PFDN2 promotes cell cycle progression via the hnRNPD-MYBL2 axis in gastric cancer.

Gastric cancer (GC) is a major health burden worldwide, but our understanding of GC is limited, and the prognosis is poor. Novel therapeutic strategies and biomarkers are urgently needed to improve GC patient outcomes. Previously, we identified PFDN2 as a novel key gene in gastric cancer based on its differential expression between cancer and normal tissues. However, the role and underlying mechanisms of PFDN2 in GC remain elusive. In this article, we demonstrated that PFDN2 is highly expressed in GC and that upregulation of PFDN2 is associated with the progression of GC. We further found that PFDN2 could promote cell cycle progression by promoting MYBL2 expression. Mechanistically, we demonstrated that PFDN2 could upregulate MYBL2 expression by facilitating the nuclear translocation of hnRNPD, and thus promoting MYBL2 transcriptional program. In conclusion, we found that PFDN2 promotes cell cycle progression via the hnRNPD-MYBL2 axis and may serve as a potential biomarker and therapeutic target for GC.

LINC00924-induced fatty acid metabolic reprogramming facilitates gastric cancer peritoneal metastasis via hnRNPC-regulated alternative splicing of Mnk2.

The molecular mechanism underlying gastric cancer (GC) peritoneal metastasis (PM) remains unclear. Here, we identified LINC00924 as a GC PM-related lncRNA through Microarray sequencing. LINC00924 was highly expressed in GC, and its high expression is associated with a broad range of PM. Via RNA sequencing, RNA pulldown assay, mass spectrometry, Seahorse, Lipidomics, spheroid formation and cell viability assays, we found that LINC00924 promoted fatty acid (FA) oxidation (FAO) and FA uptake, which was essential for matrix-detached GC cell survival and spheroid formation. Regarding the mechanism, LINC00924 regulated the alternative splicing (AS) of Mnk2 pre-mRNA by binding to hnRNPC. Specifically, LINC00924 enhanced the binding of hnRNPC to Mnk2 pre-mRNA at e14a, thus downregulating Mnk2a splicing and regulating the p38 MAPK/PPARα signaling pathway. Collectively, our results demonstrate that LINC00924 plays a role in promoting GC PM and could serve as a drug target.

Identification of a novel 10 immune-related genes signature as a prognostic biomarker panel for gastric cancer.

BACKGROUND: Emerging evidence indicates that immune infiltrating cells in tumor microenvironment (TME) correlates with the development and progression of gastric cancer (GC). This study aimed to systematically investigate the immune-related genes (IRGs) to develop a prognostic signature to predict the overall survival (OS) in GC. METHOD: The gene expression profiles of training dataset (GSE62254), validation dataset I (GSE15459), and validation dataset II (GSE84437) were retrieved from GEO and TCGA databases. In the present study, we developed a 10 IRGs prognostic signature with the combination of weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator method (LASSO) COX model. RESULTS: In the training dataset, the accuracy of the signature was 0.681, 0.741, and 0.72 in predicting 1, 3, and 5-year OS separately. The signature also had good performance in validation dataset Ⅰ with the accuracy of 0.57, 0.619, and 0.694, and in validation dataset Ⅱ with the accuracy of 0.559, 0.624, and 0.585. Then, we constructed a nomogram using the signature and clinical information which had strong discrimination ability with the c-index of 0.756. In the immune infiltration analysis, the signature was correlated with multiple immune infiltrating cells such as CD8 T cells, CD4 memory T cells, NK cells, and macrophages. Furthermore, several significant pathways were enriched in gene set enrichment analysis (GSEA) analysis, including TGF-beta signaling pathway and Wnt signaling pathway. CONCLUSION: The signature of 10 IRGs we identified can effectively predict the prognosis of GC and provides new insight into discovering candidate prognostic biomarkers of GC.

Identification and Validation of Key Genes of Differential Correlations in Gastric Cancer.

Background: Gastric cancer (GC) is aggressive cancer with a poor prognosis. Previously bulk transcriptome analysis was utilized to identify key genes correlated with the development, progression and prognosis of GC. However, due to the complexity of the genetic mutations, there is still an urgent need to recognize core genes in the regulatory network of GC. Methods: Gene expression profiles (GSE66229) were retrieved from the GEO database. Weighted correlation network analysis (WGCNA) was employed to identify gene modules mostly correlated with GC carcinogenesis. R package 'DiffCorr' was applied to identify differentially correlated gene pairs in tumor and normal tissues. Cytoscape was adopted to construct and visualize the gene regulatory network. Results: A total of 15 modules were detected in WGCNA analysis, among which three modules were significantly correlated with GC. Then genes in these modules were analyzed separately by "DiffCorr". Multiple differentially correlated gene pairs were recognized and the network was visualized by the software Cytoscape. Moreover, GEMIN5 and PFDN2, which were rarely discussed in GC, were identified as key genes in the regulatory network and the differential expression was validated by real-time qPCR, WB and IHC in cell lines and GC patient tissues. Conclusions: Our research has shed light on the carcinogenesis mechanism by revealing differentially correlated gene pairs during transition from normal to tumor. We believe the application of this network-based algorithm holds great potential in inferring relationships and detecting candidate biomarkers.