Recent Trends in Cancer Genomics and Bioinformatics Tools Development

Unraveling the immune landscape and therapeutic biomarker PMEPA1 for oxaliplatin resistance in colorectal cancer: A comprehensive approach

Oxaliplatin (OXA) is a platinum-based chemotherapeutic agent with promising applications in the treatment of various malignancies, particularly colorectal cancer (CRC). However, the management of OXA resistance remains an ongoing obstacle in CRC therapy. This study aims to comprehensively investigate the immune landscape, targeted therapeutic biomarkers, and mechanisms that influence OXA resistance in CRC. Our results demonstrated that our OXA- resistant CRC prognostic model not only provides risk assessment for patients but also reflects the immune landscape of patients. Additionally, we identified prostate transmembrane protein, androgen-induced1 (PMEPA1) as a promising molecular targeted therapeutic biomarker for patients with OXA-resistant CRC. The mechanism of PMEPA1 may involve cell adhesion, pathways in cancer, and the TGF-β signaling pathway. Furthermore, analysis of CRC clinical samples indicated that patients resistant to OXA exhibited elevated serum levels of TGF-β1, increased expression of PMEPA1 in tumors, a lower proportion of CD8+ T cell positivity, and a higher proportion of M0 macrophage positivity, in comparison to OXA-sensitive individuals. Cellular experiments indicated that selective silencing of PMEPA1, alone or in combination with OXA, inhibited proliferation and metastasis in OXA-resistant CRC cells, HCT116R. Animal experiments further confirmed that PMEPA1 silencing suppressed subcutaneous graft tumor growth and liver metastasis in mice bearing HCT116R and synergistically enhanced the efficacy of OXA. These data highlight the potential of leveraging the therapeutic biomarker PMEPA1, CD8+ T cells, and M0 macrophages as innovative targets for effectively addressing the challenges associated with OXA resistance. Our findings hold promising implications for further clinical advancements in this field.

A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities

Bronchopulmonary dysplasia (BPD) is a complex, multifactorial lung disease affecting preterm neonates that can result in long-term pulmonary and non-pulmonary complications. Current therapies mainly focus on symptom management after the development of BPD, indicating a need for innovative approaches to predict and identify neonates who would benefit most from targeted or earlier interventions. Clinical informatics, a subfield of biomedical informatics, is transforming healthcare by integrating computational methods with patient data to improve patient outcomes. The application of clinical informatics to develop and enhance clinical therapies for BPD presents opportunities by leveraging electronic health record data, applying machine learning algorithms, and implementing clinical decision support systems. This review highlights the current barriers and the future potential of clinical informatics in identifying clinically relevant BPD phenotypes and developing clinical decision support tools to improve the management of extremely preterm neonates developing or with established BPD. However, the full potential of clinical informatics in advancing our understanding of BPD with the goal of improving patient outcomes cannot be achieved unless we address current challenges such as data collection, storage, privacy, and inherent data bias.

An m1A/m6A/m5C-associated long non-coding RNA signature: Prognostic and immunotherapeutic insights into cervical cancer

BACKGROUND

Cervical cancer (CC) remains a significant clinical challenge, even though its fatality rate has been declining in recent years. Particularly in developing countries, the prognosis for CC patients continues to be suboptimal despite numerous therapeutic advances.

METHODS

Using The Cancer Genome Atlas database, we extracted CC-related data. From this, 52 methylation-related genes (MRGs) were identified, leading to the selection of a 10 long non-coding RNA (lncRNA) signature co-expressed with these MRGs. R programming was employed to filter out the methylation-associated lncRNAs. Through univariate, least absolute shrinkage and selection operator (i.e. LASSO) and multivariate Cox regression analysis, an MRG-associated lncRNA model was constructed. The established risk model was further assessed via the Kaplan-Meier method, principal component analysis, functional enrichment annotation and a nomogram. Furthermore, we explored the potential of this model with respect to guiding immune therapeutic interventions and predicting drug sensitivities.

RESULTS

The derived 10-lncRNA signature, linked with MRGs, emerged as an independent prognostic factor. Segmenting patients based on their immunotherapy responses allowed for enhanced differentiation between patient subsets. Lastly, we highlighted potential compounds for distinguishing CC subtypes.

