Identification of Early Diagnostic and Prognostic Biomarkers via WGCNA in Stomach Adenocarcinoma

Post-Transcriptional Regulator RBM47 Stabilizes FBXO2 mRNA to Advance Osteoarthritis Development: WGCNA Analysis and Experimental Validation

Osteoarthritis (OA) is a common chronic joint degenerative disease and a major cause of disability in the elderly. However, the current intervention strategies cannot effectively improve OA, and the pathogenesis of OA remains elusive. The present study identified RNA binding motif protein 47 (RBM47) as an upstream modulator of key dysregulation gene co-expression module based on weighted gene co-expression network analysis (WGCNA) analysis and least absolute shrinkage and selection operator (Lasso) modeling. Subsequently, data from real-time quantitative PCR and western blot analysis revealed that RBM47 was upregulated in OA models in vivo and in vitro compared with normal controls. Functional analysis results from the MTT assay, flow cytometry, evaluation of LDH activities and inflammatory mediators, and western blot analysis of extracellular matrix (ECM) proteins, showed that RBM47 knockdown significantly alleviated inflammation, apoptosis, and ECM degradation in interleukin 1β (IL-1β)-treated chondrocytes. Mechanistically, RBM47 bound to F box only protein 2 (FBXO2) and stabilized FBXO2 messenger RNA (mRNA) to promote the phosphorylation of signal transducer and activator of transcription 3 (STAT3) in chondrocytes. Results from the recovery assay showed that the re-activation of STAT3 signaling by overexpressing FBXO2 or STAT3 counteracted the alleviating effect of RBM47 downregulation on IL-1β-induced inflammation, apoptosis, and ECM degradation. Altogether, our findings illustrate that RBM47 stabilizes FBXO2 mRNA to advance OA development by activating STAT3 signaling, which enhances our understanding of the molecular regulatory mechanisms underlying the development of OA.

Gastric cancer fibroblasts affect the effect of immunotherapy and patient prognosis by inducing micro-vascular production

Introduction

Immunotherapy is critical for treating many cancers, and its therapeutic success is linked to the tumor microenvironment. Although anti-angiogenic drugs are used to treat gastric cancer (GC), their efficacy remains limited. Cancer-associated fibroblast (CAF)-targeted therapies complement immunotherapy; however, the lack of CAF-specific markers poses a challenge. Therefore, we developed a CAF angiogenesis prognostic score (CAPS) system to evaluate prognosis and immunotherapy response in patients with GC, aiming to improve patient stratification and treatment efficacy.

Methods

We assessed patient-derived GC CAFs for promoting angiogenesis using EdU, cell cycle, apoptosis, wound healing, and angiogenesis analysis.

Results

We then identified CAF-angiogenesis-associated differentially-expressed genes, leading to the development of CAPS, which included THBS1, SPARC, EDNRA, and VCAN. We used RT-qPCR to conduct gene-level validation, and eight GEO datasets and the HPA database to validate the CAPS system at the gene and protein levels. Six independent GEO datasets were utilized for validation. Overall survival time was shorter in the high- than the low-CAPS group. Immune microenvironment and immunotherapy response analysis showed that the high-CAPS group had a greater tendency toward immune escape and reduced immunotherapy efficacy than the low-CAPS group.

Discussion

CAPS is closely associated with GC prognosis and immunotherapy outcomes. It is therefore an independent predictor of GC prognosis and immunotherapy efficacy.

Uncovering the ceRNA Network Related to the Prognosis of Stomach Adenocarcinoma Among 898 Patient Samples

Stomach adenocarcinoma (STAD) patients are often associated with significantly high mortality rates and poor prognoses worldwide. Among STAD patients, competing endogenous RNAs (ceRNAs) play key roles in regulating one another at the post-transcriptional stage by competing for shared miRNAs. In this study, we aimed to elucidate the roles of lncRNAs in the ceRNA network of STAD, uncovering the molecular biomarkers for target therapy and prognosis. Specifically, a multitude of differentially expressed lncRNAs, miRNAs, and mRNAs (i.e., 898 samples in total) was collected and processed from TCGA. Cytoplasmic lncRNAs were kept for evaluating overall survival (OS) time and constructing the ceRNA network. Differentially expressed mRNAs in the ceRNA network were also investigated for functional and pathological insights. Interestingly, we identified one ceRNA network including 13 lncRNAs, 25 miRNAs, and 9 mRNAs. Among them, 13 RNAs were found related to the patient survival time; their individual risk score can be adopted for prognosis inference. Finally, we constructed a comprehensive ceRNA regulatory network for STAD and developed our own risk-scoring system that can predict the OS time of STAD patients by taking into account the above.

