A Two-eRNA-Based Signature Can Impact the Immune Status and Predict the Prognosis and Drug Sensitivity of Lung Adenocarcinoma

Wang, Li; Zhou, Shao-quan; Zhou, Yu; Lu, Jia-xi

doi:10.1155/2022/8069858

Cited by 3 publications

(4 citation statements)

References 44 publications

(47 reference statements)

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“…It is reported that Rapamycin inhibits the growth and metastatic progression of NSCLC [41]. Besides, a strong correlation between risk scores and anticancer medication sensitivity of KIN001-102 and KIN001-135 was found in previous study [42]. PAFAH1B3 is elevated in human pan-cancer, which is correlated with greater pathology and poor prognosis, in particular for NSCLC and liver hepatocellular carcinoma (LIHC) [43].…”

Section: Discussionmentioning

confidence: 76%

Integrated Bulk and Single-cell RNA Sequencing Data Constructs and Validates a Prognostic Model for Non-small Cell Lung Cancer

Zhu,

Yang,

Chen

et al. 2024

J. Cancer

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Background : Most of the current research on prognostic model construction for non-small cell lung cancer (NSCLC) only involves in bulk RNA-seq data without integration of single-cell RNA-seq (scRNA-seq) data. Besides, most of the prognostic models are constructed by predictive genes, ignoring other predictive variables such as clinical features. Methods : We obtained scRNA-seq data from GEO database and bulk RNA-seq data from TCGA database. We construct a prognostic model through the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. Furthermore, we performed ESTIMATE, CIBERSORT, immune checkpoint-related analyses and compared drug sensitivity using pRRophetic method judged by IC50 between different risk groups. Results : 14 tumor-related genes were extracted for model construction. The AUC for 1-, 3-, and 5 years overall survival prediction in TCGA and three validation cohorts were almost higher than 0.65, some of which were even higher than 0.7, even 0.8. Besides, calibration curves suggested no departure between model prediction and perfect fit. Additionally, immune-related and drug sensitivity results revealed potential targets and strategies for treatment, which can provide clinical guidance. Conclusion : We integrated traditional bulk RNA-seq and scRNA-seq data, along with predictive clinical features to develop a prognostic model for patients with NSCLC. According to the constructed model, patients in different groups can follow precise and individual therapeutic schedules based on immune characteristics as well as drug sensitivity.

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Section: Discussionmentioning

confidence: 76%

Integrated Bulk and Single-cell RNA Sequencing Data Constructs and Validates a Prognostic Model for Non-small Cell Lung Cancer

Zhu,

Yang,

Chen

et al. 2024

J. Cancer

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“…So far, some related researchers have also indicated that there are also many limitations in the studies on PCBP1-AS1. In addition, there is a limited amount of highly relevant Hepatocellular carcinoma Proliferation, migration PCBP1-AS1/PRL-3-AKT/PCBP1 Oncogene Up-regulation [20,21] Vulvar squamous cell carcinoma Inhibitor of proliferation, enhance apoptosis PCBP1-AS1/TRAF5/NF-kB Inhibitor Down-regulation [24] Hodgkin lymphoma Proliferation PCBP1-AS1/mRNA Unknown Down-regulation [26] Lung adenocarcinoma Inhibitor of migration PCBP1-AS1/EMT Inhibitor Down-regulation [29][30][31] Cervical cancer Invasion, proliferation, migration PCBP1-AS1/p53/notch Oncogene Up-regulation [34][35][36] Glioma Proliferation, migration and invasion Unknown Oncogene Up-regulation [40] Colorectal cancer Inhibitor, proliferation, tolerance LncRNA-miRNA-mRNA Unknown Up-regulation…”

Section: Methylation Of Lncrna Promoter Regionmentioning

confidence: 99%

“…In order to explore the correlation between PCBP1-AS1 expression and TME for lung adenocarcinoma. Li team used CIBERSORT algorithm (A method for assessing tissue cell composition from gene expression profiles) to study the level of tumor-initiating cells subpopulation, [ 31 ] and obtained 21 types of immune cell profiles in Lung adenocarcinoma patients. Through observation, it was found that the level of monocytes, neutrophils and activated NK cells in LAUD samples was abnormal.…”

