Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

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Identification of a Novel Epithelial-Mesenchymal Transition Gene Signature Regulated by KEAP1-NRF2 Pathway in Esophageal Carcinoma

10.21203/rs.3.rs-77379/v1 ◽

2020 ◽

Author(s):

Mohamed Elshaer ◽

Ahmed Hammad ◽

Xiu Jun Wang ◽

Xiuwen Tang

Keyword(s):

Cell Lines ◽

Epithelial Mesenchymal Transition ◽

Cancer Cell Line ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Mesenchymal Transition ◽

Gene Set ◽

Over Expression

Abstract BackgroundKEAP1-NRF2 pathway alterations were identified in many cancers including, esophageal cancer (ESCA). Identifying biomarkers that are associated with mutations in this pathway will aid in defining this cancer subset; and hence in supporting precision and personalized medicine. MethodsIn this study, 182 tumor samples from the Cancer Genome Atlas (TCGA)-ESCA RNA-Seq V2 level 3 data were segregated into two groups KEAP1-NRF2-mutated (22) and wild-type (160).The two groups were subjected to differential gene expression analysis, and we performed Gene Set Enrichment Analysis (GSEA) to determine all significantly affected biological pathways. Then, the enriched gene set was integrated with the differentially expressed genes (DEGs) to identify a gene signature regulated by the KEAP1-NRF2 pathway in ESCA. Furthermore, we validated the gene signature using mRNA expression data of ESCA cell lines provided by the Cancer Cell Line Encyclopedia (CCLE). The identified signature was tested in 3 independent ESCA datasets to assess its prognostic value.ResultsWe identified 11 epithelial-mesenchymal transition (EMT) genes regulated by the KEAP1-NRF2 pathway in ESCA patients. Five of the 11 genes showed significant over-expression in KEAP1-NRF2-mutated ESCA cell lines. In addition, the over-expression of these five genes was significantly associated with poor survival in 3 independent ESCA datasets, including the TCGA-ESCA dataset.ConclusionAltogether, we identified a novel EMT 5-gene signature regulated by the KEAP1-NRF2 axis and this signature is strongly associated with metastasis and drug resistance in ESCA. These 5-genes are potential biomarkers and therapeutic targets for ESCA patients in whom the KEAP1-NRF2 pathway is altered.

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Establishment and Validation of a Novel Metabolism-related Gene Signature for Predicting Overall Survival of Patients with Breast Cancer

10.21203/rs.3.rs-222322/v1 ◽

2021 ◽

Author(s):

Jian Li ◽

Yang Liu ◽

Fei Liu ◽

Qiang Tian ◽

Baojiang Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Treatment Strategies ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Current Status ◽

Lasso Regression ◽

Related Gene

Abstract It is well known that Breast cancer is a heterogeneous disease.Although the current recurrence and mortality rate have been greatly improved, many people still suffer relapse and metastasis.Metabolic reprograming is currently considered to be a new hallmark of cancer.Therefore,in this study, we comprehensively analyzed the prognostic effect of metabolic-related gene signatures in breast cancer and its relationship with the immune microenvironment.We constructed a novel metabolic-related gene signature containing 6 genes to distinguish between high and low risk groups by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression, and validated its robustness and accuracy through multiple databases.The metabolic gene signature may be an independent risk factor for BC both in the training and the testing set,the nomogram has a moderately accurate performance,and the C index was 0.757 and 0.728 respectively.The signature can reveal metabolic characteristics based on gene set enrichment analysis and at the same time monitor the status of TME.This gene signature can be used as a promising independent prognostic marker for BC patients, and can indicate the current status of TME, providing more clues for exploring new diagnostic and treatment strategies.

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PVT1 is a prognostic marker associated with immune invasion of bladder urothelial carcinoma

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2022009 ◽

2021 ◽

Vol 19 (1) ◽

pp. 169-190

Author(s):

Peiyuan Li ◽

◽

Gangjie Qiao ◽

Jian Lu ◽

Wenbin Ji ◽

...

