A Comprehensive Analysis of Pyroptosis-Related lncRNAs Signature Associated With Prognosis and Tumor Immune Microenvironment of Pancreatic Adenocarcinoma

Identification and validation of a novel anoikis-related long non-coding RNA signature for pancreatic adenocarcinoma to predict the prognosis and immune response

OBJECTIVE

To provide more precise treatment options for pancreatic adenocarcinoma (PAAD) patients and improve their prognosis,we established a novel anoikis-related long non-coding RNA signature (ARLSig) to predict the prognosis and immune response for PAAD patients.

METHODS

We downloaded information on PAAD from The Cancer Genome Atlas (TCGA) database, and screened long non-coding RNA (lncRNA) linked with anoikis, and prognostic signatures with these lncRNAs. After that, ARLSig was verified using receiver operating characteristic (ROC) and C-index curves. To further investigate the role of ARLSig, we also performed enrichment analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). Additionally, using immunological correlation analysis and single-sample genetic enrichment analysis, we investigated the effectiveness of PAAD immunotherapy.

RESULTS

We screened 7 lncRNAs to construct a novel ARLSig and utilized it to predict the efficacy of immunotherapy and the prognosis of PAAD patients.

CONCLUSION

ARLSig can identify patients who will benefit from immunotherapy and improve the prediction of PAAD patient prognosis.

Construction of a Novel Disulfidptosis-Related lncRNA Prognostic Signature in Pancreatic Cancer

Pancreatic cancer is a lethal, extremely aggressive gastrointestinal tumor with a poor prognosis and limited treatment alternatives. Disulfidptosis is a newly defined type of cell death with potential influence on cancer. Research on the association between disulfidptosis and pancreatic cancer is scarce. The expression data of disulfidptosis-related genes were downloaded from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA). Disulfidptosis-related lncRNA signature (DRLS) was developed through the Cox and the least absolute shrinkage and selection operator (LASSO) analysis. Differences in enrichment functions, mutational landscape, immune microenvironment, and predicted therapeutic efficacy between high- and low-risk groups were assessed. Consensus clustering analysis was applied to identify the DRLS-related subtypes. Among 98 disulfidptosis-related lncRNAs, 5 lncRNAs were screened thus constructing a prognostic DRLS. DRLS showed high predictive accuracy and was an independent prognostic factor for pancreatic cancer. According to the risk scores calculated from the signature, samples were categorized into high- and low- risk groups. Overall, low-risk patients had a better prognosis, lower mutational occurrences, higher immune cell infiltration and more sensitivity to anti-tumor agents. The DRLS performed well in predicting prognosis and revealed intimate correlation with biological function, mutation status and immune infiltration landscape of pancreatic cancer, providing some insights for future research on the relationship between disulfidptosis and pancreatic cancer.

Construction of a cuproptosis-associated long non-coding RNA risk prediction model for pancreatic adenocarcinoma based on the TCGA database

Cuproptosis is a recently identified controlled process of cell death that functions in tumor development and treatment. Long non-coding RNAs (lncRNAs) are RNA molecules longer than 200 nucleotides that bind to transcription factors and regulate tumor invasion, penetration, metastasis, and prognosis. However, there are limited data on the function of cuproptosis-associated lncRNAs in pancreatic adenocarcinoma. Utilizing data retrieved from the cancer genome atlas database, we devised a risk prediction model of cuproptosis-associated lncRNAs in pancreatic adenocarcinoma, determined their prognostic significance and relationship with tumor immunity, and screened potential therapeutic drugs. Overall, 178 patients were randomized to a training or test group. We then obtained 6 characteristic cuproptosis-associated lncRNAs from the training group, based on which we constructed the risk prediction model, calculated the risk score, and verified the test group results. Subsequently, we performed differential gene analysis, tumor immunoassays, functional enrichment analysis, and potential drug screening. Finally, we found that the prediction model was highly reliable for the prognostic assessment of pancreatic adenocarcinoma patients. Generally, low risk patients had better outcomes than high risk patients. A tumor immunoassay showed that immunotherapy may benefit high risk patients more as there is a greater likelihood that the tumors could escape the immune system in low-risk patients. Through drug screening, we identified ten drugs that may have therapeutic effects on patients with pancreatic adenocarcinoma. In conclusion, this study constructed a risk prediction model of cuproptosis-associated lncRNAs, which can reliably predict the prognosis of pancreatic adenocarcinoma patients, provided a clinical reference for determining treatment approach, and provided some insights into the associations between lncRNAs and cuproptosis. This provides useful insight to aid in the development of therapeutic drugs for pancreatic adenocarcinoma.

