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INTRODUCTION: Clinical guidelines offer little guidance for treatment selection following inadequate response to conventional synthetic disease-modifying antirheumatic drug (csDMARD) in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) was validated to predict tumor necrosis factor inhibitor (TNFi) inadequate response. The decision impact of MSRC results on biologic and targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) selection was evaluated. METHODS: This is an analysis of AIMS, a longitudinal, prospective database of patients with RA tested using the MSRC. This study assessed selection of b/tsDMARDs class after MSRC testing by surveying physicians, the rate of b/tsDMARD prescriptions aligning with MSRC results, and the percentage of physicians utilizing MSRC results for decision-making. RESULTS: Of 1018 participants, 70.7% (720/1018) had treatment selected after receiving MSRC results. In this MSRC-informed cohort, 75.6% (544/720) of patients received a b/tsDMARD aligned with MSRC results, and 84.6% (609/720) of providers reported using MSRC results to guide treatment selection. The most prevalent reason reported (8.2%, 59/720) for not aligning treatment selection with MSRC results from the total cohort was health insurance coverage issues. CONCLUSION: This study showed that rheumatologists reported using the MSRC test to guide b/tsDMARD selection for patients with RA. In most cases, MSRC test results appeared to influence clinical decision-making according to physician self-report. Wider adoption of precision medicine tools like the MSRC could support rheumatologists and patients in working together to achieve optimal outcomes for RA.

Among the dominant pathogenic genes (PG) in breast cancer are BRCA1/2. Knowing whether a patient carry one of these alterations is meaningful as it affects management. A substantial question is to what extent are the genomic profile of a tumor and its characteristics affected by the germline profile of BRCA1/2 and what is the possible contribution of other environmental factors. Here, we compared the molecular characteristics of two subsequent primary breast cancers in three women (6 primary breast cancers) BRCA PG carriers, and in two of them also a primary lung cancer. Comparing two different tumors in the same patient neutralizes the contribution of other germline changes, and may demonstrate possible effects of other external insults occurring between the first and second tumor. Nonetheless, epigenetic changes resulting from early life events will be present in all tumors that the patient has developed. Our analysis suggests that tumors arising from the same tissue in the same patient share similar molecular characteristics, albeit occurring in different times, as the patient is exposed to a variety of external stimuli. [ABSTRACT FROM AUTHOR]

Pulmonary arterial hypertension (PAH) is a progressive and rapidly fatal disease with an intricate etiology. Identifying biomarkers for early PAH lesions based on the exploration of subtle biological processes is significant for timely diagnosis and treatment. In the present study, nine distinct cell populations identified based on gene expression profiles revealed high heterogeneity in cell composition ratio, biological function, distribution preference, and communication patterns in PAH. Notably, compared to other cells, endothelial cells (ECs) showed prominent variation in multiple perspectives. Further analysis demonstrated the endothelial-to-mesenchymal transition (EndMT) in ECs and identified a subgroup exhibiting a contrasting phenotype. Based on these findings, a machine-learning integrated program consisting of nine learners was developed to create a PAH Endothelial-to-mesenchymal transition Signature (PETS). This study identified cell populations underlying EndMT and furnished a potential tool that might be valuable for PAH diagnosis and new precise therapies. [ABSTRACT FROM AUTHOR]

Hepatocellular adenomas (HAs) are tumors that can develop under different conditions, including in patients harboring a germline mutation in HNF1A. However, little is known about the pathogenesis of such disease. This work aims to better define what mechanisms lie under the development of this condition. Six HAs were sampled from the liver of a 17-year-old male affected by diabetes and multiple hepatic adenomatosis harboring the heterozygous pathogenic germline variant c.815G>A, p.(Arg272His) in HNF1A, which has a dominant negative effect. All HAs were molecularly characterized. Four of them were shown to harbor a second somatic HNF1A variant and one had a mutation in the ARID1A gene, while no additional somatic changes were found in the remaining HA and normal parenchyma. A transcriptomic profile of the same HA samples was also performed. HNF1A biallelic mutations were associated with the up-regulation of several pathways including the tricarboxylic acid cycle, the metabolism of fatty acids, and mTOR signaling while angiogenesis, endothelial and vascular proliferation, cell migration/adhesion, and immune response were down-regulated. Contrariwise, in the tumor harboring the ARID1A variant, angiogenesis was up-modulated while fatty acid metabolism was down-modulated. Histological analyses confirmed the molecular data. Independently of the second mutation, energetic processes and cholesterol metabolism were up-modulated, while the immune response was down-modulated. This work provides a complete molecular signature of HNF1A-associated HAs, analyzing the association between specific HNF1A variants and the development of HA while identifying potential new therapeutic targets for non-surgical treatment. [ABSTRACT FROM AUTHOR]

RNAs, especially non-coding RNAs (ncRNAs), are crucial players in regulating cellular mechanisms due to their ability to interact with and regulate other molecules. Altered expression patterns of ncRNAs have been observed in prostate cancer (PCa), contributing to the disease's initiation, progression, and treatment response. This study aimed to evaluate the ability of a specific set of RNAs, including long ncRNAs (lncRNAs), microRNAs (miRNAs), and mRNAs, to discriminate between PCa and the non-neoplastic condition benign prostatic hyperplasia (BPH). After selecting by literature mining the most relevant RNAs differentially expressed in biofluids from PCa patients, we evaluated their discriminatory power in samples of unfiltered urine from 50 PCa and 50 BPH patients using both real-time PCR and droplet digital PCR (ddPCR). Additionally, we also optimized a protocol for urine sample manipulation and RNA extraction. This two-way validation study allowed us to establish that miRNAs (i.e., miR-27b-3p, miR-574-3p, miR-30a-5p, and miR-125b-5p) are more efficient biomarkers for PCa compared to long RNAs (mRNAs and lncRNAs) (e.g., PCA3, PCAT18, and KLK3), as their dysregulation was consistently reported in the whole urine of patients with PCa compared to those with BPH in a statistically significant manner regardless of the quantification methodology performed. Moreover, a significant increase in diagnostic performance was observed when molecular signatures composed of different miRNAs were considered. Hence, the abovementioned circulating ncRNAs represent excellent potential non-invasive biomarkers in urine capable of effectively distinguishing individuals with PCa from those with BPH, potentially reducing cancer overdiagnosis. [ABSTRACT FROM AUTHOR]

Inflammatory Bowel Diseases (IBD), which encompass ulcerative colitis (UC) and Crohn's disease (CD), are characterized by chronic inflammation and tissue damage of the gastrointestinal tract. This study aimed to uncover novel disease-gene signatures, dysregulated pathways, and the immune cell infiltration landscape of inflamed tissues. Eight publicly available transcriptomic datasets, including inflamed and non-inflamed tissues from CD and UC patients were analyzed. Common differentially expressed genes (DEGs) were identified through meta-analysis, revealing 180 DEGs. DEGs were implicated in leukocyte transendothelial migration, PI3K-Akt, chemokine, NOD-like receptors, TNF signaling pathways, and pathways in cancer. Protein–protein interaction network and cluster analysis identified 14 central IBD players, which were validated using eight external datasets. Disease module construction using the NeDRex platform identified nine out of 14 disease-associated genes (CYBB, RAC2, GNAI2, ITGA4, CYBA, NCF4, CPT1A, NCF2, and PCK1). Immune infiltration profile assessment revealed a significantly higher degree of infiltration of neutrophils, activated dendritic cells, plasma cells, mast cells (resting/activated), B cells (memory/naïve), regulatory T cells, and M0 and M1 macrophages in inflamed IBD tissue. Collectively, this study identified the immune infiltration profile and nine disease-associated genes as potential modulators of IBD pathogenesis, offering insights into disease molecular mechanisms, and highlighting potential disease modulators and immune cell dynamics. [ABSTRACT FROM AUTHOR]

Abstract Among the dominant pathogenic genes (PG) in breast cancer are BRCA1/2. Knowing whether a patient carry one of these alterations is meaningful as it affects management. A substantial question is to what extent are the genomic profile of a tumor and its characteristics affected by the germline profile of BRCA1/2 and what is the possible contribution of other environmental factors. Here, we compared the molecular characteristics of two subsequent primary breast cancers in three women (6 primary breast cancers) BRCA PG carriers, and in two of them also a primary lung cancer. Comparing two different tumors in the same patient neutralizes the contribution of other germline changes, and may demonstrate possible effects of other external insults occurring between the first and second tumor. Nonetheless, epigenetic changes resulting from early life events will be present in all tumors that the patient has developed. Our analysis suggests that tumors arising from the same tissue in the same patient share similar molecular characteristics, albeit occurring in different times, as the patient is exposed to a variety of external stimuli.

