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Background: Intervertebral disc degeneration (IDD) is the leading cause of lower back pain, and an overall understanding of the molecular mechanisms related to IDD is still lacking. The purpose of this study was to explore gene signatures and immune cell infiltration related to IDD via bioinformatics analysis. Methods: A total of five expression profiles of mRNA and non-coding RNA were downloaded from the Gene Expression Omnibus (GEO) database. The potentially involved lncRNA/circRNA-miRNA-mRNA networks and protein-protein interaction networks were constructed by miRNet, circBank, STRING, and the Cytoscape database. Gene ontology, Kyoto Encyclopaedia of Genes and Genomes Analysis, Gene Set Enrichment Analysis, Gene Set Variation Analysis, Immune Infiltration Analysis, and Drug-Gene Interaction were used to analyse the top 20 hub genes. RT-qPCR was conducted to confirm the 12 differential expressions of genes both in the nucleus pulposus and annulus fibrosus tissues Results: There were 346 differentially expressed mRNAs, 12 differentially expressed miRNAs, 883 differentially expressed lncRNAs, and 916 differentially expressed circRNAs in the GEO database. Functional and enrichment analyses revealed hub genes associated with platelet activation, immune responses, focal adhesion, and PI3K-Akt signalling. The apoptotic pathway, the reactive oxygen species pathway, and oxidative phosphorylation play an essential role in IDD. Immune infiltration analysis demonstrated that the Treg cells had significant infiltration, and three levels of immune cells, including dendritic cells, Th2 cells, and tumour-infiltrating lymphocytes, were inhibited in IDD. Drug-gene interaction analysis showed that COL1A1 and COL1A2 were targeted by collagenase clostridium histolyticum, ocriplasmin, and PDGFRA was targeted by 66 drugs or molecular compounds. Finally, 24 cases of IDD tissues and 12 cases of normal disc tissues were collected, and the results of RT-qPCR were consistent with the bioinformatics results. Conclusion: Our data indicated that the 20 hub genes and immune cell infiltration were involved in the pathological process of IDD. In addition, the PDGFRA and two potential drugs were found to be significant in IDD development., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Tang, He, Zhu, Wang, Chen, Zhang, Zhou, Wang, Li, Liu, Zhou and Liu.)

Background: Myocardial infarction (MI) ranks among the most prevalent cardiovascular diseases. Insufficient blood flow to the coronary arteries always leads to ischemic necrosis of the cardiac muscle. However, the mechanism of myocardial injury after MI remains unclear. This article aims to explore the potential common genes between mitophagy and MI and to construct a suitable prediction model., Methods: Two Gene Expression Omnibus (GEO) datasets (GSE62646 and GSE59867) were used to screen the differential expression genes in peripheral blood. SVM, RF, and LASSO algorithm were employed to find MI and mitophagy-related genes. Moreover, DT, KNN, RF, SVM and LR were conducted to build the binary models, and screened the best model to further external validation (GSE61144) and internal validation (10-fold cross validation and Bootstrap), respectively. The performance of various machine learning models was compared. In addition, immune cell infiltration correlation analysis was conducted with MCP-Counter and CIBERSORT., Results: We finally identified ATG5, TOMM20, MFN2 transcriptionally differed between MI and stable coronary artery diseases. Both internal and external validation supported that these three genes could accurately predict MI withAUC = 0.914 and 0.930 by logistic regression, respectively. Additionally, functional analysis suggested that monocytes and neutrophils might be involved in mitochondrial autophagy after myocardial infarction., Conclusion: The data showed that the transcritional levels of ATG5, TOMM20 and MFN2 in patients with MI were significantly different from the control group, which might be helpful to further accurately diagnose diseases and have potential application value in clinical practice., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Yang, Sun and Wang.)