A prognostic model based on tumor microenvironment-related lncRNAs predicts therapy response in pancreatic cancer

Pancreatic cancer is an aggressive malignant tumor with high mortality and a low survival rate. The immune and stromal cells that infiltrate in the tumor microenvironment (TME) significantly impact immunotherapy and drug responses. Therefore, we identify the TME-related lncRNAs to develop a prognostic model for predicting the therapy efficacy in pancreatic cancer patients. Firstly, we identified differentially expressed genes (DEGs) for weighted gene co-expression network analysis (WGCNA) to identify the TME-related module eigengenes. According to the module eigengenes, the TME-related prognostic lncRNAs were screened through the univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses to construct a prognostic risk score (RS) model. Next, the predictive power of this model was evaluated by the time-dependent receiver operating characteristic (ROC) curve and Kaplan-Meier analyses. In addition, functional enrichment, immune cell infiltration, and somatic mutation analyses were performed. Finally, tumor immune dysfunction and exclusion (TIDE) score and drug sensitivity analyses were applied to predict therapy response. In this study, 11 TME-related prognostic lncRNAs were identified to develop the prognostic RS model. According to the RS, the low-risk patients had a better prognosis, lower rates of somatic mutation, lower TIDE scores, and higher sensitivity to gemcitabine and paclitaxel compared to high-risk patients. The findings above suggested that low-risk patients may benefit more from immunotherapy, and high-risk patients may benefit more from chemotherapy. Within this study, we established a prognostic RS model based on 11 TME-related lncRNAs, which may help improve clinical decision-making.

An Aggrephagy-Related LncRNA Signature for the Prognosis of Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is a common, highly malignant, and aggressive gastrointestinal tumor. The conventional treatment of PAAD shows poor results, and patients have poor prognosis. The synthesis and degradation of proteins are essential for the occurrence and development of tumors. Aggrephagy is a type of autophagy that selectively degrades aggregated proteins. It decreases the formation of aggregates by degrading proteins, thus reducing the harm to cells. By breaking down proteins, it decreases the formation of aggregates; thus, minimizing damage to cells. For evaluating the response to immunotherapy and prognosis in PAAD patients, in this study, we developed a reliable signature based on aggrephagy-related genes (ARGs). We obtained 298 AGGLncRNAs. Based on the results of one-way Cox and LASSO analyses, the lncRNA signature was constructed. In the risk model, the prognosis of patients in the low-risk group was noticeably better than that of the patients in the high-risk group. Additionally, the ROC curves and nomograms validated the capacity of the risk model to predict the prognosis of PAAD. The patients in the low-risk and high-risk groups showed considerable variations in functional enrichment and immunological analysis. Regarding drug sensitivity, the low-risk and high-risk groups had different half-maximal inhibitory concentrations (IC50).

System Analysis Based on Pancreatic Cancer Progression Identifies BRINP2 as a Novel Prognostic Biomarker

Pancreatic adenocarcinoma (PAAD) is a malignant tumor of the digestive system, which develops rapidly and has no obvious early symptoms. This study aims to discover the biomarkers associated with PAAD development. We obtained RNA expression of PAAD patient samples and corresponding clinical data from The cancer genome atlas (TCGA), and screened out BMP/RA-inducible neural-specific protein 2 (BRINP2) gene which is highly associated with PAAD severity. Then, gene ontology (GO) enrichment, Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis and single-sample gene set enrichment analysis (ssGSEA) analysis were performed to explore the biological functions of BRINP2. Subsequently, long non-coding RNA (lncRNAs) associated with BRINP2 were screened out via correlation analysis, and Cox regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were used to construct the risk prediction model. We further validated the expression level of BRINP2 and its associated lncRNAs in BRINP2-associated lncRNAs prognostic model in vitro. We proposed that BRINP2 might be correlated to the tumor immune microenvironment and could also be used as a biomarker for PAAD progression. GO enrichment analysis and KEGG pathway analysis showed that the prognostic model was highly correlated to immune microenvironment-related pathways. Additionally, we established a BRINP2-associated lncRNAs prognostic model consisting of three lncRNAs. We validated the expression trends of BRINP2 and its associated lncRNAs in BRINP2-associated lncRNAs prognostic model in PAAD cells with various severity of metastatic potential using the quantitative real-time PCR (qRT-PCR). Meanwhile, pRRophetic R package was employed to predict potential therapeutic drugs for BRINP2-associated lncRNAs prognostic model of PAAD. The results suggest that BRINP2 can be used as a novel prognostic biomarker for PAAD.

