PANoptosis is a newly-discovered cell death pathway that involves crosstalk and co-ordination between pyroptosis, apoptosis, and necroptosis processes. However, the roles of PANoptosis-related genes (PRGs) in prognosis and immune landscape of colon cancer remain widely unknown. Here, we performed a bioinformatics analysis of expression data of nineteen PRGs identified from previous studies and clinical data of colon cancer patients obtained from TCGA and GEO databases. Colon cancer cases were divided into two PRG clusters, and prognosis-related differentially expressed genes (PRDEGs) were identified. The patient data were then separated into two corresponding distinct gene clusters, and the relationship between the risk score, patient prognosis, and immune landscape was analyzed. The identified PRGs and gene clusters correlated with patient survival and immune system and cancer-related biological processes and pathways. A prognosis signature based on seven genes was identified, and patients were divided into high-risk and low-risk groups based on the calculated risk score. A nomogram model for prediction of patient survival was also developed based on the risk score and other clinical features. Accordingly, the high-risk group showed worse prognosis, and the risk score was related to immune cell abundance, cancer stem cell (CSC) index, checkpoint expression, and response to immunotherapy and chemotherapeutic drugs. Results of quantitative real-time polymerase chain reaction (qRT-PCR) showed that LGR5 and VSIG4 were differentially expressed between normal and colon cancer samples. In conclusion, we demonstrated the potential of PANoptosis-based molecular clustering and prognostic signatures for prediction of patient survival and tumor microenvironment (TME) in colon cancer. Our findings may improve our understanding of the role of PANoptosis in colon cancer, and enable the development of more effective treatment strategies.

Globally, gastric cancer (GC) is the fifth most common tumor. It is necessary to identify novel molecular subtypes to guide patient selection for specific target therapeutic benefits. Multi-omics data, including transcriptomics RNA-sequencing (mRNA, LncRNA, miRNA), DNA methylation, and gene mutations in the TCGA-STAD cohort were used for the clustering. Ten classical clustering algorithms were executed to recognize patients with different molecular features using the "MOVICS" package in R. The activated signaling pathways were evaluated using the single-sample gene set enrichment analysis. The differential distribution of gene mutations, copy number alterations, and tumor mutation burden was compared, and potential responses to immunotherapy and chemotherapy were also assessed. Two molecular subtypes (CS1 and CS2) were recognized by ten clustering algorithms with consensus ensembles. Patients in the CS1 group had a shorter average overall survival time (28.5 vs. 68.9 months, P = 0.016), and progression-free survival (19.0 vs. 63.9 months, P = 0.008) as compared to those in the CS2 group. Extracellular associated biological process activation was higher in the CS1 group, while the CS2 group displayed the enhanced activation of cell cycle-associated pathways. Significantly higher total mutation numbers and neoantigens were observed in the CS2 group, along with specific mutations in TTN, MUC16, and ARID1A. Higher infiltration of immunocytes was also observed in the CS2 group, reflective of the potential immunotherapeutic benefits. Moreover, the CS2 group could also respond to 5-fluorouracil, cisplatin, and paclitaxel. The similar diversity in clinical outcomes between CS1 and CS2 groups was successfully validated in the external cohorts, GSE62254, GSE26253, GSE15459, and GSE84437. The findings provided novel insights into the GC subtypes through integrative analysis of five -omics data by ten clustering algorithms. These could provide potential clinical therapeutic targets based on the specific molecular features.

