Constructing a global transcriptional regulatory landscape for early non-small cell lung cancer to identify hub genes and key pathways

Bu, Jianlong; Zhang, Pinyi; Zhu, Kaibin; Yan, Yubo; Shi, Bowen; Wang, Junfeng; Xu, Shidong

doi:10.18632/aging.103475

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…LUAD and LUSC are the two most common types of NSCLC, and LUSC is the main pathological type of lung malignancy [19][20][21]. Currently, less than 10% of patients with advanced metastatic LUSC can live more than 5 years [22].…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis identifies key genes of prognostic value in lung cancer

2023

Journal of Men's Health

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Lung cancer is the most common human malignancy worldwide and can be divided into different types of carcinomas depending on their pathological features. Advances in medical science and technology have led to the identification of some lung cancer-related marker genes, including EGFR (epidermal growth factor receptor), BRAF (B-Raf proto-oncogene), RAS (RAS proto-oncogene, GTPase) and HER2 (human epidermal growth factor receptor 2). However, the underlying biomarker and key genes associated with different types of lung cancer are still poorly understood. In this study, we analyzed a GEO (Gene Expression Omnibus) dataset and identified 28 upregulated intersection DEGs (different expression genes) and 125 downregulated intersection DEGs among AC (adenocarcinoma), PTC (primary typical carcinoid), PLCC (primary large cell carcinoma), PLCNC (primary large cell lung carcinoma) and PSCLC (primary small cell lung carcinoma). Through PPI (protein-protein interaction) network analysis, we identified 14 genes among the DEGs, namely MFAP4 (microfibril-associated protein 4), PDZD2 (PDZ domain containing 2), FBLN1 (fibulin 1), FBLN5 (fibulin 5), EFEMP1 (EGF containing fibulin extracellular matrix protein 1), KDR (kinase insert domain receptor), S1PR1 (sphingosine-1-phosphate receptor 1), CAV1 (caveolin 1), GRK5 (G protein-coupled receptor kinase 5), EDNRA (endothelin receptor type A), EDNRB (endothelin receptor type B), CALCRL (calcitonin receptor-like receptor), PTGER4 (prostaglandin E receptor 4), and ADRB1 (adrenoceptor beta 1), which were found to be downregulated in different subtypes of lung cancer and associated with poor survival outcomes. In addition, most of the screened DEGs demonstrated good predictive ability in LUAD (lung adenocarcinoma) and LUSC (lung squamous cell carcinoma). Among them, MFAP4 was found to promote cell proliferation while also suppressing cell migration and angiogenesis. In summary, we propose MFAP4, PDZD2, FBLN1, FBLN5, EFEMP1, KDR, S1PR1, CAV1, GRK5, EDNRA, EDNRB, CALCRL, PTGER4 and ADRB1 as potential prognostic markers in lung cancer patients.

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Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis identifies key genes of prognostic value in lung cancer

2023

Journal of Men's Health

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“…Cyclin B1 ( CCNB1 ) binds to specific cyclin-dependent kinases, and these interactions play crucial roles in the cell cycle regulation [ 73 ]. It is well known that CCNB1 is highly expressed in NSCLC and is a potential biomarker for both lung adenocarcinoma [ 74 ] and lung squamous cell carcinoma [ 75 ], belonging to two subtypes of NSCLC. Constructing the CCNB1 -related subnetwork revealed that PRC1 , CEP55 , KIF14 , KIF4A , and HJURP had the highest RWR scores.…”

Section: Discussionmentioning

confidence: 99%

Knowledge-Guided “Community Network” Analysis Reveals the Functional Modules and Candidate Targets in Non-Small-Cell Lung Cancer

