The role of N6-methyladenosine (m6A)-associated long-stranded non-coding RNA (lncRNA) in pancreatic cancer is unclear. Therefore, we analysed the characteristics and tumour microenvironment in pancreatic cancer and determined the value of m6A-related lncRNAs for prognosis and drug target prediction. An m6A-lncRNA co-expression network was constructed using The Cancer Genome Atlas database to screen m6A-related lncRNAs. Prognosis-related lncRNAs were screened using univariate Cox regression; patients were divided into high- and low-risk groups and randomised into training and test groups. In the training group, least absolute shrinkage and selection operator (LASSO) was used for regression analysis and to construct a prognostic model, which was validated in the test group. Tumor mutational burden (TMB), immune evasion, and immune function of risk genes were analysed using R; drug sensitivity and potential drugs were examined using the Genomics of Drug Sensitivity in Cancer database. We screened 129 m6A-related lncRNAs; 17 prognosis-related m6A-related lncRNAs were obtained using multivariate analysis and three m6A-related lncRNAs (AC092171.5, MEG9, and AC002091.1) were screened using LASSO regression. Survival rates were significantly higher (p < 0.05) in the low-risk than in the high-risk group. Risk score was an independent predictor affecting survival (p < 0.001), with the highest risk score being obtained by calculating the c-index. The TMB significantly differed between the high- and low-risk groups (p < 0.05). In the high- and low-risk groups, mutations were detected in 61 of 70 samples and 49 of 71 samples, respectively, with KRAS, TP53, and SMAD4 showing the highest mutation frequencies in both groups. A lower survival rate was observed in patients with a high versus low TMB. Immune function HLA, Cytolytic activity, and Inflammation-promoting, T cell co-inhibition, Check-point, and T cell co-stimulation significantly differed in different subgroups (p < 0.05). Immune evasion scores were significantly higher in the high-risk group than in the low-risk group. Eight sensitive drugs were screened: ABT.888, ATRA, AP.24534, AG.014699, ABT.263, axitinib, A.443654, and A.770041. We screened m6A-related lncRNAs using bioinformatics, constructed a prognosis-related model, explored TMB and immune function differences in pancreatic cancer, and identified potential therapeutic agents, providing a foundation for further studies of pancreatic cancer diagnosis and treatment.

As a new programmed death mode, pyroptosis plays an indispensable role in gastric cancer (GC) and has strong immunotherapy potential, but the specific pathogenic mechanism and antitumor function remain unclear. We comprehensively analysed the overall changes of pyroptosis-related genes (PRGs) at the genomic and epigenetic levels in 886 GC patients. We identified two molecular subtypes by consensus unsupervised clustering analysis. Then, we calculated the risk score and constructed the risk model for predicting prognostic and selected nine PRGs related genes (IL18RAP, CTLA4, SLC2A3, IL1A, KRT7,PEG10, IGFBP2, GPA33, and DES) through LASSO and COX regression analyses in the training cohorts and were verified in the test cohorts. Consequently, a highly accurate nomogram for improving the clinical applicability of the risk score was constructed. Besides, we found that multi-layer PRGs alterations were correlated with patient clinicopathological features, prognosis, immune infiltration and TME characteristics. The low risk group mainly characterized by increased microsatellite hyperinstability, tumour mutational burden and immune infiltration. The group had lower stromal cell content, higher immune cell content and lower tumour purity. Moreover, risk score was positively correlated with T regulatory cells, M1 and M2 macrophages. In addition, the risk score was significantly associated with the cancer stem cell index and chemotherapeutic drug sensitivity. This study revealed the genomic, transcriptional and TME multiomics features of PRGs and deeply explored the potential role of pyroptosis in the TME, clinicopathological features and prognosis in GC. This study provides a new immune strategy and prediction model for clinical treatment and prognosis evaluation.

Background Pyroptosis-related long noncoding RNAs (lncRNAs) (PRLs) are closely related to gastric cancer (GC). However, However, the mechanism of its role in GC has not been elaborated. This study deeply analyzed the potential role of PRL in GC. Methods A PRLs coexpression network was constructed via GC data from the TCGA dataset. Cox analysis was used to determine the prognosis related PRLs. QRT–PCR was used for quantitative verification. LASSO analysis and multivariate Cox analysis were used to construct the prognosis model of PRLs and calculate the risk score of each sample. The clinical characteristics, prognosis and tumor microenvironment (TME) of different risk groups were analyzed. Finally, we constructed a ceRNA network of lncRNA miRNA/mRNA and five histone modification modes (H3K27ac, H3K4me1, H3K17me3, H3K4me3, and H3K9me3). Results We obtained seven PRLs and constructed a prognostic model. In addition, we also drew a highly accurate nomogram to predict the prognosis of GC. The expression of lncRNAs AP000695.1 and AC087301.1 was significantly different between GC tissues and normal tissues. The immune function and TME also changed in different risk groups. We found the sub-networks of miRNAs and target genes related to AP000695.1 and AC243964.3. And we also found that the AC007277.1 enhancer region H3K27ac, H3K4me1, H3K4me3 levels increased. Conclusion This study revealed the clinical features, prognosis and tumor microenvironment of PRL in gastric cancer, and further explored its potential role in GC. This study revealed the clinical characteristics, prognosis and tumor microenvironment of PRLs in GC. The potential role in GC was discussed, which provided a new theoretical basis and ideas for immunotherapy of GC.