RIO kinase 2 has emerged as a critical kinase for ribosome maturation, and recently it has also been found to play a fundamental role in cancer, being involved in the occurrence and progression of glioblastoma, liver cancer, prostate cancer, non-small cell lung cancer, and acute myeloid leukemia. However, our knowledge in this regard is fragmented and limited and it is difficult to determine the exact role of RIO kinase 2 in tumors. Here, we conducted an integrated pan-cancer analysis comprising 33 cancer-types to determine the function of RIO kinase 2 in malignancies. The results show that RIO kinase 2 is highly expressed in all types of cancer and is significantly associated with tumor survival, metastasis, and immune cell infiltration. Moreover, RIO kinase 2 alteration via DNA methylation, and protein phosphorylation are involved in tumorigenesis. In summary, RIO kinase two serves as a promising target for the identification of cancer and increases our understanding of tumorigenesis and cancer progression and enhancing the ultimate goal of improved treatment for these diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li, Zou, Yan, Li, Li and Liu.)

Pancreatic ductal adenocarcinoma (PDAC) is the most aggressive digestive malignancy due to frequent late-stage diagnosis, rapid progression and resistance to therapy. With increasing PDAC incidence worldwide, there is an urgent need for new prognostic biomarkers and therapy targets. Recently, RNA methylation has emerged as a new tumorigenic mechanism in different cancers. 5-methylcytosine (m5C) is one of the most frequent RNA modifications and occurs on a variety of RNA species including mRNA, thereby regulating gene expression. Here we investigated the prognostic role of m5C-regulator-associated transcriptional signature in PDAC. We evaluated m5C-regulator status and expression in 239 PDAC samples from publicly available datasets. We used unsupervised consensus clustering analyses to classify PDACs based on m5C-regulator expression. From the resulting signature of differentially expressed genes (DEGs), we selected prognosis-relevant DEGs to stratify patients and build a scoring signature (m5C-score) through LASSO and multivariate Cox regression analyses. The m5C-score represented a highly significant independent prognostic marker. A high m5C-score correlated with poor prognosis in different PDAC cohorts, and was associated with the squamous/basal subtype as well as activated cancer-related pathways including Ras, MAPK and PI3K pathways. Furthermore, the m5C-score correlated with sensitivity to pathway-specific inhibitors of PARP, EGFR, AKT, HER2 and mTOR. Tumors with high m5C-score were characterized by overall immune exclusion, low CD8 + T-cell infiltration, and higher PD-L1 expression. Overall, the m5C-score represented a robust predictor of prognosis and therapy response in PDAC, which was associated with unfavorable molecular subtypes and immune microenvironment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yun, Yang, Zhang, Yang, Liu, Grützmann, Pilarsky and Britzen-Laurent.)

Background: Atopic dermatitis (AD) is a dermatological disorder characterized by symptoms such as chronically inflamed skin and frequently intolerable itching. The mechanism underlying AD development is still unclear. Our study aims to identify the diagnostic and therapeutic biomarkers for AD and provide insight into immune mechanisms at the molecular level through bioinformatics analysis. Methods: The GSE6012, GSE32924, and GSE36842 gene expression profiles were obtained for analysis from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were segregated using the "Batch correction" and "RobustRankAggreg" methods. Weighted gene co-expression network analysis (WGCNA) was performed to screen for module genes with AD traits. Then, common DEGs (co-DEGs) were screened out via combined differential expression analysis and WGCNA. Functional enrichment analysis was performed for these co-DEGs using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), followed by protein-protein interaction network analysis. Candidate hub genes were identified using the "cytoHubba" plugin in Cytoscape, and their value for AD diagnosis was validated using receiver operating characteristic curve analysis in the external database GSE120721. Immunohistochemical staining was performed for further validation. The CIBERSORT algorithm was used to evaluate skin samples obtained from healthy controls (HCs) and lesions of AD patients, to determine the extent of immune cell infiltration. The association between the identified hub genes and significant differential immune cells was analyzed using Pearson correlation analysis. Results: A total of 259 DEGs were acquired from the intersection of DEGs obtained by the two independent procedures, and 331 AD-trait module genes were separated out from the blue module via WGCNA analysis. Then, 169 co-DEGs arising from the intersection of the 259 DEGs and the 331 AD-trait module genes were obtained. We found that co-DEGs were significantly enhanced in the type I interferon and IL-17 signal transduction pathways. Thirteen potential hub genes were identified using Cytoscape. Five hub genes (CCR7, CXCL10, IRF7, MMP1, and RRM2) were identified after screening via external dataset validation and immunohistochemical analysis. We also identified four significant differential immune cells, i.e., activated dendritic cells, plasma cells, resting mast cells, and CD4 + naïve T cells, between AD patients and HCs. Moreover, the relationship between the identified hub genes and significant differential immune cells was analyzed. The results showed that the CCR7 expression level was positively correlated with the number of CD4 + naïve T cells (R = 0.42, p = 0.011). Conclusion: CCR7, CXCL10, IRF7, MMP1, and RRM2 could be potential diagnostic and therapeutic biomarkers for AD. CCR7 expression level was positively correlated with the number of CD4 + naïve T cells in AD. These findings need to be corroborated in future studies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li, Lu and Han.)

