scholarly journals A Hypoxia Signature for Predicting Prognosis and Tumor Immune Microenvironment in Adrenocortical Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xi Chen ◽  
Lijun Yan ◽  
Yu Lu ◽  
Feng Jiang ◽  
Ni Zeng ◽  
...  
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Adrenocortical Carcinoma ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Immune Microenvironment ◽  
Gene Sets ◽  
Tumor Immune Microenvironment ◽  
Hypoxia Signature

Adrenocortical carcinoma (ACC) is a rare malignancy with dismal prognosis. Hypoxia is one of characteristics of cancer leading to tumor progression. For ACC, however, no reliable prognostic signature on the basis of hypoxia genes has been built. Our study aimed to develop a hypoxia-associated gene signature in ACC. Data of ACC patients were obtained from TCGA and GEO databases. The genes included in hypoxia risk signature were identified using the Cox regression analysis as well as LASSO regression analysis. GSEA was applied to discover the enriched gene sets. To detect a possible connection between the gene signature and immune cells, the CIBERSORT technique was applied. In ACC, the hypoxia signature including three genes (CCNA2, COL5A1, and EFNA3) was built to predict prognosis and reflect the immune microenvironment. Patients with high-risk scores tended to have a poor prognosis. According to the multivariate regression analysis, the hypoxia signature could be served as an independent indicator in ACC patients. GSEA demonstrated that gene sets linked to cancer proliferation and cell cycle were differentially enriched in high-risk classes. Additionally, we found that PDL1 and CTLA4 expression were significantly lower in the high-risk group than in the low-risk group, and resting NK cells displayed a significant increase in the high-risk group. In summary, the hypoxia risk signature created in our study might predict prognosis and evaluate the tumor immune microenvironment for ACC.

scholarly journals Identification of a Ferroptosis-Related Signature Associated with Prognosis and Immune Infiltration in Adrenocortical Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xi Chen ◽  
Lijun Yan ◽  
Feng Jiang ◽  
Yu Lu ◽  
Ni Zeng ◽  
...  
Keyword(s):  
Cell Death ◽  
Regression Analysis ◽  
High Risk ◽  
Immune Cells ◽  
Adrenocortical Carcinoma ◽  
Prognostic Value ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Infiltration ◽  
Gene Sets

Adrenocortical carcinoma (ACC) is a rare malignant tumor with poor prognosis. Ferroptosis, a new form of cell death, differs from other forms of cell death and plays a vital role in tumor progress. Our study aimed to establish a ferroptosis-related signature with prognostic value in ACC. RNA-seq data and corresponding clinical characteristics for ACC were downloaded from TCGA and GEO databases. Genes included in ferroptosis risk signature were assessed by univariable and multivariable Cox regression analysis as well as lasso regression analysis. The prognostic value of the ferroptosis risk signature was assessed using K-M and ROC curves. Furthermore, we performed GSEA to discover the enriched gene sets in high-risk group. Additionally, TIMER website was applied to detect a possible connection between the signature and immune cells infiltration. ssGSEA was performed to evaluate scores of immune cells and immune-related pathways in two groups. A ferroptosis signature comprised of six genes (SLC7A11, TP53, HELLS, ACSL4, PCBP2, and HMGB1) was constructed to predict prognosis and reflect the immune infiltration in ACC. Patients in high-risk group were inclined to have worse prognosis. The ferroptosis model performed well in predicting prognosis and could be served as an independent indicator in ACC. GSEA revealed that gene sets correlated with biological processes including cell cycle, DNA replication, base excision repair, and P53 signaling pathway were highly enriched in high-risk group. In addition, we discovered that the expressional levels of hub genes were linked to six immune cells’ infiltration in ACC tumor. ssGSEA revealed that contents of most immune cells significantly decreased in the high-risk group. In conclusion, the novel ferroptosis risk signature could be useful in predicting prognosis and reflecting immune infiltration in ACC. It also brings us new insights into the possible value of targeting ferroptosis during the therapy of ACC.

