Immune-Related lncRNAs to Construct Novel Signatures and Predict the Prognosis of Rectal Cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Xing XL
PROVIDER: S-EPMC8415501 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundImmune-related long non-coding RNAs (irlncRNAs) appear valuable in predicting prognosis in patients with cancer. In this study, we used a fresh modeling algorithm to construct irlncRNAs signature and then assessed its predictive value for prognosis, tumor immune infiltration, and chemotherapy efficacy in gastric cancer (GC) patients.Materials and methodsThe raw transcriptome data were extracted from the Cancer Genome Atlas (TCGA). Patients were randomly divided into the training and testing cohort. irlncRNAs were identified through co-expression analysis, after which differentially expressed irlncRNA (DEirlncRNA) pairs were identified. Next, we developed a model to distinguish between high- or low-risk groups in GC patients through univariate and LASSO regression analyses. A ROC curve was used to verify this model. After subgrouping patients according to the median risk score, we investigated the connection between the risk score of GC and clinicopathological characteristics. Functional enrichment analysis was also performed.ResultsWe find that the results indicate that immune-related lncRNA signaling has essential value in predicting prognosis, and it may be potential to measure the Efficacy for immunotherapy. This feature may be a guide to the selection of GC immunotherapy.ConclusionOur data revealed that immune-related lncRNA signaling had essential value in predicting prognosis, and it may be potentially used to measure the efficacy for immunotherapy. This feature may also be used to guide the selection of GC immunotherapy.
| S-EPMC8985853 | biostudies-literature
Project description:BackgroundGastric cancer is a prevalent cause of tumor death. Tumor immunotherapy aims to reshape the specific immunity to tumors in order to kill the tumor. LncRNAs play a pivotal role in regulating the tumor immune microenvironment. Herein, immune-related lncRNAs were used to establish a prognosis risk-assessment model for gastric cancer and provide personalized predictions while providing insights and targets for gastric cancer treatment to enhance patient prognosis.MethodsGastric adenocarcinoma transcriptome and clinical data were acquired from the The Cancer Genome Atlas (TCGA) database to screen the immune-related lncRNAs. Then, LASSO COX regression was utilized to construct the prognosis risk-assessment model. Afterward, the reliability of the model was evaluated the relationship between immune infiltration, clinical characteristics, and the model was analyzed.ResultsWe identified 13 lncRNAs and constructed the prognosis assessment model. According to the median risk score of the training set, the patients were assigned to different risk groups. Overall survival time was shorter in the high-risk group. In the high-risk group, higher infiltration of mono-macrophages, dendritic cells, CD4+ T cells, and CD8+ T cells was observed. Moreover, the model was positively related to tumor metastasis.ConclusionThe prognosis risk-assessment model developed in this research can effectively predict the prognosis of gastric cancer patients. This tool is expected to be further applied to clinics in the future, thus providing a novel target for immunotherapy in gastric cancer patients.
| S-EPMC9318354 | biostudies-literature
Project description:Background: Breast cancer (BC) is one of the most frequently diagnosed malignancies among females. As a huge heterogeneity of malignant tumor, it is important to seek reliable molecular biomarkers to carry out the stratification for patients with BC. We surveyed immune- associated lncRNAs that may be used as potential therapeutic targets in BC. Methods: LncRNA expression data and clinical information of BC patients were downloaded from the TCGA database for a comprehensive analysis of candidate genes. A model consisting of immune-related lncRNAs enriched in BC cancerous tissues was established using the univariate Cox regression analysis and the iterative Lasso Cox regression analysis. The prognostic performance of this model was validated in two independent cohorts (GSE21653 and BC-KR), and compared with known prognostic biomarkers. A nomogram that integrated the immune-related lncRNA signature and clinicopathological factors was constructed to accurately assess the prognostic value of this signature. The correlation between the signature and immune cell infiltration in BC was also analyzed. Results: The Kaplan-Meier analysis showed that the OS of Patients in the low-risk group had significantly better survival than those in the high-risk group, Clinical subgroup analysis showed that the predictive ability was independent of clinicopathological factors. Univariate/multivariate Cox regression analysis showed immune lncRNA signature is an important prognostic factor and an independent prognostic marker. In addition, GSEA and GSVA analysis as well as comprehensive analysis of immune cells showed that the signature was significantly correlated with the infiltration of immune cells. Conclusion: We successfully constructed an immune-associated lncRNA signature that can accurately predict BC prognosis.