Abstract Pulmonary arterial hypertension (PAH) is a progressive and rapidly fatal disease with an intricate etiology. Identifying biomarkers for early PAH lesions based on the exploration of subtle biological processes is significant for timely diagnosis and treatment. In the present study, nine distinct cell populations identified based on gene expression profiles revealed high heterogeneity in cell composition ratio, biological function, distribution preference, and communication patterns in PAH. Notably, compared to other cells, endothelial cells (ECs) showed prominent variation in multiple perspectives. Further analysis demonstrated the endothelial-to-mesenchymal transition (EndMT) in ECs and identified a subgroup exhibiting a contrasting phenotype. Based on these findings, a machine-learning integrated program consisting of nine learners was developed to create a PAH Endothelial-to-mesenchymal transition Signature (PETS). This study identified cell populations underlying EndMT and furnished a potential tool that might be valuable for PAH diagnosis and new precise therapies.

Qi Huang,1– 3,* Xu-Yun Huang,1,2,* Yu-Ting Xue,1,2,* Xiao-Hui Wu,1,2,* Yu-Peng Wu,1,2,* Zhi-Bin Ke,1,2 Zhen Kang,1,2 Yi-Cheng Xu,1,2 Dong-Ning Chen,1,2 Yong Wei,1,2 Xue-Yi Xue,1,2 Zhi-Yang Huang,3 Ning Xu1,2,4 1Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People’s Republic of China; 2Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, People’s Republic of China; 3Department of Urology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, 362000, People’s Republic of China; 4Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ning Xu, Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Fuzhou, 350005, People’s Republic of China, Tel +86-059187981687, Email drxun@fjmu.edu.cn Zhi-Yang Huang, Department of Urology, Quanzhou First Hospital, Fujian Medical University, No. 250, East Street, Licheng District, Quanzhou, Fujian, 362000, People’s Republic of China, Email hzy8902@126.comIntroduction: This study aims to explore the role of cuproptosis-related genes in ACC, utilizing data from TCGA and GEO repositories, and to develop a predictive model for patient stratification.Methods: A cohort of 123 ACC patients with survival data was analyzed. RNA-seq data of 17 CRGs were examined, and univariate Cox regression identified prognostic CRGs. A cuproptosis-related network was constructed to show interactions between CRGs. Consensus clustering classified ACC into three subtypes, with transcriptional and survival differences assessed by PCA and survival analysis. Gene set variation analysis (GSVA) and ssGSEA evaluated functional and immune infiltration characteristics across subtypes. Differentially expressed genes (DEGs) were identified, and gene clusters were established. A risk score (CRG_score) was generated using LASSO and multivariate Cox regression, validated across datasets. Tumor microenvironment, stem cell index, mutation status, drug sensitivity, and hormone synthesis were examined in relation to the CRG_score. Protein expression of key genes was validated, and functional studies on ASF1B and NDRG4 were performed.Results: Three ACC subtypes were identified with distinct survival outcomes. Subtype B showed the worst prognosis, while subtype C had the best. We identified 214 DEGs linked to cell proliferation and classified patients into three gene clusters, confirming their prognostic value. The CRG_score predicted patient outcomes, with high-risk patients demonstrating worse survival and possible resistance to immunotherapy. Drug sensitivity analysis suggested higher responsiveness to doxorubicin and etoposide in high-risk patients.Conclusion: This study suggests the potential prognostic value of CRGs in ACC. The CRG_score model provides a robust tool for risk stratification, with implications for treatment strategies.Keywords: cuproptosis, overall survival, immune microenvironment, molecular signature

Abstract Gastric cancer is a significant global health concern with complex molecular underpinnings influencing disease progression and patient outcomes. Various molecular drivers were reported, and these studies offered potential avenues for targeted therapies, biomarker discovery, and the development of precision medicine strategies. However, it was posed that the heterogeneity of the disease and the complexity of the molecular interactions are still challenging. By seamlessly integrating data from single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq), we embarked on characterizing molecular signatures and establishing a prognostic signature for this complex malignancy. We offered a holistic view of gene expression landscapes in gastric cancer, identified 226 candidate marker genes from 3 different dimensions, and unraveled key players’ risk stratification and treatment decision-making. The convergence of molecular insights in gastric cancer progression occurs at multiple biological scales simultaneously. The focal point of this study lies in developing a prognostic model, and we amalgamated four molecular signatures (COL4A1, FKBP10, RNASE1, SNCG) and three clinical parameters using advanced machine-learning techniques. The model showed high predictive accuracy, with the potential to revolutionize patient care by using clinical variables. This will strengthen the reliability of the model and enable personalized therapeutic strategies based on each patient’s unique molecular profile. In summary, our research sheds light on the molecular underpinnings of gastric cancer, culminating in a powerful prognostic tool for gastric cancer. With a firm foundation in biological insights and clinical implications, our study paves the way for future validations and underscores the potential of integrated molecular analysis in advancing precision oncology.

Recently, interest in transcriptomic assessment of kidney biopsies has been growing. This study investigates the use of NGS to identify gene expression changes and analyse the pathways involved in rejection. An Illumina bulk RNA sequencing on the polyadenylated RNA of 770 kidney biopsies was conducted. Differentially-expressed genes (DEGs) were determined for AMR and TCMR using DESeq2. Genes were segregated according to their previous descriptions in known panels (microarray or the Banff Human Organ Transplant (B-HOT) panel) to obtain NGS-specific genes. Pathway enrichment analysis was performed using the Reactome and Kyoto Encyclopaedia of Genes and Genomes (KEGG) public repositories. The differential gene expression using NGS analysis identified 6,141 and 8,478 transcripts associated with AMR and TCMR. While most of the genes identified were included in the microarray and the B-HOT panels, NGS analysis identified 603 (9.8%) and 1,186 (14%) new specific genes. Pathways analysis showed that the B-HOT panel was associated with the main immunological processes involved during AMR and TCMR. The microarrays specifically integrated metabolic functions and cell cycle progression processes. Novel NGS-specific based transcripts associated with AMR and TCMR were discovered, which might represent a novel source of targets for drug designing and repurposing. [ABSTRACT FROM AUTHOR]

Gastric cancer is a significant global health concern with complex molecular underpinnings influencing disease progression and patient outcomes. Various molecular drivers were reported, and these studies offered potential avenues for targeted therapies, biomarker discovery, and the development of precision medicine strategies. However, it was posed that the heterogeneity of the disease and the complexity of the molecular interactions are still challenging. By seamlessly integrating data from single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq), we embarked on characterizing molecular signatures and establishing a prognostic signature for this complex malignancy. We offered a holistic view of gene expression landscapes in gastric cancer, identified 226 candidate marker genes from 3 different dimensions, and unraveled key players' risk stratification and treatment decision-making. The convergence of molecular insights in gastric cancer progression occurs at multiple biological scales simultaneously. The focal point of this study lies in developing a prognostic model, and we amalgamated four molecular signatures (COL4A1, FKBP10, RNASE1, SNCG) and three clinical parameters using advanced machine-learning techniques. The model showed high predictive accuracy, with the potential to revolutionize patient care by using clinical variables. This will strengthen the reliability of the model and enable personalized therapeutic strategies based on each patient's unique molecular profile. In summary, our research sheds light on the molecular underpinnings of gastric cancer, culminating in a powerful prognostic tool for gastric cancer. With a firm foundation in biological insights and clinical implications, our study paves the way for future validations and underscores the potential of integrated molecular analysis in advancing precision oncology. [ABSTRACT FROM AUTHOR]

Simple Summary: The prognosis of intrahepatic cholangiocarcinoma (ICC) depends on tumor biology, morphology, and the high rates of late diagnosis and recurrence after curative-intent surgery, with high heterogeneity. Understanding the molecular landscape of ICC and its clinical implications for treatment and prognosis remains a major challenge. Several attempts have been made to classify molecular ICC subtypes, but they have produced heterogeneous results and need a clear clinical definition. This systematic review examined the evidence for the multi-omics-based classification of ICC to assess its clinical and prognostic significance. Molecular analysis of ICC can assist in decision-making by identifying patients who are suitable for targeted pre- and postoperative chemotherapy. Identifying clinical characteristics associated with specific ICC clusters resulting from multi-omics analysis could help tailor patient management. However, there are logistical, technical, and therapeutic challenges in the routine application of ICC molecular characterization. Identifying clinical characteristics commonly found in specific ICC clusters can help define high-risk ICC subtypes quickly and assist in selecting patients who may benefit the most from molecular profiling in the disease's earlier stages. Intrahepatic cholangiocarcinoma (ICC) is a heterogeneous disease characterized by a dismal prognosis. Various attempts have been made to classify ICC subtypes with varying prognoses, but a consensus has yet to be reached. This systematic review aims to gather relevant data on the multi-omics-based ICC classification. The PubMed, Embase, and Cochrane databases were searched for terms related to ICC and multi-omics analysis. Studies that identified multi-omics-derived ICC subtypes and investigated clinicopathological predictors of long-term outcomes were included. Nine studies, which included 910 patients, were considered eligible. Mean 3- and 5-year overall survival were 25.7% and 19.6%, respectively, for the multi-omics subtypes related to poor prognosis, while they were 70.2% and 63.3%, respectively, for the subtypes linked to a better prognosis. Several negative prognostic factors were identified, such as genes' expression profile promoting inflammation, mutations in the KRAS gene, advanced tumor stage, and elevated levels of oncological markers. The subtype with worse clinicopathological characteristics was associated with worse survival (Ref.: good prognosis subtype; pooled hazard ratio 2.06, 95%CI 1.67–2.53). Several attempts have been made to classify molecular ICC subtypes, but they have yielded heterogeneous results and need a clear clinical definition. More efforts are required to build a comprehensive classification system that includes both molecular and clinical characteristics before implementation in clinical practice to facilitate decision-making and select patients who may benefit the most from comprehensive molecular profiling in the disease's earlier stages. [ABSTRACT FROM AUTHOR]