scholarly journals A 5-Gene Signature Is Closely Related to Tumor Immune Microenvironment and Predicts the Prognosis of Patients with Non-Small Cell Lung Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Jia Li ◽  
Huiyu Wang ◽  
Zhaoyan Li ◽  
Chenyue Zhang ◽  
Chenxing Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Egfr Mutations ◽  
Kaplan Meier ◽  
Tumor Immune Microenvironment ◽  
Nsclc Patients

Purpose. Establishing prognostic gene signature to predict clinical outcomes and guide individualized adjuvant therapy is necessary. Here, we aim to establish the prognostic efficacy of a gene signature that is closely related to tumor immune microenvironment (TIME). Methods and Results. There are 13,035 gene expression profiles from 130 tumor samples of the non-small cell lung cancer (NSCLC) in the data set GSE103584. A 5-gene signature was identified by using univariate survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) to build risk models. Then, we used the CIBERSORT method to quantify the relative levels of different immune cell types in complex gene expression mixtures. It was found that the ratio of dendritic cells (DCs) activated and mast cells (MCs) resting in the low-risk group was higher than that in the high-risk group, and the difference was statistically significant (P<0.001 and P=0.03). Pathway enrichment results which were obtained by performing Gene Set Variation Analysis (GSVA) showed that the high-risk group identified by the 5-gene signature had metastatic-related gene expression, resulting in lower survival rates. Kaplan–Meier survival results showed that patients of the high-risk group had shorter disease-free survival (DFS) and overall survival (OS) than those of the low-risk group in the training set (P=0.0012 and P<0.001). The sensitivity and specificity of the gene signature were better and more sensitive to prognosis than TNM (tumor/lymph node/metastasis) staging, in spite of being not statistically significant (P=0.154). Furthermore, Kaplan–Meier survival showed that patients of the high-risk group had shorter OS and PFS than those of the low-risk group (P=0.0035, P<0.001, and P<0.001) in the validating set (GSE31210, GSE41271, and TCGA). At last, univariate and multivariate Cox proportional hazard regression analyses were used to evaluate independent prognostic factors associated with survival, and the gene signature, lymphovascular invasion, pleural invasion, chemotherapy, and radiation were employed as covariates. The 5-gene signature was identified as an independent predictor of patient survival in the presence of clinical parameters in univariate and multivariate analyses (P<0.001) (hazard ratio (HR): 3.93, 95% confidence interval CI (2.17–7.1), P=0.001, (HR) 5.18, 95% CI (2.6995–9.945), P<0.001), respectively. Our 5-gene signature was also related to EGFR mutations (P=0.0111), and EGFR mutations were mainly enriched in low-risk group, indicating that EGFR mutations affect the survival rate of patients. Conclusion. The 5-gene signature is a powerful and independent predictor that could predict the prognosis of NSCLC patients. In addition, our gene signature is correlated with TIME parameters, such as DCs activated and MCs resting. Our findings suggest that the 5-gene signature closely related to TIME could predict the prognosis of NSCLC patients and provide some reference for immunotherapy.

scholarly journals Comprehensive Analysis of Inflammatory Response–Related Genes, and Prognosis and Immune Infiltration in Patients With Low-Grade Glioma

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Han ◽  
Zhifan Zuo ◽  
Meilin Qu ◽  
Yinghui Zhou ◽  
Qing Li ◽  
...  
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Grade ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Background: Although low-grade glioma (LGG) has a good prognosis, it is prone to malignant transformation into high-grade glioma. It has been confirmed that the characteristics of inflammatory factors and immune microenvironment are closely related to the occurrence and development of tumors. It is necessary to clarify the role of inflammatory genes and immune infiltration in LGG.Methods: We downloaded the transcriptome gene expression data and corresponding clinical data of LGG patients from the TCGA and GTEX databases to screen prognosis-related differentially expressed inflammatory genes with the difference analysis and single-factor Cox regression analysis. The prognostic risk model was constructed by LASSO Cox regression analysis, which enables us to compare the overall survival rate of high- and low-risk groups in the model by Kaplan–Meier analysis and subsequently draw the risk curve and survival status diagram. We analyzed the accuracy of the prediction model via ROC curves and performed GSEA enrichment analysis. The ssGSEA algorithm was used to calculate the score of immune cell infiltration and the activity of immune-related pathways. The CellMiner database was used to study drug sensitivity.Results: In this study, 3 genes (CALCRL, MMP14, and SELL) were selected from 9 prognosis-related differential inflammation genes through LASSO Cox regression analysis to construct a prognostic risk model. Further analysis showed that the risk score was negatively correlated with the prognosis, and the ROC curve showed that the accuracy of the model was better. The age, grade, and risk score can be used as independent prognostic factors (p &lt; 0.001). GSEA analysis confirmed that 6 immune-related pathways were enriched in the high-risk group. We found that the degree of infiltration of 12 immune cell subpopulations and the scores of 13 immune functions and pathways in the high-risk group were significantly increased by applying the ssGSEA method (p &lt; 0.05). Finally, we explored the relationship between the genes in the model and the susceptibility of drugs.Conclusion: This study analyzed the correlation between the inflammation-related risk model and the immune microenvironment. It is expected to provide a reference for the screening of LGG prognostic markers and the evaluation of immune response.

