Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods

MRTO4 acts as an independent prognostic and immunological biomarker and is correlated with tumor microenvironment in hepatocellular carcinoma

Liver cancer is a malignant tumor found worldwide. mRNA turnover 4 homolog (MRTO4) is highly expressed in hepatocellular carcinoma (HCC) tissues, and we explored its relationship with HCC. All cancer data were downloaded from the Cancer Genome Atlas (TCGA), the Cancer Immune Atlas (TCIA), and the Human Protein Atlas (THPA). Stromal scores, immune scores, and ESTIMATE scores were calculated by "ESTIMATE" R package. Single sample gene set enrichment analysis and CIBERSORT were used to evaluate the immune status and infiltration of cancer tissues. pRRophetic R package was used to predict the half-maximal inhibitory concentration (IC50) of different drugs in each sample. MRTO4 overexpression was associated with poor prognosis in HCC, and positively correlated with the stage and grade of HCC patients. The average immunophenoscore (IPS) of the low MRTO4 group was significantly higher than that of the high MRTO4 group. Tumor microenvironment (TME) scores were significantly higher in the low MRTO4 group than in the high MRTO4 group in HCC. MRTO4 expression was positively correlated with tumor mutation burden (TMB) and was positively correlated with most immune checkpoint gene expressions in HCC. Drug sensitivity analysis showed significantly higher IC50 values for 5-fluorouracil, gemcitabine, and sorafenib in patients with low MRTO4 expression than in those with high MRTO4 expression. MRTO4 acts as an independent prognostic and immunological biomarker and is correlated with clinical stage, tumor grade, and drug sensitivity in HCC. It may serve as a putative therapeutic target and potential biomarker for prognosis of HCC.

EBNA1BP2 identified as potential prognostic biomarker for multiple tumor types in pan-cancer analysis

BACKGROUND

Epstein-Barr virus (EBV) infection has been closely linked to the development of various types of cancer. EB nuclear antigen 1 binding protein 2 (EBNA1BP2) is a crucial molecule for stable isolation of EBV in latent infection. However, the role of EBNA1BP2 in multiple tumor types is remains unclear. In this study, we comprehensively analyzed the functional characteristics of EBNA1BP2 and investigate its potential as a prognostic biomarker in pan-cancer.

METHODS

We utilized data from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) databases and employed various bioinformatics analysis tools, including TIMER2.0, HPA, GEPIA2.0, PrognoScan, cBioPortal, CancerSEA, and BioGRID to explore the expression pattern, prognostic value, immune infiltration, and methylation level of EBNA1BP2 in pan-cancer. Additionally, we conducted enrichment analysis of genes associated with EBNA1BP2 to identify potential biological functions and pathways.

RESULTS

Our analysis revealed that EBNA1BP2 expression was significantly higher in tumor tissues compared to tumor-adjacent tissues. We observed that lower expression of EBNA1BP2 in adrenocortical carcinoma (ACC), brain lower grade glioma (LGG), sarcoma (SARC), and uterine carcinosarcoma (UCS) was significantly associated with improved overall survival (OS) and disease-free survival (DFS). Furthermore, the promoter methylation level of EBNA1BP2 was downregulated in the majority of cancer types. At the single-cell level, EBNA1BP2 was found to be positively correlated with cell cycle and DNA repair processes, while negatively correlated with hypoxia. Additionally, EBNA1BP2 was associated with the infiltration of immune cells such as B cells, cancer-associated fibroblast cells, and CD8+ T cells. Gene enrichment analysis indicated that EBNA1BP2 was mainly involved in nucleoplasm and RNA binding pathways.

CONCLUSION

Our findings suggest that EBNA1BP2 may serve as a potential prognostic biomarker for survival in pan-cancer. Further experimental studies are needed to validate these findings and explore the underlying mechanisms by which EBNA1BP2 contributes to tumorigenesis.

Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.

