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Gliomas cause high mortality around the world. The metabolic pattern of the tumor was previously suggested to be associated with the patient's survival outcome and immune activity. Yet, this relationship in glioma remains unknown. This study systematically evaluated the immune landscape in different phenotypes classified by metabolic-related pathways of 3068 glioma samples and 33 glioblastoma single-cell sequencing samples. Machine learning prediction analysis of microarray with R (pamr) was used for validating clustering results. A total of 5842 pan-cancer samples were used for external validation of the metabolic clusters. Cell Counting Kit-8 (CCK8) assay, cell clone assay, EdU assay, wound healing assay, Transwell assay, and co-culture assay were performed to verify the distinction in molecular characteristics among metabolic clusters. Metabolomics and RNA sequencing were performed on HS683 and U251 cells to annotate potential hyaluronic acid (HA)-mediated pathways. Three distinct metabolic phenotypes were identified. Metabolic cluster 1 correlated with a high number of immune infiltrating cells and poor survival of glioma patients. Metabolic clusters were proved with different levels of the macrophage markers CD68 and CD163 by multiplex immunofluorescence staining. Glioma cells from other metabolic clusters also expressed various levels of HA. HA was further found to mediate glioma proliferation, progression, and invasion. Moreover, HA potentially promoted macrophage recruitment and M2 polarization through the IL-1/CHI3L1 and TGF-b/CHI3L1 axes. HA also regulated the expression of PD-L1. This work revealed the significant connection between metabolic patterns, especially HA, and tumor immune infiltration in gliomas.

Hypoxia and angiogenesis are the leading causes of tumor progression, and their strong correlation has been discovered in many cancers. However, their collective function’s prognostic and biological roles were not reported in gastric cancer. Hence, we aimed to investigate the effects of hypoxia and angiogenesis on gastric cancer via sequencing data. This study used weighted gene coexpression network analysis and random forest regression to build a hypoxia-angiogenesis-related model (HARM) via the TCGA-STAD lncRNA data. It estimated the HARM’s correlation with clinical features and its accuracy for survival prediction. Sequential functional analyses were conducted to investigate its biological role, and we next sought the immune landscape status and immunological function variation by ESTIMATE score calculation and GSVA, respectively. Seven different algorithms were conducted to assess the immunocyte infiltration, and TIDE score and immune checkpoint levels were compared between the high- and low-HARM groups. As a result, we found that HARM predicted patient survival with high accuracy and was correlated with higher stages of gastric cancer. Various cancer-associated pathways and macrophage-related regulations were upregulated in the high-HRAM group. The high-HARM group harbored higher immune levels, and M2 macrophages and cancer-associated fibroblasts were particularly highly unfiltered. Furthermore, globally upregulated immune checkpoints and higher TIDE scores were observed in the high-HARM group. Finally, we filtered eight drugs with lower IC50 in the high-HARM group as potential drugs for the HARM-targeted therapy. We believe this study opens up novel perspectives into the interaction between hypoxia-angiogenesis and immunosuppression and will provide novel insights for gastric cancer immunotherapy.

Although immunotherapy and targeted treatments have dramatically improved the survival of melanoma patients, the intra- or intertumoral heterogeneity and drug resistance have hindered the further expansion of clinical benefits.The 96 combination frames constructed by ten machine learning algorithms identified a prognostic consensus signature based on 1002 melanoma samples from nine independent cohorts. Clinical features and 26 published signatures were employed to compare the predictive performance of our model.A machine learning-based prognostic signature (MLPS) with the highest average C-index was developed via 96 algorithm combinations. The MLPS has a stable and excellent predictive performance for overall survival, superior to common clinical traits and 26 collected signatures. The low MLPS group with a better prognosis had significantly enriched immune-related pathways, tending to be an immune-hot phenotype and possessing potential immunotherapeutic responses to anti-PD-1, anti-CTLA-4, and MAGE-A3. On the contrary, the high MLPS group with more complex genomic alterations and poorer prognoses is more sensitive to the BRAF inhibitor dabrafenib, confirmed in patients with BRAF mutations.MLPS could independently and stably predict the prognosis of melanoma, considered a promising biomarker to identify patients suitable for immunotherapy and those with BRAF mutations who would benefit from dabrafenib.

Infection susceptibility, poor vaccination efficacy, age-related disease onset, and neoplasms are linked to innate and adaptive immune dysfunction that accompanies aging (known as immunosenescence). During aging, organisms tend to develop a characteristic inflammatory state that expresses high levels of pro-inflammatory markers, termed inflammaging. This chronic inflammation is a typical phenomenon linked to immunosenescence and it is considered the major risk factor for age-related diseases. Thymic involution, naïve/memory cell ratio imbalance, dysregulated metabolism, and epigenetic alterations are striking features of immunosenescence. Disturbed T-cell pools and chronic antigen stimulation mediate premature senescence of immune cells, and senescent immune cells develop a proinflammatory senescence-associated secretory phenotype that exacerbates inflammaging. Although the underlying molecular mechanisms remain to be addressed, it is well documented that senescent T cells and inflammaging might be major driving forces in immunosenescence. Potential counteractive measures will be discussed, including intervention of cellular senescence and metabolic-epigenetic axes to mitigate immunosenescence. In recent years, immunosenescence has attracted increasing attention for its role in tumor development. As a result of the limited participation of elderly patients, the impact of immunosenescence on cancer immunotherapy is unclear. Despite some surprising results from clinical trials and drugs, it is necessary to investigate the role of immunosenescence in cancer and other age-related diseases.

With the advent of the utilization of machine learning techniques in the diagnosis of hematological diseases, endless potential can be foreseen, including digital images analysis. The application of machine-learning tool in cytogenetics contributes to the lightening of manpower burden, the improvement of recognition efficiency and the enrichment of cytogenetic maps, which paves the way for the development of digital pathology. Chromosome banding analysis is an essential technique for chromosome karyotyping, which comprises of one of important tools for the diagnostics in hematological malignancies. Its important role has been emphasized in clinic for dozens of years till now. The recognition of abnormal karyotypes is indispensable for disease classification and even diagnosis. However, a lack of abnormal karyotype images as reference dataset restricts its utilization in clinic, especially for uncommon hematological diseases. Here, to our best knowledge, we, for the first time, successfully generated abnormal karyotype images of t(9;22)(q34;q11)manually from normal karyotype images using machine learning, providing a proof-of-concept for establishing abnormal karyotypes of hematological malignancies as clinical reference. Moreover, to verify the reliability of generated abnormal dataset, artificial intelligence (AI)-recognizing models were further established based on ‘manually-built’ karyogram dataset and real karyotype dataset, respectively. The results showed that there was no difference between ‘manually-built’ karyotype dataset derived AI model (model-M) and real karyotype dataset derived AI model (model-R) regarding the recognition of t(9;22)(q34;q11) abnormality, with model-M (AUC=0.984, 95%CI 0.98-0.988) versus model-R (AUC=0.988, 95%CI 0.984-0.993) (p>0.05), which pointed out that our generated abnormal karyotype images were comparable to real images to assist the establishment of AI-recognising models. Collectively, our work demonstrates the potential application of machine learning in generating unlimited dataset from limited sources, helping to overcome the big challenge of AI in healthcare.