scholarly journals Construction of a Prognostic Gene Signature Associated with Immune Infiltration in Glioma: A Comprehensive Analysis Based on the CGGA

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Xiaoxiang Gong ◽  
Lingjuan Liu ◽  
Jie Xiong ◽  
Xingfang Li ◽  
Jie Xu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Immune Cells ◽  
Calcium Binding ◽  
Risk Group ◽  
Binding Protein ◽  
Gene Signature ◽  
High Risk Group ◽  
Training Set ◽  
Immune Infiltration

Background. Tumor microenvironment (TME) is closely related to the progression of glioma and the therapeutic effect of drugs on this cancer. The aim of this study was to develop a signature associated with the tumor immune microenvironment using machine learning. Methods. We downloaded the transcriptomic and clinical data of glioma patients from the Chinese Glioma Genome Atlas (CGGA) databases (mRNAseq_693). The single-sample Gene Set Enrichment Analysis (ssGSEA) database was used to quantify the relative abundance of immune cells. We divided patients into two different infiltration groups via unsupervised clustering analysis of immune cells and then selected differentially expressed genes (DEGs) between the two groups. Survival-related genes were determined using Cox regression analysis. We next randomly divided patients into a training set and a testing set at a ratio of 7 : 3. By integrating the DEGs into least absolute shrinkage and selection operator (LASSO) regression analysis in the training set, we were able to construct a 15-gene signature, which was validated in the testing and total sets. We further validated the signature in the mRNAseq_325 dataset of CGGA. Results. We identified 74 DEGs associated with tumor immune infiltration, 70 of which were significantly associated with overall survival (OS). An immune-related gene signature was established, consisting of 15 key genes: adenosine triphosphate (ATP)-binding cassette subfamily C member 3 (ABCC3), collagen type IV alpha 1 chain (COL4A1), podoplanin (PDPN), annexin A1 (ANXA1), COL4A2, insulin-like growth factor binding protein 2 (IGFBP2), serpin family A member 3 (SERPINA3), CXXC-type zinc finger protein 11 (CXXC11), junctophilin 3 (JPH3), secretogranin III (SCG3), secreted protein acidic and rich in cysteine (SPARC)-related modular calcium-binding protein 1 (SMOC1), Cluster of Differentiation 14 (CD14), COL1A1, S100 calcium-binding protein A4 (S100A4), and transforming growth factor beta 1 (TGF-β1). The OS of patients in the high-risk group was worse than that of patients in the low-risk group. GSEA showed that interleukin-6 (IL-6)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT3) signaling, interferon gamma (IFN-γ) response, angiogenesis, and coagulation were more highly enriched in the high-risk group and that oxidative phosphorylation was more highly enriched in the low-risk group. Conclusion. We constructed a stable gene signature associated with immune infiltration to predict the survival rates of glioma patients.

scholarly journals Identification of Hypoxia Signature to Assess the Tumor Immune Microenvironment and Predict Prognosis in Patients with Ovarian Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Chunyan Wei ◽  
Xiaoqing Liu ◽  
Qin Wang ◽  
Qipei Li ◽  
Min Xie
Keyword(s):  
Ovarian Cancer ◽  
Risk Model ◽  
Risk Group ◽  
Plasmacytoid Dendritic Cell ◽  
Gene Signature ◽  
Immune Checkpoints ◽  
Immune Microenvironment ◽  
Cell Immunity ◽  
Tumor Immune Microenvironment ◽  
Hypoxia Signature