T cells are essential tumor suppressors in cancer immunology, but their dysfunction induced by cancer cells can result in T cell exhaustion. Exhausted T cells (Tex) significantly influence the tumor immune environment, and thus, there is a need for their thorough investigation across different types of cancer. Here, we address the role of Tex cells in pan-cancer, focusing on the expression, mutations, methylation, immune infiltration, and drug sensitivity of a molecular signature comprising of the genes HAVCR2, CXCL13, LAG3, LAYN, TIGIT, and PDCD1across multiple cancer types, using bioinformatics analysis of TCGA data. Our analysis revealed that the Tex signature genes are differentially expressed across 14 cancer types, being correlated with patient survival outcomes, with distinct survival trends. Pathway analysis indicated that the Tex genes influence key cancer-related pathways, such as apoptosis, EMT, and DNA damage pathways. Immune infiltration analysis highlighted a positive correlation between Tex gene expression and immune cell infiltration in bladder cancer, while mutations in these genes were associated with specific immune cell enrichments in UCEC and SKCM. CNVs in Tex genes were widespread across cancers. We also highlight high LAYN methylation in most tumors and a negative correlation between methylation levels and immune cell infiltration in various cancers. Drug sensitivity analysis identified numerous correlations, with CXCL13 and HAVCR2 expressions influencing sensitivity to several drugs, including Apitolisib, Belinostat, and Docetaxel. Overall, these findings highlight the importance of reviving exhausted T cells to enhance the treatment efficacy to significantly boost anti-tumor immunity and achieve better clinical outcomes. [ABSTRACT FROM AUTHOR]

The bronchoalveolar organoid (BAO) model is increasingly acknowledged as an ex‐vivo platform that accurately emulates the structural and functional attributes of proximal airway tissue. The transition from bronchoalveolar progenitor cells to alveolar organoids is a common event during the generation of BAOs. However, there is a pressing need for comprehensive analysis to elucidate the molecular distinctions characterizing the pre‐differentiated and post‐differentiated states within BAO models. This study established a murine BAO model and subsequently triggered its differentiation. Thereafter, a suite of multidimensional analytical procedures was employed, including the morphological recognition and examination of organoids utilizing an established artificial intelligence (AI) image tracking system, quantification of cellular composition, proteomic profiling and immunoblots of selected proteins. Our investigation yielded a detailed evaluation of the morphologic, cellular, and molecular variances demarcating the pre‐ and post‐differentiation phases of the BAO model. We also identified of a potential molecular signature reflective of the observed morphological transformations. The integration of cutting‐edge AI‐driven image analysis with traditional cellular and molecular investigative methods has illuminated key features of this nascent model. [ABSTRACT FROM AUTHOR]

Post-traumatic stress disorder (PTSD) is a multisystem syndrome. Integration of systems-level multi-modal datasets can provide a molecular understanding of PTSD. Proteomic, metabolomic, and epigenomic assays are conducted on blood samples of two cohorts of well-characterized PTSD cases and controls: 340 veterans and 180 active-duty soldiers. All participants had been deployed to Iraq and/or Afghanistan and exposed to military-service-related criterion A trauma. Molecular signatures are identified from a discovery cohort of 218 veterans (109/109 PTSD+/-). Identified molecular signatures are tested in 122 separate veterans (62/60 PTSD+/-) and in 180 active-duty soldiers (PTSD+/-). Molecular profiles are computationally integrated with upstream regulators (genetic/methylation/microRNAs) and functional units (mRNAs/proteins/metabolites). Reproducible molecular features of PTSD are identified, including activated inflammation, oxidative stress, metabolic dysregulation, and impaired angiogenesis. These processes may play a role in psychiatric and physical comorbidities, including impaired repair/wound healing mechanisms and cardiovascular, metabolic, and psychiatric diseases.

Distinguishing between allergic and nonallergic forms of Contact Dermatitis (CD) is challenging and requires investigations based on patch-testing. Early detection of allergy biomarkers in active CD lesions could refine and simplify the management of CD patients. To characterize the molecular signatures of active CD lesions. We studied the expression of 12 allergy biomarkers by qRT-PCR in active lesions of 38 CD patients. Allergic CD (ACD) was diagnosed based on patch test (PT) results and exposure assessment. Molecular signatures of active lesions, as well as positive PT reactions, were compared with those of reference chemical allergens and irritants. Nineteen of the 38 CD patients reacted positively upon patch-testing and exposure assessment confirmed ACD diagnosis for 17 of them. Gene profiling of active CD lesions revealed 2 distinct molecular patterns: patients harboring signatures similar to reference allergens (n = 23) or irritants (n = 15). Among the 23 patients with an "allergy signature," we found the 17 patients with confirmed ACD, while no culprit allergen was identified for the 6 other patients. Interestingly, the 15 patients without biomarker induction had negative PT, suggesting that they developed nonallergic CD reactions. Molecular signatures from active skin lesions may help to stratify CD patients and predict those suffering from ACD. [ABSTRACT FROM AUTHOR]

Sepsis continues to overwhelm hospital systems with its high mortality rate and prevalence. A strategy to reduce the strain of sepsis on hospital systems is to develop a diagnostic/prognostic measure that identifies patients who are more susceptible to septic death. Current biomarkers fail to achieve this outcome, as they only have moderate diagnostic power and limited prognostic capabilities. Sepsis disrupts a multitude of pathways in many different organ systems, making the identification of a single powerful biomarker difficult to achieve. However, a common feature of many of these perturbed pathways is the increased generation of reactive oxygen species (ROS), which can alter gene expression, changes in which may precede the clinical manifestation of severe sepsis. Therefore, the aim of this study was to evaluate whether ROS-related circulating molecular signature can be used as a tool to predict sepsis survival. Here we created a ROS-related gene signature and used two Gene Expression Omnibus datasets from whole blood samples of septic patients to generate a 37-gene molecular signature that can predict survival of sepsis patients. Our results indicate that peripheral blood gene expression data can be used to predict the survival of sepsis patients by assessing the gene expression pattern of free radical–associated -related genes in patients, warranting further exploration. [ABSTRACT FROM AUTHOR]

Recently, interest in transcriptomic assessment of kidney biopsies has been growing. This study investigates the use of NGS to identify gene expression changes and analyse the pathways involved in rejection. An Illumina bulk RNA sequencing on the polyadenylated RNA of 770 kidney biopsies was conducted. Differentially-expressed genes (DEGs) were determined for AMR and TCMR using DESeq2. Genes were segregated according to their previous descriptions in known panels (microarray or the Banff Human Organ Transplant (B-HOT) panel) to obtain NGS-specific genes. Pathway enrichment analysis was performed using the Reactome and Kyoto Encyclopaedia of Genes and Genomes (KEGG) public repositories. The differential gene expression using NGS analysis identified 6,141 and 8,478 transcripts associated with AMR and TCMR. While most of the genes identified were included in the microarray and the B-HOT panels, NGS analysis identified 603 (9.8%) and 1,186 (14%) new specific genes. Pathways analysis showed that the B-HOT panel was associated with the main immunological processes involved during AMR and TCMR. The microarrays specifically integrated metabolic functions and cell cycle progression processes. Novel NGS-specific based transcripts associated with AMR and TCMR were discovered, which might represent a novel source of targets for drug designing and repurposing.

