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

Anoikis-related genes linked with patient outcome in pancreatic cancer

Anoikis is programmed cell death occurring upon cell detachment from the extracellular matrix. Cancer cells need to evade anoikis to be able to metastasize to distant sites. However, the molecular features and prognostic value of anoikis-related genes (ARGs) in pancreatic cancer remain unclear. In this study, we utilized transcriptome data from the TCGA and GSE102238 databases to identify 64 ARGs significantly associated with prognosis. We used the "ConsensusClusterPlus" R package to stratify patients into high and low-risk prognostic subgroups. The KEGG and GSEA analyses revealed that the clusters with poor prognosis were enriched for the ECM receptor interaction pathway, the TP53 signaling pathway, and the galactose metabolism pathway, and that the cell cycle pathway was upregulated. A prognostic model consisting of seven ARGs (SERPINE1, EGF, E2F1, MSLN, RAB27B, ETV7, MST1) was constructed using LASSO regression and when combined with clinicopathological parameters using Cox regression, a prognostic Nomogram was created, which demonstrated high prognostic utility. Among the biomarker candidates, we report ETV7 as a novel, independent prognostic marker in pancreatic cancer. ETV7 was highly expressed in KRAS and TP53 co-occurrent mutant TCGA patients, indicating that it may be regulated by the two major driver genes of pancreatic cancer. Therefore, targeting ETV7 could be a potential focus for future therapeutic studies.

Construction of prognostic markers for pancreatic adenocarcinoma based on mitochondrial fusion-related genes

Early detection of pancreatic adenocarcinoma (PAAD) remains a pressing clinical problem. Information on the clinical prognostic value of mitochondrial fusion-related genes in PAAD remains limited. In this study, we investigated mitochondrial fusion-related genes of PAAD to establish an optimal signature plate for the early diagnosis and prognosis of PAAD. The cancer genome atlas database was used to integrate the Fragments Per Kilobase Million data and related clinical data for patients with PAAD. Least absolute shrinkage and selection operator regression, cox regression, operating characteristic curves, and cBioPortal database was used to evaluate model performance, assess the prognostic ability and sensitivity. The levels of immune infiltration were compared by CIBERSORT, QUANTISEQ, and EPIC. Chemotherapy sensitivity between the different risk groups was compared by the Genomics of Drug Sensitivity in Cancer database and the "pRRophetic" R package. At last, a total of 4 genes were enrolled in multivariate Cox regression analysis. The risk-predictive signature was constructed as: (0.5438 × BAK1) + (-1.0259 × MIGA2) + (1.1140 × PARL) + (-0.4300 × PLD6). The area under curve of these 4 genes was 0.89. Cox regression analyses indicates the signature was an independent prognostic indicator (P < .001, hazard ratio [HR] = 1.870, 95% CI = 1.568-2.232). Different levels of immune cell infiltration in the 2 risk groups were observed using the 3 algorithms, with tumor mutation load (P = .0063), tumor microenvironment score (P = .01), and Tumor Immune Dysfunction and Exclusion score (P = .0012). The chemotherapeutic sensitivity analysis also revealed that the half-maximal inhibitory concentration of 5-fluorouracil (P = .0127), cisplatin (P = .0099), docetaxel (P < .0001), gemcitabine (P = .0047), and pacilataxel (P < .0001) were lower in the high-risk groups, indicating that the high-risk group patients had a greater sensitivity to chemotherapy. In conclude, we established a gene signature plate comprised of 4 mitochondrial fusion related genes to facilitate early diagnosis and prognostic prediction of PAAD.

Cervical cancer-specific long non-coding RNA landscape reveals the favorable prognosis predictive performance of an ion-channel-related signature model

BACKGROUND

Ion channels play an important role in tumorigenesis and progression of cervical cancer. Multiple long non-coding RNA genes are widely involved in ion channel-related signaling regulation. However, the association and potential clinical application of lncRNAs in the prognosis of cervical cancer are still poorly explored.

METHODS

Thirteen patients with cervical cancer were enrolled in current study. Whole transcriptome (involving both mRNAs and lncRNAs) sequencing was performed on fresh tumor and adjacent normal tissues that were surgically resected from patients. A comprehensive cervical cancer-specific lncRNA landscape was obtained by our custom pipeline. Then, a prognostic scoring model of ion-channel-related lncRNAs was established by regression algorithms. The performance of the predictive model as well as its association with the clinical characteristics and tumor microenvironment (TME) status were further evaluated.

