Identification of a Novel Oxidative Stress- and Anoikis-Related Prognostic Signature and Its Immune Landscape Analysis in Non-Small Cell Lung Cancer
Ontology highlight
ABSTRACT:
SUBMITTER:
PROVIDER: S-EPMC10671784 | biostudies-literature | 2023 Nov
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:PurposeAs a subgroup of lung cancer, small cell lung cancer (SCLC) is characterized by a short tumor doubling time, high rates of early occurred distant cancer spread, and poor outcomes. Despite its exquisite sensitivity to chemotherapy and radiotherapy, acquired drug resistance and tumor progression are typical. This study aimed to develop a robust signature based on immune-related genes to predict the outcome of patients with SCLC.MethodsThe expression data of 77 SCLC patients from George's cohort were divided into training set and testing set, and 1534 immune-related genes from ImmPort database were used to generate and validate the signature. Cox proportional hazards and the Kaplan-Meier analysis were used for developing and testing the prognostic signature. Single-sample gene set enrichment analysis was used to determine immune cell infiltration phenotypes.ResultsA 10-gene model comprising NR3C1, NR1D2, TANK, ARAF, HDGF, INHBE, LRSAM1, PLXNA1, PML, and SP1 with the highest frequency after 1000 interactions, was chosen to construct immune-related signature. This signature showed robust predictive value for SCLC patients' survival in both training and testing sets. This signature was weakly associated with the clinic pathological values like TNM stage. Furthermore, patients with low risk presented with activation of immune signal pathways, and specific immune cell infiltration with high levels of CD56bright NK cells but low levels of CD8+ T cells, mast cells, and helper T cells.ConclusionThe present study developed immune-related signature that may help predict the prognosis of SCLC patients, which reflects an unappreciated level of heterogeneity of immunophenotype associated with diverse prognosis for specific subsets in this highly lethal cancer type.
| S-EPMC8683526 | biostudies-literature
Project description:BackgroundAnoikis is a specialized form of programmed apoptosis that occurs in two model epithelial cell lines and plays an important role in tumors. However, the prognostic value of anoikis-related lncRNA (ARLncs) in osteosarcoma (OS) has not been reported.MethodsBased on GTEx and TARGET RNA sequencing data, we carried out a thorough bioinformatics analysis. The 27 anoikis-related genes were obtained from the Gene Set Enrichment Analysis (GSEA). Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis were successively used to screen for prognostic-related ARLncs. To create the prognostic signature of ARLncs, we performed multivariate Cox regression analysis. We calculated the risk score based on the risk coefficient, dividing OS patients into high- and low-risk subgroups. Additionally, the relationship between the OS immune microenvironment and risk prognostic models was investigated using function enrichment, including Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), single-sample gene set enrichment analysis (ssGSEA), and GSEA analysis. Finally, the potential effective drugs in OS were found by immune checkpoint and drug sensitivity screening.ResultsA prognostic signature consisting of four ARLncs (AC079612.1, MEF2C-AS1, SNHG6, and TBX2-AS1) was constructed. To assess the regulation patterns of anoikis-related lncRNA genes, we created a risk score model. According to a survival analysis, high-risk patients have a poor prognosis as they progress. By using immune functional analysis, the lower-risk group demonstrated the opposite effects compared with the higher-risk group. GO and KEGG analysis showed that the ARLncs pathways and immune-related pathways were enriched. Immune checkpoints and drug sensitivity analysis might be used to determine the better effects of the higher group.ConclusionWe identified a novel prognostic model based on a four-ARLncs signature that might serve as potential prognostic indicators that can be used to predict the prognosis of OS patients, and immunotherapy and drugs that may contribute to improving the overall survival of OS patients and advance our understanding of OS.