Anqi Lin,1,* Jiarong Yao,1,* Quan Cheng,2,3 Zaoqu Liu,4 Peng Luo,1 Jian Zhang1 1Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China; 2Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 3National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 4Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Peng Luo; Jian Zhang, Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China, Tel +86-18588447321 ; +86-13925091863, Email luopeng@smu.edu.cn; zhangjian@i.smu.edu.cnPurpose: In recent years, tumour immunotherapy has ushered in a new era of oncology treatment. However, the use of immune checkpoint inhibitors (ICIs) in the treatment of CRC remains limited. There is an urgent clinical need for precise biomarkers that can aid in the screening and treatment of CRC subtypes. Therefore, we focused on the NOTCH pathway mutation status and conducted a systematic analysis for its predictive value of ICI therapy efficacy.Methods: We collected mutational and clinical data from cohorts of CRC patients treated with ICIs. The relationship between NOTCH pathway mutations (NOTCH-MT) and CRC immunotherapy prognosis was analysed using univariate and multivariate Cox regression models. CRC cohort data from The Cancer Genome Atlas (TCGA) database were combined to obtain a comprehensive overview of immunogenicity and tumour microenvironment (TME) differences among different NOTCH pathway mutation statuses.Results: We observed greater infiltration of M1 macrophages, CD8+ T cells, neutrophils, and activated natural killer (NK) cells with NOTCH-MT status. Immunogenicity was also significantly higher in patients with NOTCH-MT, as were tumour mutational burden (TMB), neoantigen load (NAL), and the number of mutations in DNA damage repair (DDR) pathways.Conclusion: NOTCH-MT status was strongly associated with the prognosis of CRC patients treated with ICIs and is expected to serve as a novel biomarker and therapeutic target for CRC.Keywords: NOTCH, CRC, ICIs, biomarker, tumour microenvironment

Background Gastric cancer (GC) is one of the most common malignant tumors of the digestive tract which seriously endangers the health of human beings worldwide. Transcriptomic deregulation by epigenetic mechanisms plays a crucial role in the heterogeneous progression of GC. This study aimed to investigate the impact of epigenetically regulated genes on the prognosis, immune microenvironment, and potential treatment of GC. Results Under the premise of verifying significant co-regulation of the aberrant frequencies of microRNA (miRNA) correlated (MIRcor) genes and DNA methylation-correlated (METcor) genes. Four GC molecular subtypes were identified and validated by comprehensive clustering of MIRcor and METcor GEPs in 1521 samples from five independent multicenter GC cohorts: cluster 1 was characterized by up-regulated cell proliferation and transformation pathways, with good prognosis outcomes, driven by mutations, and was sensitive to 5-fluorouracil and paclitaxel; cluster 2 performed moderate prognosis and benefited more from apatinib and cisplatin; cluster 3 was featured by an up-regulated ligand–receptor formation-related pathways, poor prognosis, an immunosuppression phenotype with low tumor purity, resistant to chemotherapy (e.g., 5-fluorouracil, paclitaxel, and cisplatin), and targeted therapy drug (apatinib) and sensitive to dasatinib; cluster 4 was characterized as an immune-activating phenotype, with advanced tumor stages, benefit more from immunotherapy and displayed worst prognosis. Conclusions According to the epigenetically regulated GEPs, we developed four robust GC molecular subtypes, which facilitated the understanding of the epigenetic mechanisms underlying GC heterogeneity, offering an optimized decision-making and surveillance platform for GC patients.

Exosomes, the cell-derived small extracellular vehicles, play a vital role in intracellular communication by reciprocally transporting DNA, RNA, bioactive protein, chains of glucose, and metabolites. With great potential to be developed as targeted drug carriers, cancer vaccines and noninvasive biomarkers for diagnosis, treatment response evaluation, prognosis prediction, exosomes show extensive advantages of relatively high drug loading capacity, adjustable therapeutic agents release, enhanced permeation and retention effect, striking biodegradability, excellent biocompatibility, low toxicity, etc. With the rapid progression of basic exosome research, exosome-based therapeutics are gaining increasing attention in recent years. Glioma, the standard primary central nervous system (CNS) tumor, is still up against significant challenges as current traditional therapies of surgery resection combined with radiotherapy and chemotherapy and numerous efforts into new drugs showed little clinical curative effect. The emerging immunotherapy strategy presents convincing results in many tumors and is driving researchers to exert its potential in glioma. As the crucial component of the glioma microenvironment, tumor-associated macrophages (TAMs) significantly contribute to the immunosuppressive microenvironment and strongly influence glioma progression via various signaling molecules, simultaneously providing new insight into therapeutic strategies. Exosomes would substantially assist the TAMs-centered treatment as drug delivery vehicles and liquid biopsy biomarkers. Here we review the current potential exosome-mediated immunotherapeutics targeting TAMs in glioma and conclude the recent investigation on the fundamental mechanisms of diversiform molecular signaling events by TAMs that promote glioma progression.

Approximately 10%-15% of stage II and 25%-30% of stage III colorectal cancer (CRC) patients experience recurrence within five years after surgery, while existing taxonomies are inadequate for daily clinical application. Therefore, a more reliable classification approach with higher accuracy and prognostic potential is urgently required to stratify stage II/III CRC and identify potential patients who will benefit from postoperative adjuvant therapy. Alternatively, interactions of ligand-receptor pairs point to an emerging direction in tumor signaling with far-reaching implications for CRC, while their impact on tumor subtyping has not been elucidated. Herein, 304 normal samples, 1,386 stage II/III CRC patients, and 2,557 ligand-receptor pairs were retrospectively retrieved. Grounded on perturbations in ligand-receptor interactions, four well-characterized and robust ligand-receptor-driven subtypes (LRDS1-4) were established in the discovery cohort and validated in meta-GEO cohort comprising nine independent cohorts. Specifically, LRDS1 was characterized by an immune-cold phenotype, with cell proliferation and differentiation-related functions and higher tumor purity; LRDS2 was endowed with matrix activation-related pathways and sensitive to fluorouracil-based chemotherapy; LRDS3 was distinguished by elevated metabolic activities and frequent 13q12.2 amplification; LRDS4 was an immune-hot subtype, driven by mutations, and exhibiting dismal prognosis with MEIS2 as the prognostic feature gene. Overall, our study developed four heterogeneous LRDS and shed novel insights into the implications of intercellular communication on stage II/III CRC from a ligand-receptor interactome perspective.