Abstract The bronchoalveolar organoid (BAO) model is increasingly acknowledged as an ex‐vivo platform that accurately emulates the structural and functional attributes of proximal airway tissue. The transition from bronchoalveolar progenitor cells to alveolar organoids is a common event during the generation of BAOs. However, there is a pressing need for comprehensive analysis to elucidate the molecular distinctions characterizing the pre‐differentiated and post‐differentiated states within BAO models. This study established a murine BAO model and subsequently triggered its differentiation. Thereafter, a suite of multidimensional analytical procedures was employed, including the morphological recognition and examination of organoids utilizing an established artificial intelligence (AI) image tracking system, quantification of cellular composition, proteomic profiling and immunoblots of selected proteins. Our investigation yielded a detailed evaluation of the morphologic, cellular, and molecular variances demarcating the pre‐ and post‐differentiation phases of the BAO model. We also identified of a potential molecular signature reflective of the observed morphological transformations. The integration of cutting‐edge AI‐driven image analysis with traditional cellular and molecular investigative methods has illuminated key features of this nascent model.

This review article discusses the epigenetic regulation of quiescent stem cells. Quiescent stem cells are a rare population of stem cells that remain in a state of cell cycle arrest until activated to proliferate and differentiate. The molecular signature of quiescent stem cells is characterized by unique epigenetic modifications, including histone modifications and deoxyribonucleic acid (DNA) methylation. These modifications play critical roles in regulating stem cell behavior, including maintenance of quiescence, proliferation, and differentiation. The article specifically focuses on the role of histone modifications and DNA methylation in quiescent stem cells, and how these modifications can be dynamically regulated by environmental cues. The future perspectives of quiescent stem cell research are also discussed, including their potential for tissue repair and regeneration, their role in aging and age-related diseases, and their implications for cancer research. Overall, this review provides a comprehensive overview of the epigenetic regulation of quiescent stem cells and highlights the potential of this research for the development of new therapies in regenerative medicine, aging research, and cancer biology.

Paeonia ostii, a plant of substantial economic significance, continues to face constraints in achieving large-scale propagation. In vitro propagation offers a promising avenue for the production of disease-free plants and the genetic transformation of peonies to instill novel traits. However, significant challenges persist in tissue culture, particularly with regards to the reproduction coefficient of shoots and the rooting process. This study reports an efficacious protocol for P. ostii micropropagation, focusing on in vitro root development facilitated through the application of phloroglucinol (PG). Furthermore, the study unveils the molecular signature of P. ostii during in vitro root development. The results indicate that the modified Y3 medium (Y3M), supplemented with 1 mg/L 6-benzyladenine (BA) and 0.1 mg/L α-naphthaleneacetic acid (NAA), is optimal for adventitious bud induction, achieving a 96.67% induction rate and an average of 16.03 adventitious shoots per sample. The highest elongation percentage (92.15%) and the longest average shoot length (3.87 cm) were obtained with Y3M containing 0.3 mg/L BA and 0.03 mg/L NAA. Additionally, the optimal medium for inducing root formation in P. ostii was identified as WPM supplemented with 3 mg/L indole-3-butyric acid (IBA) and 100 mg/L phloroglucinol (PG). Lignin content detection, microscope inspection, and molecular signature results demonstrated that PG enhanced lignin biosynthesis, thereby promoting in vitro rooting of P. ostii.

Chemodiversity of dissolved organic matter (DOM) is a crucial factor controlling soil nutrient availability, greenhouse gas emissions, and pollutant migration. Microplastics (MPs) are widespread pollutants in terrestrial ecosystems in many regions. However, the effects of MPs on DOM chemodiversity are not sufficiently understood, particularly under different types of polymers. Using UV–Vis spectroscopy, 3D fluorescence spectroscopy, and Fourier-transform ion cyclotron resonance mass spectrometry, the effects of three prevalent MPs [polyethylene, polystyrene, and polyvinyl chloride (PVC)] on the chemical properties and composition of soil DOM were investigated via a 310-d soil incubation experiment. The results showed that MPs reduced the aromatic and hydrophobic soil DOM components by more than 20%, with PVC MPs having the greatest effect. Furthermore, as MP contents increase, the humification level of soil DOM significantly decreases. MPs increased DOM molecules with no heteroatom by 8.3%–14.0%, but decreased DOM molecules with nitrogen content by 17.0%–47.8%. This may be because MPs cause positive "priming effect," resulting in the breakdown of bioavailable components in soil DOM. This is also related to MPs changing microbial richness and diversity and enriching microbial communities involved in lignin compositions degradation. In the presence of MPs, soil DOM chemodiversity depended on soil pH, electrical conductivity, dissolved organic carbon, soil organic matter, bacterial Shannon, and fungal Chao index. Specifically, DOM in MP-contaminated soils featured more lipids and less condensed aromatics and proteins/amino sugars, thereby conferring a lower DOM aromaticity and higher lability. [ABSTRACT FROM AUTHOR]

Abstract Introduction Clinical guidelines offer little guidance for treatment selection following inadequate response to conventional synthetic disease-modifying antirheumatic drug (csDMARD) in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) was validated to predict tumor necrosis factor inhibitor (TNFi) inadequate response. The decision impact of MSRC results on biologic and targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) selection was evaluated. Methods This is an analysis of AIMS, a longitudinal, prospective database of patients with RA tested using the MSRC. This study assessed selection of b/tsDMARDs class after MSRC testing by surveying physicians, the rate of b/tsDMARD prescriptions aligning with MSRC results, and the percentage of physicians utilizing MSRC results for decision-making. Results Of 1018 participants, 70.7% (720/1018) had treatment selected after receiving MSRC results. In this MSRC-informed cohort, 75.6% (544/720) of patients received a b/tsDMARD aligned with MSRC results, and 84.6% (609/720) of providers reported using MSRC results to guide treatment selection. The most prevalent reason reported (8.2%, 59/720) for not aligning treatment selection with MSRC results from the total cohort was health insurance coverage issues. Conclusion This study showed that rheumatologists reported using the MSRC test to guide b/tsDMARD selection for patients with RA. In most cases, MSRC test results appeared to influence clinical decision-making according to physician self-report. Wider adoption of precision medicine tools like the MSRC could support rheumatologists and patients in working together to achieve optimal outcomes for RA.

Adequate reporting is essential for evaluating the performance and clinical utility of a prognostic prediction model. Previous studies indicated a prevalence of incomplete or suboptimal reporting in translational and clinical studies involving development of multivariable prediction models for prognosis, which limited the potential applications of these models. While reporting templates introduced by the established guidelines provide an invaluable framework for reporting prognostic studies uniformly, there is a widespread lack of qualified adherence, which may be due to miscellaneous challenges in manual reporting of extensive model details, especially in the era of precision medicine. Here, we present ReProMSig (Reproducible Prognosis Molecular Signature), a web-based integrative platform providing the analysis framework for development, validation and application of a multivariable prediction model for cancer prognosis, using clinicopathological features and/or molecular profiles. ReProMSig platform supports transparent reporting by presenting both methodology details and analysis results in a strictly structured reporting file, following the guideline checklist with minimal manual input needed. The generated reporting file can be published together with a developed prediction model, to allow thorough interrogation and external validation, as well as online application for prospective cases. We demonstrated the utilities of ReProMSig by development of prognostic molecular signatures for stage II and III colorectal cancer respectively, in comparison with a published signature reproduced by ReProMSig. Together, ReProMSig provides an integrated framework for development, evaluation and application of prognostic/predictive biomarkers for cancer in a more transparent and reproducible way, which would be a useful resource for health care professionals and biomedical researchers. [ABSTRACT FROM AUTHOR]

Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy. In this study, we collected three HNSC microarrays (n = 306) from the Gene Expression Omnibus (GEO), and HNSC RNA-Seq data (n = 566) from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) and validate our results. Two scoring methods, representative score (RS) and perturbative score (PS), were developed for DEGs to summarize their possible activation functions and influence in tumorigenesis. Based on the RS and PS scoring, we selected candidate genes to cluster TCGA samples for the identification of molecular subtypes in HNSC. We have identified 289 up-regulated DEGs and selected 88 genes (called HNSC88) using the RS and PS scoring methods. Based on HNSC88 and TCGA samples, we determined three HNSC subtypes, including one HPV-associated subtype, and two HPV-negative subtypes. One of the HPV-negative subtypes showed a relationship to smoking behavior, while the other exhibited high expression in tumor immune response. The Kaplan–Meier method was used to compare overall survival among the three subtypes. The HPV-associated subtype showed a better prognosis compared to the other two HPV-negative subtypes (log rank, p = 0.0092 and 0.0001; hazard ratio, 1.36 and 1.39). Additionally, within the HPV-negative group, the smoking-related subgroup exhibited worse prognosis compared to the subgroup with high expression in immune response (log rank, p = 0.039; hazard ratio, 1.53). The HNSC88 not only enables the identification of HPV-associated subtypes, but also proposes two potential HPV-negative subtypes with distinct prognoses and molecular signatures. This study provides valuable strategies for summarizing the roles and influences of genes in tumorigenesis for identifying molecular signatures and subtypes of HNSC. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is a fatal, progressive neurodegenerative disease with limited treatment options. Diagnosis can be difficult due to the heterogeneity and non-specific nature of the initial symptoms, resulting in delays that compromise prompt access to effective therapeutic strategies. Transcriptome profiling of patient-derived peripheral cells represents a valuable benchmark in overcoming such challenges, providing the opportunity to identify molecular diagnostic signatures. In this study, we characterized transcriptome changes in skin fibroblasts of sporadic ALS patients (sALS) and controls and evaluated their utility as a molecular classifier for ALS diagnosis. Our analysis identified 277 differentially expressed transcripts predominantly involved in transcriptional regulation, synaptic transmission, and the inflammatory response. A support vector machine classifier based on this 277-gene signature was developed to discriminate patients with sALS from controls, showing significant predictive power in both the discovery dataset and in six independent publicly available gene expression datasets obtained from different sALS tissue/cell samples. Taken together, our findings support the utility of transcriptional signatures in peripheral cells as valuable biomarkers for the diagnosis of ALS. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have allowed the discovery of marker–trait associations in crops over recent decades. However, their power is hampered by a number of limitations, with the key one among them being an overreliance on single-nucleotide polymorphisms (SNPs) as molecular markers. Indeed, SNPs represent only one type of genetic variation and are usually derived from alignment to a single genome assembly that may be poorly representative of the population under study. To overcome this, k-mer-based GWAS approaches have recently been developed. k-mer-based GWAS provide a universal way to assess variation due to SNPs, insertions/deletions, and structural variations without having to specifically detect and genotype these variants. In addition, k-mer-based analyses can be used in species that lack a reference genome. However, the use of k-mers for GWAS presents challenges such as data size and complexity, lack of standard tools, and potential detection of false associations. Nevertheless, efforts are being made to overcome these challenges and a general analysis workflow has started to emerge. We identify the priorities for k-mer-based GWAS in years to come, notably in the development of user-friendly programs for their analysis and approaches for linking significant k-mers to sequence variation. [ABSTRACT FROM AUTHOR]

We employed supervised machine learning algorithms to a cohort of colorectal cancer patients from the NCI to differentiate and classify the heterogenous disease based on anatomical laterality and multi-omics stratification, in a first of its kind. Multi-omics integrative analysis shows distinct clustering of left and right colorectal cancer with disentangled representation of methylome and delineation of transcriptome and genome. We present novel multi-omics findings consistent with augmented hypermethylation of genes in right CRC, epigenomic biomarkers on the right in conjunction with immune-mediated pathway signatures, and lymphocytic invasion which unlocks unique therapeutic avenues. Contrarily, left CRC multi-omics signature is found to be marked by angiogenesis, cadherins, and epithelial–mesenchymal transition (EMT). An integrated multi-omics molecular signature of RNF217-AS1, hsa-miR-10b, and panel of FBX02, FBX06, FBX044, MAD2L2, and MIIP copy number altered genes have been found by the study. Overall survival analysis reveals genomic biomarkers ABCA13 and TTN in 852 LCRC cases, and SOX11 in 170 RCRC cases that predicts a significant survival benefit. Our study exemplifies the translational competence and robustness of machine learning in effective translational bridging of research and clinic. [ABSTRACT FROM AUTHOR]

Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder (DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a molecular signature composed of multiple genes for lung adenocarcinoma (ADC). In independent validation cohorts, the proposed molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.

The concentration and molecular composition of soil organic matter (SOM) are important factors in mitigation against climate change as well as providing other ecosystem services. Our quantitative understanding of how land use influences SOM molecular composition and associated turnover dynamics is limited, which underscores the need for high-throughput analytical approaches and molecular marker signatures to clarify this etiology. Combining a high-throughput untargeted mass spectrometry screening and molecular markers, we show that forest, farmland and urban land uses result in distinct molecular signatures of SOM in the Lake Chaohu Basin. Molecular markers indicate that forest SOM has abundant carbon contents from vegetation and condensed organic carbon, leading to high soil organic carbon (SOC) concentration. Farmland SOM has moderate carbon contents from vegetation, and limited content of condensed organic carbon, with SOC significantly lower than that of forest soils. Urban SOM has high abundance of condensed organic carbon markers due to anthropogenic activities but relatively low in markers from vegetation. Consistently, urban soils have the highest black carbon/SOC ratio among these land uses. Overall, our results suggested that the molecular signature of SOM varies significantly with land use in the Lake Chaohu Basin, influencing carbon dynamics. Our strategy of molecular fingerprinting and marker discovery is expected to enlighten further research on SOM molecular signatures and cycling dynamics.

Abstract Background Limb-girdle muscular dystrophy (LGMD) is a rare neuromuscular disease including a growing and heterogeneous number of subtypes with variable phenotype. Their clinical and histopathological characteristics frequently overlap with other neuromuscular dystrophies. Our goal was to identify, by a non-invasive method, a molecular signature including biochemical and epigenetic parameters with potential value for patient prognosis and stratification. Results Circulating miRNome was obtained by smallRNA-seq in plasma from LGMD patients (n = 6) and matched-controls (n = 6). Data, validated by qPCR in LGMD samples, were also examined in other common muscular dystrophies: Duchenne (DMD) (n = 5) and facioscapulohumeral muscular dystrophy (FSHD) (n = 4). Additionally, biochemical and clinical parameters were analyzed. miRNome analysis showed that thirteen differentially expressed miRs could separate LGMD vs control group by hierarchical clustering. Most of differentially expressed miRs in LGMD patients were up-regulated (miR-122-5p, miR-122b-3p, miR-6511a-3p, miR-192-5p, miR-574-3p, mir-885-3p, miR-29a-3p, miR-4646-3p, miR-203a-3p and miR-203b-5p) whilst only three of sequenced miRs were significantly down-regulated (miR-19b-3p, miR-7706, miR-323b-3p) when compared to matched controls. Bioinformatic analysis of target genes revealed cell cycle, muscle tissue development, regeneration and senescence as the most affected pathways. Four of these circulating miRs (miR-122-5p, miR-192-5p, miR-19b-3p and miR-323b-3p), together with the myomiR miR-206, were further analysed by qPCR in LGMD, DMD and FSHD. The receiver operating characteristic curves (ROC) revealed high area under the curve (AUC) values for selected miRs in all groups, indicating that these miRs have good sensitivity and specificity to distinguish LGMD, DMD and FSHD patients from healthy controls. miR-122-5p, miR-192-5p and miR-323-3p were differentially expressed compared to matched-controls in all groups but apparently, each type of muscular dystrophy showed a specific pattern of miR expression. Finally, a strong correlation between miRs and biochemical data was only found in LGMD patients: while miR-192-5p and miR-122-5p negatively correlated with CK, miR-192-5p positively correlated with vitamin D3 and ALP. Conclusions Although limited by the small number of patients included in this study, we propose here a specific combination of circulating miR-122-5p/miR-192-5p/miR-323-3 and biochemical parameters as a potential molecular signature whose clinical value for LGMD patient prognosis and stratification should be further confirmed in a larger cohort of patients.

Background: The hyperproliferation featured with upregulated glycolysis is a hallmark of psoriasis. However, molecular difference of keratinocyte glycolysis amongst varied pathologic states in psoriasis remain elusive. Objectives: To characterize glycolysis status of psoriatic skin and assess the potential of glycolysis score for therapeutic decision. Methods: We analyzed 345414 cells collected from different cohorts of singlecell RNA seq database. A new method, Scissor, was used to integrate the phenotypes in GSE11903 to guide single-cell data analysis, allowing identification of responder subpopulations. AUCell algorithm was performed to evaluate the glycolysis status of single cell. Glycolysis signature was used for further ordering in trajectory analysis. The signature model was built with logistic regression analysis and validated using external datasets. Results: Keratinocytes (KCs) expressing SLC2A1 and LDH1 were identified as a novel glycolysis-related subpopulation. Scissor+ cells and Scissor− cells were defined as response and non-response phenotypes. In Scissor+ SLC2A1+ LDH1+ KCs, ATP synthesis pathway was activated, especially, the glycolysis pathway being intriguing. Based on the glycolysis signature, keratinocyte differentiation was decomposed into a three-phase trajectory of normal, non-lesional, and lesional psoriatic cells. The area under the curve (AUC) and Brier score (BS) were used to estimate the performance of the glycolysis signature in distinguishing response and non-response samples in GSE69967 (AUC =0.786, BS =17.7) and GSE85034 (AUC=0.849, BS=11.1). Furthermore, Decision Curve Analysis suggested that the glycolysis score was clinically practicable. Conclusion: We demonstrated a novel glycolysis-related subpopulation of KCs, identified 12-glycolysis signature, and validated its promising predictive efficacy of treatment effectiveness. [ABSTRACT FROM AUTHOR]