The Tumor Stemness Indice mRNAsi can Act as Molecular Typing Tool for Lung Adenocarcinoma

Due to the high heterogeneity, lung adenocarcinoma (LUAD) cannot be distinguished into precise molecular subtypes, thereby resulting in poor therapeutic effect and low 5-year survival rate clinically. Although the tumor stemness score (mRNAsi) has been shown to accurately characterize the similarity index of cancer stem cells (CSCs), whether mRNAsi can serve as an effective molecular typing tool for LUAD isn't reported to date. In this study, we first demonstrate that mRNAsi is significantly correlated with the prognosis and disease degree of LUAD patients, i.e., the higher the mRNAsi, the worse the prognosis and the higher the disease degree. Second, we identify 449 mRNAsi-related genes based on both weighted gene co-expression network analysis (WGCNA) and univariate regression analysis. Third, our results display that 449 mRNAsi-related genes can accurately distinguish the LUAD patients into two molecular subtypes: ms-H subtype (with high mRNAsi) and ms-L subtype (with low mRNAsi), particularly the ms-H subtype has a worse prognosis. Remarkably, significant differences in clinical characteristics, immune microenvironment, and somatic mutation exist between the two molecular subtypes, which might lead to the poorer prognosis of the ms-H subtype patients than that of the ms-L subtype ones. Finally, we establish a prognostic model containing 8 mRNAsi-related genes, which can effectively predict the survival rate of LUAD patients. Taken together, our work provides the first molecular subtype related to mRNAsi in LUAD, and reveals that these two molecular subtypes, the prognostic model and marker genes may have important clinical value for effectively monitoring and treating LUAD patients.

A new strategy to identify ADAM12 and PDGFRB as a novel prognostic biomarker for matrine regulates gastric cancer via high throughput chip mining and computational verification

BACKGROUND

Gastric cancer is a life-threatening disease that poses a serious risk to human health. Although there are numerous molecular targets for gastric cancer in clinical practice, they often exhibit low specificity and sensitivity. Consequently, this can result in a low early diagnosis rate, delayed treatment, and poor prognosis for patients with gastric cancer. Hence, it remains crucial to identify more precise diagnostic markers for this disease.

METHODS

This study utilized ceRNA chips and bioinformatics methods to investigate the key genes and mechanisms involved in matrine intervention in gastric cancer cells.

RESULTS

ADAM12 and PDGFRB are the key genes that are down-regulated after matrine intervention in gastric cancer cells. By conducting bioinformatics analysis, two ceRNA regulatory axes were identified, which are associated with the prognosis of gastric cancer. These axes are lncRNA DGCR5/hsa-miR-206/ADAM12 and circRNA ITGA3/hsa-miR-24-3p/PDGFRB.

CONCLUSION

The low expression of ADAM12 may weaken the digestion of extracellular matrix (ECM) molecules, which can result in the invasion and metastasis of tumor cells. This occurs without the catalysis of ECM proteases, thereby impacting the invasion and metastasis of gastric cancer cells. Additionally, the analysis of immune infiltration suggests that ADAM12 and PDGFRB may influence changes in the tumor immune microenvironment, thereby affecting the occurrence and development of gastric cancer. This study contributes to a deeper understanding of the role of the matrine-related ceRNA network in gastric cancer, providing a reference for clinical diagnosis and treatment. It holds significant importance in discovering new drug treatment targets.

Identification of Immuno-Inflammation-Related Biomarkers for Acute Myocardial Infarction Based on Bioinformatics

Purpose

Previous studies have confirmed that inflammation and immunity are involved in the pathogenesis of acute myocardial infarction (AMI). However, only few related genes are identified as biomarkers for the diagnosis and treatment of AMI.

Patients and Methods

GSE48060 and GSE60993 datasets were retrieved from Gene Expression Omnibus. The differentially expressed immuno-inflammation-related genes (DEIIRGs) were obtained from GSE48060, and the biomarkers for AMI were screened and validated using the "Neuralnet" package and GSE60993 dataset. Further, the biomarker-based nomogram was constructed, and miRNAs, transcription factors (TFs), and potential drugs targeting the biomarkers were explored. Furthermore, immune infiltration analysis was analyzed in AMI. Finally, the biomarkers were verified by assessing their mRNA levels using real-time quantitative PCR (RT-qPCR).