Background Glioblastoma (GBM) is a type of highly malignant brain tumor that is known for its significant intratumoral heterogeneity, meaning that there can be a high degree of variability within the tumor tissue. Despite the identification of several subtypes of GBM in recent years, there remains to explore a classification based on genes related to proliferation and growth. Methods The growth-related genes of GBM were identified by CRISPR-Cas9 and univariate Cox regression analysis. The expression of these genes in The Cancer Genome Atlas cohort (TCGA) was used to construct growth-related subtypes (GGSs) via consensus clustering. Validation of this subtyping was performed using the nearest template prediction (NTP) algorithm in two independent Gene Expression Omnibus (GEO) cohorts and the ZZ cohort. Additionally, copy number variations, biological functions, and potential drugs were analyzed for each of the different subtypes separately. Results Our research established multicenter-validated GGSs. GGS1 exhibits the poorest prognosis, with the highest frequency of chr 7 gain & chr 10 loss, and the lowest frequency of chr 19 & 20 co-gain. Additionally, GGS1 displays the highest expression of EGFR. Furthermore, it is significantly enriched in metabolic, stemness, proliferation, and signaling pathways. Besides, we screened CCT128930 and Foretinib as potential therapeutic agents for GGS1 which was an independent poor prognostic factor. GGS2 has a moderate prognosis, with a slightly higher proportion of chr 7 gain & chr 10 loss, and the highest proportion of chr 19 & 20 co-gain. The prognosis of GGS3 is the best, with the least chr 7 gain & 10 loss and EGFR expression. Conclusions These results enhance our understanding of the heterogeneity of GBM and offer insights for stratified management and precise treatment of GBM patients.

Background PI3K pathway is the most frequently mutated pathway in head and neck squamous cell carcinoma (HNSC), which plays a crucial role in tumorigenesis and progression. In the present study, we aimed to investigate the role of PI3K pathway mutation in clinical prognosis prediction and the relationship with immune microenvironment and response rate to immunotherapy. Methods We collected 129 samples with immunotherapy information from MSKCC-2019 cohort as well as 501 and 40 samples from TCGA-HNSC and MD-Anderson non-immunotherapy cohorts, respectively. Somatic mutation data was utilized to characterize the mutational status of the PI3K pathway. Subsequently, we further analyzed the differences in prognosis, immunotherapy response, genomic alterations, functional characteristics, and immune microenvironment between the mutation and wild groups. Results The Kaplan-Meier survival curves displayed that PI3K pathway mutation predicted observably prolonged overall survival (OS) in the immunotherapy cohort MSKCC-2019 (p = 0.012) but did not reach statistical significance in the non-immunotherapy cohorts TCGA-HNSC (p = 0.68) and MD-Anderson (p = 0.68). After incorporating several clinicopathologic features such as age, gender, and tumor mutation burden (TMB), the results of multivariate Cox regression analysis also demonstrated that the PI3K pathway mutation could indicate better immunotherapy outcomes in HNSC patients with a hazard ratio (HR) of 0.533 (95% CI: 0.313–0.910; p = 0.021) in the immunotherapy cohort MSKCC-2019, compared with 0.888 (95% CI: 0.636–1.241; p = 0.487) and 1.939 (95% CI: 0.483–7.781; p = 0.351) in the non-immunotherapy cohorts TCGA-HNSC and MD-Anderson. In addition, the results of the subclass mapping (SubMap) and the tumor immune dysfunction and exclusion (TIDE) also consistently suggested that patients in the mutation group are more likely to benefit from immunotherapy. And further studies showed that the mutation group owned significantly higher TMB, activated immune-related pathways, richer abundance of immune cells, and higher expression levels of immunomodulators. To improve the prognosis of the wild group, we identified five relatively sensitive potential drugs for the wild group, including “BMS-536924,” “linsitinib,” “NVP-TAE684,” “PLX-4720,” and “clonazepam.” Conclusions The PI3K pathway mutation status could be considered as a potential biomarker to predict better immunotherapeutic efficacy and clinical outcomes after immunotherapy in HNSC patients.

BACKGROUND: Angiographic computed tomography (CT) is useful in various medical contexts, but little research has been presented regarding the application of cone beam CT (CBCT) in airway stenting. This study set out to evaluate the clinical feasibility of using CBCT in airway stent placement in a single-center retrospective cohort. METHODS: A total of 228 patients with stenosis or fistula diseases were treated with metallic airway stents in the First Affiliated Hospital of Zhengzhou University from January 1, 2015, to December 31, 2018. Of them, 128 patients underwent fluoroscopy-guided airway stenting. CBCT scanning was performed on the other 100 patients during and after treatment, and their images were compared with those from postoperative multidetector CT (MDCT). The outcomes and complications in the CBCT-guided and fluoroscopy-guided groups were also assessed via Pearson’s χ(2) test or Fisher’s exact test. RESULTS: Tracheal stenting was performed successfully on the first attempt for 90 patients in the CBCT-guided group and 123 patients in the fluoroscopy-guided group. The mean measured diameters of the central airway in the CBCT images and MDCT images were 18.2±2.81 and 19.0±2.33 mm, respectively, and the mean lengths were 58.7±16.82 and 58.5±17.06 mm, respectively. In the CBCT-guided group and the fluoroscopy-guided group, the mean scores for visibility of the distal bronchus were 3.7±0.49 and 3.9±0.34, respectively; the mean scores for the pulmonary parenchyma were 3.3±0.71 and 3.9±0.31, respectively; and the mean scores for the airway above the upper stent graft were 1.8±0.41 and 4.0±0.20, respectively. Two of the three anatomical areas were reproduced in a diagnostically relevant way. The major complications rate was 7% and 19% in the CBCT-guided and fluoroscopy-guided groups, respectively. CONCLUSIONS: CBCT produces images with sufficient quality to replace MDCT as a reasonable control measure after stent implantation, and its use during surgery reduces complications relating to airway stent placement.

125I seeds can effectively inhibit the growth of a variety of cancer cells. It has been used in the treatment of a variety of cancers, and has achieved certain curative effect. However, to the best of our knowledge, no report has described the effects of 125I seeds on the biological functions of cholangiocarcinoma (CCA) and the mechanisms underlying the effects of the seeds on this cancer. In this study, we demonstrated that 125I seeds could inhibit the proliferation, migration and invasion of CCA cells, as well as promoting apoptosis and blocking the cell cycle in these cells. Moreover, 125I seeds inhibited the growth of CCA xenografts and promoted the apoptosis of CCA cells in vivo. Furthermore, transcriptome sequencing showed that 125I seeds could inhibit the growth of CCA by inhibiting the expression of AGR2 and regulating p38 MAPK pathway. Finally, this finding indicated that 125I seeds can inhibit proliferation and promote apoptosis in CCA cells by inhibiting the expression of AGR2 and DUSP1 and increasing the expression of p-p38 MAPK and p-p53. This study provides a new research direction for studies investigating the mechanisms underlying the effects of 125I seeds on CCA.

Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD).A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties.Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III.We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.

The incidence and mortality of cancer are the major health issue worldwide. Apart from the treatments developed to date, the unsatisfactory therapeutic effects of cancers have not been addressed by broadening the toolbox. The advent of immunotherapy has ushered in a new era in the treatments of solid tumors, but remains limited and requires breaking adverse effects. Meanwhile, the development of advanced technologies can be further boosted by gene analysis and manipulation at the molecular level. The advent of cutting-edge genome editing technology, especially clustered regularly interspaced short palindromic repeats (CRISPR-Cas9), has demonstrated its potential to break the limits of immunotherapy in cancers. In this review, the mechanism of CRISPR-Cas9-mediated genome editing and a powerful CRISPR toolbox are introduced. Furthermore, we focus on reviewing the impact of CRISPR-induced double-strand breaks (DSBs) on cancer immunotherapy (knockout or knockin). Finally, we discuss the CRISPR-Cas9-based genome-wide screening for target identification, emphasis the potential of spatial CRISPR genomics, and present the comprehensive application and challenges in basic research, translational medicine and clinics of CRISPR-Cas9.

