A prognostic model based on immune-related long noncoding RNAs for patients with epithelial ovarian cancer.

Long noncoding RNAs (lncRNAs) are important regulators of gene expression and can affect a variety of physiological processes. Recent studies have shown that immune-related lncRNAs play an important role in the tumour immune microenvironment and may have potential application value in the treatment and prognosis prediction of tumour patients. Epithelial ovarian cancer (EOC) is characterized by a high incidence and poor prognosis. However, there are few studies on immune-related lncRNAs in EOC. In this study, we focused on immune-related lncRNAs associated with survival in EOC. We downloaded mRNA data for EOC patients from The Cancer Genome Atlas (TCGA) database and mRNA data for normal ovarian tissue from the Genotype-Tissue Expression (GTEx) database and identified differentially expressed genes through differential expression analysis. Immune-related lncRNAs were obtained through intersection and coexpression analysis of differential genes and immune-related genes from the Immunology Database and Analysis Portal (ImmPort). Samples in the TCGA EOC cohort were randomly divided into a training set, validation set and combination set. In the training set, Cox regression analysis and LASSO regression were performed to construct an immune-related lncRNA signature. Kaplan-Meier survival analysis, time-dependent ROC curve analysis, Cox regression analysis and principal component analysis were performed for verification in the training set, validation set and combination set. Further studies of pathways and immune cell infiltration were conducted through Gene Set Enrichment Analysis (GSEA) and the Timer data portal. An immune-related lncRNA signature was identified in EOC, which was composed of six immune-related lncRNAs (KRT7-AS, USP30-AS1, AC011445.1, AP005205.2, DNM3OS and AC027348.1). The signature was used to divide patients into high-risk and low-risk groups. The overall survival of the high-risk group was lower than that of the low-risk group and was verified to be robust in both the validation set and the combination set. The signature was confirmed to be an independent prognostic biomarker. Principal component analysis showed the different distribution patterns of high-risk and low-risk groups. This signature may be related to immune cell infiltration (mainly macrophages) and differential expression of immune checkpoint-related molecules (PD-1, PDL1, etc.). We identified and established a prognostic signature of immune-related lncRNAs in EOC, which will be of great value in predicting the prognosis of clinical patients and may provide a new perspective for immunological research and individualized treatment in EOC.

Peng Y ，Wang H ，Huang Q ，Wu J ，Zhang M ... -  《Journal of Ovarian Research》

Acetylation model predicts prognosis of patients and affects immune microenvironment infiltration in epithelial ovarian carcinoma.

Epithelial ovarian carcinoma (EOC) is a prevalent gynaecological malignancy. The prognosis of patients with EOC is related to acetylation modifications and immune responses in the tumour microenvironment (TME). However, the relationships between acetylation-related genes, patient prognosis, and the tumour immune microenvironment (TIME) are not yet understood. Our research aims to investigate the link between acetylation and the tumour microenvironment, with the goal of identifying new biomarkers for estimating survival of patients with EOC. Using data downloaded from the tumour genome atlas (TCGA), genotypic tissue expression (GTEx), and gene expression master table (GEO), we comprehensively evaluated acetylation-related genes in 375 ovarian cancer specimens and identified molecular subtypes using unsupervised clustering. The prognosis, TIME, stem cell index and functional concentration analysis were compared among the three groups. A risk model based on differential expression of acetylation-related genes was established through minimum absolute contraction and selection operator (LASSO) regression analysis, and the predictive validity of this feature was validated using GEO data sets. A nomogram is used to predict a patient's likelihood of survival. In addition, different EOC risk groups were evaluated for timing, tumour immune dysfunction and exclusion (TIDE) score, stemness index, somatic mutation, and drug sensitivity. We used the mRNA levels of the differentially expressed genes related to acetylation to classify them into three distinct clusters. Patients with increased immune cell infiltration and lower stemness scores in cluster 2 (C2) exhibited poorer prognosis. Immunity and tumourigenesis-related pathways were highly abundant in cluster 3 (C3). We developed a prognostic model for ten differentially expressed acetylation-related genes. Kaplan-Meier analysis demonstrated significantly worse overall survival (OS) in high-risk patients. Furthermore, the TIME, tumour immune dysfunction and exclusion (TIDE) score, stemness index, tumour mutation burden (TMB), immunotherapy response, and drug sensitivity all showed significant correlations with the risk scores. Our study demonstrated a complex regulatory mechanism of acetylation in EOC. The assessment of acetylation patterns could provide new therapeutic strategies for EOC immunotherapy to improve the prognosis of patients.

Wang X ，Li X ，Wei L ，Yu Y ，Hazaisihan Y ，Tao L ，Jia W ... -  《Journal of Ovarian Research》

Immune-related lncRNAs as predictors of survival in breast cancer: a prognostic signature.

Ma W ，Zhao F ，Yu X ，Guan S ，Suo H ，Tao Z ，Qiu Y ，Wu Y ，Cao Y ，Jin F ... -  《Journal of Translational Medicine》

Identification of Five Immune-Related lncRNAs Predicting Survival and Tumor Microenvironment Characteristics in Breast Cancer.

A common cancer in females, breast cancer (BRCA) mortality has been recently reduced; however, the prognosis of BRCA patients remains poor. This study attempted to develop prognostic immune-related long noncoding RNAs (lncRNAs) for BRCA and identify the effects of these lncRNAs on the tumor microenvironment (TME). Gene expression data from The Cancer Genome Atlas (TCGA) database were collected in order to select differentially expressed lncRNAs. Immune-related lncRNAs were downloaded from the ImmLnc database, where 316 immune-related lncRNAs were identified, 12 of which were found to be significantly related to the prognosis of BRCA patients. Multivariate cox regression analysis was then applied to construct prognostic immune-related lncRNAs as the risk model, including C6orf99, LINC00987, SIAH2-AS1, LINC01010, and ELOVL2-AS1. High-risk and low-risk groups were distinguished according to the median of immune-related risk scores. Accordingly, the overall survival (OS) in the high-risk group was observed to be shorter than that in the low-risk group. qRT-PCR analysis demonstrated that lncRNA expression levels in BRCA cell lines were in basic agreement with predictions except for LINC00987. By validating numerous clinical samples, lncRNA C6orf99 was shown to be highly expressed in the advanced stage, while LINC01010 and SIAH2-AS1 decreased in the advanced T-stage and M-stage. Moreover, the expression of LINC0098 was found to be significantly decreased among the groups (>50 years old). Gene set enrichment analysis (GSEA) was applied to analyze the cancer hallmarks and immunological characteristics of the high-risk and low-risk groups. Importantly, the TIMER database demonstrated that this immune-related lncRNA risk model for breast cancer is related to the infiltration of immune cells. In conclusion, the results indicated that five immune-related lncRNAs could be used as a prognostic model and may even accelerate immunotherapy for BRCA patients.

Xiao R ，Yang M ，Tan Y ，Ding R ，Li D ... -  《-》

Bioinformatics profiling utilized a nine immune-related long noncoding RNA signature as a prognostic target for pancreatic cancer.

To identify an immune-related long noncoding RNA (lncRNA) signature with potential prognostic value for patients with pancreatic cancer. Pancreatic cancer samples with available clinical information and whole genomic mRNA expression data obtained from The Cancer Genome Atlas (TCGA) were enrolled in our research. The immune score of each sample was calculated according to the expression level of immune-related genes and used to identify the most promising immune-related lncRNAs. According to the risk score developed from screened immune-related lncRNAs, the high- and low-risk groups were separated on the basis of the median risk score. The prediction reliability was further evaluated in the validation set and combination set. Both gene set enrichment analysis (GSEA) and principal component analysis (PCA) were performed for functional annotation, and the microenvironment cell population record was applied to evaluate the immune composition and purity of the tumor. A cohort of 176 samples was included in this study. A total of 163 immune-related lncRNAs were collected according to Pearson correlation analyses between immune score and lncRNA expression |R| > 0.5, P < 0.01). Nine immune-related lncRNAs (AL138966.2, AL133520.1, AC142472.1, AC127024.5, AC116913.1, AC083880.1, AC124016.1, AC008443.5, and AC092171.5) with the most significant prognostic values (P < 0.01) were identified. In the training set, it was observed that patients in the low-risk group showed longer overall survival (OS) than those in the high-risk group (P < 0.001); meanwhile, similar results were found in the validation set, combination set and various stratified sets (P < 0.05, P < 0.001, P < 0.05, respectively). Moreover, the signature was identified as an independent prognostic factor and significantly associated with the OS of pancreatic cancer. The area under curve (AUC) of the receiver operating characteristic curve (ROC curve) for the nine lncRNA signature in predicting the 2-year survival rate was 0.703. In addition, the low-risk and high-risk groups displayed different distributed patterns in PCA and different immune statuses in the GSEA. The signature indicated decreased purity of the tumor by implying a lower proportion of cancer cells along with an increasing enrichment of fibroblasts, myeloid dendritic cells, and monocytic lineage cells. Our research suggests that the immune-related lncRNA signature possesses latent prognostic value for patients with pancreatic cancer and may provide new information for immunological research and treatment in pancreatic cancer.

Wei C ，Liang Q ，Li X ，Li H ，Liu Y ，Huang X ，Chen X ，Guo Y ，Li J ... -  《-》