Long Non-Coding RNAs in Lung Cancer: The Role in Tumor Microenvironment

Dai, Shuang; Liu, Ting; Liu, Yanyang; He, Yingying; Liu, Tao; Xu, Zihan; Wang, Zhiwu; Luo, Feng

doi:10.3389/fcell.2021.795874

Cited by 7 publications

(6 citation statements)

References 106 publications

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“…In addition, MIR4435-2HG is involved in the mechanism of action of anticancer drugs, including cisplatin for the treatment of non-small cell lung cancer and colon cancer, and carboplatin for the treatment of triple-negative breast cancer ( Zhong et al, 2022 ). Clinically, NKILA overexpression in tumor-specific CTL and TH1 cells correlates with shorter patient survival and increased apoptosis ( Huang et al, 2018 ; Dai et al, 2021 ). The NKILA lncRNA stimulates activation-induced cell death in T cells, promoting tumor immunoevasion ( Huang et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

A novel defined risk signature of cuproptosis-related long non-coding RNA for predicting prognosis, immune infiltration, and immunotherapy response in lung adenocarcinoma

Ma,

Li,

et al. 2023

Front. Pharmacol.

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Background: Cuproptosis is a newly discovered non-apoptotic form of cell death that may be related to the development of tumors. Nonetheless, the potential role of cuproptosis-related lncRNAs in tumor immunity formation and patient-tailored treatment optimization of lung adenocarcinoma (LUAD) is still unclear.Methods: RNA sequencing and survival data of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) database for model training. The patients with LUAD in GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081 were used for validation. The proofed cuproptosis-related genes were extracted from the previous studies. The Pearson correlation was applied to select cuproptosis-related lncRNAs. We chose differentially expressed cuproptosis-related lncRNAs in the tumor and normal tissues and allowed them to go to a Cox regression and a LASSO regression for a lncRNA signature that predicts the LUAD prognosis. Kaplan–Meier estimator, Cox model, ROC, tAUC, PCA, nomogram predictor, decision curve analysis, and real-time PCR were further deployed to confirm the model’s accuracy. We examined this model’s link to other regulated cell death forms. Applying TMB, immune-related signatures, and TIDE demonstrated the immunotherapeutic capabilities of signatures. We evaluated the relationship of our signature to anticancer drug sensitivity. GSEA, immune infiltration analysis, and function experiments further investigated the functional mechanisms of the signature and the role of immune cells in the prognostic power of the signature.Results: An eight-lncRNA signature (TSPOAP1-AS1, AC107464.3, AC006449.7, LINC00324, COLCA1, HAGLR, MIR4435-2HG, and NKILA) was built and demonstrated owning prognostic power by applied to the validation cohort. Each signature gene was confirmed differentially expressed in the real world by real-time PCR. The eight-lncRNA signature correlated with 2321/3681 (63.05%) apoptosis-related genes, 11/20 (55.00%) necroptosis-related genes, 34/50 (68.00%) pyroptosis-related genes, and 222/380 (58.42%) ferroptosis-related genes. Immunotherapy analysis suggested that our signature may have utility in predicting immunotherapy efficacy in patients with LUAD. Mast cells were identified as key players that support the predicting capacity of the eight-lncRNA signature through the immune infiltrating analysis.Conclusion: In this study, an eight-lncRNA signature linked to cuproptosis was identified, which may improve LUAD management strategies. This signature may possess the ability to predict the effect of LUAD immunotherapy. In addition, infiltrating mast cells may affect the signature’s prognostic power.

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Section: Discussionmentioning

confidence: 99%

A novel defined risk signature of cuproptosis-related long non-coding RNA for predicting prognosis, immune infiltration, and immunotherapy response in lung adenocarcinoma

Ma,

Li,

et al. 2023

Front. Pharmacol.

