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

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.

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

Journal of Oncology ◽

10.1155/2021/2298973 ◽

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.

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

10.21203/rs.3.rs-558845/v1 ◽

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.

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Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.616133 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xingyu Chen ◽

Hua Lan ◽

Dong He ◽

Zhanwang Wang ◽

Runshi Xu ◽

...

Keyword(s):

Ovarian Cancer ◽

Signaling Pathways ◽

Risk Model ◽

Cox Regression ◽

Malignant Tumors ◽

Clinical Importance ◽

Clinical Samples ◽

Immune Microenvironment ◽

Tumor Immune Microenvironment ◽

Cluster A

Ovarian cancer (OC) is one of the most lethal gynecologic malignant tumors. The interaction between autophagy and the tumor immune microenvironment has clinical importance. Hence, it is necessary to explore reliable biomarkers associated with autophagy-related genes (ARGs) for risk stratification in OC. Here, we obtained ARGs from the MSigDB database and downloaded the expression profile of OC from TCGA database. The k-means unsupervised clustering method was used for clustering, and two subclasses of OC (cluster A and cluster B) were identified. SsGSEA method was used to quantify the levels of infiltration of 24 subtypes of immune cells. Metascape and GSEA were performed to reveal the differential gene enrichment in signaling pathways and cellular processes of the subtypes. We found that patients in cluster A were significantly associated with higher immune infiltration and immune-associated signaling pathways. Then, we established a risk model by LASSO Cox regression. ROC analysis and Kaplan-Meier analysis were applied for evaluating the efficiency of the risk signature, patients with low-risk got better outcomes than those with high-risk in overall survival. Finally, ULK2 and GABARAPL1 expression was further validated in clinical samples. In conclusion, Our study constructed an autophagy-related prognostic indicator, and identified two promising targets in OC.

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Development And Validation of A Pyroptosis -Related Signature And Immune Microenvironment Infiltration In Cutaneous Melanoma

10.21203/rs.3.rs-772494/v1 ◽

2021 ◽

Author(s):

Wei Dalong ◽

Xiaoling Lan ◽

Qiang Tang ◽

Zhiqun Huang ◽

Qianli Tang

Keyword(s):

High Risk ◽

Cutaneous Melanoma ◽

Immune Cell ◽

Risk Model ◽

Risk Group ◽

External Validation ◽

High Risk Group ◽

Immune Cell Infiltration ◽

Immune Microenvironment ◽

Tumor Immune Microenvironment

Abstract Background: Cutaneous melanoma is cancer that is both malignant and aggressive, with a poor prognosis. Pyroptosis can affect the prognosis of cancer patients by controlling tumor cell growth, migration, and metastasis, as well as is closely related to the tumor immune microenvironment. The significance of pyroptosis-related genes (PRGs) in cutaneous melanoma, however, is unknown. Methods: The training set and external validation sets were cutaneous melanoma samples from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO), respectively. By using univariate Cox regression analysis and selection operator (Lasso) regression model, prognostic genes for overall survival (OS) were found. Candidate genes that were screened were used to calculate risk scores and construct a PRG risk model. The Kaplan Meier curve, time-dependent receiver operating characteristic (ROC) curve, and area under the curve (AUC)were used to assess the functional and prognostic usefulness of gene signatures in the risk model. Furthermore, to speculate on the activity of immune cell infiltration and immune-related pathways in the tumor immune microenvironment and calculate corresponding scores, a single sample gene set enrichment analysis (ssGSEA) was used.Results: An eight PRGs risk signature (AIM2, CASP4, CASP5, CASP8, IL18, NLRC4, NLRP6, PRKACA) were conducted and divided all cutaneous melanoma patients in the TCGA cohort into two groups: Low-risk and High-risk. Both the training and external validation sets showed that patients in the low-risk group showed a significantly higher likelihood of survival than those in the high-risk group (p < 0.001). Except for PRKACA, all the other eight PRGs in our study appeared to be longer survival times for patients. The results of ssGSEA in terms of 16 types of immune cells and the activity of 13 immune-related pathways showed that the High-risk group had lower immune pathway activity and lower levels of immune cell infiltration. In conclusion, the PRG-signature may be a significant predictor of prognosis and may play an essential role in UM patients' tumor immunity.