Cuprotosis Programmed-Cell-Death-Related lncRNA Signature Predicts Prognosis and Immune Landscape in PAAD Patients

In terms of mortality and survival, pancreatic cancer is one of the worst malignancies. Known as a unique type of programmed cell death, cuprotosis contributes to tumor cell growth, angiogenesis, and metastasis. Cuprotosis programmed-cell-death-related lncRNAs (CRLs) have been linked to PAAD, although their functions in the tumor microenvironment and prognosis are not well understood. This study included data from the TCGA-PAAD cohort. Random sampling of PAAD data was conducted, splitting the data into two groups for use as a training set and test set (7:3). We searched for differentially expressed genes that were substantially linked to prognosis using univariate Cox and Lasso regression analysis. Through the use of multivariate Cox proportional risk regression, a risk-rating system for prognosis was developed. Correlations between the CRL signature and clinicopathological characteristics, tumor microenvironment, immunotherapy response, and chemotherapy sensitivity were further evaluated. Lastly, qRT-PCR was used to compare CRL expression in healthy tissues to that in tumors. Some CRLs are thought to have strong correlations with PAAD outcomes. These CRLs include AC005332.6, LINC02041, LINC00857, and AL117382.1. The CRL-based signature construction exhibited outstanding predictive performance and offers a fresh approach to evaluating pre-immune effectiveness, paving the way for future studies in precision immuno-oncology.

Identification of pyroptosis-related lncRNA signature and AC005253.1 as a pyroptosis-related oncogene in prostate cancer

Background

Pyroptosis and prostate cancer (PCa) are closely related. The role of pyroptosis-related long non-coding RNAs (lncRNAs) (PRLs) in PCa remains elusive. This study aimed to explore the relationship between PRL and PCa prognosis.

Methods

Gene expression and clinical signatures were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. A PRL risk prediction model was established by survival random forest analysis and least absolute shrinkage and selection operator regression. Functional enrichment, immune status, immune checkpoints, genetic mutations, and drug susceptibility analyses related to risk scores were performed by the single-sample gene set enrichment analysis, gene set variation analysis, and copy number variation analysis. PRL expression was verified in PCa cells. Cell Counting Kit-8, 5-ethynyl-2′-deoxyuridine, wound healing, transwell, and Western blotting assay were used to detect the proliferation, migration, invasion, and pyroptosis of PCa cells, respectively.

Results

Prognostic features based on six PRL (AC129507.1, AC005253.1, AC127502.2, AC068580.3, LIMD1-AS1, and LINC01852) were constructed, and patients in the high-score group had a worse prognosis than those in the low-score group. This feature was determined to be independent by Cox regression analysis, and the area under the curve of the 1-, 3-, and 5-year receiver operating characteristic curves in the testing cohort was 1, 0.93, and 0.92, respectively. Moreover, the external cohort validation confirmed the robustness of the PRL risk prediction model. There was a clear distinction between the immune status of the two groups. The expression of multiple immune checkpoints was also reduced in the high-score group. Gene mutation proportion in the high-score group increased, and the sensitivity to drugs increased significantly. Six PRLs were upregulated in PCa cells. Silencing of AC005253.1 inhibited cell proliferation, migration, and invasion in DU145 and PC-3 cells. Moreover, silencing of AC005253.1 promoted pyroptosis and inflammasome AIM2 expression.

Conclusions

Overall, we constructed a prognostic model of PCa with six PRLs and identified their expression in PCa cells. The experimental verification showed that AC005253.1 could affect the proliferation, migration, and invasion abilities of PCa cells. Meanwhile, AC005253.1 may play an important role in PCa by affecting pyroptosis through the AIM2 inflammasome. This result requires further research for verification.

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

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.