| S-EPMC7873981 | biostudies-literature
Project description:The host immune response can impact cancer growth, prognosis, and response to therapy. In colorectal cancer, the presence of cells involved with T-cell-mediated adaptive immunity predicts survival better than the current staging method. We used the expression of genes recently associated with host immune responses (T(H1)-mediated adaptive immunity, inflammation, and immune suppression) to perform hierarchical clustering of multiple large cohorts of cancer specimens to determine if immune-related gene expression resulted in clinical significant groupings of tumors. Microarray data from prostate cancer (n = 79), breast cancer (n = 132), lung cancer (n = 84), glioblastoma multiforme (n = 120), and lymphoma (n = 127) were analyzed. Among adenocarcinomas, the T(H1)-mediated adaptive immunity genes were consistently associated with better prognosis, while genes associated with inflammation and immune suppression were variably associated with outcome. Specifically, increased expression of the T(H1)-mediated adaptive immunity genes was associated with good prognosis in breast cancer patients under 45 years of age (p = .04, hazard ratio [HR] = 0.42) and in prostate cancer patients (p = .03, HR = 0.36) but not in lung cancer patients (p = 0.45, HR = 1.37). In lymphoma, patients with increased expression of inflammation and immune suppression genes had better prognosis than those expressing the T(H1)-mediated adaptive immunity genes (p = .01, HR = 0.43) and in glioblastoma multiforme, the expression of inflammation genes conferred improved prognosis than those expressing immune suppression genes (p = 0.05, HR = 0.62). In aggregate, the gene expression signatures implicating specific components of the immune response hold prognostic import across solid tumors.
| S-EPMC4486063 | biostudies-literature
Project description:Genome instability is a hallmark of cancer, and the function of lncRNAs in regulating genomic stability has been gradually characterized. However, the prognostic value of lncRNAs related to genetic instability has not been found in breast cancer. Here we constructed a genetic instability-related lncRNA model including U62317.4, SEMA3B-AS1, MAPT-AS1, AC115837.2, LINC01269, AL645608.7, and GACAT2. This model can evaluate the risk and predict the survival outcomes of patients. Further analysis showed that the differentially expressed genes between the high- and low-risk groups were enriched in immunity and cornified envelope formation pathways. In addition, M2 macrophages infiltrated more obviously in the high-risk group. In summary, lncRNAs related to genetic instability may influence the development of breast cancer through immune infiltration and keratinization. This study provides a wider insight into breast cancer development and treatment.
| S-EPMC9478756 | biostudies-literature
Project description:Background: Ferroptosis is a novel regulated cell death that is characterized by iron-dependent oxidative damage. Renal cancer is the second most common cancer of the urinary system, which is highly correlated with iron metabolism. The aim of our present study was to identify suitable ferroptosis-related prognosis signatures for renal cancer. Methods: We downloaded the RNA-seq count data of renal cancer from The Cancer Genome Atlas database and used the DESeq2, Survival, and Cox regression analyses to screen the prognosis signatures. Results: We identified 5 ferroptosis-related differentially expressed lncRNAs (FR-DELs) (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) to be independently correlated with the overall survival (OS) of patients with renal cancer. The risk assessment model and diagnosis model constructed by those 5 FR-DELs could well predict the outcome and the diagnosis of renal cancer. Conclusion: Our present study not only suggested those 5 FR-DELs could be used as prognosis and diagnosis signatures for renal cancer but also provided strategies for other cancers in the screening of ferroptosis-related biomarkers.
| S-EPMC8804361 | biostudies-literature
Project description:Background Cuproptosis is a new modality of cell death regulation that is currently considered as a new cancer treatment strategy. Nevertheless, the prognostic predictive value of cuproptosis-related lncRNAs in breast cancer (BC) remains unknown. Using cuproptosis-related lncRNAs, this study aims to predict the immune microenvironment and prognosis of BC patients. and develop new therapeutic strategies that target the disease. Methods The Cancer Genome Atlas (TCGA) database provided the RNA-seq data along with the corresponding clinical and prognostic information. Univariate and multivariate Cox regression analyses were performed to acquire lncRNAs associated with cuproptosis to establish predictive features. The Kaplan-Meier method was used to calculate the overall survival rate (OS) in the high-risk and low-risk groups. High risk and low risk gene sets were enriched to explore functional discrepancies among risk teams. The mutation data were analyzed using the “MAFTools” r-package. The ties of predictive characteristics and immune status had been explored by single sample gene set enrichment analysis (ssGSEA). Last, the correlation between predictive features and treatment condition in patients with BC was analyzed. Based on prognostic risk models, we assessed associations between risk subgroups and immune scores and immune checkpoints. In addition, drug responses in at-risk populations were predicted. Results We identified a set of 11 Cuproptosis-Related lncRNAs (GORAB-AS1, AC 079922.2, AL 589765.4, AC 005696.4, Cytor, ZNF 197-AS1, AC 002398.1, AL 451085.3, YTH DF 3-AS1, AC 008771.1, LINC 02446), based on which to construct the risk model. In comparison to the high-risk group, the low-risk patients lived longer (p < 0.001). Moreover, cuproptosis-related lncRNA profiles can independently predict prognosis in BC patients. The AUC values for receiver operating characteristics (ROC) of 1-, 3-, and 5-year risk were 0.849, 0.779, and 0.794, respectively. Patients in the high-risk group had lower OS than those in the low-risk group when they were divided into groups based on various clinicopathological variables. The tumor burden mutations (TMB) correlation analysis showed that high TMB had a worse prognosis than low-TMB, and gene mutations were found to be different in high and low TMB groups, such as PIK3CA (36% versus 32%), SYNE1 (4% versus 6%). Gene enrichment analysis indicated that the differential genes were significantly concentrated in immune-related pathways. The predictive traits were significantly correlated with the immune status of BC patients, according to ssGSEA results. Finally, high-risk patients showed high sensitivity in anti-CD276 immunotherapy and conventional chemotherapeutic drugs such as imatinib, lapatinib, and pazopanib. Conclusion We successfully constructed of a cuproptosis-related lncRNA signature, which can independently predict the prognosis of BC patients and can be used to estimate OS and clinical treatment outcomes in BRCA patients. It will serve as a foundation for further research into the mechanism of cuproptosis-related lncRNAs in breast cancer, as well as for the development of new markers and therapeutic targets for the disease.