CONCLUSIONS

The risk model, associated with MRG-linked lncRNA, holds promise in forecasting clinical outcomes and gauging the efficacy of immunotherapies for CC patients.

APOL1 Induces Pyroptosis of Fibroblasts Through NLRP3/Caspase-1/GSDMD Signaling Pathway in Ulcerative Colitis

Background

Pyroptosis is a form of proinfammatory gasdermin-mediated programmed cell death. Abnormal infammation in the intestine is a critical risk factor for Ulcerative colitis (UC). However, at present, it is not clear whether pyroptosis of colonic fibroblasts is involved in the pathogenesis and progression of UC.

Methods

In this study, key genes associated with UC were identified by bioinformatics analysis. Datasets were downloaded from the Gene Expression Omnibus (GEO) database (GSE193677). The differentially expressed genes were analyzed, and the hub genes were screened by weighted gene co-expression network analysis (WGCNA) and differentially expressed genes. We also downloaded the dataset from GEO for single-cell RNA sequencing (GSE231993). The expression of key genes was verified by immunohistochemistry, immunofluorescence and Western blot, and the specific pathways of key genes inducing pyroptosis in cell lines were explored.

Results

The results of bioinformatics analysis showed that the expression of APOL1 and CXCL1 in UC tissues was significantly higher than that in normal tissues. The results of single-cell analysis showed that the two genes were co-localized to fibroblasts. These results were consistent with the results of immunohistochemistry and immunofluorescence colocalization in human intestinal mucosa specimens. Furthermore, APOL1 overexpression induced NLRP3-caspase1-GSDMD-mediated pyroptosis of fibroblasts, which was confirmed by Western blot.

Conclusion

APOL1 induces pyroptosis of fibroblasts mediated by NLRP3-Caspase1-GSDMD signaling pathway and promote the release of chemokines CXCL1. Fibroblasts may play a crucial role in the pathogenesis and progression of UC.

CDK1-SRC Interaction-Dependent Transcriptional Activation of HSP90AB1 Promotes Antitumor Immunity in Hepatocellular Carcinoma

This study aimed to analyze multiomics data and construct a regulatory network involving kinases, transcription factors, and immune genes in hepatocellular carcinoma (HCC) prognosis. The researchers used transcriptomic, proteomic, and clinical data from TCGA and GEO databases to identify immune genes associated with HCC. Statistical analysis, meta-analysis, and protein-protein interaction analyses were performed to identify key immune genes and their relationships. In vitro and in vivo experiments validated the CDK1-SRC-HSP90AB1 network's effects on HCC progression and antitumor immunity. A prognostic risk model was developed using clinicopathological features and immune infiltration. The immune genes LPA, BIRC5, HSP90AB1, ROBO1, and CCL20 were identified as the key prognostic factors. The CDK1-SRC-HSP90AB1 network promoted HCC cell proliferation and migration, with HSP90AB1 being transcriptionally activated by the CDK1-SRC interaction. Manipulating SRC or HSP90AB1 reversed the effects of CDK1 and SRC on HCC. The CDK1-SRC-HSP90AB1 network also influenced HCC tumor formation and antitumor immunity. Overall, this study highlights the importance of the CDK1-SRC-HSP90AB1 network as a crucial immune-regulatory network in the HCC prognosis.

IHGA: An interactive web server for large-scale and comprehensive discovery of genes of interest in hepatocellular carcinoma

Mining gene expression data is valuable for discovering novel biomarkers and therapeutic targets in hepatocellular carcinoma (HCC). Although emerging data mining tools are available for pan-cancer-related gene data analysis, few tools are dedicated to HCC. Moreover, tools specifically designed for HCC have restrictions such as small data scale and limited functionality. Therefore, we developed IHGA, a new interactive web server for discovering genes of interest in HCC on a large-scale and comprehensive basis. Integrative HCC Gene Analysis (IHGA) contains over 100 independent HCC patient-derived datasets (with over 10,000 tissue samples) and more than 90 cell models. IHGA allows users to conduct a series of large-scale and comprehensive analyses and data visualizations based on gene mRNA levels, including expression comparison, correlation analysis, clinical characteristics analysis, survival analysis, immune system interaction analysis, and drug sensitivity analysis. This method notably enhanced the richness of clinical data in IHGA. Additionally, IHGA integrates artificial intelligence (AI)-assisted gene screening based on natural language models. IHGA is free, user-friendly, and can effectively reduce time spent during data collection, organization, and analysis. In conclusion, IHGA is competitive in terms of data scale, data diversity, and functionality. It effectively alleviates the obstacles caused by HCC heterogeneity to data mining work and helps advance research on the molecular mechanisms of HCC.

