Microenvironment Analysis of Prognosis and Molecular Signature of Immune-Related Genes in Lung Adenocarcinoma

Ling, Bowen; Huang, Zu-Liang; Suo-yi, Huang; Li, Qian; Li, Genliang; Tang, Qianli

doi:10.3727/096504020x15907428281601

“…This suggests that the lower the amount of immune and stromal cells surrounding the tumor cells, the faster the tumor proliferates and progresses, and the worse the patient's prognosis may be. And this is relatively consistent with previous studies [20,21]. Figure 7: GSEA of TCGA cohort with oncogenic signature gene sets.…”

Section: Discussionsupporting

confidence: 90%

“…Patients who can benefit from targeted therapy or immunotherapy have more prolonged survival and higher quality of life than those on regular chemotherapy, based on gene mutations and immunohistochemistry. According to previous studies, an excellent prognostic marker or model helps in predicting patient survival and better clinical management [20,21]. Recently, Ling et al used differentially expressed genes closely related to the tumor microenvironment to construct models to predict patient prognosis and explore the relationship between patient responsiveness to immunotherapy and the tumor microenvironment [21].…”

Section: Discussionmentioning

confidence: 99%

“…According to previous studies, an excellent prognostic marker or model helps in predicting patient survival and better clinical management [20,21]. Recently, Ling et al used differentially expressed genes closely related to the tumor microenvironment to construct models to predict patient prognosis and explore the relationship between patient responsiveness to immunotherapy and the tumor microenvironment [21]. Zhao et al created a tumor immunoscore clinical prognostic signature of lung adenocarcinoma using 109 immune-associated genes and validated its accuracy in different datasets [20].…”

Section: Discussionmentioning

confidence: 99%

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Predicting the Clinical Outcome of Lung Adenocarcinoma Using a Novel Gene Pair Signature Related to RNA-Binding Protein

Meng

¹

,

He

²

,

Xiao

³

et al. 2020

BioMed Research International

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Adenocarcinoma is the most common type of lung cancer, and patients have varying prognoses. RNA-binding proteins (RBP) are deemed to be closely associated with tumorigenesis and development, but the exact mechanism is currently unknown. This study was aimed at constructing a new robust prognostic model based on RNA-binding protein-related gene pair scores for better clinical guidance. The model for this study was constructed based on data of lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database. Prognosis-related RBP gene pair models were created based on differentially expressed genes, and the accuracy of the models was verified in a different age, staging, and other subdatasets. A total of 379 RNA-binding protein-related genes were differentially expressed in tumor tissue. From these genes, we constructed a prognostic model consisting of 33 gene pairs, which were found to be significantly associated with survival in TCGA dataset ( P < 0.0001 , hazard ratio HR = 4.380 (3.139 to 6.111)) and different subdatasets. As expected, the results were verified in the GEO validation cohort ( P = 7.8 × 10 − 3 , HR = 1.597 (1.095 to 2.325)). We found that the signature exhibited an independent prognostic factor in both the univariate and multivariate Cox regression analyses ( P < 0.001 ). CIBERSORT was applied to estimate the fractions of infiltrated immune cells in bulk tumor tissues. CD8 T cells, activated dendritic cells, regulatory T cells (Tregs), and activated CD4 memory T cells presented a significantly lower fraction in the high-risk group ( P < 0.01 ). Patients in the high-risk group had significantly higher tumor mutational burden (TMB) ( P = 4.953 e − 04 ) and lower levels of immune cells ( P = 3.473 e − 05 ) and stromal cells ( P = 0.005 ) in the tumor microenvironment than those in the low-risk group. Furthermore, the Protein-protein interaction (PPI) network and various enrichment analyses have genuinely uncovered the interrelationships and potential functions of the RBP genes within the model. The results of the present study validated the importance of RNA-binding proteins in tumorigenesis and progression and support the RBP gene-related signature as a promising marker for prognosis prediction in lung adenocarcinoma.

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“…Understanding TME and recognizing genes related to TME can provide new ideas for immunotherapy. In the TME, many cancer cells and immune cells mediate signaling pathways ( Marwitz et al, 2021 ), involving in tumor progression and drug resistance ( Santaniello et al, 2019 ; Ling et al, 2020 ). In view of the low treatment rate of LUAD patients with high pathogenicity, it is imperative to find new gene markers ( Zhou C. S. et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis

Liu

¹

,

Li

²

,

Chen

³

et al. 2021

Front. Cell Dev. Biol.

