Weighted gene coexpression network analysis and machine learning reveal oncogenome associated microbiome plays an important role in tumor immunity and prognosis in pan-cancer

Predicting the molecular mechanism of ginger targeting PRMT1/BTG2 axis to inhibit gastric cancer based on WGCNA and machine algorithms

OBJECTIVE

The aim of this study was to screen the biomarkers of ginger against gastric cancer (GC) by network pharmacology, WGCNA and machine algorithms. To find the upstream transcription factors and downstream signaling proteins constituting the signaling axis, so as to predict the possible mechanism of action of ginger against GC.

METHODS

Ginger was screened for active ingredients and targets through public databases. GC genes were screened using disease database, GEO database and WGCNA. The intersection of the four was taken to obtain the potential core genes. Machine algorithms was used to screen the core genes. Clinical relevance analysis, gene mutation relationship, epigenetic regulation analysis, immune infiltration analysis and molecular docking validation were performed on the core genes. Find its upstream transcription factors and downstream signaling proteins through database.

RESULTS

35 intersecting genes were obtained by databases and WGCNA analysis. Machine algorithms and PPI were combined to finally screen the core gene PRMT1. The upstream transcription factor of PRMT1 was identified as EGR1 and the downstream protein as BTG2 by database and molecular docking.

CONCLUSION

In this study, we found that PRMT1 could be used as a biomarker for ginger against GC using network pharmacology, WGCNA and machine algorithms. We hypothesized that ginger may exert antitumor effects through PRMT1/BTG2, providing new insights into the pharmacological mechanism of ginger against GC.

Omics sciences for cervical cancer precision medicine from the perspective of the tumor immune microenvironment

Immunotherapies have demonstrated notable clinical benefits in the treatment of cervical cancer (CC). However, the development of therapeutic resistance and diverse adverse effects in immunotherapy stem from complex interactions among biological processes and factors within the tumor immune microenvironment (TIME). Advanced omic technologies offer novel insights into a more expansive and thorough layer of the TIME. Furthermore, integrating multidimensional omics within the frameworks of systems biology and computational methodologies facilitates the generation of interpretable data outputs to characterize the clinical and biological trajectories of tumor behavior. In this review, we present advanced omics technologies that utilize various clinical samples to address scientific inquiries related to immunotherapies for CC, highlighting their utility in identifying metastasis dissemination, recurrence risk, and therapeutic resistance in patients treated with immunotherapeutic approaches. This review elaborates on the strategy for integrating multi-omics data through artificial intelligence algorithms. Additionally, an analysis of the obstacles encountered in the multi-omics analysis process and potential avenues for future research in this domain are presented.

A quantitative analysis of artificial intelligence research in cervical cancer: a bibliometric approach utilizing CiteSpace and VOSviewer

Background

Cervical cancer, a severe threat to women's health, is experiencing a global increase in incidence, notably among younger demographics. With artificial intelligence (AI) making strides, its integration into medical research is expanding, particularly in cervical cancer studies. This bibliometric study aims to evaluate AI's role, highlighting research trends and potential future directions in the field.

Methods

This study systematically retrieved literature from the Web of Science Core Collection (WoSCC), employing VOSviewer and CiteSpace for analysis. This included examining collaborations and keyword co-occurrences, with a focus on the relationship between citing and cited journals and authors. A burst ranking analysis identified research hotspots based on citation frequency.

Results

The study analyzed 927 articles from 2008 to 2024 by 5,299 authors across 81 regions. China, the U.S., and India were the top contributors, with key institutions like the Chinese Academy of Sciences and the NIH leading in publications. Schiffman, Mark, featured among the top authors, while Jemal, A, was the most cited. 'Diagnostics' and 'IEEE Access' stood out for publication volume and citation impact, respectively. Keywords such as 'cervical cancer,' 'deep learning,' 'classification,' and 'machine learning' were dominant. The most cited article was by Berner, ES; et al., published in 2008.

Conclusions

AI's application in cervical cancer research is expanding, with a growing scholarly community. The study suggests that AI, especially deep learning and machine learning, will remain a key research area, focusing on improving diagnostics and treatment. There is a need for increased international collaboration to maximize AI's potential in advancing cervical cancer research and patient care.

