DataSheet1_Construction of a Necroptosis-Associated Long Non-Coding RNA Signature to Predict Prognosis and Immune Response in Hepatocellular Carcinoma.ZIP

Background: Necroptosis is a form of programmed cell death, and studies have shown that long non-coding RNA molecules (lncRNAs) can regulate the process of necroptosis in various cancers. We sought to screen lncRNAs associated with necroptosis to predict prognosis and tumor immune infiltration status in patients with hepatocellular carcinoma (HCC).Methods: Transcriptomic data from HCC tumor samples and normal tissues were extracted from The Cancer Genome Atlas database. Necroptosis-associated lncRNAs were obtained by co-expression analysis. Necroptosis-associated lncRNAs were then screened by Cox regression and least absolute shrinkage and selection operator methods to construct a risk model for HCC. The models were also validated and evaluated by Kaplan-Meier analysis, univariate and multivariate Cox regression, and time-dependent receiver operating characteristic (ROC) curves. In addition, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment, gene set enrichment, principal component, immune correlation, and drug sensitivity analyses were applied to assess model risk groups. To further differentiate the immune microenvironment of different HCC subtypes, the entire dataset was divided into three clusters, based on necroptosis-associated lncRNAs, and a series of analyses performed.Results: We constructed a model comprising four necroptosis-associated lncRNAs: POLH-AS1, DUXAP8, AC131009.1, and TMCC1-AS1. Overall survival (OS) duration was significantly longer in patients classified as low-risk than those who were high-risk, according to our model. Univariate and multivariate Cox regression analyses further confirmed risk score stability. The analyzed models had area under the ROC curve values of 0.786, 0.713, and 0.639 for prediction of 1-, 3-, and 5-year OS, respectively, and risk score was significantly associated with immune cell infiltration and ESTIMATE score. In addition, differences between high and low-risk groups in predicted half-maximal inhibitory concentration values for some targeted and chemical drugs, providing a potential basis for selection of treatment approach. Finally, cluster analysis facilitated more refined differentiation of the immune microenvironment in patients with HCC and may allow prediction of the effectiveness of immune checkpoint inhibitors.Conclusions: This study contributes to understanding of the function of necroptosis-related lncRNAs in predicting the prognosis and immune infiltration status of HCC. The risk model constructed and cluster analysis provide a basis for predicting the prognosis of patients with HCC and to inform the selection of immunotherapeutic strategies.</p

DataSheet1_Role of ARRB1 in prognosis and immunotherapy: A Pan-Cancer analysis.ZIP

Background: β-arrestin1 (ARRB1), was originally identified as a multifunctional adaptor protein. Although ARRB1 has recently been shown to also play an important role in tumor growth, metastasis, inflammation, and immunity, its relationship with distinct tumor types and the tumor immune microenvironment remains unclear.Methods: We analyzed the ARRB1 expression profile and clinical characteristics in 33 cancer types using datasets from The Cancer Genome Atlas (TCGA) database. Clinical parameters such as patient survival, tumor stage, age, and gender were used to assess the prognostic value of ARRB1. The Human Protein Atlas (HPA) database was used to explore ARRB1 protein expression data. ESTIMATE and CIBERSORT algorithms were performed to assess immune infiltration. Furthermore, putative correlations between ARRB1 and tumor-infiltrating immune cells, the signatures of T-cell subtypes, immunomodulators, the tumor mutation burden (TMB), Programmed cell death ligand 1 (PD-L1), and microsatellite instability (MSI) were also explored. Gene functional enrichment was determined using GSEA. GSE40435 and GSE13213 cohorts were used to validate the correlation of ARRB1 with KIRC and LUAD clinicopathological parameters. Finally, the relationship between ARRB1 and immunotherapeutic responses was assessed using three independent immunotherapy cohorts, namely, GSE67501, GSE168204, and IMvigor210.Results: We found that ARRB1 expression levels were lower in 17 tumor tissues than in the corresponding normal tissues. We further found that ARRB1 expression was significantly correlated with tumor stage in BRCA, ESCA, KIRC, TGCT, and THCA, while in some tumors, particularly KIRC and LUAD, ARRB1 expression was associated with better prognosis. ARRB1 expression was also positively correlated with the stromal score or the immune score in some tumors. Regarding immune cell infiltration, ARRB1 expression in DLBC was positively correlated with M1 macrophage content and negatively correlated with B-cell infiltration. Additionally, there was a broad correlation between ARRB1 expression and three classes of immunomodulators. Furthermore, high ARRB1 expression levels were significantly correlated with some tumor immune-related pathways. Finally, ARRB1 expression was significantly associated with MSI, PD-L1, and TMB in some tumors and with the efficacy of immune checkpoint inhibitors (ICIs) in melanoma.Conclusion: ARRB1 has prognostic value in malignant tumors, especially in KIRC and LUAD. At the same time, ARRB1 was closely correlated with the tumor immune microenvironment and indicators of immunotherapy efficacy, indicating its great potential as a reliable marker for predicting the efficacy of immunotherapy.</p