As a nontraditional T-cell subgroup, γδT cells have gained popularity in the field of immunotherapy in recent years. They have extraordinary antitumor potential and prospects for clinical application. Immune checkpoint inhibitors (ICIs), which are efficacious in tumor patients, have become pioneer drugs in the field of tumor immunotherapy since they were incorporated into clinical practice. In addition, γδT cells that have infiltrated into tumor tissues are found to be in a state of exhaustion or anergy, and there is upregulation of many immune checkpoints (ICs) on their surface, suggesting that γδT cells have a similar ability to respond to ICIs as traditional effector T cells. Studies have shown that targeting ICs can reverse the dysfunctional state of γδT cells in the tumor microenvironment (TME) and exert antitumor effects by improving γδT-cell proliferation and activation and enhancing cytotoxicity. Clarification of the functional state of γδT cells in the TME and the mechanisms underlying their interaction with ICs will solidify ICIs combined with γδT cells as a good treatment option.

Yuyuan Zhang,1,* Zaoqu Liu,1,* Jie Li,1 Xin Li,1 Mengjie Duo,2 Siyuan Weng,1 Peijie Lv,3 Guozhong Jiang,4 Caihong Wang,5 Yan Li,6 Shichao Liu,6 Zhen Li1 1Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China; 3Department of Radiology, Zhengzhou University First Affiliated Hospital, Zhengzhou, Henan, People’s Republic of China; 4Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China; 5Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China; 6Department Cardiology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhen Li, Email lzlyct620@163.comIntroduction: Mutation patterns have been extensively explored to decipher the etiologies of hepatocellular carcinoma (HCC). However, the study and potential clinical role of mutation patterns to stratify high-risk patients and optimize precision therapeutic strategies remain elusive in HCC.Methods: Using exon-sequencing data in public (n=362) and in-house (n=30) cohorts, mutation signatures were extracted to decipher relationships with the etiology and prognosis in HCC. The proteomics (n=159) and cell-line transcriptome data (n=1019) were collected to screen the implication of sensitive drugs. A novel multi-step machine-learning framework was then performed to construct a classification predictor, including recognizing stable reversed gene pairs, establishing a robust prediction model, and validating the robustness of the predictor in five independent cohorts (n=900).Results: Two heterogeneous mutation signature clusters were identified, and a high-risk prognosis cluster was recognized for further analysis. Notably, mutation signature cluster 1 (MSC1) was featured by activated anti-tumor immune and metabolism dysfunctional states, higher genomic instability (high TMB, SNV neoantigen, indel neoantigens, and total neoantigens), and a dismal prognosis. Notably, MSC performed as an independent risk factor than clinical traits (eg, stage, vascular invasion). Additionally, afatinib and canertinib were recognized which might have potential therapeutic implications in MSC1, and the targets of these drugs presented a higher expression in both gene and protein levels in HCC.Discussion: Our studies may provide a promising platform for improving prognosis and tailoring therapy in HCC.Keywords: hepatocellular carcinoma, mutation signature, gene pairs, precision therapy, machine learning, prognosis

Supplementary Table S1. A literature review of studies focusing on malignant meningioma. Supplementary Table S2. The specific record of malignant histologic types. Supplementary Figure S1. The proportionality of hazards was assessed for each variable and Schoenfeld residuals were visually inspected for potential time-variant biases and none were considered significant based on a p-value threshold of 0.05. Supplementary Figure S2. Univariate Cox proportional hazard regression model to identify prognostic factors for non-MMCM. Supplementary Figure S3. Univariate Fine-Gray regression model to identify prognostic factors for MMCM.

The identification of reliable tumor prognostic markers can help clinicians and researchers predict tumor development and patient survival outcomes more accurately, which plays a vital role in clinical diagnosis, treatment effectiveness assessment, and prognostic evaluation. Existing web tools supporting online survival analysis are gradually failing to meet the increasing demands of researchers in terms of the dataset size, richness of survival analysis methods, and diversity of customization features. Therefore, there is an urgent need for a large-scale, one-stop pan-cancer survival analysis web server. We developed PanCanSurvPlot (https://smuonco.shinyapps.io/PanCanSurvPlot/), a Shiny web tool that has incorporated a total of 215 cancer-related datasets from the GEO and TCGA databases, covering nearly 100,000 genes (mRNAs, miRNAs, and lncRNAs), approximately 45,000 samples, 51 different cancer types, and 13 different survival outcomes. The website also provides two cutoff methods based on median and optimal cutpoints. All survival analysis results from the log-rank test and univariate Cox regression are presented in a clear and straightforward summary table. Finally, users can customize color schemes and cutpoint levels to quickly obtain high-quality Kaplan-Meier survival plots that meet publication requirements.

BackgroundCD161 has been linked to the appearance and development of various cancers.MethodsThe mutation map and the variation of CNVs and SNVs of CD161 were displayed according to cBioportal and GSCALite. We also evaluated the pathway enrichment and drug sensitivity of CD161 according to GSCALite. We performed a single-cell sequencing analysis of cancer cells and T cells in melanoma. The cell communication patterns related to CD161 were further explored. Multiplex immunofluorescence staining of tissue microarrays was used to detect the association between CD161 expression and macrophages and T cells.ResultsA high CD161 level was related to neoantigens expression, pathway enrichment, and drug sensitivity. In addition, single-cell sequencing analysis showed that CD161 was mainly expressed in T cells, M1 and M2 Macrophages, neoplastic, microglial cells, neurons, and cancer cells in many tumor types. Further study on pseudotime trajectories and functional annotation of CD161 proved the critical role of CD161 in tumor progression and T cell immunity in melanoma. Multiplex immunofluorescence revealed that CD161 is closely correlated with the immune infiltration of T cells and macrophages in multiple cancers. In addition, high CD161 expression predicted a favorable immunotherapy response.ConclusionCD161 is involved in the immune infiltration of T cells and macrophages and might be a promising target for tumor immunotherapy.

Cancer drug resistance represents the main obstacle in cancer treatment. Drug-resistant cancers exhibit complex molecular mechanisms to hit back therapy under pharmacological pressure. As a reversible epigenetic modification, N6-methyladenosine (m6A) RNA modification was regarded to be the most common epigenetic RNA modification. RNA methyltransferases (writers), demethylases (erasers), and m6A-binding proteins (readers) are frequently disordered in several tumors, thus regulating the expression of oncoproteins, enhancing tumorigenesis, cancer proliferation, development, and metastasis. The review elucidated the underlying role of m6A in therapy resistance. Alteration of the m6A modification affected drug efficacy by restructuring multidrug efflux transporters, drug-metabolizing enzymes, and anticancer drug targets. Furthermore, the variation resulted in resistance by regulating DNA damage repair, downstream adaptive response (apoptosis, autophagy, and oncogenic bypass signaling), cell stemness, tumor immune microenvironment, and exosomal non-coding RNA. It is highlighted that several small molecules targeting m6A regulators have shown significant potential for overcoming drug resistance in different cancer categories. Further inhibitors and activators of RNA m6A-modified proteins are expected to provide novel anticancer drugs, delivering the therapeutic potential for addressing the challenge of resistance in clinical resistance.