Keyword(s):

Urothelial Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Gene Set Enrichment ◽

Cox Regression Analysis ◽

Gene Set ◽

Bladder Urothelial Carcinoma

<abstract> <p>Plasmacytoma variant translocation 1 (PVT1) is involved in multiple signaling pathways and plays an important regulatory role in a variety of malignant tumors. However, its role in the prognosis and immune invasion of bladder urothelial carcinoma (BLCA) remains unclear. This study investigated the expression of PVT1 in tumor tissue and its relationship with immune invasion, and determined its prognostic role in patients with BLCA. Patients were identified from the cancer genome atlas (TCGA). The enrichment pathway and function of PVT1 were explained by gene ontology (GO) term analysis, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA), and the degree of immune cell infiltration was quantified. Kaplan–Meier analysis and Cox regression were used to analyze the correlation between PVT1 and survival rate. PVT1-high BLCA patients had a lower 10-year disease-specific survival (DSS P < 0.05) and overall survival (OS P < 0.05). Multivariate Cox regression analysis showed that PVT1 (high vs. low) (P = 0.004) was an independent prognostic factor. A nomogram was used to predict the effect of PVT1 on the prognosis. PVT1 plays an important role in the progression and prognosis of BLCA and can be used as a medium biomarker to predict survival after cystectomy.</p> </abstract>

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Development and Validation of an Apoptosis-Related Genes Signature in Patients with Gastric Cancer

10.21203/rs.3.rs-1099716/v1 ◽

2021 ◽

Author(s):

Jianqiao Yang ◽

Liang Shang ◽

Leping Li ◽

Zixiao Wang ◽

Kangdi Dong ◽

...

Keyword(s):

Gastric Cancer ◽

Cox Regression ◽

External Validation ◽

Treatment Strategies ◽

Enrichment Analysis ◽

Malignant Tumour ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Risk Scores

Abstract Background: Gastric cancer (GC) is a common malignant tumour of the digestive tract. the prognosis of GC patients is still not optimistic. Apoptosis-related genes (ARGs) plays an important role in the development, invasion, metastasis and drug resistance of GC. Therefore, assessing the interaction between ARGs and the prognosis of GC patients may help identify specific biomarkers.Methods: Differentially expressed genes (DEGs) were identified by integrating gene expression profiling analyses from The Cancer Genome Atlas (TCGA) GC cohort and Gene Set Enrichment Analysis (GSEA) Database. Then, a risk score model was built based on Kaplan-Meier (K-M), least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. Another cohort (GSE84426) was used for external validation. By combining risk scores with clinical variables, a nomogram was constructed to predict the prognosis of GC patients. Results: We screened 39 DEGS and established a three-gene signature(CAV1、F2、LUM) based on 161 ARGs. In addition, three-gene signature was identified as an independent factor in predicting the prognosis of GC patients and validated in an external independent cohort. Finally, we developed a nomogram that can be applied to clinical practice.Conclusions: Our study established a three-gene signature of GC based on ARGs that has reference significance for in-depth research on the apoptosis mechanism of GC and the exploration of new clinical treatment strategies.

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Development and validation of 10-gene signature for predicting survival in patients with prostate cancer

10.21203/rs.3.rs-17658/v1 ◽

2020 ◽

Author(s):

Nan Li ◽

Kai Yu ◽

Ling Zhong ◽

Dingyuan Zeng

Keyword(s):

Prostate Cancer ◽

Cancer Patients ◽

High Throughput Sequencing ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Sequencing Data

Abstract Background. The prognosis for prostate cancer patients remains poor. High-throughput sequencing data provide a solid basis for identifying genes associated with cancer prognosis, but genetic markers are needed to predict the clinical outcome of prostate cancer. Methods. The Cancer Genome Atlas (TCGA) database (N = 551) was adopted to estimate the prognostic value of immune genes. RNA-seq and clinical follow-up data were downloaded from TCGA. The samples were randomly divided into training and test. Cox regression analyses and least absolute shrinkage and selection operator (LASSO) were conducted to develop an immune risk score. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single sample Gene Set Enrichment Analysis (ssGSEA) were used for functional Analysis. Tumor Immune Estimation Resource (TIMER) is used to analyze the immune score, and RMS curve and clinical decision curve analysis is used to analyze the superiority of the comparison with published models. Results. Survival analyses revealed that 19 genes significantly associated with the overall survival (OS). 10-genes signature was ultimately obtained through random forest feature selection. Riskscore effectively stratified samples in the training, test, and external verification sets and all TCGA sets. The 5-year survival AUC in the training, verification sets and all TCGA sets were around 0.7. Univariate and multivariate analysis showed that 10-genes signature has good predictive performance in clinical. TIMER analysis shows that immunosuppression may reduce the chances of survival for patients with prostate cancer. Compared with published models, our model has a higher C-index. Conclusion. We constructed a 10-gene signature as a new prognostic marker for predicting survival of prostate cancer patients.