Background. The 5-year overall survival rate of ovarian cancer (OC) patients is less than 40%. Hypoxia promotes the proliferation of OC cells and leads to the decline of cell immunity. It is crucial to find potential predictors or risk model related to OC prognosis. This study aimed at establishing the hypoxia-associated gene signature to assess tumor immune microenvironment and predicting the prognosis of OC. Methods. The gene expression data of 378 OC patients and 370 OC patients were downloaded from datasets. The hypoxia risk model was constructed to reflect the immune microenvironment in OC and predict prognosis. Results. 8 genes (AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5, and TGFBI) were included in the hypoxic gene signature. Patients in the high hypoxia risk group showed worse survival. Hypoxia signature significantly related to clinical features and may serve as an independent prognostic factor for OC patients. 2 types of immune cells, plasmacytoid dendritic cell and regulatory T cell, showed a significant infiltration in the tissues of the high hypoxia risk group patients. Most of the immunosuppressive genes (such as ARG1, CD160, CD244, CXCL12, DNMT1, and HAVCR1) and immune checkpoints (such as CD80, CTLA4, and CD274) were upregulated in the high hypoxia risk group. Gene sets related to the high hypoxia risk group were associated with signaling pathways of cell cycle, MAPK, mTOR, PI3K-Akt, VEGF, and AMPK. Conclusion. The hypoxia risk model could serve as an independent prognostic indicator and reflect overall immune response intensity in the OC microenvironment.

scholarly journals Analysis of Immune–Stromal Score-Based Gene Signature and Molecular Subtypes in Osteosarcoma: Implications for Prognosis and Tumor Immune Microenvironment

2021 ◽  
Vol 12 ◽  
Author(s):  
Dingzhao Zheng ◽  
Kaichun Yang ◽  
Xinjiang Chen ◽  
Yongwu Li ◽  
Yongchun Chen
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Immune Cell ◽  
Molecular Subtypes ◽  
Gene Signature ◽  
Risk Scores ◽  
Survival Duration ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment ◽  
Risk Patients

Objective: Infiltrating immune and stromal cells are essential for osteosarcoma progression. This study set out to analyze immune–stromal score-based gene signature and molecular subtypes in osteosarcoma.Methods: The immune and stromal scores of osteosarcoma specimens from the TARGET cohort were determined by the ESTIMATE algorithm. Then, immune-stromal score-based differentially expressed genes (DEGs) were screened, followed by univariate Cox regression analysis. A LASSO regression analysis was applied for establishing a prognostic model. The predictive efficacy was verified in the GSE21257 dataset. Associations between the risk scores and chemotherapy drug sensitivity, immune/stromal scores, PD-1/PD-L1 expression, immune cell infiltrations were assessed in the TARGET cohort. NMF clustering analysis was employed for characterizing distinct molecular subtypes based on immune-stromal score-based DEGs.Results: High immune/stromal scores exhibited the prolonged survival duration of osteosarcoma patients. Based on 85 prognosis-related stromal–immune score-based DEGs, a nine-gene signature was established. High-risk scores indicated undesirable prognosis of osteosarcoma patients. The AUCs of overall survival were 0.881 and 0.849 in the TARGET cohort and GSE21257 dataset, confirming the well predictive performance of this signature. High-risk patients were more sensitive to doxorubicin and low-risk patients exhibited higher immune/stromal scores, PD-L1 expression, and immune cell infiltrations. Three molecular subtypes were characterized, with distinct clinical outcomes and tumor immune microenvironment.Conclusion: This study developed a robust prognostic gene signature as a risk stratification tool and characterized three distinct molecular subtypes for osteosarcoma patients based on immune–stromal score-based DEGs, which may assist decision-making concerning individualized therapy and follow-up project.

scholarly journals Immune-Related LncRNAs Affect the Prognosis of Osteosarcoma, Which Are Related to the Tumor Immune Microenvironment