| S-EPMC9531154 | biostudies-literature
Project description:Background: Cuproptosis is a novel form of cell death discovered in recent. A great quantity of researches has confirmed the close relationships and crucial roles between long non-coding RNAs (lncRNAs) with the progression of colorectal cancer (CRC). However, the relationship between cuproptosis and lncRNAs remains unclear in CRC. Methods: 1,111 co-expressed lncRNAs with 16 cuproptosis regulators were retrieved from CRC samples of The Cancer Genome Atlas (TCGA) database. Through univariate Cox and least absolute shrinkage and selection operator regression analysis, a prognosis model was constructed with 15 lncRNAs. The Kaplan-Meier, receiver operating characteristic curve, C-index and principal component analysis identified the prognostic power. Furthermore, a cuproptosis-related cluster was generated based on the 15 lncRNAs by unsupervised methods. The correlations between the cuproptosis-related signatures with immune cell infiltration and anti-tumor therapy were explored by multiple algorithms. Results: A risk score and nomogram with great prediction ability were constructed for CRC prognosis evaluation. The immune activate pathways, immune infiltration cells, immune functions, immune score and immune activation genes were remarkably enriched in the high risk group. The cuproptosis-related cluster was generated, of which the cluster 2 showed longer overall survival. The immune cell infiltration analysis indicated the similar results of cluster 2 with the high risk group, implying a significant marker for "hot tumor." The cluster 2 also presented high expression of immune checkpoint molecules, MSI-H status and higher susceptibility to multiple immunotherapy drugs. Conclusion: We appraised a novel cuproptosis-related prognosis model and molecular signature associated with prognosis, immune infiltration and immunotherapy. The identification of cuproptosis-related lncRNAs improved our understanding of immune infiltration and provided a significant marker for prognosis and immunotherapy in CRC.
| S-EPMC9712968 | biostudies-literature
Project description:Acute myeloid leukemia (AML) is a complex hematologic malignancy. Survival rate of AML patients is low. N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play important roles in AML tumorigenesis and progression. However, the relationship between lncRNAs and biological characteristics of AML, as well as how lncRNAs influence the prognosis of AML patients, remain unclear. In this study. In this study, Pearson correlation analysis was used to identify lncRNAs related to m6A regulatory genes, namely m6A-related lncRNAs. And we analyzed their roles and prognostic values in AML. m6A-related lncRNAs associated with patient prognosis were screened using univariate Cox regression analysis, followed by systematic analysis of the relationship between these genes and AML clinicopathologic and biologic characteristics. Furthermore, we examined the characteristics of tumor immune microenvironment (TIME) using different IncRNA clustering models. Using LASSO regression, we identified the risk signals related to prognosis of AML patients. We then constructed and verified a risk model based on m6A-related lncRNAs for independent prediction of overall survival in AML patients. Our results indicate that risk scores, calculated based on risk-related signaling, were related to the clinicopathologic characteristics of AML and level of immune infiltration. Finally, we examined the expression level of TRAF3IP2-AS1 in patient samples through real-time polymerase chain reaction analysis and in GEO datasets, and we identified a interaction relationship between SRSF10 and TRAF3IP2-AS1 through in vitro assays. Our study shows that m6A-related lncRNAs, evaluated using the risk prediction model, can potentially be used to predict prognosis and design immunotherapy in AML patients.
| S-EPMC8810799 | biostudies-literature
Project description:Interventions: CRC group:none;pericarcinomatous:none
Primary outcome(s): OS
Study Design: Cohort study
| 87768 | ecrin-mdr-crc