Clinical M2 Macrophage-Related Genes Can Serve as a Reliable Predictor of Lung Adenocarcinoma

Xu, Chaojie; Song, Lishan; Yang, Yubin; Liu, Yi; Pei, Dongchen; Liu, Jiabang; Guo, Jianhua; Liu, Nan; Li, Xiaoyong; Liu, Yuchen; Li, Xuesong; Lin, Yao; Kang, Zhengjun

doi:10.3389/fonc.2022.919899

Cited by 7 publications

(7 citation statements)

References 51 publications

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“…We explored the association of risk signature and TMB in this study and found that risk signature and TMB had some synergistic effects in predicting patient survival. Previous studies have found that TMB can be used to predict the efficacy of immune checkpoint inhibitor therapy ( Snyder et al, 2014 ; Hugo et al, 2016 ; Carbone et al, 2017 ; Liang et al, 2021 ; Xu et al, 2022a ; Xu et al, 2022b ). It has also been found that TMB is associated with the prognosis of diffuse glioma ( Wang et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Clinical neutrophil-associated genes as reliable predictors of hepatocellular carcinoma

Song

Xu²,

Zhang³

et al. 2022

Front. Genet.

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Background: Growing evidence suggests that infiltrating neutrophils are key players in hepatocellular carcinoma (HCC) tumor progression. However, a comprehensive analysis of the biological roles of neutrophil infiltration and related genes in clinical outcomes and immunotherapy is lacking.Methods: HCC samples were obtained from the TCGA and GEO databases. The CIBERSORT algorithm was used to reveal the TIME landscape. Gene modules significantly associated with neutrophils were found using weighted gene co-expression network analysis (WGCNA), a “dynamic tree-cut” algorithm, and Pearson correlation analysis. Genes were screened using Cox regression analysis and LASSO and prognostic value validation was performed using Kaplan-Meier curves and receiver operating characteristic (ROC) curves. Risk scores (RS) were calculated and nomograms were constructed incorporating clinical variables. Gene set variation analysis (GSVA) was used to calculate signaling pathway activity. Immunophenoscore (IPS) was used to analyze differences in immunotherapy among samples with different risk scores. Finally, the relationship between RS and drug sensitivity was explored using the pRRophetic algorithm.Results: 10530 genes in 424 samples (50 normal samples, 374 tumor samples) were obtained from the TCGA database. Using WGCNA, the “MEbrown” gene module was most associated with neutrophils. Nine genes with prognostic value in HCC (PDLIM3, KLF2, ROR2, PGF, EFNB1, PDZD4, PLN, PCDH17, DOK5) were finally screened. Prognostic nomograms based on RS, gender, tumor grade, clinical stage, T, N, and M stages were constructed. The nomogram performed well after calibration curve validation. There is an intrinsic link between risk score and TMB and TIME. Samples with different risk scores differed in different signaling pathway activity, immunopharmaceutical treatment and chemotherapy sensitivity.Conclusion: In conclusion, a comprehensive analysis of neutrophil-related prognostic features will help in prognostic prediction and advance individualized treatment.

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Section: Discussionmentioning

confidence: 99%

Clinical neutrophil-associated genes as reliable predictors of hepatocellular carcinoma

Song

Xu²,

Zhang³

et al. 2022

Front. Genet.

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“…We used XCELL, TIMER, QUANTISEQ, MCP COUNTER, EPIC, CIBERSORT, and CIBERSORT-ABS to reveal the correlation between risk score and immune infiltrating cells ( 13 ).…”

Section: Methodsmentioning

confidence: 99%

Clinical value of anoikis-related genes and molecular subtypes identification in bladder urothelial carcinoma and in vitro validation

Dong

et al. 2023

Front. Immunol.