RESULTS

To comprehensively identify cervical cancer-specific lncRNAs, we sequenced 26 samples of cervical cancer patients and integrated the transcriptomic results. We built a custom analysis pipeline to improve the accuracy of lncRNA identification and functional annotation and obtained 18,482 novel lncRNAs in cervical cancer. Then, 159 ion channel- and tumorigenesis-related (ICTR-) lncRNAs were identified. Based on nine ICTR-lncRNAs, we also established a prognostic scoring model and validated its accuracy and robustness in assessing the prognosis of patients with cervical cancer. Besides, the TME was characterized, and we found that B cells, activated CD8+ T, and tertiary lymphoid structures were significantly associated with ICTR-lncRNAs signature scores.

CONCLUSION

We provided a thorough landscape of cervical cancer-specific lncRNAs. Through integrative analyses, we identified ion-channel-related lncRNAs and established a predictive model for assessing the prognosis of patients with cervical cancer. Meanwhile, we characterized its association with TME status. This study improved our knowledge of the prominent roles of lncRNAs in regulating ion channel in cervical cancer.

An anoikis-related lncRNA signature may predict the prognosis, immune infiltration, and drug sensitivity in esophageal cancer

Background

Esophageal cancer (EC) is a prevalent malignancy with heterogeneous outcomes. This study explores the significance of anoikis-related long non-coding RNAs (lncRNAs) in EC, aiming to unravel their molecular roles and clinical implications.

Methods

Transcriptome and clinical data were obtained from TCGA database for EC samples. We identified anoikis-related genes and lncRNAs by Pearson correlation analysis. The risk score model hinged on prognostic lncRNAs filtered from multiple steps. Risk scores were calculated using the derived formula, and categorized patients into low- and high-risk groups. Model robustness was assessed through Kaplan-Meier (KM) survival analysis and Receiver Operating Characteristic (ROC) curve, with clinical utility achieved via a constructed nomogram. We also explored the interplay between the risk score and immune cell infiltration, and investigated drug sensitivity.

Results

We identified 2365 anoikis-related lncRNAs through co-expression analysis, including 1415 significant lncRNAs differentially expressed between normal and tumor samples. A risk score model was constructed from ten prognostic lncRNAs. The risk score model effectively stratified patients based on the median score, and its predictive capacity was validated through KM survival, ROC curve analyses, and the external GSE53622 dataset. The nomogram provided a practical tool for individualized prognosis evaluation. We unveiled significant correlations between specific immune cell subsets and the risk score. Eosinophils and common lymphoid progenitors exhibited positive associations, while endothelial cells and myeloid dendritic cells showed negative correlations. Drug sensitivity analysis revealed potential sensitive drugs for EC treatment that aligned with the risk subgroups.

Conclusion

This study established an anoikis-related lncRNAs risk score model that may predict the prognosis, immune infiltration, and drug sensitivity in EC, in hope of facilitating tailored patient management.

Predicting the immunity landscape and prognosis with an NCLs signature in liver hepatocellular carcinoma

BACKGROUND

Activated neutrophils release depolymerized chromatin and protein particles into the extracellular space, forming reticular Neutrophil Extracellular Traps (NETs). This process is accompanied by programmed inflammatory cell death of neutrophils, known as NETosis. Previous reports have demonstrated that NETosis plays a significant role in immune resistance and microenvironmental regulation in cancer. This study sought to characterize the function and molecular mechanism of NETosis-correlated long non-coding RNAs (NCLs) in the prognostic treatment of liver hepatocellular carcinoma (LIHC).

METHODS

We obtained the transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and evaluated the expression of NCLs in LIHC. A prognostic signature of NCLs was constructed using Cox and Last Absolute Shrinkage and Selection Operator (Lasso) regression, while the accuracy of model was validated by the ROC curves and nomogram, etc. In addition, we analyzed the associations between NCLs and oncogenic mutation, immune infiltration and evasion. Finally, LIHC patients were classified into four subgroups based on consensus cluster analysis, and drug sensitivity was predicted.

RESULTS

After screening, we established a risk model combining 5 hub-NCLs and demonstrated its reliability. Independence checks suggest that the model may serve as an independent predictor of LIHC prognosis. Enrichment analysis revealed a concentration of immune-related pathways in the high-risk group. Immune infiltration indicates that immunotherapy could be more effective in the low-risk group. Upon consistent cluster analysis, cluster subgroup 4 presented a better prognosis. Sensitivity tests showed the distinctions in therapeutic effectiveness among various drugs in different subgroups.