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Identification of an Immune-Related Prognostic Gene CLEC5A Based on Immune Microenvironment and Risk Modeling of Ovarian Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.746932 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jiacheng Shen ◽

Tingwei Liu ◽

Jia Lv ◽

Shaohua Xu

Keyword(s):

Ovarian Cancer ◽

Immune Response ◽

Copy Number ◽

Risk Model ◽

Copy Number Variant ◽

Prognostic Indicator ◽

Immune Checkpoints ◽

Genomic Alteration ◽

Risk Modeling ◽

Immune Microenvironment

Objective: To understand the immune characteristics of the ovarian cancer (OC) microenvironment and explore the differences of immune-related molecules and cells to establish an effective risk model and identify the molecules that significantly affected the immune response of OC, to help guide the diagnosis.Methods: First, we calculate the TMEscore which reflects the immune microenvironment, and then analyze the molecular differences between patients with different immune characteristics, and determine the prognostic genes. Then, the risk model was established by least absolute shrinkage and selection operator (LASSO) analysis and combined with clinical data into a nomogram for diagnosis and prediction. Subsequently, the potential gene CLEC5A influencing the immune response of OC was identified from the prognostic genes by integrative immune-stromal analysis. The genomic alteration was explored based on copy number variant (CNV) and somatic mutation data.Results: TMEscore was a prognostic indicator of OC. The prognosis of patients with high TMEscore was better. The risk model based on immune characteristics was a reliable index to predict the prognosis of patients, and the nomogram could comprehensively evaluate the prognosis of patients. Besides, CLEC5A was closely related to the abundance of immune cells, immune response, and the expression of immune checkpoints in the OC microenvironment. OC cells with high expression of CLEC5A increased the polarization of M2 macrophages. CLEC5A expression was significantly associated with TTN and CDK12 mutations and affected the copy number of tumor progression and immune-related genes.Conclusion: The study of immune characteristics in the OC microenvironment and the risk model can reveal the factors affecting the prognosis and guide the clinical hierarchical treatment. CLEC5A can be used as a potential key gene affecting the immune microenvironment remodeling of OC, which provides a new perspective for improving the effect of OC immunotherapy.

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N6-Methylandenosine-Related lncRNA Signature Is a Novel Biomarkers of Prognosis and Immune Response in Colon Adenocarcinoma Patients

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.703629 ◽

2021 ◽

Vol 9 ◽

Author(s):

Peiling Zhang ◽

Guolong Liu ◽

Lin Lu

Keyword(s):

Clustering Analysis ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Colon Adenocarcinoma ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Data Set ◽

Survival Status ◽

Tumor Immune Microenvironment

BackgroundColon adenocarcinoma (COAD) is the most common type of colon cancer. To date, however, the prognostic values of m6A RNA methylation-related long non-coding RNAs (lncRNAs) in COAD are largely unknown.Materials and MethodsThe m6A-related lncRNAs were identified from The Cancer Genome Atlas (TCGA) data set. Univariate and multivariate Cox regression analyses were performed to explore the prognostic m6A-related lncRNAs. Consistent clustering analysis was performed to classify the COAD patients into different subgroups based on the expression of m6A-related lncRNAs. The potential biological functions as well as differences in the stemness index and tumor immune microenvironment between different subgroups were analyzed. The prognostic m6A-related lncRNAs were used to establish an m6A-related lncRNA risk model to predict prognosis and survival status.ResultsWe identified 31 m6A-associated lncRNAs with prognostic values from the TCGA data set. Based on the expression of prognostic m6A-associated lncRNAs, TCGA-COAD patients were classified into three clusters using consistent clustering analysis. There was a low correlation of tumor stemness between the three clusters but a significant correlation with the tumor immune microenvironment as well as the tumor mutational load. Thirty-one prognostic-related m6A-associated lncRNAs were used to construct a risk model, which was further determined by survival analysis, receiver operating characteristic (ROC) curve, and univariate and multifactor Cox analysis. The m6A-related risk model demonstrates good performance in predicting prognosis and survival status. The model-based high-risk group exhibited poorer overall survival (OS) compared with the low-risk group.ConclusionIn this study, we construct a risk model that consists of 31 m6A-related lncRNAs with independent prognostic values in COAD. Our study shows the critical roles of these 31 m6A-related lncRNAs in the tumor immune microenvironment, indicating the prospect of informing prognostic stratification and the development of immunotherapeutic strategies for COAD patients.

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A Novel Defined Risk Signature of the Ferroptosis-Related Genes for Predicting the Prognosis of Ovarian Cancer

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.645845 ◽

2021 ◽

Vol 8 ◽

Author(s):

Ying Ye ◽

Qinjin Dai ◽

Shuhong Li ◽

Jie He ◽

Hongbo Qi

Keyword(s):

