Long non‑coding RNA TMCC1‑AS1 predicts poor prognosis and accelerates epithelial‑mesenchymal transition in liver cancer

Chen, Cheng; Su, Naichuan; Li, Guiying; Shen, Yidi; Duan, Xiaoting

doi:10.3892/ol.2021.13034

Cited by 17 publications

(11 citation statements)

References 29 publications

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“…A recent study indicated that CMB9-22P13.1 could upregulate HOTTIP and activate HIF-1α/VEGF signaling, leading to enhanced hepatocellular carcinoma progression and angiogenesis [ 93 ]. The increase of TMCC1-AS1 facilitates proliferation, migration, invasion and EMT of HCC cells, resulting in poor outcome of liver cancer patients [ 94 ]. Given that the 4 lncRNAs have been selected to construct a prognostic signature for predicting HCC early recurrence, their roles in HCC progression should be intensively investigated.…”

Section: Discussionmentioning

confidence: 99%

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

Wei

et al. 2023

BMC Genomics

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Background Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence. Methods Data of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted. Results A 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC. Conclusion Combined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.

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Section: Discussionmentioning

confidence: 99%

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

Wei

et al. 2023

BMC Genomics

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“…LncRNA PRRT3-AS1 is able to inhibit the PPARγ gene to promote prostate cancer cell proliferation and inhibit apoptosis and autophagy by activating the mTOR signaling pathway [34]. TMCC1-AS1, 9 The expression of these 11 lncRNAs in HCC and hepatocyte cell lines AC099850.3 and KDM4A-AS1 found to be upregulated and promotes cell proliferation and migration [35][36][37]. MKLN1-AS promotes proliferation and EMT of HCC by mediating the expression of SOX9 [38].…”

Section: Discussionmentioning

confidence: 99%

Construction and verification of a novel circadian clock related long non-coding RNA model and prediction of treatment for survival prognosis in patients with hepatocellular carcinoma

et al. 2023

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Circadian clock genes are significant in the occurrence and development of HCC and long-non coding RNAs (lncRNAs) are closely related to HCC progression. In this study, we aimed to establish a prognostic risk model for HCC. Circadian clock-related lncRNAs expressed in HCC were extracted from The Cancer Genome Atlas. A nomogram was established to predict individual survival rate. Biological processes enriched for risk model transcripts were investigated by Gene Set Enrichment Analysis. Further, we evaluated the relationship between risk score and immune-checkpoint inhibitor-related gene expression level. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to assess the sensitivity of tumors in high- and low-risk score groups to different drugs. A total of 11 circadian clock-related lncRNAs were included in multi-Cox proportional hazards model analysis to establish a risk model. Univariate and multivariate Cox regression analysis showed that the risk model was an independent risk factor in HCC. The risk model was a significantly associated with the immune signature. Further GDSC analysis indicated that patients in each risk score group may be sensitive to different anti-cancer drugs. QRT-PCR analysis results showed that C012073.1, PRRT3-AS1, TMCC1-AS1, LINC01138, MKLN1-AS, KDM4A-AS1, AL031985.3, POLH-AS1, LINC01224, and AC099850.3 were more highly expressed in Huh-7 and HepG2, compared to LO2, while AC008549.1 were lower expressed. Our work established a prognostic model for HCC. Risk score analysis indicated that the model is significantly associated with modulation tumor immunity and could be used to guide more effective therapeutic strategies in the future.

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“…MSC-AS1 functioned as a key oncogene by inducing cell growth, cell cycle progression, and inflammatory mediators secretion ( 48 ). TMCC1-AS1 predicted poor prognosis and accelerated epithelial-mesenchymal transition in liver cancer ( 49 ). MKLN1-AS increased HDGF expression by acting as a molecular sponge for miR-654-3p, inducing pro-oncogenic effects during HCC progression ( 50 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of a CD4+ conventional T cells-related lncRNAs signature associated with hepatocellular carcinoma prognosis, therapy, and tumor microenvironment

Zhu

Zhang

et al. 2023

Front. Immunol.

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BackgroundHepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide, and CD4+ T lymphocytes can inhibit hepatocarcinogenesis and mediate tumor regression. However, few studies have focused on the prognostic power of CD4+ Tconv-related lncRNAs in HCC patients.MethodWe obtained data from TCGA and GEO databases and identified CD4+Tconv-related lncRNAs in HCC. The risk score was constructed using lasso regression and the model was validated using two validation cohorts. The RS was also assessed in different clinical subgroups, and a nomogram was established to further predict the patients’ outcomes. Furthermore, we estimated the immune cell infiltration and cancer-associated fibroblasts (CAFs) through TIMER databases and assessed the role of RS in immune checkpoint inhibitors response.ResultsWe constructed a CD4+ Tconv-related lncRNAs risk score, including six lncRNAs (AC012073.1, AL031985.3, LINC01060, MKLN1-AS, MSC-AS1, and TMCC1-AS1), and the RS had good predictive ability in validation cohorts and most clinical subgroups. The RS and the T stage were included in the nomogram with optimum prediction and the model had comparable OS prediction power compared to the AJCC. Patients in the high-risk group had a poor immune response phenotype, with high infiltrations of macrophages, CAFs, and low infiltrations of NK cells. Immunotherapy and chemotherapy response analysis indicated that low-risk group patients had good reactions to immune checkpoint inhibitors.ConclusionWe constructed and validated a novel CD4+ Tconv-related lncRNAs RS, with the potential predictive value of HCC patients’ survival and immunotherapy response.

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Long non‑coding RNA TMCC1‑AS1 predicts poor prognosis and accelerates epithelial‑mesenchymal transition in liver cancer

Cited by 17 publications

References 29 publications

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

Construction and verification of a novel circadian clock related long non-coding RNA model and prediction of treatment for survival prognosis in patients with hepatocellular carcinoma

Identification of a CD4+ conventional T cells-related lncRNAs signature associated with hepatocellular carcinoma prognosis, therapy, and tumor microenvironment

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