Comprehensive analysis of microbial content in whole-genome sequencing samples from The Cancer Genome Atlas project

In recent years, a growing number of publications have reported the presence of microbial species in human tumors and of mixtures of microbes that appear to be highly specific to different cancer types. Our recent re-analysis of data from three cancer types revealed that technical errors have caused erroneous reports of numerous microbial species found in sequencing data from The Cancer Genome Atlas (TCGA) project. Here we have expanded our analysis to cover all 5,734 whole-genome sequencing (WGS) data sets currently available from TCGA, covering 25 distinct types of cancer. We analyzed the microbial content using updated computational methods and databases, and compared our results to those from two major recent studies that focused on bacteria, viruses, and fungi in cancer. Our results expand upon and reinforce our recent findings, which showed that the presence of microbes is far smaller than had been previously reported, and that many species identified in TCGA data are either not present at all, or are known contaminants rather than microbes residing within tumors. As part of this expanded analysis, and to help others avoid being misled by flawed data, we have released a dataset that contains detailed read counts for bacteria, viruses, archaea, and fungi detected in all 5,734 TCGA samples, which can serve as a public reference for future investigations.

An immune biomarker associated with EMT serves as a predictor for prognosis and drug response in bladder cancer

BACKGROUND

Bladder cancer (BLCA), which develops from the upper endometrial of the bladder, is the sixth most prevalent cancer across the globe. WDHD1 (WD repeat and HMG-box DNA binding protein 1 gene) directly affects signaling, the cell cycle, and the development of the cell skeleton. Uncertainty surrounds WDHD1's function in BLCA immunity and prognosis, though.

MATERIALS AND METHODS

Using weighed gene co-expression network analysis (WGCNA), initially, we first identified 32 risk factors in genes with differential expression for this investigation. Then, using a variety of bioinformatic techniques and experimental validation, we examined the connections between WDHD1 and BLCA expression, clinical pathological traits, WDHD1-related proteins, upper-skin-intermediate conversion (EMT), immune cell immersion, convergence factors, immune markers, and drug sensitivity.

RESULT

The findings demonstrated that we constructed a 32-gene risk-predicting model where WDHD1 was elevated as a representative gene expression in BLCA and related to a range of clinical traits. Furthermore, high WDHD1 expression was a standalone predictor associated with a worse survival rate. The most commonly recruited cells and their evolutionary patterns were highlighted to better comprehend WDHD1's function in cancer. High WDHD1 expression was associated with many aspects of immunology. Finally, the study found that individuals with high expression of WDHD1 were drug-sensitive to four different broad-spectrum anti-cancer drugs.

CONCLUSION

These results describe dynamic changes in the tumor microenvironment in BLCA and provide evidence for the hypothesis that WDHD1 is a novel biomarker of tumor development. WDHD1 may therefore be a useful target for the detection and management of BLCA.

Unveiling the gastric microbiota: implications for gastric carcinogenesis, immune responses, and clinical prospects

High-throughput sequencing has ushered in a paradigm shift in gastric microbiota, breaking the stereotype that the stomach is hostile to microorganisms beyond H. pylori. Recent attention directed toward the composition and functionality of this 'community' has shed light on its potential relevance in cancer. The microbial composition in the stomach of health displays host specificity which changes throughout a person's lifespan and is subject to both external and internal factors. Distinctive alterations in gastric microbiome signature are discernible at different stages of gastric precancerous lesions and malignancy. The robust microbes that dominate in gastric malignant tissue are intricately implicated in gastric cancer susceptibility, carcinogenesis, and the modulation of immunosurveillance and immune escape. These revelations offer fresh avenues for utilizing gastric microbiota as predictive biomarkers in clinical settings. Furthermore, inter-individual microbiota variations partially account for differential responses to cancer immunotherapy. In this review, we summarize current literature on the influence of the gastric microbiota on gastric carcinogenesis, anti-tumor immunity and immunotherapy, providing insights into potential clinical applications.

Dysconnectivity of the brain functional network and abnormally expressed peripheral transcriptional profiles in patients with anxious depression

BACKGROUND

Major depressive disorder (MDD) is a heterogeneous mental disorder, and accompanying anxiety symptoms, known as anxious depression (AD), are the most common subtype. However, the pathophysiology of AD may be distinct in depressed patients without anxiety (NAD) and remains unknown. This study aimed to investigate the relationship between functional connectivity and peripheral transcriptional profiles in patients with AD and NAD.

METHODS

Functional imaging data were collected to identify differences in functional networks among patients with AD (n = 66), patients with NAD (n = 115), and healthy controls (HC, n = 200). The peripheral transcriptional data were clustered as co-expression modules, and their associations with AD, AND, and HC were analyzed. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses of the genes in the significant module were performed. Correlation analysis was performed to identify functional network-associated gene co-expression modules.

RESULTS

A network was identified which consisted of 23 nodes and 28 edges that were significantly different among three sample groups. The regions of the network were located in temporal and occipital lobe. Two gene co-expression modules were shown to be associated with NAD, and one of which was correlated with the disrupted network in the AD group. The biological function of this module was enriched in immune regulation pathways.

CONCLUSION

The results suggested that immune-related mechanisms were associated with functional networks in AD.