CONCLUSION

Overall, we have developed a prognostic signature that can discriminate different LIHC subgroups through the 5 selected NCLs, with the objective of providing LIHC patients a more precise, personalized treatment regimen.

PANoptosis-related long non-coding RNA signature to predict the prognosis and immune landscapes of pancreatic adenocarcinoma

Background

Cancer growth is significantly influenced by processes such as pyroptosis, apoptosis, and necroptosis that underlie PANoptosis, a proinflammatory programmed cell death. Several studies have examined the long non-coding RNAs (lncRNAs) associated with pancreatic adenocarcinoma (PAAD). However, the predictive value of lncRNAs related to PANoptosis for PAAD has not been established.

Methods

The Clinical Genome Atlas database was used to obtain the transcriptome 、clinical data and the corresponding mutation data of the patients with PAAD in this study. The least absolute shrinkage and selection operator regression analysis was employed to obtain prognosis-related lncRNAs for constructing a risk signature. According to the median risk score of the signature, patients with PAAD were grouped into low- and high-risk groups to further compare the survival prognosis of different risk groups. Time-dependent receiver operating characteristic curves, c-index analysis, nomograms, principal component analysis and univariate Cox and multivariate Cox regression were performed for the internal validation of the signature. In addition, enrichment analysis of different genes was performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Lastly, differences in tumor mutation burden (TMB), immune function, tumor immune dysfunction and rejection (TIDE), and drug response were determined for the two risk groups.

Results

The signature was constructed with six PANoptosis-related lncRNAs (AC067817.2、LINC02004、AC243829.1、AC092171.5、AP005233.2、AC004687.1) that predicted the prognosis of the patients with PAAD. Survival curves showed that patients in the two risk groups had statistically significant differences in prognosis (P < 0.05), and multi-cox regression analysis identified risk score as an independent risk factor for PAAD prognosis, and internal validation of nomograms showed high confidence in the signature. GO and KEGG enrichment analysis showed functional and pathway differences between the high- and low-risk groups. TMB evaluation demonstrated that patients in the high-risk group had a higher frequency of mutations. The TIDE score indicated that the high-risk group had a lower risk of immunotherapy escape and better immunotherapy outcomes. Additionally, the two risk groups revealed significantly different responses to 11 anticancer drugs.

Conclusion

We identified a novel risk signature for PANoptosis-related lncRNAs, which is a standalone prognostic indicator for PAAD. The PANoptosis-related lncRNA risk signature may be relevant for immunotherapy and a therapeutic target for PAAD.

A novel PANoptosis-related long non-coding RNA index to predict prognosis, immune microenvironment and personalised treatment in hepatocellular carcinoma

BACKGROUND

PANoptosis is involved in the interaction of apoptosis, necroptosis and pyroptosis, playing a role in programmed cell death. Moreover, long non-coding RNAs (lncRNAs) regulate the PCD. This work aims to explore the role of PANoptosis-associated lncRNAs in hepatocellular carcinoma (HCC).

METHODS

Co-expression analysis identified PANoptosis-associated lncRNAs in HCC. Cox and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms were utilised to filter lncRNAs and establish a PANoptosis-related lncRNA index (PANRI). Additionally, Cox, Kaplan-Meier and receiver operating characteristic (ROC) curves were utilised to systematically evaluate the PANRI. Furthermore, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), single sample gene set enrichment analysis (ssGSEA) and immune checkpoints were performed to analyse the potential of the PANRI in differentiating different tumour immune microenvironment (TIME) populations. The consensus clustering algorithm was used to distinguish individuals with HCC having different TIME subtypes. Finally, HCC cell lines HepG2 were utilised for further validation in in vitro experiments.

RESULTS

The PANRI differentiates patients according to risk. Notably, ESTIMATE and ssGSEA algorithms revealed a high immune infiltration status in high-risk patients. Additionally, consensus clustering divided the patients into three clusters to identify different subtypes of TIME. Moreover, in vitro results showed that siRNA-mediated silencing of AL049840.4 inhibited the viability and migration of HepG2 cells and promoted apoptosis.

CONCLUSIONS

This is the first PANoptosis-related, lncRNA-based risk index in HCC to assess patient prognosis, TIME and response to immunotherapy. This study offers novel perspectives on the role of PANoptosis-associated lncRNAs in HCC.