Background The expression of long non-coding RNAs (lncRNAs) can function as diagnostic and therapeutic biomarkers of tumours, this research explored the effects of programmed cell death ligand-1(PD-L1) related lncRNAs on glioma. Methods Downloading gene expression profiles and clinicopathological information of glioma from TCGA and CGGA databases, 6 PD-L1-related lncRNAs were screened out through correlation analysis, Cox and LASSO regression analysis. The risk score model was established based on 6 PD-L1-related lncRNAs. Using GSVA and GSEA analyses to investigate the biological function. LINC01271 was selected as the target, and bioinformatics analysis and cell experiments in vitro were adopted to verify its effects on glioma. Results Risk scores based on 6 PD-L1-related lncRNAs (AL355974.3, LINC01271, AC011899.3, MIR4500HG, LINC02594, AL357055.3) can predict the prognosis of glioma(LGG and GBM). The high-risk score group has more typical malignant features in the immune-inflammatory microenvironment and is prone to be sensitive to anti-PD-1 treatment. The nomogram combining these lncRNAs and clinical parameters has good forecasting efficiency. LINC01271 expression can be used as a risk stratification index of glioma. Experiments in vitro confirmed its positive regulatory effect on the proliferation and migration of glioma cells. Conclusions This study demonstrates the predictive value of the risk score model based on 6 PD-L1-related lncRNAs for glioma characteristics, prognosis and immunotherapy responsiveness. LncRNA LINC01271 can independently be used as a new target for prognosis evaluation and therapy of glioma.

Gliomas, originating from the glial cells, are the most lethal type of primary tumors in the central nervous system. Standard treatments like surgery have not significantly improved the prognosis of glioblastoma patients. Recently, immune therapy has become a novel and effective option. As a conserved group of transcriptional regulators, the Sry-type HMG box (SOX) family has been proved to have a correlation with numerous diseases. Based on the large-scale machine learning, we found that the SOX family, with significant immune characteristics and genomic profiles, can be divided into two distinct clusters in gliomas, among which SOX10 was identified as an excellent immune regulator of macrophage in gliomas. The high expression of SOX10 is related to a shorter OS in LGG, HGG, and pan-cancer groups but benefited from the immunotherapy. It turned out in single-cell sequencing that SOX10 is high in neurons, M1 macrophages, and neural stem cells. Also, macrophages are found to be elevated in the SOX10 high-expression group. SOX10 has a positive correlation with macrophage cytokine production and negative regulation of macrophages’ chemotaxis and migration. In conclusion, our study demonstrates the outstanding cluster ability of the SOX family, indicating that SOX10 is an immune regulator of macrophage in gliomas, which can be an effective target for glioma immunotherapy.

CD93 is a transmembrane receptor that is mainly expressed on endothelial cells. A recent study found that upregulated CD93 in tumor vessels is essential for tumor angiogenesis in several cancers. However, the underlying mechanisms are largely unexplored. Our present research systematically analyzed the characteristics of CD93 in tumor immunotherapy among 33 cancers. CD93 levels and co-expression of CD93 on cancer and stromal cells were detected using public databases and multiple immunofluorescence staining. The Kaplan-Meier (KM) analysis identified the predictive role of CD93 in these cancer types. The survival differences between CD93 mutants and WT, CNV groups, and methylation were also investigated. The immune landscape of CD93 in the tumor microenvironment was analyzed using the SangerBox, TIMER 2.0, and single-cell sequencing. The immunotherapy value of CD93 was predicted through public databases. CD93 mRNA and protein levels differed significantly between cancer samples and adjacent control tissues in multiply cancer types. CD93 mRNA expression associated with patient prognosis in many cancers. The correlation of CD93 levels with mutational status of other gene in these cancers was also analyzed. CD93 levels significantly positively related to three scores (immune, stromal, and extimate), immune infiltrates, immune checkpoints, and neoantigen expression.. Additionally, single-cell sequencing revealed that CD93 is predominantly co-expressed on tumor and stromal cells, such as endothelial cells, cancer-associated fibroblasts (CAFs), neutrophils, T cells, macrophages, M1 and M2 macrophages. Several immune-related signaling pathways were enriched based on CD93 expression, including immune cells activation and migration, focal adhesion, leukocyte transendothelial migration, oxidative phosphorylation, and complement. Multiple immunofluorescence staining displayed the relationship between CD93 expression and CD8, CD68, and CD163 in these cancers. Finally, the treatment response of CD93 in many immunotherapy cohorts and sensitive small molecules was predicted from the public datasets. CD93 expression is closely associated with clinical prognosis and immune infiltrates in a variety of tumors. Targeting CD93-related signaling pathways in the tumor microenvironment may be a novel therapeutic strategy for tumor immunotherapy.

Advances in multi-omics and big-data technologies have led to numerous prognostic signatures aimed at improving current clinicopathological staging systems. Due to the lack of reproducibility and independent confirmation, few signatures have been translated into clinical routine. As high-quality datasets accumulate, identifying robust signatures across multiple independent cohorts becomes possible. Nonetheless, inaccurate data retrieval, different versions of genome annotations, disparate expression distributions, difficult data cleaning, inconsistent clinical information, algorithm selection, and parameter tuning have impeded model development and validation in multi-center datasets. Hence, for the first time, we introduced SurvivalML (https://rookieutopia.com/app_direct/SurvivalML/), a web application for helping develop and validate prognostic models across multi-center datasets. SurvivalML included 37,325 samples (253 eligible datasets) with both transcriptome data and survival information from 21 cancer types, which were renewedly and uniformly re-annotated, normalized, and cleaned. This application provided 10 survival machine-learning algorithms for flexibly training models via tuning essential parameters online and delivered four aspects for model evaluation, including Kaplan-Meier survival analysis, time-dependent ROC, calibration curve, and decision curve analysis. Overall, we believe that SurvivalML can serve as an attractive platform for model discovery from multi-center datasets.

As the most aggressive tumor, the outcome of pancreatic cancer (PACA) has not improved observably over the last decade. Anatomy-based TNM staging does not exactly identify treatment-sensitive patients, and an ideal biomarker is urgently needed for precision medicine. Based on expression files of 1280 patients from 10 multicenter cohorts, we screened 32 consensus prognostic genes. Ten machine-learning algorithms were transformed into 76 combinations, of which we selected the optimal algorithm to construct an artificial intelligence-derived prognostic signature (AIDPS) according to the average C-index in the nine testing cohorts. The results of the training cohort, nine testing cohorts, Meta-Cohort, and three external validation cohorts (290 patients) consistently indicated that AIDPS could accurately predict the prognosis of PACA. After incorporating several vital clinicopathological features and 86 published signatures, AIDPS exhibited robust and dramatically superior predictive capability. Moreover, in other prevalent digestive system tumors, the nine-gene AIDPS could still accurately stratify the prognosis. Of note, our AIDPS had important clinical implications for PACA, and patients with low AIDPS owned a dismal prognosis, higher genomic alterations, and denser immune cell infiltrates as well as were more sensitive to immunotherapy. Meanwhile, the high AIDPS group possessed observably prolonged survival, and panobinostat may be a potential agent for patients with high AIDPS. Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment of PACA.

