Identification of autophagy-related long non-coding RNA prognostic signature for breast cancer.
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ABSTRACT:
SUBMITTER: Wu Q
PROVIDER: S-EPMC8051719 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Glioma is one of the most common types of malignant primary central nervous system tumor, and prognosis for this disease is poor. As autophagic drugs have been reported to induce glioma cell death, we investigated the potential prognostic role of autophagy-associated long non-coding RNA (lncRNA) in glioma patients. In this study, we obtained 879 lncRNAs and 216 autophagy genes from the Chinese Glioma Genome Atlas microarray, and found that 402 lncRNAs are correlated with the autophagy genes. Subsequently, 10 autophagy-associated lncRNAs with prognostic value (PCBP1-AS1, TP53TG1, DHRS4-AS1, ZNF674-AS1, GABPB1-AS1, DDX11-AS1, SBF2-AS1, MIR4453HG, MAPKAPK5-AS1 and COX10-AS1) were identified in glioma patients using multivariate Cox regression analyses. A prognostic signature was then established based on these prognostic lncRNAs, dividing patients into low-risk and high-risk groups. The overall survival time was shorter in the high-risk group than that in the low-risk group [hazard ratio (HR) = 5.307, 95% CI: 4.195-8.305; P < 0.0001]. Gene set enrichment analysis revealed that the gene sets were significantly enriched in cancer-related pathways, including interleukin (IL) 6/Janus kinase/signal transducer and activator of transcription (STAT) 3 signaling, tumor necrosis factor ? signaling via nuclear factor ?B, IL2/STAT5 signaling, the p53 pathway and the KRAS signaling pathway. The Cancer Genome Atlas dataset was used to validate that high-risk patients have worse survival outcomes than low-risk patients (HR = 1.544, 95% CI: 1.110-2.231; P = 0.031). In summary, our signature of 10 autophagy-related lncRNAs has prognostic potential for glioma, and these autophagy-related lncRNAs may play a key role in glioma biology.
| S-EPMC6443865 | biostudies-literature
Project description:Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer. Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer. Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs. Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P < 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway. Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.
| S-EPMC8007922 | biostudies-literature
Project description:Colorectal cancer (CRC) is a commonly occurring tumour with poor prognosis. Autophagy-related long non-coding RNAs (lncRNAs) have received much attention as biomarkers for cancer prognosis and diagnosis. However, few studies have focused on their prognostic predictive value specifically in CRC. This research aimed to construct a robust autophagy-related lncRNA prognostic signature for CRC. Autophagy-related lncRNAs from The Cancer Genome Atlas database were screened using univariate Cox, LASSO, and multivariate Cox regression analyses, and the resulting key lncRNAs were used to establish a prognostic risk score model. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) analysis was performed to detect the expression of several lncRNAs in cancer tissues from CRC patients and in normal tissues adjacent to the cancer tissues. A prognostic signature comprising lncRNAs AC125603.2, LINC00909, AC016876.1, MIR210HG, AC009237.14, and LINC01063 was identified in patients with CRC. A graphical nomogram based on the autophagy-related lncRNA signature was developed to predict CRC patients' 1-, 3-, and 5-year survival. Overall survival in patients with low risk scores was significantly better than in those with high risk scores (P < 0.0001); a similar result was obtained in an internal validation sample. The nomogram was shown to be suitable for clinical use and gave correct predictions. The 1- and 3-year values of the area under the receiver operating characteristic curve were 0.797 and 0.771 in the model sample, and 0.656 and 0.642 in the internal validation sample, respectively. The C-index values for the verification samples and training samples were 0.756 (95% CI = 0.668-0.762) and 0.715 (95% CI = 0.683-0.829), respectively. Gene set enrichment analysis showed that the six autophagy-related lncRNAs were greatly enriched in CRC-related signalling pathways, including p53 and VEGF signalling. The qRT-PCR results showed that the expression of lncRNAs in CRC was higher than that in adjacent tissues, consistent with the expression trends of lncRNAs in the CRC data set. In summary, we established a signature of six autophagy-related lncRNAs that could effectively guide clinical prediction of prognosis in patients with CRC. This lncRNA signature has significant clinical implications for improving the prediction of outcomes and, with further prospective validation, could be used to guide tailored therapy for CRC patients.
