Long non-coding RNA profile study identifies a metabolism-related signature for colorectal cancer

Lu, Yongqu; Wang, Wendong; Liu, Zhenzhen; Ma, Junren; Zhou, Xin; Fu, Wei

doi:10.1186/s10020-021-00343-x

Cited by 25 publications

(17 citation statements)

References 38 publications

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“…The lncRNAs play a crucial role in transcriptional regulation, epigenetic gene regulation, and disease, a kind of non-coding RNAs greater than 200 nucleotides, with no protein-coding function (10,11). With the deepening of research, the relationship between lncRNA and progression, metastasis, and prognosis of various tumors has been found, such as the digestive, nervous, bone, and urinary system (12)(13)(14)(15)(16)(17).…”

Section: Introductionmentioning

confidence: 99%

A Prognostic Model of Bladder Cancer Based on Metabolism-Related Long Non-Coding RNAs

Lai

Shen

et al. 2022

Front. Oncol.

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BackgroundSome studies have revealed a close relationship between metabolism-related genes and the prognosis of bladder cancer. However, the relationship between metabolism-related long non-coding RNAs (lncRNA) regulating the expression of genetic material and bladder cancer is still blank. From this, we developed and validated a prognostic model based on metabolism-associated lncRNA to analyze the prognosis of bladder cancer.MethodsGene expression, lncRNA sequencing data, and related clinical information were extracted from The Cancer Genome Atlas (TCGA). And we downloaded metabolism-related gene sets from the human metabolism database. Differential expression analysis is used to screen differentially expressed metabolism-related genes and lncRNAs between tumors and paracancer tissues. We then obtained metabolism-related lncRNAs associated with prognosis by correlational analyses, univariate Cox analysis, and logistic least absolute shrinkage and selection operator (LASSO) regression. A risk scoring model is constructed based on the regression coefficient corresponding to lncRNA calculated by multivariate Cox analysis. According to the median risk score, patients were divided into a high-risk group and a low-risk group. Then, we developed and evaluated a nomogram including risk scores and Clinical baseline data to predict the prognosis. Furthermore, we performed gene-set enrichment analysis (GSEA) to explore the role of these metabolism-related lncRNAs in the prognosis of bladder cancer.ResultsBy analyzing the extracted data, our research screened out 12 metabolism-related lncRNAs. There are significant differences in survival between high and low-risk groups divided by the median risk scoring model, and the low-risk group has a more favorable prognosis than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk score was closely related to the prognosis of bladder cancer. Then we established a nomogram based on multivariate analysis. After evaluation, the modified model has good predictive efficiency and clinical application value. Furthermore, the GSEA showed that these lncRNAs affected bladder cancer prognosis through multiple links.ConclusionsA predictive model was established and validated based on 12 metabolism-related lncRNAs and clinical information, and we found these lncRNA affected bladder cancer prognosis through multiple links.

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Section: Introductionmentioning

confidence: 99%

A Prognostic Model of Bladder Cancer Based on Metabolism-Related Long Non-Coding RNAs

Lai

Shen

et al. 2022

Front. Oncol.

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“…Fortunately, the screening of metabolic biomarkers can specifically detect abnormal changes in organisms to prevent malignant diseases with pathophysiological characteristics ( 13 ). The metabolic markers have been well displayed in a variety of tumors, such as hepatocellular carcinoma ( 14 ), colorectal cancer ( 15 ), endometrial cancer ( 16 ), and clear cell renal cell carcinoma ( 17 ), but research in PCa is still relatively scarce. Therefore, it is of great clinical significance to find a new metabolic marker to predict the prognosis of PCa.…”

Section: Introductionmentioning

confidence: 99%

Identification of Metabolism-Related Gene-Based Subgroup in Prostate Cancer

Liang

Yin

et al. 2022

Front. Oncol.