SummaryData mining from RNA-seq or microarray data has become an essential part of cancer biomarker exploration. Certain existing web servers are valuable and broadly utilized, but the meta-analysis of multiple datasets is absent. Most web servers only contain tumor samples from the TCGA database with only one cohort for each cancer type, which also means that the analysis results mainly derived from a single cohort are thin and unstable. Indeed, consistent performance across multiple independent cohorts is the foundation for an excellent biomarker. Moreover, many analytical functions researchers require remain adequately unmet by these tools. Thus, we introduce BEST (Biomarker Exploration for Solid Tumors), a web application for comprehensive biomarker exploration on large-scale data in solid tumors. BEST includes more than 50,000 samples of 27 cancer types. To ensure the comparability of genes between different sequencing technologies and the legibility of clinical traits, we re-annotated transcriptome data based on the GRCh38 patch 13 sequences and unified the nomenclature of clinical traits. BEST delivers fast and customizable functions, including clinical association, survival analysis, enrichment analysis, cell infiltration, immunomodulator, immunotherapy, candidate agents, and genomic alteration. Together, our web server provides multiple cleaned-up independent datasets and diverse analysis functionalities, helping unleash the value of current data resources. It is freely available athttps://rookieutopia.com/.The bigger pictureBioinformatics web servers enable researchers without computational programming skills to conduct various bioinformatics analyses. However, most web servers only contain tumor samples from the TCGA database with only one cohort for each cancer type, which also means that the analysis results mainly derived from a single cohort are thin and unstable. Thus, we introduce BEST (Biomarker Exploration for Solid Tumors), a web application for comprehensive biomarker exploration on large-scale data in solid tumors. BEST includes more than 50,000 samples of 27 cancer types that have been uniformly re-annotated based on the GRCh38 patch 13 sequences, which ensures the comparability of genes between different sequencing technologies. BEST also offers prevalent functions including clinical association, survival analysis, enrichment analysis, cell infiltration, immunomodulator, immunotherapy, candidate agents, and genomic alteration. Together, BEST provides a curated database and innovative analytical pipelines to explore cancer biomarkers at high resolution.

Chimeric antigen receptor (CAR) T cell (CAR-T cell) therapy based on gene editing technology represents a significant breakthrough in personalized immunotherapy for human cancer. This strategy uses genetic modification to enable T cells to target tumor-specific antigens, attack specific cancer cells, and bypass tumor cell apoptosis avoidance mechanisms to some extent. This method has been extensively used to treat hematologic diseases, but the therapeutic effect in solid tumors is not ideal. Tumor antigen escape, treatment-related toxicity, and the immunosuppressive tumor microenvironment (TME) limit their use of it. Target selection is the most critical aspect in determining the prognosis of patients receiving this treatment. This review provides a comprehensive summary of all therapeutic targets used in the clinic or shown promising potential. We summarize CAR-T cell therapies’ clinical trials, applications, research frontiers, and limitations in treating different cancers. We also explore coping strategies when encountering sub-optimal tumor-associated antigens (TAA) or TAA loss. Moreover, the importance of CAR-T cell therapy in cancer immunotherapy is emphasized.

Background Mounting evidence has revealed the dynamic variations in the cellular status and phenotype of the smooth muscle cell (SMC) are vital for shaping the atherosclerotic plaque microenvironment and ultimately mapping onto heterogeneous clinical outcomes in coronary artery disease. Currently, the underlying clinical significance of SMC evolutions remains unexplored in atherosclerosis. Methods The dissociated cells from diseased segments within the right coronary artery of four cardiac transplant recipients and 1070 bulk samples with atherosclerosis from six bulk cohorts were retrieved. Following the SMC fate trajectory reconstruction, the MOVICS algorithm integrating the nearest template prediction was used to develop a stable and robust molecular classification. Subsequently, multi-dimensional potential biological implications, molecular features, and cell landscape heterogeneity among distinct clusters were decoded. Results We proposed an SMC cell fate decision signature (SCFDS)-based atherosclerosis stratification system and identified three SCFDS subtypes (C1–C3) with distinguishing features: (i) C1 (DNA-damage repair type), elevated base excision repair (BER), DNA replication, as well as oxidative phosphorylation status. (ii) C2 (immune-activated type), stronger immune activation, hyper-inflammatory state, the complex as well as varied lesion microenvironment, advanced stage, the most severe degree of coronary stenosis severity. (iii) C3 (stromal-rich type), abundant fibrous content, stronger ECM metabolism, immune-suppressed microenvironment. Conclusions This study uncovered atherosclerosis complex cellular heterogeneity and a differentiated hierarchy of cell populations underlying SMC. The novel high-resolution stratification system could improve clinical outcomes and facilitate individualized management.

Long noncoding ribonucleic acids (RNAs; lncRNAs) have been associated with cancer immunity regulation. However, the roles of immune cell-specific lncRNAs in glioblastoma (GBM) remain largely unknown. In this study, a novel computational framework was constructed to screen the tumor-infiltrating immune cell-associated lncRNAs (TIIClnc) for developing TIIClnc signature by integratively analyzing the transcriptome data of purified immune cells, GBM cell lines and bulk GBM tissues using six machine learning algorithms. As a result, TIIClnc signature could distinguish survival outcomes of GBM patients across four independent datasets, including the Xiangya in-house dataset, and more importantly, showed superior performance than 95 previously established signatures in gliomas. TIIClnc signature was revealed to be an indicator of the infiltration level of immune cells and predicted the response outcomes of immunotherapy. The positive correlation between TIIClnc signature and CD8, PD-1 and PD-L1 was verified in the Xiangya in-house dataset. As a newly demonstrated predictive biomarker, the TIIClnc signature enabled a more precise selection of the GBM population who would benefit from immunotherapy and should be validated and applied in the near future.

Accumulating evidence has demonstrated that the immune cells have an emerging role in controlling anti-tumor immune responses and tumor progression. The comprehensive role of mast cell in glioma has not been illustrated yet. In this study, 1,991 diffuse glioma samples were collected from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). xCell algorithm was employed to define the mast cell-related genes. Based on mast cell-related genes, gliomas were divided into two clusters with distinct clinical and immunological characteristics. The survival probability of cluster 1 was significantly lower than that of cluster 2 in the TCGA dataset, three CGGA datasets, and the Xiangya cohort. Meanwhile, the hypoxic and metabolic pathways were active in cluster 1, which were beneficial to the proliferation of tumor cells. A potent prognostic model based on mast cell was constructed. Via machine learning, DRG2 was screened out as a characteristic gene, which was demonstrated to predict treatment response and predict survival outcome in the Xiangya cohort. In conclusion, mast cells could be used as a potential effective prognostic factor for gliomas.