Results

First, eight biomarkers were screened via bioinformatics, and the artificial neural network model indicated a higher prediction accuracy for AMI even in the validation dataset. Nomogram had accurate forecasting ability for AMI as well. The TFs GTF2I, PHOX2B, RUNX1, and FOS targeting hsa-miR-1297 could regulate the expressions of ADM and CBLB, and RORA could effectively interact with melatonin and citalopram. RT-qPCR results for ADM, PI3, MMP9, NRG1 and CBLB were consistent with those of bioinformatic analysis.

Conclusion

In conclusion, eight key immuno-inflammation-related genes, namely, SH2D1B, ADM, PI3, MMP9, NRG1, CBLB, RORA, and FASLG, may serve as the potential biomarkers for AMI, in which the downregulation of CBLB and upregulation of ADM, PI3, and NRG1 in AMI was detected for the first time, providing a new strategy for the diagnosis and treatment of AMI.

Identification of miR-143-3p as a diagnostic biomarker in gastric cancer

BACKGROUND

Gastric cancer (GC) is among the most common types of gastrointestinal cancers and has a high incidence and mortality around the world. To suppress the progression of GC, it is essential to develop diagnostic markers. MicroRNAs regulate GC development, but a clearer insight into their role is needed before they can be applied as a molecular markers and targets.

METHODS

In this study, we assessed the diagnostic value of differentially expressed microRNAs as potential diagnostic biomarkers for GC using data for 389 tissue samples from the Cancer Genome Atlas (TCGA) and 21 plasma samples from GC patients.

RESULTS

The expression of hsa-miR-143-3p (also known as hsa-miR-143) was significantly downregulated in GC according to the TCGA data and plasma samples. The 228 potential target genes of hsa-miR-143-3p were analyzed using a bioinformatics tool for miRNA target prediction. The target genes correlated with extracellular matrix organization, the cytoplasm, and identical protein binding. Furthermore, the pathway enrichment analysis of target genes showed that they were involved in pathways in cancer, the phosphoinositide 3-kinase (PI3K)-protein kinase B (Akt) signaling pathway, and proteoglycans in cancer. The hub genes in the protein-protein interaction (PPI) network, were matrix metallopeptidase 2 (MMP2), CD44 molecule (CD44), and SMAD family member 3 (SMAD3).

CONCLUSIONS

This study suggests that hsa-miR-143-3p may be used as a diagnostic marker for GC, contributing via the pathways involved in the development of GC.

Novel biomarkers for early detection of gastric cancer

Gastric cancer (GC) remains a leading cause of cancer-related death worldwide. Less than half of GC cases are diagnosed at an advanced stage due to its lack of early symptoms. GC is a heterogeneous disease associated with a number of genetic and somatic mutations. Early detection and effective monitoring of tumor progression are essential for reducing GC disease burden and mortality. The current widespread use of semi-invasive endoscopic methods and radiologic approaches has increased the number of treatable cancers: However, these approaches are invasive, costly, and time-consuming. Thus, novel molecular noninvasive tests that detect GC alterations seem to be more sensitive and specific compared to the current methods. Recent technological advances have enabled the detection of blood-based biomarkers that could be used as diagnostic indicators and for monitoring postsurgical minimal residual disease. These biomarkers include circulating DNA, RNA, extracellular vesicles, and proteins, and their clinical applications are currently being investigated. The identification of ideal diagnostic markers for GC that have high sensitivity and specificity would improve survival rates and contribute to the advancement of precision medicine. This review provides an overview of current topics regarding the novel, recently developed diagnostic markers for GC.

Identification of molecular subtypes of ischaemic stroke based on immune-related genes and weighted co-expression network analysis

Immune system has been reported to play a key role in the development of ischaemic stroke (IS). Nevertheless, its exact immune-related mechanism has not yet been fully revealed. Gene expression data of IS and healthy control samples was downloaded from Gene Expression Omnibus database and differentially expressed genes (DEGs) was obtained. Immune-related genes (IRGs) data was downloaded from the ImmPort database. The molecular subtypes of IS were identified based on IRGs and weighted co-expression network analysis (WGCNA). 827 DEGs and 1142 IRGs were obtained in IS. Based on 1142 IRGs, 128 IS samples were clustered into two molecular subtypes: clusterA and clusterB. Based on the WGCNA, the authors found that the blue module had the highest correlation with IS. In the blue module, 90 genes were screened as candidate genes. The top 55 genes were selected as the central nodes according to gene degree in protein-protein interactions network of all genes in blue module. Through taking overlap, nine real hub genes were obtained that might distinguish between clusterA subtype and clusterB subtype of IS. The real hub genes (IL7R, ITK, SOD1, CD3D, LEF1, FBL, MAF, DNMT1, and SLAMF1) may be associated with molecular subtypes and immune regulation of IS.