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Identification of a Novel Epithelial-mesenchymal Transition Gene Signature Regulated by KEAP1-NRF2 Pathway in Esophageal Carcinoma

10.21203/rs.3.rs-76086/v1 ◽

2020 ◽

Author(s):

Mohamed Elshaer ◽

Ahmed Hammad ◽

Xiu Jun Wang ◽

xiuwen Tang

Keyword(s):

Cell Lines ◽

Epithelial Mesenchymal Transition ◽

Cancer Cell Line ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Mesenchymal Transition ◽

Gene Set ◽

Over Expression

Abstract BackgroundKEAP1-NRF2 pathway alterations were identified in many cancers including, esophageal cancer (ESCA). Identifying biomarkers that are associated with mutations in this pathway will aid in defining this cancer subset; and hence in supporting precision and personalized medicine. MethodsIn this study, 182 tumor samples from the Cancer Genome Atlas (TCGA)-ESCA RNA-Seq V2 level 3 data were segregated into two groups KEAP1-NRF2-mutated (22) and wild-type (160).The two groups were subjected to differential gene expression analysis and we performed Gene Set Enrichment Analysis (GSEA). Then, the enriched gene set was integrated with the differentially expressed genes (DEGs) to identify a gene signature regulated by the KEAP1-NRF2 pathway in ESCA. Furthermore, we validated the gene signature using mRNA expression data of ESCA cell lines provided by the Cancer Cell Line Encyclopedia (CCLE). The identified signature was tested in 3 independent ESCA datasets to assess its prognostic value.ResultsWe identified 11 epithelial-mesenchymal transition (EMT) genes regulated by the KEAP1-NRF2 pathway in ESCA patients. Five of the 11 genes showed significant over-expression in KEAP1-NRF2-mutated ESCA cell lines. In addition, over-expression of these five genes was significantly associated with poor survival in 3 independent ESCA datasets, including the TCGA-ESCA dataset.ConclusionAltogether, we identified a novel EMT 5-gene signature regulated by the KEAP1-NRF2 axis and this signature is strongly associated with metastasis and drug resistance in ESCA. These 5-genes are potential biomarkers and therapeutic targets for ESCA patients in whom the KEAP1-NRF2 pathway is altered.

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Pathological and genetic aggressiveness of left-sided breast cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e12579 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e12579-e12579

Author(s):

Yara Abdou ◽

Mariko Asaoka ◽

Kazuaki Takabe

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Enrichment Analysis ◽

Tumor Grade ◽

Mitotic Rate ◽

Cytolytic Activity ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Gene Sets ◽

Significant Difference

e12579 Background: Breast Cancer in women consistently occurs more frequently in the left breast, with the ratio of left to right sided breast cancer cases ranging from 1.05 to 1.26. In spite of the difference in frequency, prior studies have failed to show any significant difference in clinical characteristics between left sided and right sided cancer. Methods: Genomic and clinical features were collected from The Cancer Genome Atlas breast cancer project. LVI status, mitotic rate, nuclear score and tubular score were collected from pathology reports in TIES client 5.8. Fisher's exact test was used for group comparison and survival analysis was performed with Cox regression. Cytolytic activity (CYT) indicates anti-cancer immune response and was quantified from gene expression data. Hallmark gene-sets were used for gene set enrichment analysis (GSEA). Results: Among the 1081 women with unilateral invasive breast cancer, 561 had tumor on the left side compared to 520 on the right. Our results didn’t show any significant differences between left and right side with regards to tumor location, histology, race, and tumor characteristics including stage, tumor size, nodal status and receptor status. No statistical significant differences were observed in mitotic rate, LVI status and tubular score, however, the tumor grade was significantly higher in the left side. Moreover, there were no significant differences in mutation count, CYT and overall survival between both sides. GSEA revealed cell-cycle related gene sets like G2M checkpoint, Mitotic spindle, E2F targets and MYC targets which were significantly enriched in left sided tumor. Furthermore, out of the 865 genes which were highly expressed on the left side, we identified specific genes including BRCA1, BRCA2, BRIP1, CHEK2, FANCC, PALB2, TP53 and MSH6 which are associated primarily with breast cancer genesis and mostly have established clinical management guidelines. Conclusions: Our results suggest a more aggressive nature to left sided breast cancer with a higher pathological grade perhaps requiring more aggressive treatment. Such a hypothesis needs further study to confirm or refute its validity. If confirmed, it may have a major impact with regard to biology of breast cancer and its subsequent management.

