A novel pyroptosis gene expression-based risk score for survival in gastric cancer

The construction and experimental verification of a 6-LncRNA model based on Lactic acid metabolism in the tumor microenvironment of Wilms tumor

BACKGROUND

Lactic acid (LA) can promote the malignant progression of tumors through the crosstalk with the tumor microenvironment (TME). However, the function of long non-coding RNAs (lncRNAs) related to LA metabolism in Wilms tumor (WT) remains unclear.

METHODS

Gene expression data and clinical data of WT patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Through the ESTIMATE algorithm and Pearson correlation analysis, lncRNAs related to tumor immunity and LA metabolism were screened. Subsequently, Cox regression analysis and Lasso Cox regression analysis were used to construct a model. Furthermore, candidate genes were identified and a competitive endogenous RNA (ceRNA) network was conducted to explore the specific mechanism of characteristic genes. Finally, based on the strong clinical relevance of UNC5B-AS1, its expression and function were experimentally verified.

RESULTS

The immune score and stromal score were found to be closely related to the prognosis of WT. Eventually, a prognostic model (TME-LA-LM) consisting of 6 lncRNAs was successfully identified. The model demonstrated favorable predictive ability and accuracy, with significant variation in immune infiltration and drug susceptibility observed between risk groups. Additionally, the study revealed the involvement of 2 candidate genes and 5 microRNAs (miRNAs) in the tumor's development. Notably, UNC5B-AS1 was highly expressed and found to promote the proliferation and migration of tumor cells.

CONCLUSION

This study, for the first time, elucidated the prognostic signatures of WT using lncRNAs related to TME and LA metabolism. The fundings of this research offer valuable insights for future studies on immunotherapy, personalized chemotherapy and mechanism research.

Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer

Gastric cancer (GC) is characterized by notable heterogeneity and the impact of molecular subtypes on treatment and prognosis. The role of programmed cell death (PCD) in cellular processes is critical, yet its specific function in GC is underexplored. This study applied multiomics approaches, integrating transcriptomic, epigenetic, and somatic mutation data, with consensus clustering algorithms to classify GC molecular subtypes and assess their biological and immunological features. A machine learning model was developed to create the Gastric Cancer Multi-Omics Programmed Cell Death Signature (GMPS), targeting PCD-related genes. We verified the expression of the GMPS hub genes using the RT-qPCR method. The prognostic influence of GMPS on GC was then evaluated. Single-cell analysis was performed to examine the heterogeneity of PCD characteristics in GC. Findings indicate that GMPS notably correlates with patient survival rates, tumor mutational burden (TMB), and copy number variations (CNV), demonstrating substantial prognostic predictive power. Moreover, GMPS is closely associated with the tumor microenvironment (TME) and immune therapy response. This research elucidates the molecular subtypes of GC, highlighting PCD's critical role in prognosis assessment. The relationship between GMPS and immune therapy response, alongside gastric cancer's microenvironmental features, provides insights for personalized treatment.

Genomic and transcriptomic profiling of inflammatory breast cancer reveals distinct molecular characteristics to non-inflammatory breast cancers

PURPOSE

Inflammatory breast cancer (IBC), a rare and highly aggressive form of breast cancer, accounts for 10% of breast cancer-related deaths. Previous omics studies of IBC have focused solely on one of genomics or transcriptomics and did not discover common differences that could distinguish IBC from non-IBC.

METHODS

Seventeen IBC patients and five non-IBC patients as well as additional thirty-three Asian breast cancer samples from TCGA-BRCA were included for the study. We performed whole-exon sequencing (WES) to investigate different somatic genomic alterations, copy number variants, and large structural variants between IBC and non-IBC. Bulk RNA sequencing (RNA-seq) was performed to examine the differentially expressed genes, pathway enrichment, and gene fusions. WES and RNA-seq data were further investigated in combination to discover genes that were dysregulated in both genomics and transcriptomics.

RESULTS

Copy number variation analysis identified 10 cytobands that showed higher frequency in IBC. Structural variation analysis showed more frequent deletions in IBC. Pathway enrichment and immune infiltration analysis indicated increased immune activation in IBC samples. Gene fusions including CTSC-RAB38 were found to be more common in IBC. We demonstrated more commonly dysregulated RAS pathway in IBC according to both WES and RNA-seq. Inhibitors targeting RAS signaling and its downstream pathways were predicted to possess promising effects in IBC treatment.

CONCLUSION

We discovered differences unique in Asian women that could potentially explain IBC etiology and presented RAS signaling pathway as a potential therapeutic target in IBC treatment.

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results, through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells. The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD.

