Identification of specific modules and significant genes associated with colon cancer by weighted gene co‑expression network analysis

TMEM147 Correlates with Immune Infiltration and Serve as a Potential Prognostic Biomarker in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies and is associated with high mortality. Transmembrane protein 147 (TMEM147) is a seven-transmembrane protein that may mediate immune regulation. However, the relevance of TMEM147 to immune regulation in HCC and the prognosis of HCC patients are unclear.

Methods

We analyzed TMEM147 expression in HCC by using the Wilcoxon rank-sum test. Real time quantitative PCR (RT-qPCR) and Western blot analysis of tumor tissues and cell lines were used to verify TMEM147 expression in HCC. The influence of TMEM147 on HCC prognosis was assessed using Kaplan-Meier analysis, Cox regression analysis, and a prognostic nomogram. The functions of the TMEM147-related differentially expressed genes (DEGs) were identified by Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and gene set enrichment analysis (GSEA). In addition, we examined the associations between TMEM147 expression and immune infiltration using single-sample gene set enrichment analysis (ssGSEA) and immunofluorescence staining of HCC tissues.

Results

Our results showed that the expression of TMEM147 was significantly higher in human HCC tissues than in adjacent normal liver tissues, with similar findings in human HCC cell lines. High TMEM147 expression was correlated with T stage, pathological stage, histological grade, race, alpha-fetoprotein level, and vascular invasion in HCC. Moreover, we revealed that high TMEM147 expression was associated with shorter survival times and that TMEM147 could be a risk factor for overall survival, along with T stage, M stage, pathological stage, and tumor status. Mechanistic studies revealed that high TMEM147 expression was linked to the B lymphocyte, antigen response, IL6 signaling pathway, cell cycle, Kirsten rat sarcoma viral oncogene homolog (KRAS) signaling pathway, and myelocytomatosis oncogene (MYC) targets. Correspondingly, TMEM147 expression was positively associated with the infiltration of immune cells, including Th2 cells, follicular helper T cells, macrophages, and NK CD56 bright cells in HCC.

Conclusions

TMEM147 might be a biomarker for poor prognosis and is related to immune cell infiltration in HCC.

A transmembrane protein family gene signature for overall survival prediction in osteosarcoma

The transmembrane (TMEM) protein family is constituted by a large number of proteins that span the lipid bilayer. Dysregulation of TMEM protein genes widely occurs and is associated with clinical outcomes of patients with multiple tumors. Nonetheless, the significance of TMEM genes in the prognosis prediction of patients with osteosarcoma remains largely unclear. Here, we comprehensively analyzed TMEM protein family genes in osteosarcoma using public resources and bioinformatics methods. Prognosis-related TMEM protein family genes were identified by the univariate Cox regression analysis and were utilized to construct a signature based on six TMEM protein family genes (TMEM120B, TMEM147, TMEM9B, TMEM8A, TMEM59, and TMEM39B) in osteosarcoma. The prognostic signature stratified patients into high- and low-risk groups, and validation in the internal and external cohorts confirmed the risk stratification ability of the signature. Functional enrichment analyses of differentially expressed genes between high- and low-risk groups connected immunity with the prognostic signature. Moreover, we found that M2 and M0 macrophages were the most abundant infiltrated immune cell types in the immune microenvironment, and samples of the high-risk group showed a decreased proportion of M2 macrophages. Single-sample gene set enrichment analysis revealed that the scores of neutrophils and Treg were markedly lower in the high-risk group than these in the low-risk group in The Cancer Genome Atlas and GSE16091 cohorts. As for the related immune functions, APC co-inhibition and cytolytic activity exhibited fewer active levels in the high-risk group than that in the low-risk group in both cohorts. Of the six TMEM genes, the expression of TMEM9B was lower in the high-risk group than in the low-risk group and was positively associated with the overall survival of osteosarcoma patients. In conclusion, our TMEM protein family gene-based signature is a novel and clinically useful prognostic biomarker for osteosarcoma patients, and TMEM9B might be a potential therapeutic target in osteosarcoma.

Immune-Related lncRNAs with WGCNA Identified the Function of SNHG10 in HBV-Related Hepatocellular Carcinoma

Objective. The hepatitis B virus (HBV) infection led to hepatitis, which was one of common reasons for hepatocellular carcinoma (HCC). The immune microenvironment alteration played a crucial role in this process. The study aimed to identify immune-related long noncoding RNAs (lncRNAs) in HBV-related HCC and explore potential mechanisms. Methods. In total, 1,072 immune‐related genes (IRGs) were enriched in different co-expression modules with weighted gene co-expression network analysis (WGCNA) combining the corresponding clinical features in HBV-related HCC. The immune-related lncRNAs were selected from the crucial co-expression model based on the correlation analysis with IRGs. The immune-related lncRNAs were furtherly used to construct prognostic signature by the Cox proportional hazards regression and Lasso regression. Furthermore, the proliferation and migration ability of lncRNA SNHG10 were verified in vitro. Results. A total of nine co-expression modules were identified by WGCNA of which the “red” co-expression module was most correlated with various clinical characteristics. Additionally, the IRGs in this module were significantly enriched in multiple immune-related pathways. The twelve immune-related lncRNAs prognostic signature (HAND2-AS1, LINC00844, SNHG10, MALAT1, LINC00460, LBX2-AS1, MIR31HG, SEMA6A-AS1, LINC1278, LINC00514, CTBP-AS2, and LINC00205) was constructed. The risk score was an independent risk factor in HBV-related HCC and verified by principal components analysis (PCA), nomogram, and PCR between different cell lines. Moreover, the proportion of immune cells were significantly different between high-risk score group and low-risk score group. The malignant behavior of Hep3B was significantly different between si-lncRNA SNHG10 and control group. Conclusions. The immune-related lncRNAs prognostic signature provided some potential biomarkers and molecular mechanisms in HBV-related HCC.