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BackgroundTumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD).Methods“GEO2R,” “limma” R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value <0.01, LogFC>2 or <-2 were included for further analyses. The function analysis of 250 overlapping mRNAs was shown by DAVID and Metascape software. By UALCAN, Oncomine and R packages, we explored the expression levels, survival analyses of CDK2 in 33 cancers. “Survival,” “survminer,” “rms” R packages were used to construct a Nomogram model of age, gender, stage, T, M, N. Univariate and multivariate Cox regression were used to establish prognosis-related immune forecast model in LUAD. CeRNA network was constructed by various online databases. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore correlations between CDK2 expression and IC50 of anti-tumor drugs.ResultsA total of 250 differentially expressed genes (DEGs) were identified to participate in many cancer-related pathways, such as activation of immune response, cell adhesion, migration, P13K-AKT signaling pathway. The target molecule CDK2 had prognostic value for the survival of patients in LUAD (P = 5.8e-15). Through Oncomine, TIMER, UALCAN, PrognoScan databases, the expression level of CDK2 in LUAD was higher than normal tissues. Pan-cancer analysis revealed that the expression, stage and survival of CDK2 in 33 cancers, which were statistically significant. Through TISIDB database, we selected 13 immunodepressants, 21 immunostimulants associated with CDK2 and explored 48 genes related to these 34 immunomodulators in cBioProtal database (P < 0.05). Gene Set Enrichment Analysis (GSEA) and Metascape indicated that 49 mRNAs were involved in PUJANA ATM PCC NETWORK (ES = 0.557, P = 0, FDR = 0), SIGNAL TRANSDUCTION (ES = –0.459, P = 0, FDR = 0), immune system process, cell proliferation. Forest map and Nomogram model showed the prognosis of patients with LUAD (Log-Rank = 1.399e-08, Concordance Index = 0.7). Cox regression showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were used to construct the forecast model to predict the prognosis of patients (P < 0.05). LUAD patients were divided into two different risk groups (low and high) had a statistical significance (P = 6.223e-04). By “survival ROC” R package, the total risk score of this prognostic model was AUC = 0.729 (SIT1 = 0.484, SNAI3 = 0.485, ASB2 = 0.267, CDK2 = 0.579). CytoHubba selected ceRNA mechanism medicated by potential biomarkers, 6 lncRNAs-7miRNAs-CDK2. The expression of CDK2 was associated with IC50 of 89 antitumor drugs, and we showed the top 20 drugs with P < 0.05.ConclusionIn conclusion, our study identified CDK2 related immune forecast model, Nomogram model, forest map, ceRNA network, IC50 of anti-tumor drugs, to predict the prognosis and guide targeted therapy for LUAD patients.

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“…Accumulating evidence indicates that the tumor microenvironment (TME) has significant clinical value in predicting prognosis ( Pu et al, 2019 ; Hong et al, 2020 ; Ling et al, 2020 ; Qu et al, 2020 ). Previous studies indicated that T cells were positively correlated with good clinical outcomes in OC ( Fridman et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Guo

¹

,

Wang

²

,

Wang

³

et al. 2020

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Ovarian cancer (OC) is the deadliest gynecological cancer in women. Immune cell infiltration has a critical role in regulating carcinogenesis and prognosis in OC. To identify prognostic genes relevant to the tumor microenvironment in OC, we investigated the association between OC and gene expression profiles. Results obtained with the ESTIMATE R tool showed that immune score and stromal score were correlated with lymphatic invasion, and high immune score predicted a favorable prognosis. A total of 342 common differentially expressed genes were identified according to the two scores; these genes were mainly involved in immune response, extracellular region, and serine-type endopeptidase activity. Three immune-related prognostic genes were selected by univariate and multivariate Cox regression analysis. We further established a prognostic model and validated the prognostic value of three hub genes in different databases; our results showed that this model could accurately predict survival and evaluate prognosis independent of clinical characteristics. Three hub genes have prognostic value in OC. TIMER analysis revealed that the three genes were correlated with different immune cells. Low levels of macrophage infiltration and high levels of CD4+ T cell infiltration were associated with favorable survival outcomes. Arm-level gain of GYPC was correlated with neutrophils and dendritic cells. These findings indicate that CXCR4, GYPC, and MMP12 modulate prognosis via effects on the infiltration of immune cells. Thus, these genes represent potential targets for immune therapy in OC.

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Microenvironment Analysis of Prognosis and Molecular Signature of Immune-Related Genes in Lung Adenocarcinoma

Cited by 16 publications

References 35 publications

Predicting the Clinical Outcome of Lung Adenocarcinoma Using a Novel Gene Pair Signature Related to RNA-Binding Protein

Predicting the Clinical Outcome of Lung Adenocarcinoma Using a Novel Gene Pair Signature Related to RNA-Binding Protein

Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

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