Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma

Darzi, M.M.; Gorgin, Saeid; Majidzadeh‐A, Keivan; Esmaeili, Rezvan

doi:10.1038/s41598-021-81952-8

Cited by 17 publications

(10 citation statements)

References 50 publications

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“…KLRB1 (killer cell lectin-like receptor B1) is a surface marker produced by the vast majority of T cell subsets that can signify innate immunity ( 45 , 46 ). Several studies ( 45 , 47 , 48 ) found that it had a possible prognostic value for good survival in non-uterine leiomyosarcoma and BC, which was consistent with our results that KLRB1 had a protective function in BC. BIRC3 (baculoviral IAP repeat-containing 3) belongs to the apoptosis inhibitor family ( 49 ).…”

Section: Discussionsupporting

confidence: 93%

Prognostic and Immune Implications of a Novel Pyroptosis-Related Five-Gene Signature in Breast Cancer

Zheng

Wang

et al. 2022

Front. Surg.

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BackgroundBreast cancer (BC) is the most common cancer among women worldwide, with enormous heterogeneity. Pyroptosis has a significant impact on the development and progression of tumors. Nonetheless, the possible correlation between pyroptosis-related genes (PRGs) and the BC immune microenvironment has yet to be investigated.Materials and methodsIn The Cancer Genome Atlas Breast Cancer cohort, 38 PRGs were shown to be significantly different between malignant and non-malignant breast tissues. The 38 PRGs’ consensus clustering grouped 1,089 individuals into two pyroptosis-related (PR) patterns. Using univariate and LASSO-Cox analyses, a PR five-gene predictive signature was constructed based on the differentially expressed genes between two clusters. The tools estimation of stromal and immune cells in malignant tumours using expression data (ESTIMATE), cell type identification by estimating relative subsets Of RNA transcripts (CIBERSORT), and single-sample gene set enrichment analysis (ssGSEA) were used to investigate the BC tumor microenvironment (TME).ResultsIn TME, the two PR clusters displayed distinct clinicopathological characteristics, survival outcomes, and immunocyte infiltration features. The developed five-signature model (SEMA3B, IGKC, KLRB1, BIRC3, and PSME2) classified BC patients into two risk groups based on the estimated median risk score. Patients in the low-scoring category had a higher chance of survival and more extensive immunocyte infiltration. An external validation set can yield similar results.ConclusionOur data suggest that PRGs have a significant impact on the BC immunological microenvironment. The PR clusters and associated predictive signature stimulate additional research into pyroptosis in order to optimize therapeutic strategies for BC patients and their responses to immune therapy.

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Section: Discussionsupporting

confidence: 93%

Prognostic and Immune Implications of a Novel Pyroptosis-Related Five-Gene Signature in Breast Cancer

Zheng

Wang

et al. 2022

Front. Surg.

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“…Knockdown of KDM1A promoted the ferroptosis sensitivity of lung cancers. The analysis of gene co-expression network is an outstanding approach for identifying the co-expression patterns of candidate genes in different phenotypes, and has been widely used for gene function annotation 22,23 . The co-expressed genes of KDM1A (Table S2) were downloaded from a lung adenocarcinoma dataset, TCGA Pan-Cancer Atlas, to further evaluate the underlying mechanisms regulated by KDM1A co-expression molecules.…”

Section: Resultsmentioning

confidence: 99%

Aberrant expression of KDM1A inhibits ferroptosis of lung cancer cells through up-regulating c-Myc

Lü

Cai

Liu

et al. 2022

Sci Rep

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Ferroptosis is a cell death process caused by metabolic dysfunction with the feature of aberrant iron accumulation. Emerging studies have identified that ferroptosis is an important biological function involving in the tumorigenesis, and targeting ferroptosis could provide promising therapeutic targets for lung cancer. However, such therapeutic strategies show limited therapeutic effect owing to drug resistance and other unknown underlying mechanisms. In this study, lysine-specific demethylase 1 (LSD1/KDM1A) was found to be significantly upregulated in lung cancer cells and tissues. The patients with KDM1A downregulation displayed the good prognosis. Using gene set enrichment analysis (GSEA), we demonstrated that KDM1A-associated genes might participate in the regulation of cell ferroptosis and Myc signaling in lung cancer. Knockdown of KDM1A inhibited the level of c-Myc and increased the concentration of malondialdehyde (MDA) and irons in human lung cancer cells H1299 and A549. Downregulation of c-Myc could facilitate KDM1A knockdown-mediated ferroptosis. Our study has elucidated the effect of KDM1A/c-Myc regulatory axis in the ferroptosis resistance of lung cancer cells.

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“…Although this study identified both functional modules and regulatory relationships between individual gene pairs, most other studies only reported functional modules of cancers using co-expression analysis [17][18][19]. In addition to using the CC for the estimation of GCNs, some modifications are applied for better estimation of co-expression relationships [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Weighted gene co-expression analysis (WGCNA) is one of the most widely used methods.…”

Section: ( )mentioning

confidence: 99%

Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Cho

2021

Processes

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Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

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Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma

Cited by 17 publications

References 50 publications

Prognostic and Immune Implications of a Novel Pyroptosis-Related Five-Gene Signature in Breast Cancer

Prognostic and Immune Implications of a Novel Pyroptosis-Related Five-Gene Signature in Breast Cancer

Aberrant expression of KDM1A inhibits ferroptosis of lung cancer cells through up-regulating c-Myc

Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

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