Immune checkpoint inhibitors (ICIs) combined with the anti-angiogenesis drug bevacizumab is one of the future directions of immunotherapy. However, the potential adverse drug reactions (ADRs) caused by combination therapy remain unclear. Current research on ADRs of combination therapy in cancer patients is extremely limited. Our study aims to help determine the safety of combination therapy. We downloaded the ADR reports on combination therapy, from the first quarter of 2012 to the fourth quarter of 2021, from the FDA adverse event reporting system (FAERS) database and conducted a large-scale retrospective study. The ADR signals were monitored by reporting odds ratio (ROR) and analyzing the risk of different ADRs in patients with Pan-cancer. A total of 2094 cases were selected, after excluding duplicate data and the use of chemotherapy drugs. We evaluated the risk of ADR in Pan-cancer patients. Combination therapy was an independent risk factor for adverse drug reactions associated with interstitial lung disease (OR: 8.62; 95% CI: 6.14-12.10, P .0001), hypertension (OR: 1.35; 95% CI: 1.11-1.65, P .01) and gastrointestinal bleeding (OR: 3.16; 95% CI: 2.21-4.51, P .0001). A subgroup analysis revealed that the risk of endocrine system-related ADRs was elevated in patients receiving different combination therapies or with certain tumor types. We retrospectively studied the ADR of combination therapy in Pan-cancer patients and analyzed the distribution characteristics of ADR from the perspectives of treatment strategy and cancer types to provide recommendations for the individualized management of patients receiving combination therapy.

CUGBP Elav‐like family member 2(CELF2) plays crucial roles in the development and activation of T cell. However, the impacts of CELF2 on tumour‐infiltrating immune cells (TIICs) and clinical outcomes of tumours remain unclear. In this study, we found that elevated CELF2 expression was markedly correlated with prolonged survival in multiple tumours, particularly in breast and lung cancers. Notably, CELF2 only impacted the prognosis of triple‐negative breast cancer (TNBC) with lymph node metastasis. Further investigation showed CELF2 expression was positively correlated with the infiltration abundance of dendritic cells (DCs), CD8+ T cells and neutrophils in breast invasive carcinoma (BRCA) and DCs in lung squamous cell carcinoma (LUSC). CELF2 also had strong correlations with markers of diverse TIICs such as T cells, tumour‐associated macrophages and DCs in BRCA and LUSC. Importantly, CELF2 was significantly associated with plenty of immune checkpoint molecules (ICMs) and outperformed five prevalent biomarkers including PD‐1, PD‐L1, CTLA‐4, CD8 and tumour mutation burden in predicting immunotherapeutic responses. Immunohistochemistry also revealed lower protein levels of CELF2 in TNBC and LUSC compared to normal tissues, and patients with high expression showed significantly prolonged prognosis. In conclusion, we demonstrated that increased CELF2 expression was closely related to better prognosis and superior TIIC infiltration and ICM expression, particularly in BRCA and LUSC. CELF2 also performed well in evaluating the immunotherapeutic efficacy, suggesting CELF2 might be a promising biomarker.

As essential regulators of gene expression, miRNAs are engaged in the initiation and progression of colorectal cancer (CRC), including antitumour immune response. In this study, we proposed an integrated algorithm, ImmuMiRNA, for identifying miRNA modulators of immune‐associated pathways. Based on these immune‐associated miRNAs, we applied the LASSO algorithm to develop a reliable and individualized signature for evaluating overall survival (OS) and inflammatory landscape of CRC patients. An external public data set and qRT‐PCR data from 40 samples were further utilized to validate this signature. As a result, an immune‐associated miRNA prognostic signature (IAMIPS) consisting of three miRNAs (miR‐194‐3P, miR‐216a‐5p and miR‐3677‐3p) was established and validated. Patients in the high‐risk group possessed worse OS. After stratification for clinical factors, the signature remained a powerful independent predictor for OS. IAMIPS displayed much better accuracy than the traditional clinical stage in assessing the prognosis of CRC. Further analysis revealed that patients in the high‐risk group were characterized by inflammatory response, abundance immune cell infiltration, and higher immune checkpoint profiles and tumour mutation burden (TMB). In conclusion, the IAMIPS is highly predictive of OS in patients with CRC, which may serve as a powerful prognostic tool to further optimize immunotherapies for cancer.

The discovery of immune checkpoint inhibitors (ICIs) has now been universally acknowledged as a significant breakthrough in tumor therapy after the targeted treatment of checkpoint molecules: anti-programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) and anti-cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) on several cancer types achieved satisfying results. However, there are still quite a lot of patients suffering from severe side effects and ineffective treatment outcomes. Although the current ICI therapy is far from satisfying, a series of novel immune checkpoint molecules with remarkable preclinical and clinical benefits are being widely investigated, like the V-domain Ig suppressor of T cell activation (VISTA), which can also be called PD-1 homolog (PD-1H), and ectonucleotidases: CD39, CD73, and CD38, which belong to the ribosyl cyclase family, etc. In this review, we systematically summarized and discussed these molecules' biological structures, molecular features, and the corresponding targeted drugs, aiming to help the in-depth understanding of immune checkpoint molecules and promote the clinical practice of ICI therapy.

To accurately predict the prognosis and further improve the clinical outcomes of bladder cancer (BLCA), we leveraged large-scale data to develop and validate a robust signature consisting of small gene sets. Ten machine-learning algorithms were enrolled and subsequently transformed into 76 combinations, which were further performed on eight independent cohorts (n = 1218). We ultimately determined a consensus artificial intelligence-derived gene signature (AIGS) with the best performance among 76 model types. In this model, patients with high AIGS showed a higher risk of mortality, recurrence, and disease progression. AIGS is not only independent of traditional clinical traits [(e.g., American Joint Committee on Cancer (AJCC) stage)] and molecular features (e.g., TP53 mutation) but also demonstrated superior performance to these variables. Comparisons with 58 published signatures also indicated that AIGS possessed the best performance. Additionally, the combination of AIGS and AJCC stage could achieve better performance. Patients with low AIGS scores were sensitive to immunotherapy, whereas patients with high AIGS scores might benefit from seven potential therapeutics: BRD-K45681478, 1S,3R-RSL-3, RITA, U-0126, temsirolimus, MRS-1220, and LY2784544. Additionally, some mutations (TP53 and RB1), copy number variations (7p11.2), and a methylation-driven target were characterized by AIGS-related multi-omics alterations. Overall, AIGS provides an attractive platform to optimize decision-making and surveillance protocol for individual BLCA patients.

The aberrant protein disulfide isomerase A5 (PDIA5) expression was relevant to the poor prognosis of patients with human cancers. However, its relationship with the epigenetic and genetic alterations and its effect on tumor immunity is still lacking. In the present study, we comprehensively analyzed the immune infiltration role of PDIA5 in human cancers based on large-scale bioinformatics analyses and in vitro experiments. Obvious DNA methylation and moderate alteration frequency of PDIA5 were observed in human cancers. The expression level of PDIA5 was significantly correlated with infiltrated immune cells, immune pathways, and other immune signatures. We found that cancer cells and macrophages exhibited high PDIA5 expression in human cancers using the single-cell RNA sequencing analysis. We also demonstrated the interaction between PDIA5 and immune cells in glioblastoma multiforme (GBM). Multiplex immunofluorescence staining showed the upregulated expression level of PDIA5 and the increased number of M2 macrophage markers-CD163 positive cells in pan-cancer samples. Notably, PDIA5 silencing resulted in upregulated expression of PD-L1 and SPP1 in U251 cells. Silencing of PDIA5 in hepG2 cells, U251 cells, and PC3 cells contributed to a decline in their ability of proliferation, clone formation, and invasion and inhibited the migration of cocultured M2 macrophages. Additionally, PDIA5 also displayed predictive value in the immunotherapy response of both murine and human cancer cohorts. Overall, our findings indicated that PDIA5 might be a potential target for immunotherapies in cancers.