Section: Performance Of Lncrna Pcbp1-as1 In Different Cancermentioning

confidence: 99%

Association of LncRNA PCBP1-AS1 with cancer occurrence and development: A review

Wu,

Mou,

Liu

et al. 2023

Medicine

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Long-stranded noncoding RNAs (LncRNAs) are noncoding RNAs >200 nucleotides in length. Polycytidine binding protein 1 antisense LncRNA is abbreviated as LncRNA polycytosine binding protein 1 antisense1 (PCBP1-AS1). Since studies in recent years have revealed the importance of PCBP1-AS1 in human genetic analysis, it is an important member of the LncRNA family. Genetically engineered group analysis of PCBP1-AS1 regulates the progression of cancer in biology. Therefore, it may be an important RNA in the regulation of human cancer. This article summarizes the molecular mechanism and clinical role of PCBP1-AS1 in various tumor types. Taking “PCBP1-AS1” and “cancer” as keywords, this paper analyzed the relationship between PCBP1-AS1 and various tumors by searching PubMed and Geen Medical, and summarized the related regulatory mechanism of PCBP1-AS1. PCBP1-AS1 is a valuable tumor-associated LncRNA that plays different biological roles in different cancers. Overall, it can both promote and inhibit the development of cancer. For example, abnormally high expression in castration-resitant prostate cancer, hepatocellular carcinoma, cervical cancer, glioma, and colorectal cancer promotes the proliferation and progression of these cancers; in contrast, PCBP1-AS1 inhibits cancer proliferation, metastasis, invasion, and recurrence when highly expressed in vulvar squamous cell carcinoma, Hodgkin lymphoma, and lung adenocarcinoma. PCBP1-AS1 regulates the development of multiple tumors, and the specific mechanism needs to be further investigated, which may become a new tumor marker and potential therapeutic target.

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“…Accumulating evidence has showed that eRNAs are associated with multiple traits, characteristics, and cancers. For example, NET1 eRNA regulates the expression of neuroepithelial cell transforming 1 (NET1) oncogene to promote tumorigenesis in breast cancer (Zhang et al 2019 ) and HPSE eRNA regulates the expression of heparinase (HPSE) to promote the invasion and metastasis of cancer (Jiao et al 2018 ). The eRNA disorders can affect biological processes, including cell cycle and cancer cell growth, or change the expression of target genes (Hsieh et al 2014 ; Lam et al 2013 ; Melo et al 2013 ), adding new insights into the action mechanisms of enhancers.…”

Section: Introductionmentioning

confidence: 99%

Prognostic and onco-immunological value of immune-related eRNAs-driven genes in lung adenocarcinoma

Wu,

Zhao,

Zhou

et al. 2024

J Cancer Res Clin Oncol

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Background We aimed to comprehensively analyze the clinical value of immune-related eRNAs-driven genes in lung adenocarcinoma (LUAD) and find the potential biomarkers for prognosis and therapeutic response to improve the survival of this malignant disease. Materials and methods Pearson’s correlation analysis was performed to identify the immune-related eRNAs-driven genes. Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were used to construct this prognostic risk signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to investigate the underlying molecular mechanism. The single sample gene set enrichment analysis (ssGSEA) algorithm was conducted to evaluate the immune status based on the signature. The quantitative real-time PCR (qRT-PCR) analysis was performed to evaluate the expression value of the signature genes between LUAD tissues and adjacent lung tissues. Results Five immune-related eRNAs-driven genes (SHC1, GDF10, CCL14, FYN, and NOD1) were identified to construct a prognostic risk signature with favorable predictive capacity. The patients with high-risk scores based on the signature were significantly associated with the malignant clinical features compared with those with low-risk scores. Kaplan–Meier analysis demonstrated that the sample in the low-risk group had a prolonged survival compared with those in the high-risk group. This risk signature was validated to have a promising predictive capacity and reliability in diverse clinical situations and independent cohorts. The functional enrichment analysis demonstrated that humoral immune response and intestinal immune network for IgA production pathway might be the underlying molecular mechanism related to the signature. The proportion of the vast majority of immune infiltrating cells in the high-risk group was significantly lower than that in the low-risk group, and the immunotherapy response rate in the low-risk group was significantly higher than that in the high-risk group. Moreover, BI-2536, sepantronium bromide, and ULK1 were the potential drugs for the treatment of patients with higher risk scores. Finally, the experiment in vivo and database analysis indicated that CCL14, FYN, NOD1, and GDF10 are the potential LUAD suppressor and SHC1 is a potential treatment target for LUAD. Conclusion Above all, we constructed a prognostic risk signature with favorable predictive capacity in LUAD, which was significantly associated with malignant features, immunosuppressive tumor microenvironment, and immunotherapy response and may provide clinical benefit in clinical decisions.

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A Two-eRNA-Based Signature Can Impact the Immune Status and Predict the Prognosis and Drug Sensitivity of Lung Adenocarcinoma

Cited by 3 publications

References 44 publications

Integrated Bulk and Single-cell RNA Sequencing Data Constructs and Validates a Prognostic Model for Non-small Cell Lung Cancer

Integrated Bulk and Single-cell RNA Sequencing Data Constructs and Validates a Prognostic Model for Non-small Cell Lung Cancer

Association of LncRNA PCBP1-AS1 with cancer occurrence and development: A review

Prognostic and onco-immunological value of immune-related eRNAs-driven genes in lung adenocarcinoma

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