| S-EPMC10076677 | biostudies-literature
Project description:BackgroundSuppressive tumor microenvironment is closely related to the progression and poor prognosis of lung adenocarcinoma (LUAD). Novel individual and universal immune-related biomarkers to predict the prognosis and immune landscape of LUAD patients are urgently needed. Two-gene pairing patterns could integrate and utilize various gene expression data.MethodsThe RNA-seq and relevant clinicopathological data of the LUAD project from the TCGA and well-known immune-related genes list from the ImmPort database were obtained. Co-expression analysis followed by an analysis of variance was performed to identify differentially expressed immune-related lncRNA (irlncRNA) (DEirlncRNA) between tumor and normal tissues. Two arbitrary DEirlncRNAs (DEirlncRNAs pair) in a tumor sample underwent pairwise comparison to generate a score (0 or 1). Next, Univariate analysis, Lasso regression and Multivariate analysis were used to screen survival-related DEirlncRNAs pairs and construct a prognostic model. The Acak information standard (AIC) values of the receiver operating characteristic (ROC) curve for 3 years are calculated to determine the cut-off point for high- or low-risk score. Finally, we evaluated the relationship between the risk score and overall survival, clinicopathological features, immune landscape, and chemotherapy efficacy.ResultsData of 54 normal and 497 tumor samples of LUAD were enrolled. After a strict screening process, 15 survival-independent-related DEirlncRNA pairs were integrated to construct a prognostic model. The AUC value of the 3-year ROC curve was 0.828. Kaplan-Meier analysis showed that patients with low risk lived longer than patients with high risk (p <0.001). Univariate and Multivariate Cox analysis suggested that the risk score was an independent factor of survival. The risk score was negatively associated with most tumor-infiltrating immune cells, immune score, and microenvironment scores. The low-risk group was correlated with increased expression of ICOS. The high-risk group had a connection with lower half inhibitory centration (IC50) of most chemotherapy drugs (e.g., etoposide, paclitaxel, vinorelbine, gemcitabine, and docetaxel) and targeted medicine-erlotinib, but with higher IC50 of methotrexate.ConclusionThe established irlncRNA pairs-based model is a promising prognostic signature for LUAD patients. Furthermore, the prognostic signature has great potential in the evaluation of tumor immune landscape and guiding individualized treatment regimens.
| S-EPMC8784752 | biostudies-literature
Project description:Ovarian cancer (OV) is the most lethal gynecological malignancy worldwide, with high recurrence rates. Anoikis, a newly-acknowledged form of programmed cell death, plays an essential role in cancer progression, though studies focused on prognostic patterns of anoikis in OV are still lacking. We filtered 32 potential anoikis-related genes (ARGs) among the 6406 differentially expressed genes (DEGs) between the 180 normal controls and 376 TCGA-OV samples. Through the LASSO-Cox analysis, a 2-gene prognostic signature, namely AKT2, and DAPK1, was finally distinguished. We then demonstrated the promising prognostic value of the signature through the K-M survival analysis and time-dependent ROC curves (p-value < 0.05). Moreover, based on the signature and clinical features, we constructed and validated a nomogram model for 1-year, 3-year, and 5-year overall survival, with reliable prognostic values in both TCGA-OV training cohort (p-value < 0.001) and ICGC-OV validation cohort (p-value = 0.030). We evaluated the tumor immune landscape through the CIBERSORT algorithm, which indicated the upregulation of resting Myeloid Dendritic Cells (DCs), memory B cells, and naïve B cells and high expression of key immune checkpoint molecules (CD274 and PDCD1LG2) in the high-risk group. Interestingly, the high-risk group exhibited better sensitivity toward immunotherapy and less sensitivity toward chemotherapies, including Cisplatin and Bleomycin. Especially, based on the IHC of tissue microarrays among 125 OV patients at our institution, we reported that aberrant upregulation of DAPK1 was related to poor prognosis. Conclusively, the anoikis-related signature was a promising tool to evaluate prognosis and predict therapy responses, thus assisting decision-making in the realm of OV precision medicine.
| S-EPMC10837903 | biostudies-literature
Project description:Stomach adenocarcinoma (STAD) is the most prevalent gastrointestinal malignancy and seriously threatens the life of the global population. Anoikis, a process of programmed cell death that occurs when cells detach from the extracellular matrix, is closely associated with tumor invasion and metastasis. In this study, we used the TCGA-STAD database to identify the expression patterns and prognostic relevance of anoikis-related genes (ARGs) in STAD. Functional enrichment analysis was used to explore the potential pathway. LASSO and Cox regression were used to construct anoikis-related prognostic signature. The anoikis risk score (ARS) incorporated 7 genes and stratified patients into highand low-risk subgroups by median value splitting. In addition, external validation was performed based on GSE66229, GSE15459, and GSE84437 cohorts. Nomograms were created based on risk characteristics in combination with clinical variants and the performance of the model was validated with time-dependent AUC, calibration curves, and decision curve analysis (DCA). The prognostic signature indicated that the low-risk subgroup had better outcomes and significant correlations with tumor microenvironment, immune landscape, immunotherapy response, and drug sensitivity. In addition, single-cell analysis displayed the cell types, the subcellular localization of prognostic genes, and the cellular interaction to reveal the potential molecular communication mechanism of anoikis resistance. Finally, in vitro experiments confirmed the critical role of CRABP2 in STAD. The results indicated that CRABP2 knockdown inhibited gastric cancer cell proliferation, migration and invasion, and promoted apoptosis. In summary, ARS can serve as a biomarker for predicting survival outcomes in STAD patients, providing new tools for personalized treatment decisions for STAD patients.
