Role of gasdermin family proteins in cancers (Review)

The Role and Therapeutic Potential of Pyroptosis in Colorectal Cancer: A Review

Colorectal cancer (CRC) is one of the leading causes of cancer-related mortality worldwide. The unlimited proliferation of tumor cells is one of the key features resulting in the malignant development and progression of CRC. Consequently, understanding the potential proliferation and growth molecular mechanisms and developing effective therapeutic strategies have become key in CRC treatment. Pyroptosis is an emerging type of regulated cell death (RCD) that has a significant role in cells proliferation and growth. For the last few years, numerous studies have indicated a close correlation between pyroptosis and the occurrence, progression, and treatment of many malignancies, including CRC. The development of effective therapeutic strategies to inhibit tumor growth and proliferation has become a key area in CRC treatment. Thus, this review mainly summarized the different pyroptosis pathways and mechanisms, the anti-tumor (tumor suppressor) and protective roles of pyroptosis in CRC, and the clinical and prognostic value of pyroptosis in CRC, which may contribute to exploring new therapeutic strategies for CRC.

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data

The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.

GSDMB: A novel, independent prognostic marker and potential new therapeutic target in clear cell renal cell carcinoma

Gasdermin (GSDM) family members are involved in numerous biological processes, including pyroptosis, as well as in the initiation and progression of various types of cancer. However, the specific role of GSDM genes in clear cell renal cell carcinoma (ccRCC) has yet to be fully clarified. The present study investigated the differential expression and genetic alterations GSDM genes, their effects on prognosis and immune modulation, and their functional enrichment in ccRCC. Several bioinformatics databases were used, including UALCAN, The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, Metascape, Tumor Immune Estimation Resource, GSCALite and cBioPortal. The results revealed that the expression levels of GSDMA, GSDMB, GSDMC and GSDMD were significantly upregulated in cancer tissues compared with those in paracancerous tissues in patients with ccRCC, whereas the expression of DFNB59 exhibited the opposite trend. The results were experimentally validated in patients with ccRCC, and it was confirmed that the expression levels of GSDMA, GSDMB, GSDMC, GSDMD and GSDME (DFNA5) were significantly enhanced, whereas (PJVK, DFNB59) expression was reduced. In addition, elevated GSDMB, GSDMD and DFNA5 expression levels were clearly associated with worse pathological characteristics of ccRCC, including a high pathological stage and high tumor grade. Furthermore, the high expression levels of GSDMB, GSDMC, GSDMD, DFNA5 and PJVK were shown to be associated with worse overall survival (OS) and progression-free interval in patients with ccRCC. Both univariate and multivariate analyses indicated that the expression of GSDMB was independently associated with the OS of patients with ccRCC. Additionally, a high mutation rate of GSDM genes (33%) was observed in patients with ccRCC, and GSDM gene mutations were also significantly associated with a poor OS in patients with ccRCC. Significant associations between GSDM genes and ccRCC immunoprofiling and drug sensitivity were also determined. In conclusion, the findings of the present study indicated that GSDMB, GSDMD and DFNA5 may be considered promising therapeutic agents and potential biomarkers for patients with ccRCC. Furthermore, GSDMB could act as an independent predictor for the OS of patients with ccRCC.