Xiaojuan Dai,1 Jinghua Wang,1 Xiao Zhang,1 Li Wang,1 Sifan Wu,1 Huiyong Chen,1 Ying Sun,1 Lili Ma,1 Lingying Ma,1 Xiufang Kong,1 Lindi Jiang1,2 1Department of Rheumatology, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China; 2Center of Clinical Epidemiology and Evidence-Based Medicine, Fudan University, Shanghai, People’s Republic of ChinaCorrespondence: Lindi Jiang; Xiufang Kong, Email zsh-rheum@hotmail.com; kongxiufang2007@163.comObjective: This study aimed to analyze biomarker changes in patients with TAK following treatment with glucocorticoids (GCs) and tofacitinib (TOF).Methods: Seventeen patients from a prospective TAK cohort treated with GCs and TOF and 12 healthy individuals were recruited. TAK associated cytokines, chemokines, growth factors, and MMPs were analyzed in these patients before and after GCs and TOF treatment, and healthy controls. Molecular signatures associated with clinical features were evaluated.Results: Patients’ cytokines (PTX3, IL-6, IFN-γ), chemokines (IL-16, CCL22, CCL2), growth factors (VEGF), and MMP9 levels were significantly higher at baseline (all p < 0.05), while patients’ FGF-2 levels were significantly lower (p = 0.02). After treatment, IL-10 was significantly increased at 6 months (p=0.007), and inflammatory cytokines such as PTX3, IL-6 demonstrated a downward trend. Patients without vascular occlusion had higher baseline CCL22 levels than patients with it (p = 0.05), which remained persistently higher after treatment. Radar plot analysis demonstrated that PTX3 was closely correlated with disease activity. In addition, patients without imaging improvement had relatively higher baseline levels of CCL22, FGF-2, and PDGF-AB (p = 0.056, p = 0.06 and p = 0.08 respectively) and lower baseline levels of TNFα, ESR, and CRP (p=0.04, p=0.056, p=0.07, respectively) compared with patients without it.Conclusion: GCs and TOF are effective in decreasing inflammatory molecules but have limited efficacy in regulating multiple other markers involved in TAK. PTX3 is a prominent marker for disease activity, and CCL22 may have a predictive value for vascular progression.Keywords: Takayasu arteritis, tofacitinib, molecular signature, biomarker changes

Type 2 diabetes (T2DM) defined as the adult-onset type that is primarily not insulin-dependent, comprises over 95% of all diabetes mellitus (DM) cases. According to global records, 537 million adults aged 20-79 years are affected by DM that means at least 1 out of 15 persons. This number is projected to grow by 51% by the year 2045. One of the most common complications of T2DM is diabetic retinopathy (DR) with an overall prevalence over 30%. The total number of the DR-related visual impairments is on the rise, due to the growing T2DM population. Proliferative diabetic retinopathy (PDR) is the progressing DR and leading cause of preventable blindness in working-age adults. Moreover, PDR with characteristic systemic attributes including mitochondrial impairment, increased cell death and chronic inflammation, is an independent predictor of the cascading DM-complications such as ischemic stroke. Therefore, early DR is a reliable predictor appearing upstream of this "domino effect". Global screening, leading to timely identification of DM-related complications, is insufficiently implemented by currently applied reactive medicine. A personalised predictive approach and cost-effective targeted prevention shortly - predictive, preventive and personalised medicine (PPPM / 3PM) could make a good use of the accumulated knowledge, preventing blindness and other severe DM complications. In order to reach this goal, reliable stage- and disease-specific biomarker panels are needed characterised by an easy way of the sample collection, high sensitivity and specificity of analyses. In the current study, we tested the hypothesis that non-invasively collected tear fluid is a robust source for the analysis of ocular and systemic (DM-related complications) biomarker patterns suitable for differential diagnosis of stable DR versus PDR. Here, we report the first results of the comprehensive ongoing study, in which we correlate individualised patient profiles (healthy controls versus patients with stable D as well as patients with PDR with and without co-morbidities) with their metabolic profiles in the tear fluid. Comparative mass spectrometric analysis performed has identified following metabolic clusters which are differentially expressed in the groups of comparison: acylcarnitines, amino acid & related compounds, bile acids, ceramides, lysophosphatidyl-choline, nucleobases & related compounds, phosphatidyl-cholines, triglycerides, cholesterol esters, and fatty acids. Our preliminary data strongly support potential clinical utility of metabolic patterns in the tear fluid indicating a unique metabolic signature characteristic for the DR stages and PDR progression. This pilot study creates a platform for validating the tear fluid biomarker patterns to stratify T2DM-patients predisposed to the PDR. Moreover, since PDR is an independent predictor of severe T2DM-related complications such as ischemic stroke, our international project aims to create an analytical prototype for the "diagnostic tree" (yes/no) applicable to healthrisk assessment in diabetes care. [ABSTRACT FROM AUTHOR]

Cancer-associated fibroblasts (CAFs), a prominent population of stromal cells, play a crucial role in tumor progression, prognosis, and treatment response. However, the relationship among CAF-based molecular signatures, clinical outcomes, and tumor microenvironment infiltration remains largely elusive in pancreatic cancer (PC). Here, we collected multicenter PC data and performed integrated analysis to investigate the role of CAF-related genes (CRGs) in PC. Firstly, we demonstrated that α-SMA+ CAFs were the most prominent stromal components and correlated with the poor survival rates of PC patients in our tissue microarrays. Then, we discriminated two diverse molecular subtypes (CAF clusters A and B) and revealed the significant differences in the tumor immune microenvironment (TME), four reported CAF subpopulations, clinical characteristics, and prognosis in PC samples. Furthermore, we analyzed their association with the immunotherapy response of PC patients. Lastly, a CRG score was constructed to predict prognosis, immunotherapy responses, and chemosensitivity in pancreatic cancer patients. In summary, these findings provide insights into further research targeting CAFs and their TME, and they pave a new road for the prognosis evaluation and individualized treatment of PC patients. [ABSTRACT FROM AUTHOR]

Background: Limb-girdle muscular dystrophy (LGMD) is a rare neuromuscular disease including a growing and heterogeneous number of subtypes with variable phenotype. Their clinical and histopathological characteristics frequently overlap with other neuromuscular dystrophies. Our goal was to identify, by a non-invasive method, a molecular signature including biochemical and epigenetic parameters with potential value for patient prognosis and stratification. Results: Circulating miRNome was obtained by smallRNA-seq in plasma from LGMD patients (n = 6) and matched-controls (n = 6). Data, validated by qPCR in LGMD samples, were also examined in other common muscular dystrophies: Duchenne (DMD) (n = 5) and facioscapulohumeral muscular dystrophy (FSHD) (n = 4). Additionally, biochemical and clinical parameters were analyzed. miRNome analysis showed that thirteen differentially expressed miRs could separate LGMD vs control group by hierarchical clustering. Most of differentially expressed miRs in LGMD patients were up-regulated (miR-122-5p, miR-122b-3p, miR-6511a-3p, miR-192-5p, miR-574-3p, mir-885-3p, miR-29a-3p, miR-4646-3p, miR-203a-3p and miR-203b-5p) whilst only three of sequenced miRs were significantly down-regulated (miR-19b-3p, miR-7706, miR-323b-3p) when compared to matched controls. Bioinformatic analysis of target genes revealed cell cycle, muscle tissue development, regeneration and senescence as the most affected pathways. Four of these circulating miRs (miR-122-5p, miR-192-5p, miR-19b-3p and miR-323b-3p), together with the myomiR miR-206, were further analysed by qPCR in LGMD, DMD and FSHD. The receiver operating characteristic curves (ROC) revealed high area under the curve (AUC) values for selected miRs in all groups, indicating that these miRs have good sensitivity and specificity to distinguish LGMD, DMD and FSHD patients from healthy controls. miR-122-5p, miR-192-5p and miR-323-3p were differentially expressed compared to matched-controls in all groups but apparently, each type of muscular dystrophy showed a specific pattern of miR expression. Finally, a strong correlation between miRs and biochemical data was only found in LGMD patients: while miR-192-5p and miR-122-5p negatively correlated with CK, miR-192-5p positively correlated with vitamin D3 and ALP. Conclusions: Although limited by the small number of patients included in this study, we propose here a specific combination of circulating miR-122-5p/miR-192-5p/miR-323-3 and biochemical parameters as a potential molecular signature whose clinical value for LGMD patient prognosis and stratification should be further confirmed in a larger cohort of patients. [ABSTRACT FROM AUTHOR]