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“…Tumorigenesis and metastasis rely heavily on TME’s interactions with tumor cells [ 33 ]. Tumor onset and progression are mostly accounted for by lncRNAs which have been shown to alter the TME (tumor microenvironment) and partake in a significant duty in immune identification and evasion in tumor-infiltrating immune cells [ 34 , 35 ]. In one study, LINC00662 stimulates the production and release of WNT3A.…”

Section: Backgroudmentioning

confidence: 99%

Identification and validation of a novel prognosis model based on m5C-related long non-coding RNAs in colorectal cancer

Di,

Xu,

Ding

et al. 2023

Cancer Cell Int

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Background The prognosis of tumor patients can be assessed by measuring the levels of lncRNAs (long non-coding RNAs), which play a role in controlling the methylation of the RNA. Prognosis in individuals with colorectal adenocarcinoma (CRC) is strongly linked to lncRNA expression, making it imperative to find lncRNAs that are associated with RNA methylation with strong prognostic value. Methods In this study, by analyzing TCGA dataset, we were able to develop a risk model for lncRNAs that are associated with m5C with prognostic significance by employing LASSO regression and univariate Cox proportional analysis. There were a number of methods employed to ensure the model was accurate, including multivariate and univariate Cox regression analysis, Kaplan analysis, and receiver operating characteristic curve analysis. The principal component analysis, GSEA and GSVA analysis were used for risk model analysis. The CIBERSORT instrument and the TIMER database were used to evaluate the link between the immune cells that infiltrate tumors and the risk model. In vitro experiments were also performed to validate the predicted m5C-related significant lncRNAs. Results The m5c regulators were differentially expressed in colorectal cancer and normal tissue. Based on the screening criteria and LASSO regression, 11 m5c-related lncRNAs were identified for developing the prognostic risk model. Multivariate and univariate Cox regression analysis showed the risk score is a crucial prognostic factor in CRC patients. The 1-year, 3-year, and 5-year AUC curves showed the risk score was higher than those identified for other clinicopathological characteristics. A nomogram using the risk score as a quantitative tool was developed for predicting patients' outcomes in clinical settings. In addition, the risk profile of m5C-associated lncRNAs can discriminate between tumor immune cells’ characteristics in CRC. Mutation patterns and chemotherapy were analyzed between high- and low- risk groups of CRC patients. Moreover, TNFRSF10A-AS1 was chosen for the in vitro verification of the m5C-connected lncRNA to demonstrate impressive effects on the proliferation, migration and invasion of CRC cells. Conclusion A risk model including the prognostic value of 11 m5C-associated lncRNAs proves to be a useful prognostic tool for CRC and improves the care of patients suffering from CRC based on these findings.

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“…LncRNAs and exosomal lncRNAs regulate tumor progression, drug sensitivity and TME remodeling in lung cancer ( Entezari et al, 2022 ). The role of lncRNAs in the regulation of PD-1 and PD-L1 pathways and TME in cancer immunotherapy has been discussed ( Jiang W. et al, 2021 ; Dai et al, 2021 ; Zhang P. et al, 2022 ). For example, lncRNA C5orf64 was characterized as a potential indicator for TME and mutation pattern remodeling in lung cancer ( Pang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Targeting immune cell types of tumor microenvironment to overcome resistance to PD-1/PD-L1 blockade in lung cancer

Wang

Zhu²,

Yang³

et al. 2023

Front. Pharmacol.

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Lung cancer is the common malignant tumor with the highest mortality rate. Lung cancer patients have achieved benefits from immunotherapy, including immune checkpoint inhibitors (ICIs) therapy. Unfortunately, cancer patients acquire adaptive immune resistance, leading to poor prognosis. Tumor microenvironment (TME) has been demonstrated to play a critical role in participating in acquired adaptive immune resistance. TME is associated with molecular heterogeneity of immunotherapy efficacy in lung cancer. In this article, we discuss how immune cell types of TME are correlated with immunotherapy in lung cancer. Moreover, we describe the efficacy of immunotherapy in driven gene mutations in lung cancer, including KRAS, TP53, EGFR, ALK, ROS1, KEAP1, ZFHX3, PTCH1, PAK7, UBE3A, TNF-α, NOTCH, LRP1B, FBXW7, and STK11. We also emphasize that modulation of immune cell types of TME could be a promising strategy for improving adaptive immune resistance in lung cancer.

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Long Non-Coding RNAs in Lung Cancer: The Role in Tumor Microenvironment

Cited by 7 publications

References 106 publications

A novel defined risk signature of cuproptosis-related long non-coding RNA for predicting prognosis, immune infiltration, and immunotherapy response in lung adenocarcinoma

A novel defined risk signature of cuproptosis-related long non-coding RNA for predicting prognosis, immune infiltration, and immunotherapy response in lung adenocarcinoma

Identification and validation of a novel prognosis model based on m5C-related long non-coding RNAs in colorectal cancer

Targeting immune cell types of tumor microenvironment to overcome resistance to PD-1/PD-L1 blockade in lung cancer

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