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BackgroundAnoikis is a programmed cell death process that was proven to be associated with cancer. Uroepithelial carcinoma of the bladder (BLCA) is a malignant disease of the urinary tract and has a strong metastatic potential. To determine whether anoikis-associated genes can predict the prognosis of BLCA accurately, we evaluated the prognostic value of anoikis-associated genes in BLCA and constructed the best model to predict prognosis.MethodThe BLCA transcriptome data were downloaded from TCGA and GEO databases, and genes with differential expression were selected and then clustered using non-negative matrix factorization (NMF). The genes with the most correlation with anoikis were screened and identified using univariate Cox regression, lasso regression, and multivariate Cox regression. The GEO dataset was used for external validation. Nomograms were created based on risk characteristics in combination with clinical variants and the performance of the model was validated with receiver operating characteristic (ROC) curves. The immunotherapeutic significance of this risk score was assessed using the immune phenomenon score (IPS). IC50 values of predictive chemotherapeutic agents were calculated. Finally, we used RT-qPCR to determine the mRNA expression of four genes, CALR, FASN, CASP6, and RAD9A.ResultWe screened 406 tumor samples and 19 normal tissue samples from the TCGA database. Based on anoikis-associated genes, we classified patients into two subtypes (C1 and C2) using NMF method. Subsequently, nine core genes were screened by multiple methods after analysis, which were used to construct risk profiles. The design of nomograms based on risk profiles and clinical variables, ROC, and calibration curves confirmed that the model could well have the ability to predict the survival of BLCA patients at 1, 3, and 5 years. By predicting the IC50 values of chemotherapeutic drugs, it was learned that the high-risk group (HRG) was more susceptible to paclitaxel, gemcitabine, and cisplatin, and the low-risk group (LRG) was more susceptible to veriparib and afatinib.ConclusionIn summary, the risk score of anoikis-associated genes can be applied as a predictor to predict the prognosis of BLCA in clinical practice.

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“…We used the GSVA to estimate the activity of pathways so that we could compare the activity of pathways in different samples, which are listed in the MSigDB database ( Xu et al, 2022c ).…”

Section: Methodsmentioning

confidence: 99%

Clinical value of M1 macrophage-related genes identification in bladder urothelial carcinoma and in vitro validation

Huang

et al. 2022

Front. Genet.

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Background: Tumor microenvironment (TME) takes a non-negligible role in the progression and metastasis of bladder urothelial carcinoma (BLCA) and tumor development could be inhibited by macrophage M1 in TME. The role of macrophage M1-related genes in BLCA adjuvant therapy has not been studied well.Methods: CIBERSOR algorithm was applied for identification tumor-infiltrating immune cells (TICs) subtypes of subjects from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data sets. We identified potential modules of M1 macrophages by weighted gene co-expression network analysis (WGCNA). Nomogram was determined by one-way Cox regression and lasso regression analysis for M1 macrophage genes. The data from GEO are taken to verify the models externally. Kaplan-Meier and receiver operating characteristic (ROC) curves validated prognostic value of M1 macrophage genes. Finally, we divided patients into the low-risk group (LRG) and the high-risk group (HRG) based on the median risk score (RS), and the predictive value of RS in patients with BLCA immunotherapy and chemotherapy was investigated. Bladder cancer (T24, 5637, and BIU-87) and bladder uroepithelial cell line (SV-HUC-1) were used for in vitro validation. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was employed to validate the associated genes mRNA level.Results: 111 macrophage M1-related genes were identified using WGCNA. RS model containing three prognostically significant M1 macrophage-associated genes (FBXO6, OAS1, and TMEM229B) was formed by multiple Cox analysis, and a polygenic risk model and a comprehensive prognostic line plot was developed. The calibration curve clarified RS was a good predictor of prognosis. Patients in the LRG were more suitable for programmed cell death protein 1 (PD1) and cytotoxic T lymphocyte associate protein-4 (CTLA4) combination immunotherapy. Finally, chemotherapeutic drug models showed patients in the LRG were more sensitive to gemcitabine and mitomycin. RT-qPCR result elucidated the upregulation of FBXO6, TMEM229B, and downregulation of OAS1 in BLCA cell lines.Conclusion: A predictive model based on M1 macrophage-related genes can help guide us in the treatment of BLCA.

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Clinical M2 Macrophage-Related Genes Can Serve as a Reliable Predictor of Lung Adenocarcinoma

Cited by 7 publications

References 51 publications

Clinical neutrophil-associated genes as reliable predictors of hepatocellular carcinoma

Clinical neutrophil-associated genes as reliable predictors of hepatocellular carcinoma

Clinical value of anoikis-related genes and molecular subtypes identification in bladder urothelial carcinoma and in vitro validation

Clinical value of M1 macrophage-related genes identification in bladder urothelial carcinoma and in vitro validation

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