| S-EPMC7738973 | biostudies-literature
Project description:Ovarian cancer (OC), the most lethal gynecologic malignancy, ranks fifth in cancer deaths among women, largely because of late diagnosis. Recent studies suggest that the expression levels of immune-related long non-coding RNAs (lncRNAs) play a significant role in the prognosis of OC; however, the potential of immune-related lncRNAs as prognostic factors in OC remains unexplored. In this study, we aimed to identify a potential immune-related lncRNA prognostic signature for OC patients. We used RNA sequencing and clinical data from The Cancer Genome Atlas and the Gene Expression Omnibus database to identify immune-related lncRNAs that could serve as useful biomarkers for OC diagnosis and prognosis. Univariate Cox regression analysis was used to identify the immune-related lncRNAs with prognostic value. Functional annotation of the data was performed through the GenCLiP310 website. Seven differentially expressed lncRNAs (AC007406.4, AC008750.1, AL022341.2, AL133351.1, FAM74A7, LINC02229, and HOXB-AS2) were found to be independent prognostic factors for OC patients. The Kaplan-Meier curve indicated that patients in the high-risk group had a poorer survival outcome than those in the low-risk group. The receiver operating characteristic curve revealed that the predictive potential of the immune-related lncRNA signature for OC was robust. The prognostic signature of the seven lncRNAs was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the signature of the seven lncRNAs was an independent prognostic factor for OC patients. Finally, we constructed a nomogram model and a competing endogenous RNA network related to the lncRNA prognostic signature. In conclusion, our study reveals novel immune-related lncRNAs that may serve as independent prognostic factors in OC.
| S-EPMC8335165 | biostudies-literature
Project description:BackgroundAutophagy and long non-coding RNA (lncRNA) play a critical role in tumor progression and microenvironment. However, the role of autophagy-related lncRNAs (ARLs) in glioma microenvironment remains unclear.MethodsA total of 988 diffuse glioma samples were extracted from TCGA and CGGA databases. Consensus clustering was applied to reveal different subgroups of diffuse gliomas. Kaplan-Meier analysis was used to evaluate survival differences between groups. The infiltration of immune cells was estimated by ssGSEA, TIMER, and CIBERSORT algorithms. The construction of ARL signature was conducted using principal component analysis.ResultsConsensus clustering revealed two clusters of diffuse gliomas, in which cluster 1 was associated with poor prognosis and enriched with malignant subtypes of gliomas. Moreover, cluster 1 exhibited high apoptotic and immune characteristics, and it had a low purity and high infiltration of several immune cells. The constructed ARL signature showed a promising accuracy in predicting the prognosis of glioma patients. ARL score was significantly elevated in the malignant subtype of glioma and the high ARL score indicated a poor prognosis. Besides, the high ARL score notably indicated low tumor purity and high infiltration of macrophages and neutrophils.ConclusionOur study developed and validated a novel ARL signature for the classification of diffuse glioma, which was closely associated with glioma immune microenvironment and could serve as a promising prognostic biomarker for glioma patients.
| S-EPMC8239411 | biostudies-literature
Project description:Traditional clinicopathological indices are insufficient in predicting the prognosis of patients diagnosed with oral and oropharyngeal squamous cell carcinoma (OSCC/OPSCC). Notably, autophagy and long non-coding RNAs (lncRNAs) regulate the development and progression of various types of cancer. The present study aimed to assess the association between autophagy-related lncRNAs and the prognosis of patients diagnosed with OSCC/OPSCC. Gene sequencing and clinicopathological data of patients with OSCC/OPSCC were downloaded from The Cancer Genome Atlas database, while gene set functional classification was downloaded from the Gene Set Enrichment Analysis database. Out of the 413 transcriptome data samples and 402 clinicopathological data samples retrieved, a total of nine autophagy-related lncRNAs, including PTCSC2, AC099850.3, LINC01963, RTCA-AS1, AP002884.1, UBAC2-AS1, AL512274.1, MIR600HG and AL354733.3, were screened. This was geared towards establishing a signature through gene co-expression network, univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses. Based on this signature, the patients were subdivided into a high-risk group and a low-risk group. Kaplan-Meier survival analysis revealed that the overall survival of the high-risk group was significantly lower than that of the low-risk group. Furthermore, principal components analysis demonstrated that the patients diagnosed with OSCC/OPSCC could be distinguished into low-survival and high-survival groups according to the signature. Univariate and multivariate Cox regression analyses of clinicopathological data and the signature revealed that the signature could potentially be used as an independent prognostic factor for OSCC/OPSCC. In addition, reverse transcription-quantitative PCR analysis of clinical samples demonstrated the validity of the signature. In summary, the present study revealed that the signature based on autophagy-related lncRNAs potentially acts as an independent prognostic indicator for patients with OSCC/OPSCC. Furthermore, it promotes research on targeted diagnosis and treatment of patients diagnosed with OSCC/OPSCC.
| S-EPMC7681235 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is the primary malignancy in the liver with high rate of death and recurrence. Novel prognostic model would be crucial for early diagnosis and improved clinical decision. The study aims to provide an effective lncRNA-based signature to predict survival time and tumor recurrence for HCC. Based on public database, lncRNA-based classifiers for overall survival and tumor recurrence were built with regression analysis and cross validation strategy. According to the risk-score of the classifiers, the whole cohorts were divided into groups with high and low risk. Afterwards, the efficiency of the lncRNA-based classifiers was evaluated and compared with other clinical factors. Finally, candidate small molecules for high risk groups were further screened using drug response databases to explore potential drugs for HCC treatment.