Identification of 5 hub genes for diagnosis of coronary artery disease

Background

Coronary artery disease (CAD) is a main cause leading to increasing mortality of cardiovascular disease (CVD) worldwide. We aimed to discover marker genes and develop a diagnostic model for CAD.

Methods

CAD-related target genes were searched from DisGeNET. Count expression data and clinical information were screened from the GSE202626 dataset. edgeR package identified differentially expressed genes (DEGs). Using online STRING tool and Cytoscape, protein-protein reactions (PPI) were predicted. WebGestaltR package was employed to functional enrichment analysis. We used Metascape to conduct module-based network analysis. VarElect algorithm provided genes-phenotype correlation analysis. Immune infiltration was assessed by ESTIMATE package and ssGSEA analysis. mRNAsi was determined by one class logistic regression (OCLR). A diagnostic model was constructed by SVM algorithm.

Results

162 target genes were screened by intersection 1,714 DEGs and 1,708 CAD related target genes. 137 target genes of the 162 target genes were obtained using PPI analysis, in which those targets were enriched in inflammatory cytokine pathways, such as chemokine signaling pathway, and IL-17 signaling pathway. From the above 137 target genes, four functional modules (MCODE1-4) were extracted. From the 162 potential targets, CAD phenotype were directly and indirectly associated with 161 genes and 22 genes, respectively. Finally, 5 hub genes (CCL2, PTGS2, NLRP3, VEGFA, LTA) were screened by intersections with the top 20, directly and indirectly, and genes in MCODE1. PTGS2, NLRP3 and VEGFA were positively, while LTA was negatively correlated with immune cells scores. PTGS2, NLRP3 and VEGFA were negatively, while LTA was positively correlated with mRNAsi. A diagnostic model was successfully established, evidenced by 92.59% sensitivity and AUC was 0.9230 in the GSE202625 dataset and 94.11% sensitivity and AUC was 0.9706 in GSE120774 dataset.

Conclusion

In this work, we identified 5 hub genes, which may be associated with CAD development.

Medical Genetics, Genomics and Bioinformatics-2022

The analysis of molecular mechanisms of disease progression challenges the development of bioinformatics tools and omics data integration [...].

Research Topics of the Bioinformatics of Gene Regulation

The study of gene expression regulation raises the challenge of developing bioinformatics tools and algorithms, demanding data integration [...].

Multi-Omics Analysis of Molecular Characteristics and Carcinogenic Effect of NFE2L3 in Pan-Cancer

NFE2L3, also known as NFE2L3, is a nuclear transcription factor associated with the pathogenesis and progression of human tumors. To systematically and comprehensively investigate the role of NFE2L3 in tumors, a pan-cancer analysis was performed using multi-omics data, including gene expression analysis, diagnostic and prognostic analysis, epigenetic methylation analysis, gene alteration analysis, immune feature analysis, functional enrichment analysis, and tumor cell functional status analysis. Furthermore, the molecular mechanism of NFE2L3 in liver hepatocellular carcinoma (LIHC) was explored. The relationship between NFE2L3 expression and survival prognosis of patients with LIHC was analyzed and a nomogram prediction model was constructed. Our study showed that NFE2L3 expression was upregulated in most cancers, suggesting that NFE2L3 may play an important role in promoting cancer progression. NFE2L3 expression is closely related to DNA methylation, genetic alteration, immune signature, and tumor cell functional status in pan-cancers. Furthermore, NFE2L3 was demonstrated to be an independent risk factor for LIHC, and the nomogram model based on NFE2L3 expression had good prediction efficiency for the overall survival of patients with LIHC. In summary, our study indicated that NFE2L3 may be an important molecular biomarker for the diagnosis and prognosis of pan-cancer. NFE2L3 is expected to be a potential molecular target for the treatment of tumors.