PPIH acts as a potential predictive biomarker for patients with common solid tumors

BACKGROUND

Our previous studies have indicated that mRNA and protein levels of PPIH are significantly upregulated in Hepatocellular Carcinoma (LIHC) and could act as predictive biomarkers for patients with LIHC. Nonetheless, the expression and implications of PPIH in the etiology and progression of common solid tumors have yet to be explored, including its potential as a serum tumor marker.

METHODS

We employed bioinformatics analyses, augmented with clinical sample evaluations, to investigate the mRNA and protein expression and gene regulation networks of PPIH in various solid tumors. We also assessed the association between PPIH expression and overall survival (OS) in cancer patients using Kaplan-Meier analysis with TCGA database information. Furthermore, we evaluated the feasibility and diagnostic efficacy of PPIH as a serum marker by integrating serological studies with established clinical tumor markers.

RESULTS

Through pan-cancer analysis, we found that the expression levels of PPIH mRNA in multiple tumors were significantly different from those in normal tissues. This study is the first to report that PPIH mRNA and protein levels are markedly elevated in LIHC, Colon adenocarcinoma (COAD), and Breast cancer (BC), and are associated with a worse prognosis in these cancer patients. Conversely, serum PPIH levels are decreased in patients with these tumors (LIHC, COAD, BC, gastric cancer), and when combined with traditional tumor markers, offer enhanced sensitivity and specificity for diagnosis.

CONCLUSION

Our findings propose that PPIH may serve as a valuable predictive biomarker in tumor patients, and its secreted protein could be a potential serum marker, providing insights into the role of PPIH in cancer development and progression.

Unveiling the link between lactate metabolism and rheumatoid arthritis through integration of bioinformatics and machine learning

Rheumatoid arthritis (RA) is a persistent autoimmune condition characterized by synovitis and joint damage. Recent findings suggest a potential link to abnormal lactate metabolism. This study aims to identify lactate metabolism-related genes (LMRGs) in RA and investigate their correlation with the molecular mechanisms of RA immunity. Data on the gene expression profiles of RA synovial tissue samples were acquired from the gene expression omnibus (GEO) database. The RA database was acquired by obtaining the common LMRDEGs, and selecting the gene collection through an SVM model. Conducting the functional enrichment analysis, followed by immuno-infiltration analysis and protein-protein interaction networks. The results revealed that as possible markers associated with lactate metabolism in RA, KCNN4 and SLC25A4 may be involved in regulating macrophage function in the immune response to RA, whereas GATA2 is involved in the immune mechanism of DC cells. In conclusion, this study utilized bioinformatics analysis and machine learning to identify biomarkers associated with lactate metabolism in RA and examined their relationship with immune cell infiltration. These findings offer novel perspectives on potential diagnostic and therapeutic targets for RA.

PPIH gene regulation system and its prognostic significance in hepatocellular carcinoma: a comprehensive analysis

BACKGROUND

Peptidyl-prolyl isomerase H (PPIH) is a member of the cyclophilin protein family, which functions as a molecular chaperone and is involved in the splicing of pre-mRNA. According to reports, the malignant progression of HCC related to hepatitis B virus (HBV) is tightly associated with RNA-binding proteins. Nevertheless, there is no research on PPIH expression or its function in the occurrence and progression of HCC.

RESULTS

We are the first to reveal that the mRNA and protein levels of Ppih are substantially overexpressed in HCC, as the outcomes show. A significant correlation existed between enriched expression of Ppih within HCC and more advanced, poorly differentiated, and TP53-mutated tumors.

CONCLUSION

These findings, which suggest that Ppih may serve as a predictive biomarker for people with HCC, serve as a starting point for further investigation into the function of Ppih in the progression of carcinogenesis.

METHODS

Accordingly, we utilized clinical samples and bioinformatics analysis to assess Ppih's mRNA, protein expression, and gene regulatory system in HCC. Additionally, Wilcoxon signed-rank testing and logistic regression were utilized to inspect the association between clinicopathological factors and Ppih. Clinical pathological traits linked to overall survival (OS) among HCC patients were examined via TCGA data via Cox regression and the Kaplan-Meier approach. Additionally, via TCGA data collection, gene set enrichment assessment was also conducted.