| S-EPMC8351707 | biostudies-literature
Project description:Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis. Considerable evidence indicates that autophagy and non-coding RNA play essential roles in the biological processes involved in cancers, but associations between autophagy-related long non-coding RNAs (lncRNAs) and HNSCC remain unclear. In the present study, HNSCC RNA sequences and autophagy-related gene data were extracted from The Cancer Genome Atlas database and the Human Autophagy Database. A total of 1,153 autophagy-related lncRNAs were selected via calculating Pearson's correlation coefficient. Three prognosis-related autophagy lncRNAs were identified via univariate Cox regression, least absolute shrinkage and selection operator analysis, and multivariate Cox regression analysis. We also constructed a prognostic model based on these autophagy-related lncRNAs and evaluated its ability to accurately and independently predict the prognosis of HNSCC patients. The area under the curve (AUC) was 0.864 (3-year) and 0.836 (5-year), and our model can independently predict the prognosis of HNSCC. The prognostic value of the three autophagy lncRNAs was confirmed via analysis of samples from five databases. To further identify the functions of the three lncRNAs, a co-expression network was constructed and pathway analysis was performed. In that analysis the lncRNAs were correlated with 189 related genes and 20 autophagy-related genes, and these lncRNAs mainly involved homologous recombination, the Fanconi anemia pathway, the autophagy-related pathway, and immune-related pathways. In addition, we validated the expression levels of three lncRNAs and autophagy markers (ATG12, BECN1, and MAP1LC3B) based on TIMER, Oncomine, and HPA database analysis. Our results indicated that TTTY15 was increased in HPV positive and HPV negative HNSCC patients, and three autophagy markers were up-regulated in all HNSCCC patients. Lastly, association between three lncRNAs and autophagy markers was performed, and our results showed that TTTY15 and MIF-AS1 were associated with autophagy markers. Collectively, these results suggested that three autophagy-related lncRNAs have prognostic value in HNSCC patients.
| S-EPMC7871905 | biostudies-literature
Project description:This study aimed to develop an autophagy-associated long non-coding RNA (lncRNA) signature to predict the prognostic outcomes of uveal melanoma (UM). The data of UM from The Cancer Genome Atlas (TCGA) were enrolled to obtain differentially expressed genes (DEGs) between metastasizing and non-metastasizing UM patients. A total of 13 differentially expressed autophagy genes were identified and validated in Gene Expression Omnibus, and 11 autophagy-related lncRNAs were found to be associated with overall survival. Through performing least absolute shrinkage and selection operator regression analyses, a six-autophagy-related lncRNA signature was built, and its efficacy was confirmed by receiver-operating characteristic, Kaplan-Meier analysis, and univariate and multivariate Cox regression analyses. A comprehensive nomogram was established and its clinical net benefit was validated by decision curve analysis. GSEA revealed that several biological processes and signaling pathways including Toll-like receptor signaling pathway, natural killer cell-mediated cytotoxicity, and B- and T-cell receptor signaling pathway were enriched in the high-risk group. CIBERSORT results showed that the signature was related to the immune response especially HLA expression. This signature could be deployed to assist clinicians to identify high-risk UM patients and help scientists to explore the molecular mechanism of autophagy-related lncRNAs in UM pathogenesis.
| S-EPMC8047156 | biostudies-literature
Project description:Long non-coding RNAs (lncRNAs) serve key roles in tumorigenesis and are differentially expressed in cancer. Using bioinformatics and statistical methods, the present study aimed to identify an lncRNA signature to predict breast cancer survival. The gene expression data of 768 patients with breast cancer were downloaded from The Cancer Genome Atlas database, and Cox regression, Kaplan-Meier and receiver operating characteristic (ROC) analyses were performed to construct and validate a predictive model. Gene Ontology term enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis were employed to predict the functions of the indicated lncRNAs. A signature consisting of four lncRNAs, including PVT1, MAPT-AS1, LINC00667 and LINC00938, was identified, and patients were subsequently divided into high- and low-risk groups according to the median risk score. Kaplan-Meier analysis confirmed that patients in the high-risk group exhibited significantly poorer overall survival rate in both the training (P=0.0151) and the validation set (P=0.0016); furthermore, ROC analysis confirmed that the model could predict patient survival with a certain sensitivity and specificity. In conclusion, the four-lncRNA signature presents a potential prognostic biomarker for breast cancer that may be relevant for clinical application.
| S-EPMC6924049 | biostudies-literature