DataSheet1_Construction of a cancer-associated fibroblasts-related long non-coding RNA signature to predict prognosis and immune landscape in pancreatic adenocarcinoma.ZIP

Background: Cancer-associated fibroblasts (CAFs) are an essential cell population in the pancreatic cancer tumor microenvironment and are extensively involved in drug resistance and immune evasion mechanisms. Long non-coding RNAs (lncRNAs) are involved in pancreatic cancer evolution and regulate the biological behavior mediated by CAFs. However, there is a lack of understanding of the prognostic signatures of CAFs-associated lncRNAs in pancreatic cancer patients.Methods: Transcriptomic and clinical data for pancreatic adenocarcinoma (PAAD) and the corresponding mutation data were obtained from The Cancer Genome Atlas database. lncRNAs associated with CAFs were obtained using co-expression analysis. lncRNAs were screened by Cox regression analysis using least absolute shrinkage and selection operator (LASSO) algorithm for constructing predictive signature. According to the prognostic model, PAAD patients were divided into high-risk and low-risk groups. Kaplan-Meier analysis was used for survival validation of the model in the training and validation groups. Clinicopathological parameter correlation analysis, univariate and multivariate Cox regression, time-dependent receiver operating characteristic (ROC) curves, and nomogram were performed to evaluate the model. The gene set variation analysis (GSVA) and gene ontology (GO) analyses were used to explore differences in the biological behavior of the risk groups. Furthermore, single-sample gene set enrichment analysis (ssGSEA), tumor mutation burden (TMB), ESTIMATE algorithm, and a series of immune correlation analyses were performed to investigate the relationship between predictive signature and the tumor immune microenvironment and screen for potential responders to immune checkpoint inhibitors. Finally, drug sensitivity analyses were used to explore potentially effective drugs in high- and low-risk groups.Results: The signature was constructed with seven CAFs-related lncRNAs (AP005233.2, AC090114.2, DCST1-AS1, AC092171.5, AC002401.4, AC025048.4, and CASC8) that independently predicted the prognosis of PAAD patients. Additionally, the high-risk group of the model had higher TMB levels than the low-risk group. Immune correlation analysis showed that most immune cells, including CD8+ T cells, were negatively correlated with the model risk scores. ssGSEA and ESTIMATE analyses further indicated that the low-risk group had a higher status of immune cell infiltration. Meanwhile, the mRNA of most immune checkpoint genes, including PD1 and CTLA4, were highly expressed in the low-risk group, suggesting that this population may be “hot immune tumors” and have a higher sensitivity to immune checkpoint inhibitors (ICIs). Finally, the predicted half-maximal inhibitory concentrations of some chemical and targeted drugs differ between high- and low-risk groups, providing a basis for treatment selection.Conclusion: Our findings provide promising insights into lncRNAs associated with CAFs in PAAD and provide a personalized tool for predicting patient prognosis and immune microenvironmental landscape.</p