• Our knowledge of their taxonomic and phylogenetic relationships is still deficient. • Some genus and species from Moewusinia clade differ specific feature combinations. • The ITS2 of Eubrownia and Chlorococcum genera is unique within the chlorophytes. Despite that the diversity of chlorophycean microalgae has been studied for a long time, our knowledge of their taxonomic and phylogenetic relationships is still deficient. This study represents one of the first efforts to examine the congruence of the morphological identification of microalgal species from genera Eubrownia, Spongiococcum , and Chlorococcum with DNA-based species delimitation methods. We found unique combinations of morphological, molecular, and ecological features, distinguishing some members of the Moewusinia clade at the genus and species levels. Most genetic groups recovered by GMYC, ABGD, and PTP analyses of 18S rRNA and ITS2 were consistent with their morphological identification. The ITS2 region of genera Eubrownia and Chlorococcum is among the longest within the chlorophytes theretofore studied and has a unique secondary structure with branched helix III and uncommonly long helix IV. Moreover, the molecular signatures were found in the basal stem region of helix I, distinguishing two genera. The CBC analysis is able to effectively discriminate between E. isobilateralis, E. aggregata , and all Chlorococcum species, except for C. infusionum and C. echinozygotum. Thus, we confirmed tentative hypotheses of species, obtained by DNA-based delimitation methods, with morphological data, molecular signatures, ITS2 secondary structure, and CBC criterion. [ABSTRACT FROM AUTHOR]

Abstract Background To evaluate the specificity of the expression patterns of microRNAs (miRNAs) in circulating CD4+ T cells in aged patients with atherosclerosis obliterans (ASO). Methods A comprehensive miRNA expression study was conducted using a miRNA microarray of CD4+ T cells isolated from peripheral blood mononuclear cells (PBMCs) of 33 patients with ASO and 24 healthy donors. A t test was used for statistical analysis, and the average linkage method was used for hierarchical clustering. The results were validated by qRT–PCR. Putative targeted pathways associated with validated miRNAs were predicted with the online software DIANA miRPath. Results We identified 44 miRNAs based on a cutoff value of a 1.3-fold change in expression between the two groups, with 18 miRNAs showing a false discovery rate (FDR) p value

Vlad Horia Schitcu,1– 3,&ast; Lajos Raduly,1,&ast; Andreea Nutu,1,&ast; Oana Zanoaga,1 Cristina Ciocan,1 Vlad Cristian Munteanu,2,3 Roxana Cojocneanu,1 Bogdan Petrut,2,3 Ioan Coman,2 Cornelia Braicu,1 Ioana Berindan-Neagoe1 1Research Center for Functional Genomics, Biomedicine, and Translational Medicine, “Iuliu Hațieganu” University of Medicine and Pharmacy, Cluj-Napoca, 400337, Romania; 2Department of Urology, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, 400012, Romania; 3Department of Urology, “Prof. Dr. Ion Chiricuta” Oncology Institute, Cluj-Napoca, Romania&ast;These authors contributed equally to this workCorrespondence: Cornelia Braicu, Research Center for Functional Genomics, Biomedicine, and Translational Medicine, “Iuliu Hațieganu” University of Medicine and Pharmacy, 23 Gh. Marinescu Street, Cluj-Napoca, 400337, Romania, Tel +40-264-597-256, Fax +40-264-597-257, Email cornelia.braicu@umfcluj.ro; braicucornelia@yahoo.comAbstract: Prostate cancer biology is complex, and needs to be deciphered. The latest evidence reveals the significant role of non-coding RNAs, particularly microRNAs (miRNAs), as key regulatory factors in cancer. Therefore, the identification of altered miRNA patterns involved in prostate cancer will allow them to be used for development of novel diagnostic and prognostic biomarkers. Patients and Methods: We performed a miRNAs transcriptomic analysis, using microarray (10 matched pairs tumor tissue versus normal adjacent tissue, selected based on inclusion criteria), followed by overlapping with TCGA data. A total of 292 miRNAs were differentially expressed, with 125 upregulated and 167 downregulated in TCGA patients’ cohort with PRAD (prostate adenocarcinoma), respectively for the microarray experiments; 16 upregulated and 44 downregulated miRNAs were found in our cohort. To confirm our results obtained for tumor tissue, we performed validation with qRT-PCR at the tissue and plasma level of two selected transcripts, and finally, we focused on the identification of altered miRNAs involved in key biological processes. Results: A common signature identified a panel of 12 upregulated and 1 downregulated miRNA, targeting and interconnected in a network with the TP53, AGO2, BIRC5 gene and EGFR as a core element. Among this signature, the overexpressed transcripts (miR-20b-5p, miR-96-5p, miR-183-5p) and the downregulated miR-542-5p were validated by qRT-PCR in an additional patients’ cohort of 34 matched tumor and normal adjacent paired samples. Further, we performed the validation of the expression level for miR-20b-5p, miR-96-5p, miR-183-5p plasma, on the same patients’ cohort versus a healthy control group, confirming the overexpression of these transcripts in the PRAD group, demonstrating the liquid biopsy as a potential investigational tool in prostate cancer. Conclusion: In this pilot study, we provide evidence on miRNA dysregulation and its association with key functional components of the PRAD landscape, where an important role is acted by miR-20b-5p, miR-542-5p, or the oncogenic cluster miR-183-96-182.Keywords: prostate adenocarcinoma, microRNA, tissue, plasma, molecular signature, biological network

In this paper, we discuss what biomarkers to choose if there is a need to describe the results of laser therapy targeting keloid skin. We elevate the known cytomarkers (Krt14, Lgals7, Krt5, Dcn, Lum, Igfbp5, Cd31, Vwf, Stambpl1, Uqcrb, Cd3 and Acta2), biomarkers of the inflammatory response (Cd45/Ptprc, Adgre1, Ly6g, Il1b, Il4, Il13, Il22, Cxcl2 и Ccl17), as well as the proteins of extracellular matrix (type I and III collagens; precursors of COL5A1 and COLA1A; FTL, COL3A1, PGLS, CNN2, ANXA2, TPSAB1, COL12A1, precursors of APCS and ALB), and their encoding genes (FGF7, BAX, CCND1, MMP3, MMP9, CXCL1, -2, -5, -6 and -12; IL8, S100A7 and IL1A), those expression and co-location may potentially change the appearance and internal structure of damaged skin. We also describe how to choose biomarkers using the results genomic studies and their limitations. Moreover, we provide examples of how different groups of gene and protein biomarkers are used in experimental biology and clinical practice. According to the previously published data, well-known biomarkers verified on animal models, depend on their biological effects, let to characterize structural changes and changes in the composition of cells represented at the site of damage before and after the treatment. In addition, the published experimental and clinical data provide an opportunity to analyze the efficiency of new experimental approaches and compare them to each other.

BackgroundTP53 is one of the most frequent mutated genes in colon cancer. Although colon cancer with TP53 mutations has a high risk of metastasis and worse prognosis generally, it showed high heterogeneity clinically.MethodsA total of 1,412 colon adenocarcinoma (COAD) samples were obtained from two RNA-seq cohorts and three microarray cohorts, including the TCGA-COAD (N = 408), the CPTAC-COAD (N = 106), GSE39582 (N = 541), GSE17536 (N = 171) and GSE41258 (N = 186). The LASSO-Cox method was used to establish the prognostic signature based on the expression data. The patients were divided into high-risk and low-risk groups based on the median risk score. The efficiency of the prognostic signature was validated in various cohorts, including TP53-mutant and TP53 wild-type. The exploration of potential therapeutic targets and agents was performed by using the expression data of TP53-mutant COAD cell lines obtained from the CCLE database and the corresponding drug sensitivity data obtained from the GDSC database.ResultsA 16-gene prognostic signature was established in TP53-mutant COAD. The high-risk group had significantly inferior survival time compared to the low-risk group in all TP53-mutant datasets, while the prognostic signature failed to classify the prognosis of COAD with TP53 wild-type properly. Besides, the risk score was the independent poor factor for the prognosis in TP53-mutant COAD and the nomogram based on the risk score was also shown good predictive efficiency in TP53-mutant COAD. Moreover, we identified SGPP1, RHOQ, and PDGFRB as potential targets for TP53-mutant COAD, and illuminated that the high-risk patients might benefit from IGFR-3801, Staurosporine, and Sabutoclax.ConclusionA novel prognostic signature with great efficiency was established especially for COAD patients with TP53 mutations. Besides, we identified novel therapeutic targets and potential sensitive agents for TP53-mutant COAD with high risk. Our findings provided not only a new strategy for prognosis management but also new clues for drug application and precision treatment in COAD with TP53 mutations.