Comprehensive analysis of cuproptosis-related long non-coding RNAs in prognosis, immune microenvironment infiltration and chemotherapy response of hepatocellular carcinoma

The objective of this study is to explore the relationship between cuproptosis-related long noncoding RNAs (lncRNAs) in hepatocellular carcinoma (HCC). RNA-seq data, including lncRNAs and related clinical information of HCC patients, were downloaded from The Cancer Genome Atlas database. A signature composed 3 cuproptosis-related lncRNAs was constructed by LASSO analysis, and HCC patients were classified into high- and low-risk groups. Patients in the high-risk group had a poorer prognosis compared with the low-risk group. Univariate Cox and multivariate Cox regression analyses confirmed that the signature model was an independent risk factor compared to other clinical biomarkers. Furthermore, gene set enrichment analysis indicated that metabolism-related pathways were enriched in low-risk group, including drug metabolism, and fatty acid metabolism. Further research demonstrated that there were markedly differences in drug response between the high- and low-risk group. Immune related analysis showed that the most type of immune cells and immunological function in the high-risk group were different with the risk-group. Finally, TP53 mutation rate and the tumor mutational burden in the high-risk group were higher compared with the low-risk group. In conclusion, we constructed a prognostic signature based on the expression of cuproptosis-related lncRNAs to predict HCC patients' prognosis, drug response and immune microenvironment, and further research will be conducted to uncover the mechanisms.

Cuproptosis-related lncRNA scoring system to predict the clinical outcome and immune landscape in pancreatic adenocarcinoma

Cuproptosis is a recently discovered novel programmed cell death pathway that differs from traditional programmed cell death and has an important role in cancer and immune regulation. Long noncoding RNA (lncRNA) is considered new potential prognostic biomarkers in pancreatic adenocarcinoma (PAAD). However, the prognostic role and immune landscape of cuproptosis-related lncRNA in PAAD remain unclear. The transcriptome and clinical data of PAAD were obtained from The Cancer Genome Atlas (TCGA) database. Cuproptosis-related lncRNA was identified using Pearson correlation analysis. The optimal lncRNA was screened by Cox and the Least Absolute Shrinkage and Selection Operator (LASSO) regression mode, and for the construction of risk scoring system. PAAD patients were divided into high- and low-risk groups according to the risk score. Clinicopathological parameter correlation analysis, univariate and multivariate Cox regression, time-dependent receiver operating characteristic (ROC) curves, and nomogram were performed to evaluate the model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to explore differences in biological function between different risk groups. Single-sample gene set enrichment analysis (ssGSEA) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were used to analyze the differences in tumor immune microenvironment (TIME) in different risk groups of PAAD. Additionally, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict immunotherapy response and identify potential immune beneficiaries. Immune checkpoints and tumor mutation burden (TMB) were also systematically analyzed. Finally, drug sensitivity analysis was used to explore the reactivity of different drugs in high- and low-risk groups to provide a reference for the selection of precise therapeutic drugs. Six cuproptosis-related lncRNAs (AL117335.1, AC044849.1, AL358944.1, ZNF236-DT, Z97832.2, and CASC8) were used to construct risk model. Survival analysis showed that overall survival and progression-free survival in the low-risk group were better than those in the high-risk group, and it is suitable for PAAD patients with different clinical characteristics. Univariate and multifactorial Cox regression analysis showed that risk score was an independent prognostic factor in PAAD patients. ROC analysis showed that the AUC values of the risk score in 1 year, 3 years and 5 years were 0.707,0.762 and 0.880, respectively. Nomogram showed that the total points of PAAD patients at 1 year, 3 years, and 5 years were 0.914,0.648, and 0.543. GO and KEGG analyses indicated that the differential genes in the high- and low-risk groups were associated with tumor proliferation and metastasis and immune regulatory pathway. Immune correlation analysis showed that the amount of pro-inflammatory cells, including CD8+ T cells, was significantly higher in the low-risk group than in the high-risk group, and the expression of immune checkpoint genes, including PD-1 and CTLA-4, was increased in the low-risk group. TIDE analysis suggests that patients in the low-risk group may benefit from immunotherapy. Finally, there was significant variability in multiple chemotherapeutic and targeted drugs across the risk groups, which informs our clinical drug selection. Our cuproptosis-related lncRNA scoring system (CRLss) could predict the clinical outcome and immune landscape of PAAD patients, identify the potential beneficiaries of immunotherapy, and provide a reference for precise therapeutic drug selection.