2021 ◽  
Vol 9 ◽  
Author(s):  
Qingshan Huang ◽  
Yilin Lin ◽  
Chenglong Chen ◽  
Jingbing Lou ◽  
Tingting Ren ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Immune Escape ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Background: Abnormal expression of lncRNA is closely related to the occurrence and metastasis of osteosarcoma. The tumor immune microenvironment (TIM) is considered to be an important factor affecting the prognosis and treatment of osteosarcoma. This study aims to explore the effect of immune-related lncRNAs (IRLs) on the prognosis of osteosarcoma and its relationship with the TIM.Methods: Ninety-five osteosarcoma samples from the TARGET database were included. Iterative LASSO regression and multivariate Cox regression analysis were used to screen the IRLs signature with the optimal AUC. The predict function was used to calculate the risk score and divide osteosarcoma into a high-risk group and low-risk group based on the optimal cut-off value of the risk score. The lncRNAs in IRLs signature that affect metastasis were screened for in vitro validation. Single sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithms were used to evaluate the role of TIM in the influence of IRLs on osteosarcoma prognosis.Results: Ten IRLs constituted the IRLs signature, with an AUC of 0.96. The recurrence and metastasis rates of osteosarcoma in the high-risk group were higher than those in the low-risk group. In vitro experiments showed that knockdown of lncRNA (AC006033.2) could increase the proliferation, migration, and invasion of osteosarcoma. ssGSEA and ESTIMATE results showed that the immune cell content and immune score in the low-risk group were generally higher than those in the high-risk group. In addition, the expression levels of immune escape-related genes were higher in the high-risk group.Conclusion: The IRLs signature is a reliable biomarker for the prognosis of osteosarcoma, and they alter the prognosis of osteosarcoma. In addition, IRLs signature and patient prognosis may be related to TIM in osteosarcoma. The higher the content of immune cells in the TIM of osteosarcoma, the lower the risk score of patients and the better the prognosis. The higher the expression of immune escape-related genes, the lower the risk score of patients and the better the prognosis.

scholarly journals Exploring the Pyroptosis-related Genes of Breast Cancer and their Effects on Tumor Immune Microenvironment

2021 ◽  
Author(s):  
Congli Jia ◽  
Fu Yang ◽  
Ruining Li
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Abstract Background: Breast cancer （BC） is the most common cancer among women, with high rates of metastasis and recurrence. Some studies have confirmed that pyroptosis is an immune-related programmed cell death. However, the correlation between the expression of pyroptosis-related genes in BC and its prognosis remains unclear. Methods: In this study, we identified 38 pyroptosis-related genes that were differentially expressed between BC and normal tissues. The prognostic value of each pyroptosis-related gene was evaluated using patient data from The Cancer Genome Atlas (TCGA). The Cox regression method was performed to establish a prognostic model for 16-gene signature, classifying all BC patients in the TCGA database into a low-or high-risk group. Results: The survival rate of BC patients in the high-risk group was significantly lower than that in the low-risk group (P＜0.01). Prognostic model is independent prognostic factor for BC patients compared to clinical features. Single sample gene set enrichment analysis (ssGSEA) showed a decrease for immune cells and immune function in the high-risk group. Conclusions: Pyroptosis-related genes influence the tumor immune microenvironment and can predict the prognosis of BC.

scholarly journals A Mitophagy-Related Gene Signature Associated With Prognosis and Immune Microenvironment in Colorectal Cancer

2022 ◽  
Author(s):  
Cong Zhang ◽  
Cailing Zeng ◽  
Shaoquan Xiong ◽  
Zewei Zhao ◽  
Guoyu Wu
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene ◽  
Prognostic Signature

