Dataset Information

Identification of key lncRNAs as prognostic prediction models for colorectal cancer based on LASSO.

ABSTRACT: Colorectal cancer (CRC) is one of the most common malignancies, with varying prognoses and a high mortality. There is an urgent need to establish a new prediction model to predict the survival risk of CRC patients. The long non-coding RNAs (lncRNAs) expression profiles and corresponding clinical information of CRC patients were obtained from The Cancer Genome Atlas, TCGA. We identified a total of 1,176 lncRNAs differentially expressed between 480 CRC and 41 normal tissues. In the training test, we combined these differentially expressed lncRNAs with overall survival of CRC patients. Six lncRNAs (AL356270.1, LINC02257, AC020891.2, LINC01485, AC083967.1 and RBAKDN) were finally screened out by using LASSO regression mode to establish a novel prediction model as a prognostic indicator for CRC patients. The area under the curve (AUC) of 3- and 5-year ROC analysis in CRC were 0.6923 and 0.7328 for training set, and were 0.6803 and 0.7035 for testing set, respectively. K-M analysis revealed a significant difference between high risk and low risk in the training set (P-value = 5.0e-05) and testing set (P-value = 0.00052), respectively. Our study shows that the six lncRNAs model can improve the survival prediction mechanism of patients with CRC and provide help for patients through personalized treatment.

SUBMITTER: Huang X

PROVIDER: S-EPMC7191144 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of key lncRNAs as prognostic prediction models for colorectal cancer based on LASSO.

Huang Xiao X Cai Wei W Yuan Wenliang W Peng Sihua S

International journal of clinical and experimental pathology 20200401 4

Colorectal cancer (CRC) is one of the most common malignancies, with varying prognoses and a high mortality. There is an urgent need to establish a new prediction model to predict the survival risk of CRC patients. The long non-coding RNAs (lncRNAs) expression profiles and corresponding clinical information of CRC patients were obtained from The Cancer Genome Atlas, TCGA. We identified a total of 1,176 lncRNAs differentially expressed between 480 CRC and 41 normal tissues. In the training test, ...[more]

PMID: 32355515

Similar Datasets

Project description:The aim of this study is to identify potential biomarkers for early diagnosis of gynecologic cancer in order to improve survival. Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanisms in both cervical and endometrial cancer remain unclear, a comprehensive and systematic bioinformatics analysis is required. In our study, gene expression profiles of GSE9750, GES7803, GES63514, GES17025, GES115810, and GES36389 downloaded from Gene Expression Omnibus (GEO) were utilized to analyze differential gene expression between cancer and normal tissues. A total of 78 differentially expressed genes (DEGs) common to CC and EC were identified to perform the functional enrichment analyses, including gene ontology and pathway analysis. KEGG pathway analysis of 78 DEGs indicated that three main types of pathway participate in the mechanism of gynecologic cancer such as drug metabolism, signal transduction, and tumorigenesis and development. Furthermore, 20 diagnostic signatures were confirmed using the least absolute shrink and selection operator (LASSO) regression with 10-fold cross validation. Finally, we used the GEPIA2 online tool to verify the expression of 20 genes selected by the LASSO regression model. Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (p < 0.05). The two genes have their function: (1.) PAMR1 is a tumor suppressor gene, and many studies have proven that overexpression of the gene markedly suppresses cell growth, especially in breast cancer and polycystic ovary syndrome; (2.) SLC24A3 is a sodium-calcium regulator of cells, and high SLC24A3 levels are associated with poor prognosis. In our study, the gene signatures can be used to predict CC and EC prognosis, which could provide novel clinical evidence to serve as a potential biomarker for future diagnosis and treatment.

Project description:BackgroundColorectal cancer (CRC) is one of the most common malignant tumors with a poor prognosis. At present, the pathogenesis is not completely clear. Therefore, finding reliable prognostic indicators for CRC is of important clinical significance. In this study, bioinformatics methods were used to screen the prognostic immune-related lncRNAs of CRC, and a prognostic risk scoring model based on immune-related lncRNAs signatures were constructed to provide a basis for prognostic evaluation and immunotherapy of CRC patients.MethodsThe clinical information and RNA-seq data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database. The information of immune-related lncRNA was downloaded from the immunology database and analysis portal. The differentially expressed immune-related lncRNAs (IRLs) were screened by the edgeR package of R software. The prognostic value of IRLs was studied. Based on Cox regression analysis, a prognostic index (IRLPI) based on IRLs was established, and the relationship between the risk score and the clinicopathological characteristics of CRC was analyzed to determine the effectiveness of the risk score model as an independent prognostic factor.ResultsA total of 240 differentially expressed IRLs were identified between normal colorectal cancer tissues and normal colorectal cancer tissues, in which 8 were significantly associated with the survival of CRC patients (P < 0.05), including LINC00461, LINC01055, ELFN1-AS1, LMO7-AS1, CYP4A22-AS1, AC079612.1, LINC01351, and MIR31HG. And most of the lncRNAs related to survival were risk factors for the prognosis of CRC. The index established based on the 7 survival-related IRLs found to be highly accurate in monitoring CRC prognosis. Besides, IRLPI was significantly correlated with a variety of pathological factors and immune cell infiltration.ConclusionEight immune-related lncRNAs closely related to the prognosis of CRC patients were identified from the TCGA database. At the same time, an independent IRLPI was constructed, which may be helpful for clinicians to assess the prognosis of patients with CRC and to formulate individualized treatment plans.

Dataset Information

Identification of key lncRNAs as prognostic prediction models for colorectal cancer based on LASSO.

Publications

Identification of key lncRNAs as prognostic prediction models for colorectal cancer based on LASSO.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets