Integration of scRNA-seq and bulk RNA-seq constructs a stemness-related signature for predicting prognosis and immunotherapy responses in hepatocellular carcinoma.

PURPOSE: Cancer stem cells are associated with unfavorable prognosis in hepatocellular carcinoma (HCC). However, existing stemness-related biomarkers and prognostic models are limited. METHODS: The stemness-related signatures were derived from taking the union of the results obtained by performing WGCNA and CytoTRACE analysis at the bulk RNA-seq and scRNA-seq levels, respectively. Univariate Cox regression and the LASSO were applied for filtering prognosis-related signatures and selecting variables. Finally, ten gene signatures were identified to construct the prognostic model. We evaluated the differences in survival, genomic alternation, biological processes, and degree of immune cell infiltration in the high- and low-risk groups. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) database was used to evaluate the protein expressions. RESULTS: A stemness-related prognostic model was constructed with ten genes including YBX1, CYB5R3, CDC20, RAMP3, LDHA, MTHFS, PTRH2, SRPRB, GNA14, and CLEC3B. Kaplan-Meier and ROC curve analyses showed that the high-risk group had a worse prognosis and the AUC of the model in four datasets was greater than 0.64. Multivariate Cox regression analyses verified that the model was an independent prognostic indicator in predicting overall survival, and a nomogram was then built for clinical utility in predicting the prognosis of HCC. Additionally, chemotherapy drug sensitivity and immunotherapy response analyses revealed that the high-risk group exhibited a higher likelihood of benefiting from these treatments. CONCLUSION: The novel stemness-related prognostic model is a promising biomarker for estimating overall survival in HCC.

Molecular cluster mining of high-grade serous ovarian cancer via multi-omics data analysis aids precise medicine.

PURPOSE: HGSOC is a kind of gynecological cancer with high mortality and strong heterogeneity. The study used multi-omics and multiple algorithms to identify novel molecular subtypes, which can help patients obtain more personalized treatments. METHODS: Firstly, the consensus clustering result was obtained using a consensus ensemble of ten classical clustering algorithms, based on mRNA, lncRNA, DNA methylation, and mutation data. The difference in signaling pathways was evaluated using the single-sample gene set enrichment analysis (ssGSEA). Meanwhile, the relationship between genetic alteration, response to immunotherapy, drug sensitivity, prognosis, and subtypes was further analyzed. Finally, the reliability of the new subtype was verified in three external datasets. RESULTS: Three molecular subtypes were identified. Immune desert subtype (CS1) had little enrichment in the immune microenvironment and metabolic pathways. Immune/non-stromal subtype (CS2) was enriched in the immune microenvironment and metabolism of polyamines. Immune/stromal subtype (CS3) not only enriched anti-tumor immune microenvironment characteristics but also enriched pro-tumor stroma characteristics, glycosaminoglycan metabolism, and sphingolipid metabolism. The CS2 had the best overall survival and the highest response rate to immunotherapy. The CS3 had the worst prognosis and the lowest response rate to immunotherapy but was more sensitive to PARP and VEGFR molecular-targeted therapy. The similar differences among three subtypes were successfully validated in three external cohorts. CONCLUSION: We used ten clustering algorithms to comprehensively analyze four types of omics data, identified three biologically significant subtypes of HGSOC patients, and provided personalized treatment recommendations for each subtype. Our findings provided novel views into the HGSOC subtypes and could provide potential clinical treatment strategies.

Metabolism-associated molecular classification of gastric adenocarcinoma.

Most gastric cancers (GC) are adenocarcinomas, whereas GC is a highly heterogeneous disease due to its molecular heterogeneity. However, traditional morphology-based classification systems, including the WHO classification and Lauren's classification, have limited utility in guiding clinical treatment. We performed nonnegative matrix factorization (NMF) clustering based on 2752 metabolism-associated genes. We characterized each of the subclasses from multiple angles, including subclass-associated metabolism signatures, immune cell infiltration, clinic10al characteristics, drug sensitivity, and pathway enrichment. As a result, four subtypes were identified: immune suppressed, metabolic, mesenchymal/immune exhausted and hypermutated. The subtypes exhibited significant prognostic differences, which suggests that the metabolism-related classification has clinical significance. Metabolic and hypermutated subtypes have better overall survival, and the hypermutated subtype is likely to be sensitive to anti-PD-1 immunotherapy. In addition, our work showed a strong connection with previously established classifications, especially Lei's subtype, to which we provided an interpretation based on the immune cell infiltration perspective, deepening the understanding of GC heterogeneity. Finally, a 120-gene classifier was generated to determine the GC classification, and a 10-gene prognostic model was developed for survival time prediction.