ObjectivesThis study aims to identify a robust signature that performs well in predicting overall survival across tumor phenotypes and treatment strata, and validates the application of Monte Carlo cross validation (MCCV) as a means of identifying molecular signatures when utilizing small and highly heterogeneous datasets.Materials and methodsRNA sequence gene expression data for 264 patient tumors were acquired from The Cancer Genome Atlas (TCGA). 100 iterations of Monte Carlo cross validation were applied to differential expression and Cox model validation. The association between the gene signature risk score and overall survival was measured using Kaplan-Meier survival curves, univariate, and multivariable Cox regression analyses.ResultsPathway analysis findings indicate that ligand-gated ion channel pathways are the most significantly enriched with the genes in the aggregated signature. The aggregated signature described in this study is predictive of overall survival in oral cancer patients across demographic and treatment strata.ConclusionThis study reinforces previous findings supporting the role of ion channel gating, interleukin, calcitonin receptor, and keratinization pathways in tumor progression and treatment response in oral cancer. These results strengthen the argument that differential expression of genes within these pathways reduces tumor susceptibility to treatment. Conducting differential gene expression (DGE) with Monte Carlo cross validation, as this study describes, offers a potential solution to decreasing the variability in DGE results across future studies that are reliant upon highly heterogeneous datasets. This improves the ability of studies reliant upon similarly structured datasets to reach results that are reproducible.

The prognosis of poorly differentiated thyroid carcinomas (PDTC) defined by the Turin criteria is variable. The aim of this study on 51 PDTC patients was to determine clinical, histological and molecular prognostic factors associated with recurrence in patients with localized disease at initial treatment and with overall survival in patients with distant metastases. Of 40 patients for whom next-generation sequencing (NGS) by ThyroSeq v3 was able to be performed on historical samples, we identified high-risk molecular signature (TERT, TP53 mutations) in 24 (60%) cases, intermediate risk signature in 9 (22.5%) cases and low-risk signature in 7 (17.5%) cases. Potentially actionable mutations were identified in 10% of cases. After a median follow-up of 57.5 mon ths, recurrence occurred in 11 (39%) of the 28 patients with localized disease. The Amer ican Thyroid Association (ATA) high risk of relapse, high mitotic count, high molecular risk signature and CD163 expression were associated with recurrence (P = 0.009, 0.01, 0.049, 0.03 respectively). After a median follow-up of 49.5 months, thyroid cancer-related death occurred in 53% of the patients with distant metastases. There was no signi ficant prognostic factor associated with death in univariate analysis. However, none of the patients with intermediate ATA risk of recurrence and none of the patients wi th low-risk molecular signature died from the disease. In addition, high molecular-risk signature was associated with the presence of synchronous or metachronous distant metastasis (P = 0.007) and with poor overall survival (P = 0.01). In conclusion, ATA risk of relapse and high mitotic count was associated with higher rate of recurrence in localized PDTC. High molecularrisk signature was associated with the presence of distant metastasis and poor overall survival. Further studies are needed to determine if molecular testing adds to ATA risk stratification or response to therapy in predicting outcomes. [ABSTRACT FROM AUTHOR]

The molecular signature response classifier (MSRC) predicts tumor necrosis factor-ɑ inhibitor (TNFi) non-response in rheumatoid arthritis. This study evaluates decision-making, validity, and utility of MSRC testing. This comparative cohort study compared an MSRC-tested arm (N = 627) from the Study to Accelerate Information of Molecular Signatures (AIMS) with an external control arm (N = 2721) from US electronic health records. Propensity score matching was applied to balance baseline characteristics. Patients initiated a biologic/targeted synthetic disease-modifying antirheumatic drug, or continued TNFi therapy. Odds ratios (ORs) for six-month response were calculated based on clinical disease activity index (CDAI) scores for low disease activity/remission (CDAI-LDA/REM), remission (CDAI-REM), and minimally important differences (CDAI-MID). In MSRC-tested patients, 59% had a non-response signature and 70% received MSRC-aligned therapy. In TNFi-treated patients, the MSRC had an 88% PPV and 54% sensitivity. MSRC-guided patients were significantly (p < 0.0001) more likely to respond to b/tsDMARDs than those treated according to standard care (CDAI-LDA/REM: 36.0% vs 21.9%, OR 2.01[1.55–2.60]; CDAI-REM: 10.4% vs 3.6%, OR 3.14 [1.94–5.08]; CDAI-MID: 49.5% vs 32.8%, OR 2.01[1.58–2.55]). MSRC clinical validity supports high clinical utility: guided treatment selection resulted in significantly superior outcomes relative to standard care; nearly three times more patients reached CDAI remission. Clinicians can offer rheumatoid arthritis patients many types of therapies but the response rate for each of these drugs is low. For example, within the first year of treatment, just about one-half of patients respond to the first-line drug, csDMARD. Only one-third of methotrexate-unresponsive patients will respond to the most common second-line agent, a tumor necrosis factor-α inhibitor. These low response rates present a critical challenge to treating patients. Clinicians try different cs- and b/tsDMARD and fail to quickly identify the most effective options. Then, disease will progress, irreversibly destroying patient joints, diminishing patient health-related quality of life, and increasing risks of cardiovascular disease, cancer, and death. To help clinicians quickly identify the best drugs for patients in a treat-to-target approach, a precision-medicine test was developed to identify patients unlikely to respond to tumor necrosis factor-α inhibitors. This molecular signature response classifier considers both molecular features (patient RNA-expression levels) and clinical features (e.g. body mass index, sex) to predict patient response. To evaluate the effectiveness of this test, the outcomes of patients treated with classifier-selected drugs (in a large, tested cohort) were compared with outcomes of patients treated with conventionally selected therapies (in an external cohort of electronic-health-record data). Patients treated with classifier-selected therapies were approximately three times as likely to achieve remission than were patients treated with conventionally selected drugs. These results suggest that this molecular signature response classifier is a valuable tool for more quickly identifying optimal therapies to treat rheumatoid arthritis. [ABSTRACT FROM AUTHOR]

Summary: With the development of various targeted therapies in breast cancer, detection of biomarkers for predicting treatment efficacy gains importance. With the definition of intrinsic subtypes, breast cancer has paved the way for personalized treatment. Already known and recently recognized targets play an important role both in metastatic and early breast cancer. As a result, early molecular profiling is becoming a part of early diagnostic work-up. Notwithstanding, further treatment targets and agents are needed, particularly in the most aggressive subtype encompassing heterogenous diseases under the group of triple negative breast cancer. [ABSTRACT FROM AUTHOR]

Background: Kidney renal papillary cell carcinoma (KIRP), kidney chromophobe (KICH), and kidney renal clear cell carcinoma (KIRC) are three most common subtypes of renal cell carcinomas (RCC), and its development is a multifaceted process that intricately involves the interplay of numerous genes. Despite recent advances in research on renal cell carcinoma, the prognosis of KIRC patients remains dismal. Therefore, there is an urgent need to explore new prognostic biomarkers and treatment strategies to help clinicians choose more effective treatment methods and accurately predict long-term efficacy. Our study aimed to systematically evaluate the gene expression profiles of three RCC subtypes, especially KIRC, and to identify survival-related biomarker., Methods: In our present study, we systematically evaluate the genes expression profile difference among three subtypes of RCC, and identify the survival-related key genes signature based on GEPIA2. GeneMANIA was used to identify the functionality-related differentially expressed genes (DEGs). Furthermore, focusing on KIRC, we intersected functionality-related and survival-related DEGs based on two datasets., Results: We ascertained five DEGs ( ANK3, FREM2, KIF13B, MPP7 and SOX6 ) as key survival-related genes in KIRC. High levels of these five DEGs expressions were strongly associated with favorable prognosis, but not correlated to metastasis. Downregulation of these five DEGs expressions was closely associated with immunomodulators, chemokines, and infiltrating levels of different immune cells, which indicated that these five DEGs were key immune-related novel prognostic biomarkers for KIRC., Conclusions: The five identified DEGs serve as potential novel prognostic biomarkers for KIRC. However, the crucial factors that lead to the downregulation and functional inactivation of these five key genes need to be explored in future studies., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-24-225/coif). The authors have no conflicts of interest to declare., (2024 AME Publishing Company. All rights reserved.)

In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using integrated bioinformatic and machine learning analysis. The results showed that a total of 88 functionally related ALI-associated hub genes in sepsis were identified by MCODE analysis and they were enriched in infection and inflammtory responses, lung and cardiovascular disease pathways. These hub genes stratified ALI-sepsis and sepsis and further stratified two subtypes of sepsis-ALI with differential ALI scores, hub gene expression patterns, and levels of immune cells. A seven-gene signature including TNFRSF1A, NFKB1, FCGR2A, NFE2L2, ICAM1 and SOCS3 and PDCD1 was derived from the hub genes. These genes were significantly implicated in immune and metabolism pathways. They were expressed in six circulatory immune cells based on analysis of a single cell RNA sequencing dataset. Furthermore, the seven-gene signature was corrobarated using by integrating 12 machine learning algorithms. A premium three-gene signature NFE2L2, FCGR2A and PDCD1 for differentiating ALI-sepsis from sepsis were also derived from the seven-gene signature based on analysis of the seven core hub genes by the machine learning algorithms. Furthermore, the expressions of hub genes were verified in sepsis mice models. Therefore, our study provided an avenue to develop a molecular tool for identify and characterize progression of acute lung injury associated with sepsis., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)