Screening of Diabetic Nephropathy Progression-Related Genes Based on Weighted Gene Co-expression Network Analysis

The purpose of this study is to explore the progression-related genes of diabetic nephropathy (DN) through weighted gene co-expression network analysis (WGCNA). The gene expression dataset GSE14202 was downloaded from the GEO database for differential expression analysis. WGCNA v1.69 was used to perform co-expression analysis on differentially expressed genes. 25 modular genes were selected through WGCNA. The motif enrichment analysis was performed on 25 genes, and 34 motifs were obtained, of which 8 transcription factors (TFs) were differentially expressed. GENIE3 was applied to analyze the expression correlation of 8 differentially expressed TFs and 25 genes. Combined with the predicted TF-target gene relationship, 69 interactions between 8 TFs and 18 genes were obtained. The functional enrichment analysis of 18 genes showed that 7 key genes were obviously enriched in adaptive immune response and were clearly up-regulated in advanced DN patients. The expression of C1S, LAIR1, CD84, SIT1, SASH3, and CD180 in glomerular samples from DN patients was significantly up-regulated in compared with normal samples, and the expression of these genes was negatively correlated with GFR. We observed that in the in vitro cell model of DN, the relative expression levels of 5 key genes (except SASH3) were obviously elevated in the high-glucose group. Five key genes were identified to be related to the progression of DN. The findings of this study may provide new ideas and therapeutic targets for exploring the pathogenesis of DN.

Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis

Background

Uterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated.

Methods

To identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the "gdcDEAnalysis" package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles.

Results

There were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts.

Conclusions

Based on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy.

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.

Combined bulk RNA-seq and single-cell RNA-seq identifies a necroptosis-related prognostic signature associated with inhibitory immune microenvironment in glioma

Necroptosis is a programmed cell death playing a significant role in cancer. Although necroptosis has been related to tumor immune environment (TIME) remodeling and cancer prognosis, however, the role of necroptosis-related genes (NRGs) in glioma is still elusive. In this study, a total of 159 NRGs were obtained, and parameters such as mutation rate, copy number variation (CNV), and relative expression level were assessed. Then, we constructed an 18-NRGs-based necroptosis-related signature (NRS) in the TCGA dataset, which could predict the patient's prognosis and was validated in two external CGGA datasets. We also explored the correlation between NRS and glioma TIME, chemotherapy sensitivity, and certain immunotherapy-related factors. The two necroptosis-related subtypes were discovered and could also distinguish the patients' prognosis. Through the glioblastoma (GBM) scRNA-seq data analysis, NRGs' expression levels in different GBM patient tissue cell subsets were investigated and the relative necroptosis status of different cell subsets was assessed, with the microglia score culminating among all. Moreover, we found a high infiltration level of immunosuppressive cells in glioma TIME, which was associated with poor prognosis in the high-NRS glioma patient group. Finally, the necroptosis suppressor CASP8 exhibited a high expression in glioma and was associated with poor prognosis. Subsequent experiments were performed in human glioma cell lines and patients' tissue specimens to verify the bioinformatic analytic findings about CASP8. Altogether, this study provides comprehensive evidence revealing a prognostic value of NRGs in glioma, which is associated with TIME regulation.

Identification of Glucose Metabolism-Related Genes in the Progression from Nonalcoholic Fatty Liver Disease to Hepatocellular Carcinoma

Nonalcoholic fatty liver disease (NAFLD) is a manifestation of hepatic metabolic syndrome that varies in severity. Hepatocellular carcinoma progresses from NAFLD when there is heterogeneity in the infiltration of immune cells and molecules. A precise molecular classification of NAFLD remains lacking, allowing further exploration of the link between NAFLD and hepatocellular carcinoma. In this work, a weighted gene coexpression network analysis was used to identify two coexpression modules based on multiple omics data used to differentiate NAFLD subtypes. Additionally, key genes in the process of glucose metabolism and NAFLD were used to construct a prognostic model in a cohort of patients with hepatocellular carcinoma. Furthermore, the specific expression of signature genes in hepatocellular carcinoma cells was analyzed using a single-cell RNA sequencing approach. A total of 19 liver tissues of NAFLD patients were obtained from the GEO database, and 81 glucose metabolism-related genes were downloaded from the CTD database. In addition, based on nine signature genes, we constructed a prognostic model to divide the HCC cohort into high and low-risk groups. We also demonstrated a significant correlation between prognostic models and clinical phenotypes. Furthermore, we integrated single-cell RNA-sequencing data and immunology data to assess potential relationships between different molecular subtypes and hepatocellular carcinoma. Finally, our study discovered that the glucose metabolism pathway may play an important role in the process of NAFLD-hepatocellular carcinoma. In addition, three glucose metabolism-related genes (SERPINE1, VCAN, and TFPI2) may be the potential targets for the immunotherapy of patients with NAFLD-hepatocellular carcinoma.