M6A methylation of FKFB3 reduced pyroptosis of gastric cancer by NLRP3

Gastric cancer is a kind of malignant tumor that seriously endangers human life and health. Its incidence rate and mortality rate are among the highest in the global malignant tumors. Therefore, this study explored the role of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) in the progression of gastric cancer and its underlying mechanism. Patients with gastric cancer were collected, and human GC cell lines (stomach gastric carcinoma 7901, stomach gastric carcinoma 823 , human gastric carcinoma cell line 803 and adenocarcinoma gastric stomach) were used in this study. We utilized glucose consumption, cell migration, and ELISA assay kits to investigate the function of GC. To understand its mechanism, we employed quantitative PCR (qPCR), western blot, and m6A methylated RNA immunoprecipitation assay. FKFB3 protein expression levels in patients with gastric cancer were increased. The induction of PFKFB3 mRNA expression levels in patients with gastric cancer or gastric cancer cell lines. Gastric cancer patients with high PFKFB3 expression had a lower survival rate. PFKFB3 high expression possessed the probability of pathological stage, lymph node metastasis or distant metastasis in patients with gastric cancer. PFKFB3 upregulation promoted cancer progression and Warburg effect progression of gastric cancer. PFKFB3 upregulation reduced pyroptosis and suppressed nucleotidebinding domain, leucinerich repeat containing protein 3-induced pyroptosis of gastric cancer. M6A-forming enzyme methyltransferase-like 3 increased PFKFB3 stability. Taken together, the M6A-forming enzyme methyltransferase-like 3 increased PFKFB3 stability and reduced pyroptosis in the model of gastric cancer through the Warburg effect. The PFKFB3 gene represents a potential therapeutic strategy for the treatment of gastric cancer.

Machine learning-driven prognostic analysis of cuproptosis and disulfidptosis-related lncRNAs in clear cell renal cell carcinoma: a step towards precision oncology

Cuproptosis and disulfidptosis, recently discovered mechanisms of cell death, have demonstrated that differential expression of key genes and long non-coding RNAs (lncRNAs) profoundly influences tumor development and affects their drug sensitivity. Clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, presently lacks research utilizing cuproptosis and disulfidptosis-related lncRNAs (CDRLRs) as prognostic markers. In this study, we analyzed RNA-seq data, clinical information, and mutation data from The Cancer Genome Atlas (TCGA) on ccRCC and cross-referenced it with known cuproptosis and disulfidptosis-related genes (CDRGs). Using the LASSO machine learning algorithm, we identified four CDRLRs-ACVR2B-AS1, AC095055.1, AL161782.1, and MANEA-DT-that are strongly associated with prognosis and used them to construct a prognostic risk model. To verify the model's reliability and validate these four CDRLRs as significant prognostic factors, we performed dataset grouping validation, followed by RT-qPCR and external database validation for differential expression and prognosis of CDRLRs in ccRCC. Gene function and pathway analysis were conducted using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) for high- and low-risk groups. Additionally, we have analyzed the tumor mutation burden (TMB) and the immune microenvironment (TME), employing the oncoPredict and Immunophenoscore (IPS) algorithms to assess the sensitivity of diverse risk categories to targeted therapeutics and immunosuppressants. Our predominant objective is to refine prognostic predictions for patients with ccRCC and inform treatment decisions by conducting an exhaustive study on cuproptosis and disulfidptosis.

Construction and Validation of a Reliable Disulfidptosis-Related LncRNAs Signature of the Subtype, Prognostic, and Immune Landscape in Colon Cancer

Disulfidptosis, a novel form of regulated cell death (RCD) associated with metabolism, represents a promising intervention target in cancer therapy. While abnormal lncRNA expression is associated with colon cancer development, the prognostic potential and biological characteristics of disulfidptosis-related lncRNAs (DRLs) remain unclear. Consequently, the research aimed to discover a novel indication of DRLs with significant prognostic implications, and to investigate their possible molecular role in the advancement of colon cancer. Here, we acquired RNA-seq data, pertinent clinical data, and genomic mutations of colon adenocarcinoma (COAD) from the TCGA database, and then DRLs were determined through Pearson correlation analysis. A total of 434 COAD patients were divided in to three subgroups through clustering analysis based on DRLs. By utilizing univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) algorithm, and multivariate Cox regression analysis, we ultimately created a prognostic model consisting of four DRLs (AC007728.3, AP003555.1, ATP2B1.AS1, and NSMCE1.DT), and an external database was used to validate the prognostic features of the risk model. According to the Kaplan-Meier curve analysis, patients in the low-risk group exhibited a considerably superior survival time in comparison to those in the high-risk group. Enrichment analysis revealed a significant association between metabolic processes and the genes that were differentially expressed in the high- and low-risk groups. Additionally, significant differences in the tumor immune microenvironment landscape were observed, specifically pertaining to immune cells, function, and checkpoints. High-risk patients exhibited a low likelihood of immune evasion, as indicated by the Tumor Immune Dysfunction and Exclusion (TIDE) analysis. Patients who exhibit both a high risk and high Tumor Mutational Burden (TMB) experience the least amount of time for survival, whereas those belonging to the low-risk and low-TMB category demonstrate the most favorable prognosis. In addition, the risk groups determined by the 4-DRLs signature displayed distinct drug sensitivities. Finally, we confirmed the levels of expression for four DRLs through rt-qPCR in both tissue samples from colon cancer patients and cell lines. Taken together, the first 4-DRLs-based signature we proposed may serve for a hopeful instrument for forecasting the prognosis, immune landscape, and therapeutic responses in colon cancer patients, thereby facilitating optimal clinical decision-making.