Construction of a new immune-related lncRNA model and prediction of treatment and survival prognosis of human colon cancer

Liu, Sicheng; Peng, Xingyu; Wu, Xun; Bu, Fanqin; Yu, Zhenting; Zhu, Jinfeng; Luo, Chen; Zhang, Wenjun; Liu, Jiang; Huang, Jun

doi:10.1186/s12957-022-02508-2

Cited by 3 publications

(2 citation statements)

References 56 publications

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“…The following six methods were used to investigate immune infiltration status: TIMER, CIBERSORT, EPIC, MCPCOUNTER, XCELL, and CIRBER-ABS [ 26 ]. Differences immune cell infiltration content between the two groups were investigated using the Wilcoxon signed-rank test.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…Studies have examined genes that affect stromal, immune cell in ltration, and the way they affect the prognosis of colon cancer patients, and these genes have the potential to be prognostic markers for colon cancer patients [38,39] .Immune-related long non-coding ribonucleic acids (irlncRNAs) are potential prognostic factors for colon cancer, and related genes can improve the sensitivity of prognostic models [40] . A feature model can be constructed by pairing the corresponding lncRNAs to better predict survival and chemotherapy e cacy and prognosis of colon cancer patients [41,42] .…”

Section: Discussionmentioning

confidence: 99%

Prediction of colon cancer prognosis and treatment based on immune-related lncRNAs and tumor microenvironment

wang,

Qin,

Zhou

et al. 2024

Preprint

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Background Increasing evidence suggests that long non-coding RNAs (lncRNAs) are closely related to the development of tumors, and no exception is made in colon cancer, which plays a crucial role in the development, metastasis, and prognosis of colon cancer. In this study, we analyzed the relationship between immune-related genes and lncRNAs and the prognosis of colon cancer based on the tumor microenvironment, and further explored possible small molecule inhibitors in order to provide more directions for clinical medication. Method We used transcriptomic and clinical data from colon cancer patients from The Cancer Genome Atlas (TCGA). Using weighted gene co-expression network analysis(WGCNA) correlation analysis, the relationship between relevant clinical traits and prognosis was analyzed, and risk-related genes and lncRNAs were identified. Subsequently, we constructed a prognostic correlation model using LASSO regression and validated the model using univariate and multivariate Cox regression analysis. In addition, we performed correlation analyses of risk scores and characteristics for survival and prognosis. Results We found 147 immune genes and lncRNAs associated with prognosis by WGCNA analysis, and then used LASSO regression analysis and cross-validation to find 37 immune genes and lncRNAs corresponding to the points with the smallest errors for the construction of prognosis-related models, risk scoring was performed for each sample, and patients were divided into two groups according to the median value: low-risk group and high-risk group. Then differential analysis, survival analysis, prognostic analysis and bioinformatics analysis of tumor microenvironment were performed for genes and lncRNAs in the high and low risk groups to obtain differences in expression levels as well as risk scores in the high and low risk groups, which had a good predictive ability for the prognosis of colon cancer patients compared with other clinical biomarkers. By enrichment analysis, we found that these prognostically relevant genes were associated with multiple biological functions and multiple signaling pathways. In addition, small molecule drugs that can inhibit high-risk genes were found by analyzing genes in the CMAP database in high and low risk groups. Conclusion Our study systematically assessed the role of immune-related lncRNAs in the tumor microenvironment and colon cancer prognosis. Risk scores based on genes and lncRNAs can reflect the survival and prognosis of patients with colon cancer. Model validation also shows that the corresponding genes and lncRNAs can be used as reliable biomarkers for predicting the prognosis and treatment response of patients with colon cancer.

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Exploration of Identification and Prognostic Analysis of a Novel Immune-Related lncRNA Pair Signature and Immune Landscape in Esophageal Adenocarcinoma: A New Method Based on “Continuous Learning” Model

Peng

et al. 2023

Int J Comput Intell Syst

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With the rapid development of information technology, many medical systems have emerged one after another with the support of continuous learning. A method of medical data privacy protection and resource utilization based on continuous learning is proposed to initialize the depth model of specific medical tasks. The depth model includes feature sampling model, data review model and task expression model, Finally, the depth model is trained according to the data from n institutions in turn. This method can overcome the obstacles of data sharing. The intelligent medical system of medical knowledge sharing will greatly improve the level of existing medical technology. An increasing body of evidence suggests that long non-coding RNAs (lncRNAs) participate in various physiological processes and pathological diseases. Esophageal adenocarcinoma develops rapidly with poor prognosis and high mortality in the near and long term. Immunotargeted therapy is a research hotspot. However, it is necessary to explore the immunomodulatory molecules of esophageal adenocarcinoma and analyze their relationship with clinicopathological characteristics and prognosis. We aimed to construct a robust immune-related lncRNA signature associated with survival outcomes in esophageal adenocarcinoma. We identified an immune-related lncRNA pairs signature with prognostic value from The Cancer Genome Atlas. Differentially expressed immune-related lncRNAs (DEirlncRNAs) were identified and paired, followed by prognostic assessment using univariate Cox regression analysis. We used least absolute shrinkage and selection operator penalized Cox analysis for constructing a risk score prognostic model and drew receiver operating characteristic (ROC) curves to predict overall survival. Then, we evaluated our signature in several settings: chemotherapy, tumor-infiltrating immune cells, and immune-mediated gene expression. In total, 339 DEirlncRNA pairs were identified, 11 of which were involved in the risk score prognostic signature. The area under ROC curves representing the predictive effect for 1-, 2-, and 3-year survival rates were 0.942, 0.987, and 0.977, respectively. The risk score model was confirmed as an independent prognostic factor and was significantly superior to clinicopathological characteristics. Correlation analyses showed disparities in drug sensitivity, tumor-infiltrating immune cells, and immune-related gene expression. We identified a novel prognostic immune-related lncRNA pair signature for esophageal adenocarcinoma. The risk score-based groups displayed different immune statuses, drug sensitivity, and immune-mediated gene expression. These findings may offer insights into the prognostic evaluation of esophageal adenocarcinoma and may provide a basis for creating personalized treatment plans.

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Construction of a new immune-related lncRNA model and prediction of treatment and survival prognosis of human colon cancer

Cited by 3 publications

References 56 publications

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

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

Prediction of colon cancer prognosis and treatment based on immune-related lncRNAs and tumor microenvironment

Exploration of Identification and Prognostic Analysis of a Novel Immune-Related lncRNA Pair Signature and Immune Landscape in Esophageal Adenocarcinoma: A New Method Based on “Continuous Learning” Model

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