Metabolism-related lncRNAs signature to predict the prognosis of colon adenocarcinoma.

Cell metabolism and long noncoding RNA (lncRNA) played crucial roles in cancer development. However, their association in colon adenocarcinoma (COAD) remains unclear. The COAD gene expression data and corresponding clinical data were retrieved from The Cancer Genome Atlas (TCGA) database. Differential expression of metabolic genes and lncRNA were identified by comparing tumor and normal colon tissues. Pearson correlation analysis was performed to identify metabolism-associated lncRNA. COAD patients were divided into training cohort and validation cohort by randomization. Then, a univariate Cox regression analysis was introduced to evaluate the correlations between metabolism-related lncRNAs and overall survival (OS) of the patients in the training cohort. The least absolute shrinkage and selection operator (LASSO) method was introduced to determine and establish a prognostic prediction model. Subsequently, survival analysis, receiver operating characteristic (ROC) curve analysis, and Cox regression analysis were generated to estimate the prognostic role of the LncRNA risk score in training, validation, and entire cohorts. We identified 152 differentially expressed metabolism-associated lncRNAs (MRLncRNAs). A prognostic prediction model involving four metabolism-related lncRNAs were established using LASSO. In each cohort, COAD patients in the high-risk group had worse OS compared to those in the low-risk group. The ROC analyses demonstrated that the lncRNA signature performed well in predicting OS. Uni- and multivariate analysis indicated that the lncRNA signature as an independent prognostic factor. Furthermore, a correlation analysis demonstrated that LINC01138 was the most closely lncRNA related to metabolic genes. In vitro assays demonstrated that LINC01138 affects tumor progression in COAD. In summary, we established a metabolism-associated lncRNAs model to predict the prognosis in COAD patients.

Sun Y ，Liu B ，Xiao B ，Jiang X ，Xiang JJ ，Xie J ，Hu XM ... -  《Cancer Medicine》

Developing a RiskScore Model based on Angiogenesis-related lncRNAs for Colon Adenocarcinoma Prognostic Prediction.

We screened key angiogenesis-related lncRNAs based on colon adenocarcinoma (COAD) to construct a RiskScore model for predicting COAD prognosis and help reveal the pathogenesis of the COAD as well as optimize clinical treatment. Regulatory roles of lncRNAs in tumor progression and prognosis have been confirmed, but few studies have probed into the role of angiogenesis-related lncRNAs in COAD. To identify key angiogenesis-related lncRNAs and build a RiskScore model to predict the survival probability of COAD patients and help optimize clinical treatment. Sample data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The HALLMARK pathway score in the samples was calculated using the single sample gene set enrichment analysis (ssGSEA) method. LncRNAs associated with angiogenesis were filtered by an integrated pipeline algorithm. LncRNA-based subtypes were classified by ConsensusClusterPlus and then compared with other established subtypes. A RiskScore model was created based on univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and stepwise regression analysis. The Kaplan-Meier curve was drawn by applying R package survival. The time-dependent ROC curves were drawn by the timeROC package. Finally, immunotherapy benefits and drug sensitivity were analyzed using tumor immune dysfunction and exclusion (TIDE) software and pRRophetic package. Pathway analysis showed that the angiogenesis pathway was a risk factor affecting the prognosis of COAD patients. A total of 66 lncRNAs associated with angiogenesis were screened, and three molecular subtypes (S1, S2, S3) were obtained. The prognosis of S1 and S2 was better than that of S3. Compared with the existing subtypes, the S3 subtype was significantly different from the other two subtypes. Immunoassay showed that immune cell scores of the S2 subtype were lower than those of the S1 and S3 subtypes, which also had the highest TIDE scores. We recruited 8 key lncRNAs to develop a RiskScore model. The high RiskScore group with inferior survival and higher TIDE scores was predicted to benefit limitedly from immunotherapy, but it may be more sensitive to chemotherapeutics. A nomogram designed by RiskScore signature and other clinicopathological characteristics shed light on rational predictive power for COAD treatment. We constructed a RiskScore model based on angiogenesis-related lncRNAs, which could serve as potential prognostic predictors for COAD patients and may offer clues for the intervention of anti-angiogenic application. Our results may help evaluate the prognosis of COAD and provide better treatment strategies.

Li X ，Lei J ，Shi Y ，Peng Z ，Gong M ，Shu X ... -  《-》

Identification of long noncoding RNAs biomarkers for diagnosis and prognosis in patients with colon adenocarcinoma.

In the study, we aimed to find key long noncoding RNAs (lncRNAs) significantly associated with the diagnosis of colon adenocarcinoma (COAD) and lncRNA signatures to provide survival risk prognosis for patients with COAD. The lncRNA and messenger RNA (mRNA) expression profiles and clinical information of patients with COAD were obtained from the Cancer Genome Atlas database. The differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) between COAD and normal tissues were identified. The optimal diagnostic and prognostic lncRNA biomarkers for COAD were identified by using feature selection procedure and classification model. Functional enrichment analysis revealed the possible roles of mRNAs coexpressed with lncRNAs in some cancer-related biological processes and pathways. Receiver operating characteristic curve of these lncRNA biomarkers were obtained by using 10-fold cross-validation. Univariate and multivariate Cox regression analysis for these DElncRNAs were performed to obtain the lncRNAs related to overall survival time. The expression levels of selected DElncRNAs were validated by quantitative real time polymerase chain reaction (qRT-PCR). A total of 169 DElncRNAs (60 downregulated and 109 upregulated) and 1236 DEmRNAs (708 downregulated and 528 upregulated) between COAD and normal tissues were identified. Eight lncRNAs of XXbac-B476C20.9, PP7080, CDKN2B-AS1, LINC00092, CA3-AS1, HAND2-AS1, CTD-2269F5.1, and LINC01082 were selected as optimal diagnostic lncRNA biomarkers for COAD. The expression of eight optimal diagnostic lncRNA biomarkers in qRT-PCR validation was consistent with our integrated analysis. Among them, XXbac-B476C20.9 was not only one of the eight optimal diagnostic lncRNA biomarkers, but also related to the prognosis of COAD. Our study demonstrated the promising potential of XXbac-B476C20.9 as an independent biomarker for diagnosis and prognosis of patients with COAD.

Huang W ，Liu Z ，Li Y ，Liu L ，Mai G ... -  《-》

Lactate Metabolism-Associated lncRNA Pairs: A Prognostic Signature to Reveal the Immunological Landscape and Mediate Therapeutic Response in Patients With Colon Adenocarcinoma.

Lactate metabolism is critically involved in the tumor microenvironment (TME), as well as cancer progression. It is important to note, however, that lactate metabolism-related long non-coding RNAs (laRlncRNAs) remain incredibly understudied in colon adenocarcinoma (COAD). A gene expression profile was obtained from the Cancer Genome Atlas (TCGA) database to identify laRlncRNA expression in COAD patients. A risk signature with prognostic value was identified from TCGA and Gene Expression Omnibus (GEO) cohort based on laRlncRNA pairs by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) and functional experiments were carried out to verify the expression of laRlncRNAs in COAD. The relationship of laRlncRNA pairs with immune landscape as well as the sensitivity of different therapies was explored. In total, 2378 laRlncRNAs were identified, 1,120 pairs of which were studied to determine their prognostic validity, followed by a risk signature established based on the screened 5 laRlncRNA pairs. The laRlncRNA pairs-based signature provided a better overall survival (OS) prediction than other published signatures and functioned as a prognostic marker for COAD patients. According to the calculated optimal cut-off point, patients were divided into high- and low-risk groups. The OS of COAD patients in the high-risk group were significantly shorter than that of those in the low-risk group (P=4.252e-14 in the TCGA cohort and P=2.865-02 in the GEO cohort). Furthermore, it remained an effective predictor of survival in strata of gender, age, TNM stage, and its significance persisted after univariate and multivariate Cox regressions. Additionally, the risk signature was significantly correlated with immune cells infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) as well as immunotherapeutic efficacy and chemotherapy sensitivity. Finally, one of the laRlncRNA, LINC01315, promotes proliferation and migration capacities of colon cancer cells. The newly identified laRlncRNAs pairs-based signature exhibits potential effects in predicting prognosis, deciphering patients' immune landscape, and mediating sensitivity to immunotherapy and chemotherapy. Findings in our study may provide evidence for the role of laRlncRNAs pairs as novel prognostic biomarkers and potentially individualized therapy targets for COAD patients.

Xiao J ，Wang X ，Liu Y ，Liu X ，Yi J ，Hu J ... -  《Frontiers in Immunology》

Construction and validation of a cuproptosis-related lncRNA signature as a novel and robust prognostic model for colon adenocarcinoma.

Cuproptosis, a newly identified form of programmed cell death, is thought to play a role in tumorigenesis. Long non-coding RNAs (lncRNAs) are reported to be associated with tumor progression and prognosis in colon adenocarcinoma (COAD). However, the role and prognostic value of cuproptosis-related lncRNAs in COAD remains unknown. This study is devoted to constructing and validating a cuproptosis-related lncRNA signature that can predict COAD patient outcomes using bioinformatics methods. The COAD mRNA and lncRNA expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database and 2,567 cuproptosis-related lncRNAs were obtained. A 10 cuproptosis-related-lncRNA prognostic signature was then constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model and patients were divided into high- and low-risk groups. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, and a nomogram were employed to evaluate the predictive power of the signature. The immune characteristics and drug sensitivity were also investigated based on the signature. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to verify the risk model. In vitro experiments were conducted to validate the expression of the ten lncRNAs during cuproptosis. The high-risk group was associated with shorter overall survival (OS) time in COAD patients (p<0.001). Multivariate Cox regression indicated that a high-risk score was an independent risk factor for poor prognosis (p<0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 3 years: 0.879). Gene Ontology (GO) enrichment analysis revealed that the signature was highly correlated with the immune response in biological processes. The immune function, the score of the immune cells, and the expression of immune checkpoints were significantly different between the two risk groups. Three drugs, LAQ824, FH535, YM155, were found to be more sensitive in the high-risk group. Finally, the expression levels of the ten lncRNAs comprising the signature were tested by qRT-PCR. A ten-cuproptosis-related lncRNA signature was constructed that provided promising insights into personalized prognosis and drug selection among COAD patients.

Xu M ，Mu J ，Wang J ，Zhou Q ，Wang J ... -  《Frontiers in Oncology》