| S-EPMC11799156 | biostudies-literature
Project description:Background: Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma (RCC). Anoikis plays an essential function in tumourigenesis, whereas the role of anoikis in ccRCC remains unclear. Methods: Anoikis-related genes (ARGs) were collected from the MSigDB database. According to univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to select the ARGs associated with the overall rate (OS). Multivariate Cox regression analysis was conducted to identify 5 prognostic ARGs, and a risk model was established. The Kaplan-Meier survival analysis was used to evaluate the OS rate of ccRCC patients. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSVA) were utilized to investigate the molecular mechanism of patients in the low- and high-risk group. ESTIMATE, CIBERSOT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape. Consensus clustering analysis was performed to divide the patients into different subgroups. Results: A fresh risk model was constructed based on the 5 prognostic ARGs (CHEK2, PDK4, ZNF304, SNAI2, SRC). The Kaplan-Meier survival analysis indicated that the OS rate of patients with a low-risk score was significantly higher than those with a high-risk score. Consensus clustering analysis successfully clustered the patients into two subgroups, with a remarkable difference in immune infiltration landscape and prognosis. The ESTIMATE, CIBERSORT, and ssGSEA results illustrated a significant gap in immune infiltration landscape of patients in the low- and high-risk group. Enrichment analysis and GSVA revealed that immune-related signaling pathways might mediate the role of ARGs in ccRCC. The nomogram results illustrated that the ARGs prognostic signature was an independent prognostic predictor that distinguished it from other clinical characteristics. TIDE score showed a promising immunotherapy response of ccRCC patients in different risk subgroups and cluster subgroups. Conclusion: Our study revealed that ARGs play a carcinogenic role in ccRCC. Additionally, we firstly integrated multiple ARGs to establish a risk-predictive model. This study highlights that ARGs could be implemented as a stratification factor for individualized and precise treatment in ccRCC patients.
| S-EPMC9627172 | biostudies-literature
Project description:BackgroundBoth metastasis and immune resistance are huge obstacle in lung adenocarcinoma (LUAD) treatment. Multiple studies have shown that the ability of tumor cells to resist anoikis is closely related to the metastasis of tumor cells.MethodsIn this study, the risk prognosis signature related to anoikis and immune related genes (AIRGs) was constructed by cluster analysis and the least absolute shrinkage and selection operator (LASSO) regression by using The Cancer Genome Atlas (TCGA) Program and the Gene Expression Omnibus (GEO) database. Kaplan-Meier (K-M) curve described the prognosis in the different groups. Receiver operating characteristic (ROC) was applied to evaluate the sensitivity of this signature. Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), independent prognostic analysis, and nomogram were utilized to assess the validity of the signature. In addition, we used multiple bioinformatic tools to analyze the function between different groups. Finally, mRNA levels were analyzed by quantitative real-time PCR (qRT-PCR).ResultsThe K-M curve showed a worse prognosis for the high-risk group compared to that for the low-risk group. ROC, PCA, t-SNE, independent prognostic analysis and nomogram showed well predictive capabilities. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that differential genes were mainly enriched in immunity, metabolism, and cell cycle. In addition, multiple immune cells and targeted drugs differed in the two risk groups. Finally, we found that the mRNA levels of AIRGs were remarkably different in normal versus cancer cells.ConclusionsIn short, we established a new model about anoikis and immune, which can well predict prognosis and immune response.
| S-EPMC10174758 | biostudies-literature
Project description:BackgroundAnoikis, as a specific form of programmed cell death, involves in tumor metastasis. However, there is still lacking of anoikis-related long non-coding RNA (lncRNA) risk signature in the diagnosis and prognosis of lung adenocarcinoma (LUAD). This study constructed a prognostic risk model by comprehensively analyzing anoikis-related lncRNAs which could effectively diagnose and predict the outcomes of LUAD patients.MethodsA list of anoikis-related genes (ARGs) was retrieved from literatures. Anoikis-related lncRNAs were selected using co-expression analysis from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate regression analyses were used to construct a prognostic model. The performance of the risk signature in predicting the prognosis and clinical significance were determined by Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, univariate and multivariate regression analyses. Moreover, the differences of tumor immune microenvironment between the high- and low-risk groups were explored. Finally, a novel nomogram was developed by combining the signature and clinicopathological factors, and the association between lncRNAs and differential N6-methyladenosine (m6A) genes was analyzed by Spearman's analysis.ResultsA total of 1,694 anoikis-related lncRNAs were identified from 479 cases of LUAD. According to the univariate and multivariate Cox analyses, we established a prognostic risk model consisting of seven lncRNAs (AC026355.2, AL606489.1, AL031667.3, LINC02802, LINC01116, AC018529.1, and AP000844.2). This prognostic risk model could efficiently classify low- and high-risk patients. The area under the curve (AUC) value was 0.717, which indicated more powerful predictive capability than commonly used clinicopathological factors. The high- and low-risk groups demonstrated different immune microenvironment. Moreover, the nomogram also demonstrated good performance in predicting the prognosis. Twelve differential m6A regulators were identified, and RBM15 was found to be correlated positively with the hub lncRNA AL606489.1.ConclusionsOur study constructed a prognostic risk model based on anoikis-related lncRNAs, which could provide novel perspective on the prognosis of LUAD patients.
| S-EPMC11543027 | biostudies-literature
Project description:The role of N7-methylguanosine(m7G)-related miRNAs in lung adenocarcinoma (LUAD) remains unclear. We used LUAD data from The Cancer Genome Atlas (TCGA) to establish a risk model based on the m7G-related miRNAs, and divided patients into high-risk or low-risk subgroups. A nomogram for predicting overall survival (OS) was then constructed based on the independent risk factors. In addition, we performed a functional enrichment analysis and defined the oxidative stress-related genes, immune landscape as well as a drug response profile in the high-risk and low-risk subgroups. This study incorporated 28 m7G-related miRNAs into the risk model. The data showed a significant difference in the OS between the high-risk and low-risk subgroups. The receiver operating characteristic curve (ROC) predicted that the area under the curve (AUC) of one-year, three-year and five-year OS was 0.781, 0.804 and 0.853, respectively. The C-index of the prognostic nomogram for predicting OS was 0.739. We then analyzed the oxidative stress-related genes and immune landscape in the high-risk and low-risk subgroups. The data demonstrated significant differences in the expression of albumin (ALB), estimated score, immune score, stromal score, immune cell infiltration and functions between the high-risk and low-risk subgroups. In addition, the drug response analysis showed that low-risk subgroups may be more sensitive to tyrosine kinase inhibitor (TKI) and histone deacetylase (HDAC) inhibitors. We successfully developed a novel risk model based on m7G-related miRNAs in this study. The model can predict clinical prognosis and guide therapeutic regimens in patients with LUAD. Our data also provided new insights into the molecular mechanisms of m7G in LUAD.
| S-EPMC10295992 | biostudies-literature
Project description:BackgroundThe immunological tumour microenvironment (TME) has occupied a very important position in the beginning and progression of non-small cell lung cancer (NSCLC). Prognosis of lung adenocarcinoma (LUAD) remains poor for the local progression and widely metastases at the time of clinical diagnosis. Our objective is to identify a potential signature model to improve prognosis of LUAD.MethodsWith the aim to identify a novel immune prognostic signature associated with overall survival (OS), we analysed LUADs extracted from The Cancer Genome Atlas (TCGA). Immune scores and stromal scores of TCGA-LUAD were downloaded from Estimation of STromal and Immune cells in MAlignant Tumour tissues Expression using data (ESTIMATE). LASSO COX regression was applied to build the prediction model. Then, the prognostic gene signature was validated in the GSE68465 dataset.ResultsThe data from TCGA datasets showed patients in stage I and stage II had higher stromal scores than patients in stage IV (P < 0.05), and for immune score patients in stage I were higher than patients in stage III and stage IV (P < 0.05). The improved overall survivals were observed in high stromal score and immune score groups. Patients in the high-risk group exhibited the inferior OS (P = 2.501e - 05). By validating the 397 LUAD patients from GSE68465, we observed a better OS in the low-risk group compared to the high-risk group, which is consistent with the results from the TCGA cohort. Nomogram results showed that practical and predicted survival coincided very well, especially for 3-year survival.ConclusionWe obtained an 11 immune score related gene signature model as an independent element to effectively classify LUADs into different risk groups, which might provide a support for precision treatments. Moreover, immune score may play a potential valuable sole for estimating OS in LUADs.
| S-EPMC7825366 | biostudies-literature