Transcription Factor Driven Gene Regulation in COVID-19 Patients

SARS-CoV-2 and its many variants have caused a worldwide emergency. Host cells colonised by SARS-CoV-2 present a significantly different gene expression landscape. As expected, this is particularly true for genes that directly interact with virus proteins. Thus, understanding the role that transcription factors can play in driving differential regulation in patients affected by COVID-19 is a focal point to unveil virus infection. In this regard, we have identified 19 transcription factors which are predicted to target human proteins interacting with Spike glycoprotein of SARS-CoV-2. Transcriptomics RNA-Seq data derived from 13 human organs are used to analyse expression correlation between identified transcription factors and related target genes in both COVID-19 patients and healthy individuals. This resulted in the identification of transcription factors showing the most relevant impact in terms of most evident differential correlation between COVID-19 patients and healthy individuals. This analysis has also identified five organs such as the blood, heart, lung, nasopharynx and respiratory tract in which a major effect of differential regulation mediated by transcription factors is observed. These organs are also known to be affected by COVID-19, thereby providing consistency to our analysis. Furthermore, 31 key human genes differentially regulated by the transcription factors in the five organs are identified and the corresponding KEGG pathways and GO enrichment are also reported. Finally, the drugs targeting those 31 genes are also put forth. This in silico study explores the effects of transcription factors on human genes interacting with Spike glycoprotein of SARS-CoV-2 and intends to provide new insights to inhibit the virus infection.

The m6A methylation profiles of immune cells in type 1 diabetes mellitus

BACKGROUND

Type 1 diabetes mellitus (T1DM) is caused by immune cell-mediated β-cell dysfunction. In recent decades, N6-methyladenosine (m6A) has attracted widespread attention in the scientific research field because it plays vital roles in the pathogenesis of immunity-related diseases, including autoimmune diseases. However, neither the m6A modification profile nor the potential role it plays in T1DM pathogenesis has been investigated to date.

MATERIALS AND METHODS

An m6A mRNA epitranscriptomic microarray analysis was performed to analyze m6A regulator expression patterns and m6A methylation patterns in immune cells of T1DM patients (n=6) and healthy individuals (n=6). A bioinformatics analysis was subsequently performed to explore the potential biological functions and signaling pathways underlying T1DM pathogenesis. Furthermore, mRNA expression and m6A methylation levels were subsequently verified by qRT-PCR and methylated RNA immunoprecipitation-qPCR (MeRIP-qPCR), respectively, in the T1DM and healthy groups (n=6 per group).

RESULTS

Among the multiple m6A regulators, METTL3 and IGF2BP2 had significantly downregulated expression, and YTHDC1 and HNRNPA2B1 had significantly upregulated expression in the T1DM group relative to the healthy group. The microarray analysis revealed 4247 differentially methylated transcripts, including 932 hypermethylated and 3315 hypomethylated transcripts, and 4264 differentially expressed transcripts, including 1818 upregulated transcripts and 2446 downregulated transcripts in the T1DM group relative to the healthy group. An association analysis between methylation and gene expression demonstrated that the expression of 590 hypermethylated transcripts was upregulated, and that of 1890 hypomethylated transcripts was downregulated. Pearson correlation analysis showed significant correlations between the expression levels of differentially expressed m6A regulators and the methylation levels of differentially methylated transcripts and significant correlations between the expression levels of differentially expressed m6A regulators and that of differentially expressed transcripts. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses demonstrated that differentially methylated transcripts were involved in pathways related to immunity, including some closely associated with T1DM.

CONCLUSIONS

Our study presents m6A regulator expression patterns and m6A methylation patterns of immune cells in T1DM, showing that the m6A mark and m6A regulators are promising targets for T1DM diagnosis and treatment.