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Prostate cancer is still the main male health problem in the world. The role of metabolism in the occurrence and development of prostate cancer is becoming more and more obvious, but it is not clear. Here we firstly identified a metabolism-related gene-based subgroup in prostate cancer. We used metabolism-related genes to divide prostate cancer patients from The Cancer Genome Atlas into different clinical benefit populations, which was verified in the International Cancer Genome Consortium. After that, we analyzed the metabolic and immunological mechanisms of clinical beneficiaries from the aspects of functional analysis of differentially expressed genes, gene set variation analysis, tumor purity, tumor microenvironment, copy number variations, single-nucleotide polymorphism, and tumor-specific neoantigens. We identified 56 significant genes for non-negative matrix factorization after survival-related univariate regression analysis and identified three subgroups. Patients in subgroup 2 had better overall survival, disease-free interval, progression-free interval, and disease-specific survival. Functional analysis indicated that differentially expressed genes in subgroup 2 were enriched in the xenobiotic metabolic process and regulation of cell development. Moreover, the metabolism and tumor purity of subgroup 2 were higher than those of subgroup 1 and subgroup 3, whereas the composition of immune cells of subgroup 2 was lower than that of subgroup 1 and subgroup 3. The expression of major immune genes, such as CCL2, CD274, CD276, CD4, CTLA4, CXCR4, IL1A, IL6, LAG3, TGFB1, TNFRSF4, TNFRSF9, and PDCD1LG2, in subgroup 2 was almost significantly lower than that in subgroup 1 and subgroup 3, which is consistent with the results of tumor purity analysis. Finally, we identified that subgroup 2 had lower copy number variations, single-nucleotide polymorphism, and neoantigen mutation. Our systematic study established a metabolism-related gene-based subgroup to predict outcomes of prostate cancer patients, which may contribute to individual prevention and treatment.

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“…Currently, senescent cells are identified by a combination of multiple traits, including expression and secretion of senescence-related proteins, DNA damage, and β-galactosidase activity; however, these traits are neither exclusively nor universally present in senescent cells (20,(37)(38)(39)(40). In addition, although several lncRNA signatures associated with CRC have been reported (9)(10)(11)(41)(42)(43)(44), only a few studies have focused on senescent-related lncRNA predictive signatures in CRC, prompting us to construct a SenALSig signature for simultaneous characterization of senescence and CRC. Based on the ROC curve, the SenALSig demonstrated a moderate predictive performance for CRC OS by using a six lncRNA senescenceassociated signature (SNHG16, AL590483.1, ZEB1-AS1, AC107375.1, AC068580.3, and AC147067.1).…”

Section: Discussionmentioning

confidence: 99%

A novel senescence-associated LncRNA signature predicts the prognosis and tumor microenvironment of patients with colorectal cancer: a bioinformatics analysis

Huang¹,

Ma²,

Zhou³

et al. 2022

J Gastrointest Oncol

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Background: Accumulating evidence suggests that cellular senescence promotes tumor formation and that long non-coding RNAs (lncRNAs) expression predicts tumor prognosis. However, senescence-related variables, particularly lncRNAs, are still largely unknown. Therefore, the present study developed a novel senescence-associated lncRNA signature to predict colorectal cancer (CRC) prognosis.Methods: A co-expression network of senescence-associated mRNAs and lncRNAs was built using RNAsequence data from The Cancer Genome Atlas (TCGA). By using the prognosis outcomes data of overall survival (OS) and disease-free survival (DFS) from TCGA, we constructed a prognostic senescenceassociated lncRNA signature (SenALSig). The OS and DFS were compared between the low-and high-risk groups defined by SenALSig. A single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithm were used to investigate the relationship between the predictive signature and immune status.Finally, the relationship between SenALSig and drug treatment options was investigated. An independent CRC cohort and three CRC cell lines were recruited to perform real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) analysis to validate the results discovered in silico.Results: A prognostic risk model consisting of six senescence-associated lncRNAs was constructed, including SNHG16, AL590483.1, ZEB1-AS1, AC107375.1, AC068580.3, and AC147067.1. High-risk scores according to the SenALSig were significantly associated with poor OS (hazard ratio =1.218, 95% confidence interval: 1.140-1.301; P<0.001). The model's accuracy was further supported by receiver operating characteristic (ROC) curves (the area under the curve is 0.714) and a principal component analysis (PCA). In univariate and multivariate Cox regression analyses, SenALSig was further found to be a prognostic factor independent of other clinical factors. Furthermore, we discovered that immune checkpoint expression and

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Long non-coding RNA profile study identifies a metabolism-related signature for colorectal cancer

Cited by 25 publications

References 38 publications

A Prognostic Model of Bladder Cancer Based on Metabolism-Related Long Non-Coding RNAs

A Prognostic Model of Bladder Cancer Based on Metabolism-Related Long Non-Coding RNAs

Identification of Metabolism-Related Gene-Based Subgroup in Prostate Cancer

A novel senescence-associated LncRNA signature predicts the prognosis and tumor microenvironment of patients with colorectal cancer: a bioinformatics analysis

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