Construction of an HCC recurrence model based on the investigation of immune-related lncRNAs and related mechanisms

Wang, Xiang-Xu; Wu, Lihong; Ai, Li; Pan, Wei; Ren, Jing-Yi; Zhang, Qiong; Zhang, Hongmei

doi:10.1016/j.omtn.2021.11.006

Cited by 10 publications

(6 citation statements)

References 67 publications

(91 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, a 3-lncRNA signature by Gu et al could well predict both recurrence free survival and overall survival in small HCC patients [ 76 ], a 15-lncRNA classifier by Zhang et al effectively identified HCC recurrence [ 77 ], a 7-lncRNA classifier by Lv et al predict early recurrence within 2 years after surgical resection for HCC [ 78 ]. The prognostic performance of this 4-lncRNA signature is comparable to other developed lncRNA-based signatures [ 76 – 80 ], while the differences of selected lncRNAs in each lncRNA-based signature might be due to the specific feature gene selection strategy and different training set. Moreover, the combined model of the 4-lncRNA signature risk score, AFP and TNM stages further improve the 2-year DFS prediction with an AUC of 72.02%.…”

Section: Discussionmentioning

confidence: 81%

“…Kaplan-Meier plot confirmed that the 4-lncRNA signature could successfully classified HCC patients into the low- and high-risk groups and predict early recurrence. Several previous developed lncRNA-based signatures were reported for HCC survival prediction and showed better performance than clinicopathological factors [ 76 – 80 ]. For example, a 3-lncRNA signature by Gu et al could well predict both recurrence free survival and overall survival in small HCC patients [ 76 ], a 15-lncRNA classifier by Zhang et al effectively identified HCC recurrence [ 77 ], a 7-lncRNA classifier by Lv et al predict early recurrence within 2 years after surgical resection for HCC [ 78 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 81%

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

View full text Add to dashboard Cite

show abstract

“…Twenty-three identified genes were associated with fibrosis and potentially modulated by transforming growth factor beta, platelet-derived growth factors, sonic hedgehog protein, or Notch pathways[ 97 ]. Given the emerging evidence suggesting that lncRNAs participate in cancer immunity, another study used RNA-seq to look at immune-related lncRNAs and related mechanisms from the TCGA database, identifying nine immune-related lncRNAs associated with HCC recurrence via Cox regression analysis[ 98 ]. The authors also created a recurrence prediction model based on their findings that was validated in an independent patient cohort.…”

Section: Rna-seq Analysis Of Recurrent Hccmentioning

confidence: 99%

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

Chiang¹,

Yeh²,

Lim³

et al. 2023

World J Gastroenterol

View full text Add to dashboard Cite

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

show abstract

“…The clinical characteristics included age, gender, pathology grade and TNM stage. The performance of the combined nomogram was evaluated by calibration plot using bootstrap method (21). And the time-ROC curve was performed to compare the prediction value among the combined nomogram model, IMPS, and clinical characteristics.…”

Section: Construction and Evaluation Of The Combined Nomogram Conside...mentioning

confidence: 99%

Construction and validation of a prognosis signature based on the immune microenvironment in gastric cancer

Wang

et al. 2023

Front. Surg.

Self Cite

View full text Add to dashboard Cite

BackgroundGastric cancer (GC) is an aggressive malignant tumor with a high degree of heterogeneity, and its immune microenvironment is closely associated with tumor growth, development and drug resistance. Therefore, a classification system of gastric cancer based explicitly on the immune microenvironment context might enrich the strategy for gastric cancer prognosis and therapy.MethodsA total of 668 GC patients were collected from TCGA-STAD (n = 350), GSE15459 (n = 192), GSE57303 (n = 70) and GSE34942 (n = 56) datasets. Three immune-related subtypes (immunity-H, -M, and -L) were identified by hierarchical cluster analysis based on the ssGSEA score of 29 immune microenvironment-related gene sets. The immune microenvironment-related prognosis signature (IMPS) was constructed via univariate Cox regression, Lasso-Cox regression and multivariate Cox regression, and nomogram model combining IMPS and clinical variables was further constructed by the “rms” package. RT-PCR was applied to validate the expression of 7 IMPS genes between two human GC cell lines (AGS and MKN45) and one normal gastric epithelial cell line (GES-1).ResultsThe patients classified as immunity-H subtype exhibited highly expressed immune checkpoint and HLA-related genes, with enriched naïve B cells, M1 macrophages and CD8 T cells. We further constructed and validated a 7-gene (CTLA4, CLDN6, EMB, GPR15, ENTPD2, VWF and AKR1B1) prognosis signature, termed as IMPS. The patients with higher IMPS expression were more likely to be associated with higher pathology grade, more advanced TNM stages, higher T and N stage, and higher ratio of death. In addition, the prediction values of the combined nomogram in predicting 1-year (AUC = 0.750), 3-year (AUC = 0.764) and 5-year (AUC = 0.802) OS was higher than IMPS and individual clinical characteristics.ConclusionsThe IMPS is a novel prognosis signature associated with the immune microenvironment and clinical characteristics. The IMPS and the combined nomogram model provide a relatively reliable predictive index for predicting the survival outcomes of gastric cancer.

show abstract

Construction of an HCC recurrence model based on the investigation of immune-related lncRNAs and related mechanisms

Cited by 10 publications

References 67 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

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

Construction and validation of a prognosis signature based on the immune microenvironment in gastric cancer

Contact Info

Product

Resources

About