BackgroundCLEC5A is a member of the C-type lectin superfamily. It can activate macrophages and lead to a series of immune-inflammation reactions. Previous studies reveal the role of CLEC5A in infection and inflammation diseases.MethodWe acquire and analyze data from The Cancer Genome Atlas (TCGA) database, Genotype-Tissue Expression (GTEx) database, and other comprehensive databases via GSCALite, cBioPortal, and TIMER 2.0 platforms or software. Single-cell sequencing analysis was performed for quantifying the tumor microenvironment of several types of cancers.ResultsCLEC5A is differentially expressed in a few cancer types, of which overexpression accompanies low overall survival of patients. DNA methylation mainly negatively correlates with CLEC5A expression. Moreover, CLEC5A is positively related to immune infiltration, including macrophages, cancer-associated fibroblasts (CAFs), and regulatory T cells (Tregs). Immune checkpoint genes are significantly associated with CLEC5A expression in diverse cancers. In addition, CLEC5A expression correlates with mismatch repair (MMR) in several cancers. Tumor mutation burden (TMB), microsatellite instability (MSI), and neoantigens show a positive association with CLEC5A expression in several cancers. Furthermore, CLEC5A in cancer correlates with signal transduction, the immune system, EMT, and apoptosis process. The drug sensitivity analysis screens out potential therapeutic agents associated with CLEC5A expression, including FR-180204, Tivozanib, OSI-930, Linifanib, AC220, VNLG/124, Bexarotene, omacetaxine mepesuccinate, narciclasine, leptomycin B, PHA-793887, LRRK2-IN-1, and CR-1-31B.ConclusionCLEC5A overexpresses in multiple cancers in contrast to normal tissues, and high CLEC5A expression predicts poor prognosis of patients and immune infiltration. CLEC5A is a potential prognostic biomarker of diverse cancers and a target for anti-tumor therapy.

Cancer drug resistance represents the main obstacle in cancer treatment. Drug-resistant cancers exhibit complex molecular mechanisms to hit back therapy under pharmacological pressure. As a reversible epigenetic modification, N

The interplay between long non-coding RNAs (lncRNAs) and the Notch pathway involves a variety of malignancies. However, Notch-derived lncRNAs and their latent clinical significance remain elusive in colorectal cancer (CRC). In this study, we introduced a framework that could screen Notch-derived lncRNAs (named “NLncer”) and ultimately identified 24 NLncers. To further explore the clinical significance of these NLncers, we performed LASSO and Cox regression in TCGA-CRC cohort (n = 584) and then retained six lncRNAs tightly associated with prognosis. The final model (termed “NLncS”) was subsequently tested in GSE38832 (n = 122), GSE39582 (n = 573), and an in-house clinical cohort (n = 115). Ultimately, our NLncS model could serve as an independent risk factor and afford a robust performance for assessing the prognosis of CRC patients. Additionally, patients with high NLncS risk scores were characterized by upregulation of immune pathways, strong immunogenicity, abundant CD8 + T-cell infiltration, and potentially higher response rates to CTLA4 blockers, which turned out to be suitable for immunotherapy. Aiming at globally observing the characteristics of high-risk patients, somatic mutation and methylation modification analysis provide us with evidence at the genomic and transcriptomic levels. To facilitate the clinical transformability, we mined deeply into the sensitive compounds targeting high-risk individuals and identified dasatinib as a candidate agent for patients with a high Notch risk score. In conclusion, our NLncS model is a promising biomarker for optimizing the clinical management of CRC patients.

Immunogenic cell death (ICD) is a type of cell death that leads to the regulation and activation of the immune response, which is marked by the exposure and delivery of damage‐associated molecular patterns (DAMPs) in the tumor microenvironment. Accumulating evidence has revealed the significance of ICD-related genes in tumor progression and therapeutic response. In this study, we obtained two ICD-related clusters for glioblastoma (GBM) by applying consensus clustering, and further constructed a risk signature on account of the prognostic ICD genes. Based on the risk signature, we found that higher risk scores were associated with worse patient prognosis. Besides, the results illustrated that ferroptosis regulators/markers were highly enriched the high-risk group, and ferroptosis were correlated with cytokine signaling pathway and other immune-related pathways. We also discovered that high-risk scores were correlated to specific immune infiltration patterns and good response to immune checkpoint blockade (ICB) treatment. In conclusion, our study highlights the significance of ICD-related genes as prognostic biomarkers and immune response indicators in GBM. And the risk signature integrating prognostic genes possessed significant potential value to predict the prognosis of patients and the efficacy of ICB treatment.

Metastatic dissemination represents a hallmark of cancer that is responsible for the high mortality rate. Recently, emerging evidence demonstrates a time-series event—pre-metastatic niche (PMN) has a profound impact on cancer metastasis. Exosomes, cell-free DNA (cfDNA), circulating tumor cells (CTC), and tumor microenvironment components, as critical components in PMN establishment, could be monitored by liquid biopsy. Intensive studies based on the molecular profile of liquid biopsy have made it a viable alternative to tissue biopsy. Meanwhile, the complex molecular mechanism and intercellular interaction are great challenges for applying liquid biopsy in clinical practice. This article reviews the cellular and molecular components involved in the establishment of the PMN and the promotion of metastasis, as well as the mechanisms of their interactions. Better knowledge of the characteristics of the PMN may facilitate the application of liquid biopsy for clinical diagnosis, prognosis, and treatment.

Pentraxin-3 (PTX3) is the prototype of the long pentraxin subfamily, an acute-phase protein consisting of a C-terminal pentraxin domain and a unique N-terminal domain. PTX3 was initially isolated from human umbilical vein endothelial cells and human FS-4 fibroblasts. It was subsequently found to be also produced by synoviocytes, chondrocytes, osteoblasts, smooth muscle cells, myeloid dendritic cells, epithelial cells, and tumor cells. Various modulatory factors, such as miRNAs, cytokines, drugs, and hypoxic conditions, could regulate the expression level of PTX3. PTX3 is essential in regulating innate immunity, inflammation, angiogenesis, and tissue remodeling. Besides, PTX3 may play dual (pro-tumor and anti-tumor) roles in oncogenesis. PTX3 is involved in the occurrence and development of many non-cancerous diseases, including COVID-19, and might be a potential biomarker indicating the prognosis, activity,and severity of diseases. In this review, we summarize and discuss the potential roles of PTX3 in the oncogenesis and pathogenesis of non-cancerous diseases and potential targeted therapies based on PTX3.