Identified lncRNAs functional modules and genes in prediabetes with hypertriglyceridemia by weighted gene co-expression network analysis

Background

Hypertriglyceridemia (HTG) is one of the most important comorbidities in abnormal glucose patients. The aim of this study was to identify lncRNAs functional modules and hub genes related to triglyceride (TG) in prediabetes.

Methods

The study included 12 prediabetic patients: 6 participants with HTG and 6 participants with normal triglyceride (NTG). Whole peripheral blood RNA sequencing was performed for these samples to establish a lncRNA library. WGCNA, KEGG pathways analysis and the PPI network were used to construct co‐expression network, to obtain modules related to blood glucose, and to detect key lncRNAs. Meanwhile, GEO database and qRT-PCR were used to validate above key lncRNAs.

Results

We found out that the TCONS_00334653 and PVT1, whose target mRNA are MYC and HIST1H2BM, were downregulating in the prediabetes with HTG. Moreover, both of TCONS_00334653 and PVT1 were validated in the GEO database and qRT-PCR.

Conclusions

Therefore, the TCONS_00334653 and PVT1 were detected the key lncRNAs for the prediabetes with HTG, which might be a potential therapeutic or diagnostic target for the treatment of prediabetes with HTG according to the results of validation in the GEO database, qRT-PCR and ROC curves.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12986-022-00665-5.

Gene Signature and Identification of Clinical Trait-Related m6 A Regulators in Pancreatic Cancer

Pancreatic cancer (PC) has a very poor prognosis and is usually diagnosed only at an advanced stage. The discovery of new biomarkers for PC will help in early diagnosis and a better prognosis for patients. Recently, N6-methyladenosine (m⁶A) RNA modifications and their regulators have been implicated in the development of many cancers. To investigate the functions and mechanisms of m⁶A modifications in the development of PC, 19 m⁶A regulators, including m⁶A-methyltransferases (ZC3H13, RBM15/15B, WTAP, KIAA1429, and METTL3/14), demethylases (FTO and ALKBH5), and binding proteins (YTHDF1/2/3, YTHDC1/2, IGF2BP1/2/3, HNRNPC, and HNRNPA2B1) were analyzed in 178 PC tissues from the cancer genome atlas (TCGA) database. The results were verified in PC cell lines Mia-PaCa-2, BXPC-3, and the control cell line HDE-CT. The m⁶A regulators-based sample clusters were significantly related to overall survival (OS). Further, lasso regression identified a six-m⁶A-regulator-signature prognostic model (KIAA1429, HNRNPC, METTL3, YTHDF1, IGF2BP2, and IGF2BP3). Model-based high-risk and low-risk groups were significantly correlated with OS and clinical traits (pathologic M, N, and clinical stages and vital status). The risk signature was verified as an independent prognostic marker for patients with PC. Finally, gene set enrichment analysis revealed m⁶A regulators (KIAA1429, HNRNPC, and IGF2BP2) were related to multiple biological behaviors in PC, including adipocytokine signaling, the well vs. poorly differentiated tumor pathway, tumor metastasis pathway, epithelial mesenchymal transition pathway, gemcitabine resistance pathway, and stemness pathway. In summary, the m6A regulatory factors which related to clinical characteristics can be involved in the malignant progression of PC, and the constructed risk markers may be a promising prognostic biomarker that can guide the individualized treatment of PC patients.

Identification of hub methylated-CpG sites and associated genes in oral squamous cell carcinoma

To improve personalized diagnosis and prognosis for oral squamous cell carcinoma (OSCC) by identification of hub methylated‐CpG sites and associated genes, weighted gene comethylation network analysis (WGCNA) was performed to examine and identify hub modules and CpG sites correlated with OSCC. Here, WGCNA modeling yielded blue and brown comethylation modules that were significantly associated with OSCC status. Following screening of the differentially expressed genes (DEGs) from gene expression microarrays and differentially methylated‐CpG sites (DCGs), integrated multiomics analysis of the DEGs, DCGs, and hub CpG sites from the modules was performed to investigate their correlations. Expression levels of 16 CpG sites‐associated genes were negatively correlated with methylation patterns of promoter. Moreover, Kaplan‐Meier survival analysis of the hub CpG sites and associated genes was carried out using 2 public databases, MethSurv and GEPIA. Only 5 genes, ACTA1, ACTN2, OSR1, SYNGR1, and ZNF677, had significant overall survival using GEPIA. Hypermethylated‐CpG sites ACTN2‐cg21376883 and OSR1‐cg06509239 were found to be associated with poor survival by MethSurv. Methylation status of specific site and expression levels of associated genes were determined using clinical samples by quantitative methylation‐specific PCR and real‐time PCR. Pearson's correlation analysis showed that methylation levels of cg06509239 and cg18335068 were negatively related to OSR1 and ZNF677 expression levels, respectively. Our classification schema using multiomics analysis represents a screening framework for identification of hub CpG sites and associated genes.

Identification of Prognostic Candidate Genes in Breast Cancer by Integrated Bioinformatic Analysis

Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module as the genes with the highest betweenness, suggesting that they play a central role in information transfer in the network. In the present study, eight candidate biomarkers were identified for further basic and advanced understanding of the molecular pathogenesis of breast cancer by using co-expression network analysis.