Oxidative stress and ferroptosis exhibit crosstalk in many types of human diseases, including malignant tumors. We aimed to develop an oxidative stress- and ferroptosis-related gene (OFRG) prognostic signature to predict the prognosis and therapeutic response in patients with colorectal cancer (CRC). Thirty-four insertion genes between oxidative stress-related genes and ferroptosis-related genes were identified as OFRGs. We then performed bioinformatics analysis of the expression profiles of 34 OFRGs and clinical information of patients obtained from multiple datasets. Patients with CRC were divided into three OFRG clusters, and differentially expressed genes (DEGs) between clusters were identified. OFRG clusters correlated with patient survival and immune cell infiltration. Prognosis-related DEGs in three clusters were used to calculate the risk score, and a prognostic signature was constructed according to the risk score. In this study, patients in the low-risk group had better prognosis, higher immune cell infiltration levels, and better responses to fluorouracil-based chemotherapy and immune checkpoint blockade therapy than high-risk patients; these results were successfully validated with multiple independent datasets. Thus, low-risk CRC could be defined as hot tumors and high-risk CRC could be defined as cold tumors. To further identify potential biomarkers for CRC, the expression levels of five signature genes in CRC and adjacent normal tissues were further verified via an in vitro experiment. In conclusion, we identified 34 OFRGs and constructed an OFRG-related prognostic signature, which showed excellent performance in predicting survival and therapeutic responses for patients with CRC. This could help to distinguish cold and hot tumors in CRC, and the results might be helpful for precise treatment protocols in clinical practice.

Most nonalcoholic steatohepatitis (NASH) patients develop severe fibrosis through extracellular matrix (ECM) accumulation, which can lead to hepatocellular carcinoma (HCC). Fibroblast growth factor 9 (FGF9) is involved in serial types of cancer; however, the specific role of FGF9 in NASH‐driven HCC is not fully understood. This study finds that FGF9 is increased in patients with NASH‐associated HCC. Furthermore, NASH‐driven HCC mice models by feeding wildtype mice with high‐fat/high‐cholesterol (HFHC) diet and low dose carbon tetrachloride (CCl₄) treatment is established; and identified that hepatic FGF9 is increased; with severe fibrosis. Additionally, AAV‐mediated knockdown of FGF9 reduced the hepatic tumor burden of NASH‐driven HCC mice models. Hepatocyte‐specific FGF9 transgenic mice (FGF9^Alb) fed with a HFHC diet without CCl₄ treatment exhibited an increased hepatic ECM and tumor burden. However, XAV‐939 treatment blocked ECM accumulation and NASH‐driven HCC in FGF9^Alb mice fed with HFHC diet. Molecular mechanism studies show that FGF9 stimulated the expression of ECM related genes in a β‐catenin dependent manner; and FGF9 exerts its effect on β‐catenin stability via the ERK1/2‐GSK‐3β signaling pathway. In summary, the data provides evidence for the critical role of FGF9 in NASH‐driven HCC pathogenesis; wherein it promotes the tumors formation through the ECM pathway.

BACKGROUND: Previous studies have demonstrated that platelets play an important role in growth, invasion, and angiogenesis of a variety of tumors. Nevertheless, the prognostic role of platelet indices in hepatocellular carcinoma (HCC) has not been ex

Aim: Immunotherapy is currently being explored as a potential treatment for hepatocellular carcinoma (HCC). This study investigated the prognostic value of immune-related long non-coding RNAs (lncRNAs) in patients with HCC. Methods: The Wilcoxon test was used to compare differentially expressed lncRNAs between HCC tissue and non-tumor tissue. Moreover, co-expression analysis was used to determine immune-related lncRNA. Univariate cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression were used to identify immune-related prognostic lncRNA. The immune risk score was calculated by the sum of the product from each lncRNA expression and its coefficient. Furthermore, the prognostic significance of the lncRNA signature was determined in the training group, testing group, and the entire group. A prognostic nomogram was established by integrating immune risk score and clinicopathological features. Results: PRRT3-AS1 and AL031985.3 were identified as immune-related prognostic lncRNAs in HCC patients. HCC patients were divided into high and low-risk groups based on the optimal cutoff value of risk score in the training group. The prognosis of HCC patients in the high-risk group was worse compared with the low-risk group. Besides, the immune-related lncRNA score was regarded as an independent risk factor for the prognosis of HCC patients. The predictive nomogram showed satisfactory discrimination and consistency. Gene enrichment analysis results indicated that the high-risk group was associated with immune-related signaling pathways. Conclusion: This study screened a 2-lncRNA signature and constructed a nomogram to predict the survival of HCC patients, thereby provided guidelines for undertaking medical decisions.