Comprehensive Analysis of Tumor Microenvironment Reveals Prognostic ceRNA Network Related to Immune Infiltration in Sarcoma

Leng, Dongliang; Yang, Ziyi; Sun, Heng; Song, Chengcheng; Huang, Chen; Ip, Ka U.; Chen, Guokai; Deng, Chu-Xia; Zhang, Xiaohua Douglas; Zhao, Qi

doi:10.1158/1078-0432.ccr-22-3396

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…The results showed that compared to IOSE80 cells, exosomes from SKOV3 and OVCAR8 cells had higher expression of AC134312.1, AC010834.3, LEMD1.AS1, PCOLCE.AS1, LINC00892, and AL138820.1. Research has shown that high expression of lncRNA is associated with the proliferation of ovarian cancer cells, activation of the PI3K-AKT pathway, T cell activation, and immune infiltration in the tumor microenvironment (63)(64)(65)(66). However, this trend was not observed in OC tissues from the TCGA datasets except LINC00892.…”

Section: Discussionmentioning

confidence: 96%

An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer

Cui,

Zhang,

et al. 2024

Front. Immunol.

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BackgroundOvarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust.Methods420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed.Results64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes.ConclusionOur study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.

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Section: Discussionmentioning

confidence: 96%

An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer

Cui,

Zhang,

et al. 2024

Front. Immunol.

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Upregulation of LncRNA-LHX2 Promotes Hepatocellular Carcinoma Proliferation, Migration, Invasion and Metastasis via Targeting VEGFA by Sequestering of MiR-939-5p

Fang,

Lin,

Gan

et al. 2024

j biomed nanotechnol

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Due to the challenges in early diagnosis and lack of specific biomarkers, liver cancer remains one of the most prevalent and lethal tumor types. Numerous studies have shown that long noncoding RNA (lncRNA) plays a crucial role in the regulation of various malignant tumors, including liver cancer. Here, we discussed the function and effect of LncRNA-LHX2 in the tumorigenesis and progression of liver cancer, which was significantly upregulated in liver cancer tissues, compared to the benign liver tissues. To improve the accuracy and efficiency of tests like qRT-PCR, we employed nano-magnetic beads for nucleic acid extraction from tissues and cells. In our experiments using HepG2 cells, silencing of LncRNA-LHX2 effectively suppressed cell proliferation, migration, and invasion by interacting with miR-939-5p, which targets VEGFA. Interestingly, overexpression of miR-939-5p also impaired malignant functions of HepG2 cells. However, simultaneously inhibition of miR-939-5p expression can partially restored the inhibitory effect on HepG2 cells resulting from LncRNA-LHX2 knockdown. Consistently, our in vivo results from tumor mice model also suggested that knockout of LncRNA-LHX2 inhibited the tumor growth and suppressed epithelial mesenchymal transition (EMT) process, while silencing of miR-939-5p exhibited the opposite effect. However, when both LncRNA-LHX2 and miR-939-5p were simultaneously interfered with, the tumor growth was partially alleviated. Based on these results, our study highlights the malignant impact of LncRNA-LHX2 in the progression of liver cancer, indicating its potential as a candidate biomarker for liver cancer diagnosis.

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Comprehensive Analysis of Tumor Microenvironment Reveals Prognostic ceRNA Network Related to Immune Infiltration in Sarcoma

Cited by 2 publications

References 48 publications

An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer

An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer

Upregulation of LncRNA-LHX2 Promotes Hepatocellular Carcinoma Proliferation, Migration, Invasion and Metastasis via Targeting VEGFA by Sequestering of MiR-939-5p

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