Pancreatic cancer (PC) has a very poor prognosis and is usually diagnosed only at an advanced stage. The discovery of new biomarkers for PC will help in early diagnosis and a better prognosis for patients. Recently, N6-methyladenosine (m 6 A) RNA modifications and their regulators have been implicated in the development of many cancers. To investigate the functions and mechanisms of m 6 A modifications in the development of PC, 19 m 6 A regulators, including m 6 A-methyltransferases (ZC3H13, RBM15/15B, WTAP, KIAA1429, and METTL3/14), demethylases (FTO and ALKBH5), and binding proteins (YTHDF1/2/3, YTHDC1/2, IGF2BP1/2/3, HNRNPC, and HNRNPA2B1) were analyzed in 178 PC tissues from the cancer genome atlas (TCGA) database. The results were verified in PC cell lines Mia-PaCa-2, BXPC-3, and the control cell line HDE-CT. The m 6 A regulators-based sample clusters were significantly related to overall survival (OS). Further, lasso regression identified a six-m 6 A-regulator-signature prognostic model (KIAA1429, HNRNPC, METTL3, YTHDF1, IGF2BP2, and IGF2BP3). Model-based high-risk and low-risk groups were significantly correlated with OS and clinical traits (pathologic M, N, and clinical stages and vital status). The risk signature was verified as an independent prognostic marker for patients with PC. Finally, gene set enrichment analysis revealed m 6 A regulators (KIAA1429, HNRNPC, and IGF2BP2) were related to multiple biological behaviors in PC, including adipocytokine signaling, the well vs. poorly differentiated tumor pathway, tumor metastasis pathway, epithelial mesenchymal transition pathway, gemcitabine resistance pathway, and stemness pathway. In summary, the m6A regulatory factors which related to clinical characteristics can be involved in the malignant progression of PC, and the constructed risk markers may be a promising prognostic biomarker that can guide the individualized treatment of PC patients.
Objective. The hepatitis B virus (HBV) infection led to hepatitis, which was one of common reasons for hepatocellular carcinoma (HCC). The immune microenvironment alteration played a crucial role in this process. The study aimed to identify immune-related long noncoding RNAs (lncRNAs) in HBV-related HCC and explore potential mechanisms. Methods. In total, 1,072 immune‐related genes (IRGs) were enriched in different co-expression modules with weighted gene co-expression network analysis (WGCNA) combining the corresponding clinical features in HBV-related HCC. The immune-related lncRNAs were selected from the crucial co-expression model based on the correlation analysis with IRGs. The immune-related lncRNAs were furtherly used to construct prognostic signature by the Cox proportional hazards regression and Lasso regression. Furthermore, the proliferation and migration ability of lncRNA SNHG10 were verified in vitro. Results. A total of nine co-expression modules were identified by WGCNA of which the “red” co-expression module was most correlated with various clinical characteristics. Additionally, the IRGs in this module were significantly enriched in multiple immune-related pathways. The twelve immune-related lncRNAs prognostic signature (HAND2-AS1, LINC00844, SNHG10, MALAT1, LINC00460, LBX2-AS1, MIR31HG, SEMA6A-AS1, LINC1278, LINC00514, CTBP-AS2, and LINC00205) was constructed. The risk score was an independent risk factor in HBV-related HCC and verified by principal components analysis (PCA), nomogram, and PCR between different cell lines. Moreover, the proportion of immune cells were significantly different between high-risk score group and low-risk score group. The malignant behavior of Hep3B was significantly different between si-lncRNA SNHG10 and control group. Conclusions. The immune-related lncRNAs prognostic signature provided some potential biomarkers and molecular mechanisms in HBV-related HCC.
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