Exploring the biological function of immune cell-related genes in human immunodeficiency virus (HIV)-1 infection based on weighted gene co-expression network analysis (WGCNA)

Exploration of drug repurposing for Mpox outbreaks targeting gene signatures and host-pathogen interactions

Monkeypox (Mpox) is a growing public health concern, with complex interactions within host systems contributing to its impact. This study employs multi-omics approaches to uncover therapeutic targets and potential drug repurposing opportunities to better understand Mpox's molecular pathogenesis. We developed an in silico host-pathogen interaction (HPI) network and applied weighted gene co-expression network analysis (WGCNA) to explore interactions between Mpox and host proteins. Subtype-specific host-pathogen protein-protein interaction networks were constructed, and key modules from the HPI and WGCNA were integrated to identify significant host proteins. To predict upstream signaling pathways and transcription factors, we used eXpression2Kinases and ChIP-X Enrichment Analysis. The multi-Steiner trees method was applied to compare our findings with those from FDA-approved antiviral drugs. Analysis of 55 differentially expressed genes in Mpox infection revealed 11 kinases and 15 transcription factors as key regulators. We identified 16 potential drug targets, categorized into 8 proviral genes (ESR2, ERK1, ERK2, P38, JNK1, CDK4, GSK3B, STAT3) designated for inhibition, and 8 antiviral genes (IKKA, HDAC1, HIPK2, TF65, CSK21, HIPK2, ESR2, GSK3B) designated for activation. Proviral genes are involved in the AKT, Wnt, and STAT3 pathways, while antiviral genes impact the AP-1, NF-κB, apoptosis, and IFN pathways. Promising FDA-approved candidates were identified, including kinase inhibitors, steroid hormone receptor agonists, STAT3 inhibitors, and notably Niclosamide. This study enhances our understanding of Mpox by identifying key therapeutic targets and potential repurposable drugs, providing a valuable framework for developing new treatments.

Molecular subtype identification and prognosis stratification by a immunogenic cell death-related gene expression signature in colorectal cancer

OBJECTIVES

This study intended to develop a new immunogenic cell death (ICD)-related prognostic signature for colorectal cancer (CRC) patients.

RESEARCH DESIGN AND METHODS

The Non-Negative Matrix Factorization (NMF) algorithm was adopted to cluster tumor samples based on ICD gene expression to obtain ICD-related subtypes. Survival analysis and immune microenvironment analysis were conducted among different subtypes. Regression analysis was used to construct the model. Based on riskscore median, cancer patients were classified into high and low risk groups, and independent prognostic ability of the model was analyzed. The CIBERSORT algorithm was adopted to determine the immune infiltration level of both groups.

RESULTS

We analyzed the differential genes between cluster 4 and cluster 1-3 and obtained 12 genes with the best prognostic features finally (NLGN1, SLC30A3, C3orf20, ADAD2, ATOH1, ATP6V1B1, KCNQ2, MUCL3, RGCC, CLEC17A, COL6A5, and INSL4). In addition, patients with lower risk had higher levels of infiltration of most immune cells, lower Tumor Immune Dysfunction and Exclusion (TIDE) level and higher immunophenscore (IPS) level than those with higher risk.

CONCLUSIONS

This study constructed and validated the ICD feature signature predicting CRC prognosis and provide a reference criteria for guiding the prognosis and immunotherapy of CRC cancer patients.

Recent advances in hematopoietic cell kinase in cancer progression: Mechanisms and inhibitors

Hematopoietic cell kinase (Hck), a non-receptor tyrosine kinase belonging to the Src kinase family, is intricately linked to the pathogenesis of numerous human diseases, with a particularly pronounced association with cancer. Hck not only directly impacts the proliferation, migration, and apoptosis of cancer cells but also interacts with JAK/STAT, MEK/ERK, PI3K/AKT, CXCL12/CXCR4, and other pathways. Hck also influences the tumor microenvironment to facilitate the onset and progression of cancer. This paper delves into the functional role and regulatory mechanisms of Hck in various solid tumors. Additionally, it explores the implications of Hck in hematological malignancies. The review culminates with a summary of the current research status of Hck inhibitors, the majority of which are in the pre-clinical phase of investigation. Notably, these inhibitors are predominantly utilized in the therapeutic management of leukemia, with their combinatorial potential indicating promising avenues for future research. In conclusion, this review underscores the significance of the mechanism of Hck in solid tumors. This insight is crucial for comprehending the current research trends regarding Hck: targeted therapy against Hck shows great promise in both diagnosis and treatment of malignant tumors. Further investigation into the role of Hck in cancer, coupled with the development of specific inhibitors, has the potential to revolutionize approaches to cancer treatment.

Investigating Key Targets of Dajianzhong Decoction for Treating Crohn’s Disease Using Weighted Gene Co-Expression Network

Background: Crohn’s disease (CD) is an inflammatory bowel disease, cases of which have substantially increased in recent years. The classical formula Dajianzhong decoction (DD, Japanese: Daikenchuto) is often used to treat CD, but few studies have evaluated related therapeutic mechanisms. In this study, we investigated the potential targets and mechanisms of DD used for treating CD at the molecular level through the weighted gene co-expression network. Methods: The main chemical components of the three DD herbs (Zanthoxylum bungeanum Maxim., Zingiber officinale (Willd.) Rosc., and Ginseng Radix et Rhizoma) were searched for using the HERB database. The targets for each component were identified using the SwissTargetPrediction and HERB databases, whereas the disease targets for CD were retrieved from the GeneCards and DisGeNET databases. The functional enrichment analysis was performed on the common targets of DD and CD. High-throughput sequencing data for CD patients were retrieved from the Gene Expression Omnibus database, and WGCNA was performed to identify the key targets. The association between the key targets and DD ingredients was verified using molecular docking. Results: By analyzing the interaction targets between DD and CD, 196 overlapping genes were identified. The enrichment results indicated that the PI3K-AKT, TNF, MAPK, and IL-17 signaling pathways influenced the mechanism of action of DD in counteracting CD. Combined with WGCNA, four differentially expressed genes (SLC6A4, NOS2, SHBG, and ABCB1) and their corresponding 24 compounds were closely related to the occurrence of CD. Conclusions: By integrating gene co-expression network analysis, this study preliminarily reveals the internal molecular mechanism of DD in treating CD from a systematic perspective, validated by molecular docking. However, these findings require further validation.