Ovarian Cancer ◽

High Risk ◽

Risk Model ◽

Cox Regression ◽

Ovarian Tissue ◽

Gene Signature ◽

High Risk Group ◽

Cancer Prognosis ◽

Consensus Clustering ◽

Gynecologic Tumor

Ferroptosis is an iron-dependent, regulated form of cell death, and the process is complex, consisting of a variety of metabolites and biological molecules. Ovarian cancer (OC) is a highly malignant gynecologic tumor with a poor survival rate. However, the predictive role of ferroptosis-related genes in ovarian cancer prognosis remains unknown. In this study, we demonstrated that the 57 ferroptosis-related genes were expressed differently between ovarian cancer and normal ovarian tissue, and based on these genes, all OC cases can be well divided into 2 subgroups by applying consensus clustering. We utilized the least absolute shrinkage and selection operator (LASSO) cox regression model to develop a multigene risk signature from the TCGA cohort and then validated it in an OC cohort from the GEO database. A 5-gene signature was built and reveals a favorable predictive efficacy in both TCGA and GEO cohort (P < 0.001 and P = 0.03). The GO and KEGG analysis revealed that the differentially expressed genes (DEGs) between the low- and high-risk subgroup divided by our risk model were associated with tumor immunity, and lower immune status in the high-risk group was discovered. In conclusion, ferroptosis-related genes are vital factors predicting the prognosis of OC and could be a novel potential treatment target.

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RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.650499 ◽

2022 ◽

Vol 12 ◽

Author(s):

Lan-Xin Mu ◽

You-Cheng Shao ◽

Lei Wei ◽

Fang-Fang Chen ◽

Jing-Wei Zhang

Keyword(s):

Breast Cancer ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Impact ◽

Immune Microenvironment ◽

Model Based ◽

Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

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Identification of a Glycolysis-Related Gene Signature for Survival Prediction of Ovarian Cancer Patients

10.21203/rs.3.rs-76061/v1 ◽

2020 ◽

Author(s):

Dai Zhang ◽

Si Yang ◽

Yiche Li ◽

Meng Wang ◽

Jia Yao ◽

...

Keyword(s):

Ovarian Cancer ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

Gynecological Cancer ◽

Predictive Ability ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Test Sets

Abstract Background: Ovarian cancer (OV) is deemed as the most lethal gynecological cancer in women. The aim of this study was construct an effective gene prognostic model for OV patients.Methods: The expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed in training and test sets.Results: Based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4), a gene risk signature was identified to predict the outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve of 0.709, 0.762, and 0.808 for 3-, 5- and 10-year survival, respectively. Similar results were found in the test sets, and the signature was also an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was constructed.Conclusion: Our study established a nine-GRG risk model and a nomogram to better perform on OV patients’ survival prediction. The risk model represents a promising and independent prognostic predictor for OV patients. Moreover, our study of GRGs could offer guidances for underlying mechanisms explorations in the future.

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Alteration in the immune microenvironment based on APC status in MSS/pMMR colon cancer data retrieved from TCGA

10.21203/rs.3.rs-56379/v1 ◽

2020 ◽

Author(s):

Haishan Lin ◽

Hongchao Zhen ◽

Kun Shan ◽

Xiaoting Ma ◽

Bangwei Cao

Keyword(s):

Colon Cancer ◽

Cancer Patients ◽

Enrichment Analysis ◽

Tumor Immunotherapy ◽

Immune Checkpoints ◽

Immune Microenvironment ◽

Cancer Data ◽

T Cell Infiltration ◽

Colon Cancers ◽

Tumor Immune Microenvironment

Abstract Immunotherapy is currently the most advanced anti-tumor treatment approach. The efficacy of anti-tumor immunotherapy is closely related to the tumor immune microenvironment, including immune cells, infiltration of immune factors, and expression of immune checkpoints. At present, the biomarkers for predicting the efficacy of colon cancer immunotherapy do not cover all colon cancer patients suitable for immunotherapy. In this study, TCGA database was used to identify tumor genotypes suitable for anti-tumor immunotherapy. We found that some of the MSS/pMMR populations, that were initially considered unsuitable for immunotherapy, might actually be suitable. In APC-wt/MSS colon cancer, the expression of PD-1, PD-L1, CTLA4 and CYT（GZMA and PRF1）were increased. Based on calculations done by ESTIMATE and CIBERSORT algorithms, the ImmunoScore and the proportion of CT8+ T cell infiltration is increased in these patients. Enrichment analysis was done to screen signaling pathways involved in immune response, extracellular matrix, and cell adhesion. Tumors from 42 colon cancer patients, including 22 APC-mt/MSS and 20 APC-wt/MSS, were immunohistochemically evaluated for expression of CD8 and PD-L1. And APC-wt/MSS tumors showed significantly higher expression of CD8 and PD-L1 than APC-mt/MSS tumor. Based on the results, we found that some colon cancers of APC-wt/MSS are classified by Tumor Immune Microenvironment types (TIMTs) TMIT I. So that we speculate that APC-wt/MSS colon cancer patients could benefit from anti-tumor immunotherapy.

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