Wang

Han

Yang

et al. 2021

Cells

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Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies that are the leading cause of cancer-related death worldwide. Although many NSCLC-related genes and pathways have been identified, there remains an urgent need to mechanistically understand how these genes and pathways drive NSCLC. Here, we propose a knowledge-guided and network-based integration method, called the node and edge Prioritization-based Community Analysis, to identify functional modules and their candidate targets in NSCLC. The protein–protein interaction network was prioritized by performing a random walk with restart algorithm based on NSCLC seed genes and the integrating edge weights, and then a “community network” was constructed by combining Girvan–Newman and Label Propagation algorithms. This systems biology analysis revealed that the CCNB1-mediated network in the largest community provides a modular biomarker, the second community serves as a drug regulatory module, and the two are connected by some contextual signaling motifs. Moreover, integrating structural information into the signaling network suggested novel protein–protein interactions with therapeutic significance, such as interactions between GNG11 and CXCR2, CXCL3, and PPBP. This study provides new mechanistic insights into the landscape of cellular functions in the context of modular networks and will help in developing therapeutic targets for NSCLC.

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“…Lung cancer is the most common chest tumor in the world, with a high incidence and mortality. More than 2.2 million people in the world have been newly diagnosed with lung cancer in 2021, accounting for 11.1% of all newly diagnosed cancer ( Bu et al, 2020 ; Xu et al, 2020 ). Lung squamous cell carcinoma (LUSC), deriving from chronic stimulation and injury of bronchial columnar epithelial cells, takes up a large percentage of lung cancer ( Sánchez Danés and Blanpain, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of a 5-lncRNA-Based Signature for Immune Characteristics and Prognosis of Lung Squamous Cell Carcinoma and Verification of the Function of lncRNA SPATA41

et al. 2022

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Lung squamous cell carcinoma (LUSC) is one of the most lethal cancers worldwide. Traditional tumor-node-metastasis (TNM) staging system has many insufficiencies in predicting immune characteristics, overall survival (OS), and prognosis of LUSC. LncRNA is currently found involved in tumor development and effectively predicts tumor prognosis. We screened potential tumor-related lncRNAs for immune characteristics and constructed a nomogram combining lncRNA and traditional clinical indicators for prognosis prediction. We obtained the large-scale gene expression profiles of samples from 492 LUSC patients in The Cancer Genome Atlas database. SPATA41, AL034550.2, AP003721.2, AC106786.1, and AC078889.1 were finally screened to construct a 5-lncRNA-based signature. The risk score of the signature divided patients into subgroups of high-risk and low-risk with significant differences in OS. Their area under the curve (AUC) reached more than 0.70 in 1, 3, and 5 years. In addition, compared with the high-risk subgroup, the low-risk subgroup exhibited a remarkably favorable prognosis and TME score, along with a higher immune infiltration score and lower TIDE score. The signature also significantly related to chemotherapy response, especially in cisplatin, vinorelbine, and paclitaxel. Importantly, the nomogram we constructed had good reliability with the assessment of the calibration chart and consistency index (c-index). GO and KEGG enrichment analysis indicated that co-expression mRNAs of the 5 lncRNAs were mainly focused on RNA splicing, DNA replication, and protein serine/threonine kinase activity. Functional assays demonstrated that SPATA41, one of the five OS-related lncRNAs, regulated invasion, migration, proliferation, and programmed death in vitro. In summary, our 5-lncRNA-based signature has a good performance in predicting immune characteristics and prognosis of LUSC patients.

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Constructing a global transcriptional regulatory landscape for early non-small cell lung cancer to identify hub genes and key pathways

Cited by 6 publications

References 26 publications

Bioinformatics analysis identifies key genes of prognostic value in lung cancer

Bioinformatics analysis identifies key genes of prognostic value in lung cancer

Knowledge-Guided “Community Network” Analysis Reveals the Functional Modules and Candidate Targets in Non-Small-Cell Lung Cancer

Identification of a 5-lncRNA-Based Signature for Immune Characteristics and Prognosis of Lung Squamous Cell Carcinoma and Verification of the Function of lncRNA SPATA41

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