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

CD8+ T cell-associated genes MS4A1 and TNFRSF17 are prognostic markers and inhibit the progression of colon cancer

Background

Colon cancer (CC) is among the top three diseases with the highest morbidity and mortality rates worldwide. Its increasing incidence imposes a major global health burden. Immune checkpoint inhibitors, such as anti-PD-1 and anti-PD-L1, can be used for the treatment of CC; however, most patients with CC are resistant to immunotherapy. Therefore, identification of biomarkers that can predict immunotherapy sensitivity is necessary for selecting patients with CC who are eligible for immunotherapy.

Methods

Differentially expressed genes associated with the high infiltration of CD8+ T cells were identified in CC and para-cancerous samples via bioinformatic analysis. Kaplan–Meier survival analysis revealed that MS4A1 and TNFRSF17 were associated with the overall survival of patients with CC. Cellular experiments were performed for verification, and the protein expression of target genes was determined via immunohistochemical staining of CC and the adjacent healthy tissues. The proliferation, migration and invasion abilities of CC cells with high expression of target genes were determined via in vitro experiments.

Results

Differential gene expression, weighted gene co-expression and survival analyses revealed that patients with CC with high expression of MS4A1 and TNFRSF17 had longer overall survival. The expression of these two genes was lower in CC tissues than in healthy colon tissues and was remarkably associated with the infiltration of various immune cells, including CD8+ T cells, in the tumour microenvironment (TME) of CC. Patients with CC with high expression of MS4A1 and TNFRSF17 were more sensitive to immunotherapy. Quantitative reverse transcription-polymerase chain reaction, western blotting and immunohistochemical staining validated the differential expression of MS4A1 and TNFRSF17. In addition, Cell Counting Kit-8, wound healing and transwell assays revealed that the proliferation, migration and invasion abilities of CC cells were weakened after overexpression of MS4A1 and TNFRSF17.

Conclusions

The core genes MS4A1 and TNFRSF17 can be used as markers to predict the sensitivity of patients with CC to immunotherapy and have potential applications in gene therapy to inhibit CC progression.

Exploration of hub genes, lipid metabolism, and the immune microenvironment in stomach carcinoma and cholangiocarcinoma

Background

Gastric cancer (GC) is the 5th most common cause of cancer in the world and the 3rd largest cause of cancer-related death. It is usually associated with a variety of cancers, of which cholangiocarcinoma (CCA) combined with GC accounts for about 1.6%. This study sought to examine the hub genes and role of lipid metabolism in the development and diagnosis of GC and CCA.

Methods

To screen potential hub genes, The Cancer Genome Atlas (TCGA) data sets, including the GC (STAD, dataset of GC) and CCA (CHOL, dataset of CCA) data sets, were used to conduct a differentially expressed gene (DEG) analysis and an enrichment analysis of the DEGs. A weighted-gene co-expression network analysis (WGCNA) was conducted to identify the significant gene module and then find the hub genes in the module. To verify the 4 hub genes, we conducted a differentiation analysis of the 4 genes in GC and CCA and found that there were differences. A survival analysis of the hub genes was performed and mutations were mapped. Additionally, tumor immune microenvironment (TIME) and immune analyses were performed to evaluate how lipid metabolism affects the development of GC with CCA.

Results

The principal component analysis showed that both GC and CCA had distinct up-regulated and down-regulated genes, which are involved in a variety of metabolic processes. Upon WGCNA, the turquoise and blue modules were meaningful, and the hub genes were identified from these 2 modules. Four hub genes were identified: amyloid beta precursor protein binding family B member 1 (APBB1), Homo sapiens armadillo repeat containing X-linked 1 (ARMCX1), DAZ interacting zinc finger protein 1 (DZIP1), and methionine sulfoxide reductase B3 (MSRB3). In survival analysis, increased expression of the 4 hub genes was associated with inferior survival outcomes, with variations in all 4 genes. Additionally, we demonstrated that genes related to lipid metabolism had an effect on immune function.

Conclusions

APBB1, ARMCX1, DZIP1, and MSRB3 affect the development of GC and CCA and can be used as biomarkers. The expression of lipid metabolism genes is related to the TIME of patients with GC and CCA.