Abstract Background: Colorectal cancer (CRC) is a heterogeneous disease and one of the most common malignancies in the world. Previous studies have found that mitophagy plays an important role in the progression of colorectal cancer. This study is aimed to investigate the relationship between mitophagy-related genes and the prognosis of patients with CRC.Methods: Gene expression profiles and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) regression analysis were used to establish the prognostic signature composed of mitophagy related genes. Kaplan-Meier curve and receiver operating characteristic (ROC) curve were used to analyze patient survival and verify the predictive accuracy of the signature, respectively. Construction of a nomogram prognostic prediction model was based on risk scores and clinicopathological parameters. Using the Genomics of Drug Sensitivity in Cancer (GDSC) database and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to estimate the sensitivity of chemotherapy, targeted therapy and immunotherapy. Results: A total of 44 mitophagy-driven genes connected with CRC survival were identified, and prognostic signature was established based on the expression of 10 of them (AMBRA1, ATG14, MAP1LC3A, MAP1LC3B, OPTN, VDAC1, ATG5, CSNK2A2, MFN1, TOMM22). Patients were divided into high-risk and low-risk groups based on the median risk score, and the survival of patients in the high-risk group was significantly shorter than that of the low-risk group among the TCGA cohort (median OS 67.3 months vs not reached, p=0.00059) and two independent cohorts from GEO (median OS in GSE17536: 54.0 months vs not reached, p=0.0082; in GSE245: 7.7 months vs not reached, p=0.025). ROC curve showed that the area under the curves (AUC) of 1-, 3- and 5-year survival were 0.66, 0.66 and 0.64, respectively. Multivariate Cox regression analysis confirmed the independent prognostic value of the signature. Then we constructed a nomogram combining the risk score, age and M stage, which had a concordance index of survival prediction of 0.77 (95% CI=0.71-0.83) and more robust predictive sensitivity and specificity. Results showed that CD8+ T cells, regulatory T cells and activated NK cells were significantly more abundant in the high-risk group. Furthermore, patients in the high-risk group were more sensitive to potential targeted therapies, including Motesanib, ATRA, Olaparib, Selumetinib, AZD8055 and immunotherapy. Conclusion: In conclusion, we constructed and validated a novel mitophagy related gene signature that can be used as an independent prognostic biomarker for CRC, and may lead to better stratification and selection of precise treatment for CRC patients.

scholarly journals Comprehensive Analysis of Tumor Immune Microenvironment and Prognosis of m6A-Related lncRNAs in Gastric Cancer

2021 ◽  
Author(s):  
Yi Wang ◽  
Gui-Qi Zhu ◽  
Di Tian ◽  
Chang-Wu Zhou ◽  
Na Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Immune Checkpoints ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Abstract Background N6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play pivotal role in gastric cancer (GC) progression. The emergence of immunotherapy in GC has created a paradigm shift in the approach of treatment, whereas there is significant heterogeneity with regard to degree of treatment responses, which results from the variability of tumor immune microenvironment (TIME). How the interplay between them enrolled in the shaping of TIME remains unclear. Methods The RNA sequencing and clinical data of GC patients were collected from TCGA database. Pearson correlation test and univariate Cox analysis were used to screen out m6A-related lncRNAs. Consensus clustering was implemented to classify GC patients into 2 subtypes. Survival analysis, the infiltration of immune cells, Gene set enrichment analysis (GSEA) and the mutation profiles were analyzed and compared between two clusters. Then least absolute shrinkage and selection operator (LASSO) COX regression was implemented to select pivotal genes and risk score model was constructed accordingly. The prognosis value of the risk model was explored. In addition, the discrepancies of response to immune checkpoints inhibitor (ICIs) therapy were compared between different risk groups. Finally, we performed qRT-PCR to detect the expression pattern in 35 tumor tissues and paired adjacent normal tissue, and validated the prognostic value of risk model in the our cohort (N=35). Results The expression profiles of 23 lncRNAs were included to cluster patients into different subtypes. Cluster1 with worse prognosis harbored higher immune score, stromal score, ESTIMATE score and mutation rate of genes. Different immune cell infiltration pattern were also displayed between different clusters. GSEA showed that cluster1 was preferentially enriched with tumor hallmarks and tumor correlated biological pathways. Next, 9 lncRNAs were selected by LASSO regression model to construct risk model. Patients in the high risk group had poor prognosis. The prognosis value of this model was also validated in our cohort. As for predicting responses to the ICIs therapy, we found that patients from high risk group had lower TMB score and lower proportion of MSI-H subtype. Moreover, patients had distinct immunophenoscores in different risk groups. Conclusion Our study revealed that the potential interplay between m6A modification and lncRNAs might have critical role in predicting GC prognosis, sculpting TIME landscape and predicting the responses to immune checkpoints inhibitors therapy.

Sign in / Sign up

Export Citation Format

Share Document