Immune checkpoint blocking (ICB) immunotherapy has achieved great success in the treatment of various malignancies. Although not have been approved for the treatment of ovarian cancer (OC), it has been actively tested for the treatment of OC. However, biomarkers that could indicate the immune status of OC and predict the response to ICB are rare. We downloaded RNAseq and clinical data of OC from The Cancer Genome Atlas (TCGA). Data analysis revealed both TMB high and immunity high were significantly related to better survival of OC. Up-regulated differentially expressed genes (Up-DEGs) were identified by analyzing the gene expression levels. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in the "GSVA" and "limma" package in R software. The correlation of genes with overall survival was also analyzed by conducted Kaplan-Meier survival analysis. Four genes, CXCL13, FCRLA, MS4A1, and PLA2G2D were found positively correlated with better prognosis of OC and mainly involved in immune response-related pathways. Finally, TIMER and TIDE were used to predict gene immune function and its association with immunotherapy. We found that these four genes were positively correlated with better response to immune checkpoint blockade-based immunotherapy. Altogether, CXCL13, FCRLA, MS4A1, and PLA2G2D may be used as potential therapeutic genes for reflecting OC immune status and predicting response to immunotherapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Fan, Lei, Lin, Zhou, Shu, Yan, Chen, Zhang and Yin.)

Background: Cervical squamous cell carcinoma (CESC) is one of the most frequent malignancies in women worldwide. The level of immune cell infiltration and immune-related genes (IRGs) can significantly affect the prognosis and immunotherapy of CESC patients. Thus, this study aimed to identify an immune-related prognostic signature for CESC. Methods: TCGA-CESC cohorts, obtained from TCGA database, were divided into the training group and testing group; while GSE44001 dataset from GEO database was viewed as external validation group. ESTIMATE algorithm was applied to evaluate the infiltration levels of immune cells of CESC patients. IRGs were screened out through weighted gene co-expression network analysis (WGCNA). A multi-gene prognostic signature based on IRGs was constructed using LASSO penalized Cox proportional hazards regression, which was validated through Kaplan-Meier, Cox, and receiver operating characteristic curve (ROC) analyses. The abundance of immune cells was calculated using ssGSEA algorithm in the ImmuCellAI database, and the response to immunotherapy was evaluated using immunophenoscore (IPS) analysis and the TIDE algorithm. Results: In TCGA-CESC cohorts, higher levels of immune cell infiltration were closely associated with better prognoses. Moreover, a prognostic signature was constructed using three IRGs. Based on this given signature, Kaplan-Meier analysis suggested the significant differences in overall survival (OS) and the ROC analysis demonstrated its robust predictive potential for CESC prognosis, further confirmed by internal and external validation. Additionally, multivariate Cox analysis revealed that the three IRGs signature served as an independent prognostic factor for CESC. In the three-IRGs signature low-risk group, the infiltrating immune cells (B cells, CD4/8 + T cells, cytotoxic T cells, macrophages and so on) were much more abundant than that in high-risk group. Ultimately, IPS and TIDE analyses showed that low-risk CESC patients appeared to present with a better response to immunotherapy and a better prognosis than high-risk patients. Conclusion: The present prognostic signature based on three IRGs (CD3E, CD3D, LCK) was not only reliable for survival prediction but efficient to predict the clinical response to immunotherapy for CESC patients, which might assist in guiding more precise individual treatment in the future., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Zuo, Xiong, Zeng, Jiang, Xiong, Tao and Guo.)