Upregulation of CCNB2 and Its Perspective Mechanisms in Cerebral Ischemic Stroke and All Subtypes of Lung Cancer: A Comprehensive Study

Cyclin B2 (CCNB2) belongs to type B cell cycle family protein, which is located on chromosome 15q22, and it binds to cyclin-dependent kinases (CDKs) to regulate their activities. In this study, 103 high-throughput datasets related to all subtypes of lung cancer (LC) and cerebral ischemic stroke (CIS) with the data of CCNB2 expression were collected. The analysis of standard mean deviation (SMD) and summary receiver operating characteristic (SROC) reflecting expression status demonstrated significant up-regulation of CCNB2 in LC and CIS (Lung adenocarcinoma: SMD = 1.40, 95%CI [0.98–1.83], SROC = 0.92, 95%CI [0.89–0.94]. Lung squamous cell carcinoma: SMD = 2.56, 95%CI [1.64–3.48]. SROC = 0.97, 95%CI [0.95–0.98]. Lung small cell carcinoma: SMD = 3.01, 95%CI [2.01–4.01]. SROC = 0.98, 95%CI [0.97–0.99]. CIS: SMD = 0.29, 95%CI [0.05–0.53], SROC = 0.68, 95%CI [0.63–0.71]). Simultaneously, protein-protein interaction (PPI) analysis indicated that CCNB2 is the hub molecule of crossed high-expressed genes in CIS and LC. Through Multiscale embedded gene co-expression network analysis (MEGENA), a gene module of CIS including 76 genes was obtained and function enrichment analysis of the CCNB2 module genes implied that CCNB2 may participate in the processes in the formation of CIS and tissue damage caused by CIS, such as “cell cycle,” “protein kinase activity,” and “glycosphingolipid biosynthesis.” Afterward, via single-cell RNA-seq analysis, CCNB2 was found up-regulated on GABAergic neurons in brain organoids as well as T cells expressing proliferative molecules in LUAD. Concurrently, the expression of CCNB2 distributed similarly to TOP2A as a module marker of cell proliferation in cell cluster. These findings can help in the field of the pathogenesis of LC-related CIS and neuron repair after CIS damage.

Ogt Demonstrated Conspicuous Clinical Significance in Cancers, from Pan-Cancer to Small-Cell Lung Cancer

The clinical progression of small-cell lung cancer (SCLC) remains pessimistic. The aim of the present study was to promote the understanding of the clinical significance and mechanism of O-linked N-acetylglucosamine (GlcNAc) transferase (OGT) in SCLC. Wilcoxon tests, standardized mean difference (SMD), and Kruskal–Wallis tests were utilized to compare OGT level differences among the experimental and control groups. The univariate Cox regression analysis, Kaplan–Meier curves, and receiver operating characteristic curves were applied to determine OGT's clinical relevance in cancers. The Spearman correlation analysis and enrichment analysis were utilized to explore the underlying mechanisms of OGT in cancers. For the first time in the field, we provide an overview of OGT in 32 cancers using a large number of samples (n = 21,196), determining distinct OGT expression in 25 cancers and its prognosis effects in 12 cancers. Furthermore, using 950 samples from multiple sources, upregulated OGT was found in both mRNA and protein levels in SCLC (SMD = 0.93, 95% CI [0.24, 1.63]). Higher OGT levels represented a more unfavorable disease-free interval for SCLC patients (p < 0.001). The research also identified OGT expression as a potential marker for SCLC prediction (sensitivity = 0.79, specificity = 0.86, and AUC = 0.88). The high expression of OGT in SCLC may result from the positive regulation of two transcription factors—DEK and XRN2. We primarily investigated the underlying mechanisms of OGT in SCLC. Herein, based on the analyses from pan-cancer to SCLC, OGT demonstrated conspicuous clinical significance. OGT may be an underlying biomarker for the treatment and identification of some cancers, including SCLC.