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High expression of PIMREG predicts poor survival outcomes and is correlated with immune infiltrates in lung adenocarcinoma

PeerJ ◽

10.7717/peerj.11697 ◽

2021 ◽

Vol 9 ◽

pp. e11697

Author(s):

Feng Jiang ◽

Min Liang ◽

Xiaolu Huang ◽

Wenjing Shi ◽

Yumin Wang

Keyword(s):

Prognostic Value ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

High Expression ◽

Gene Set Enrichment ◽

Gene Set ◽

Kaplan Meier ◽

Cancer Genome Atlas

Background PIMREG is upregulated in multiple cancer types. However, the potential role of PIMREG in lung adenocarcinoma (LUAD) remains unclear. The present study aimed to explore its clinical significance in LUAD. Methods Using the Cancer Genome Atlas (TCGA) databases, we obtained 513 samples of LUAD and 59 normal samples from the Cancer Genome Atlas (TCGA) databases to analyze the relationship between PIMREG and LUAD. We used t and Chi-square tests to evaluate the level of expression of PIMREG and its clinical implication in LUAD. The prognostic value of PIMREG in LUAD was identified through the Kaplan–Meier method, Cox regression analysis, and nomogram. Gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were performed to screen biological pathways and analyze the correlation of the immune infiltrating level with the expression of PIMREG in LUAD. Results PIMREG was highly expressed in patients with LUAD. Specifically, the level of PIMREG gradually increased from pathological stage I to IV. Further, we validated the higher expression of PIMREG expressed in LUAD cell lines. Moreover, PIMREG had a high diagnostic value, with an -AUC of 0.955. Kaplan–Meier survival and Cox regression analyses revealed that the high expression of PIMREG was independently associated with poor clinical outcomes. In our prognostic nomogram, the expression of PIMREG implied a significant prognostic value. Gene set enrichment analysis (GSEA) identified that the high expression PIMREG phenotype was involved in the mitotic cell cycle, mRNA splicing, DNA repair, Rho GTPase signaling, TP53 transcriptional regulation, and translation pathways. Next, we also explored the correlation of PIMREG and tumor-immune interactions and found a negative correlation between PIMREG and the immune infiltrating level of T cells, macrophages, B cells, dendritic cells (DCs) , and CD8+ T cells in LUAD. Conclusions High levels of PIMREG correlated with poor prognosis and immune infiltrates in LUAD.

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Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer

Scientific Reports ◽

10.1038/s41598-021-83628-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Feng Jiang ◽

Chuyan Wu ◽

Ming Wang ◽

Ke Wei ◽

Jimei Wang

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Cox Regression ◽

Single Gene ◽

Prediction Method ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cox Models ◽

Normal Tissues ◽

Predictive Risk

AbstractOne of the most frequently identified tumors and a contributing cause of death in women is breast cancer (BC). Many biomarkers associated with survival and prognosis were identified in previous studies through database mining. Nevertheless, the predictive capabilities of single-gene biomarkers are not accurate enough. Genetic signatures can be an enhanced prediction method. This research analyzed data from The Cancer Genome Atlas (TCGA) for the detection of a new genetic signature to predict BC prognosis. Profiling of mRNA expression was carried out in samples of patients with TCGA BC (n = 1222). Gene set enrichment research has been undertaken to classify gene sets that vary greatly between BC tissues and normal tissues. Cox models for additive hazards regression were used to classify genes that were strongly linked to overall survival. A subsequent Cox regression multivariate analysis was used to construct a predictive risk parameter model. Kaplan–Meier survival predictions and log-rank validation have been used to verify the value of risk prediction parameters. Seven genes (PGK1, CACNA1H, IL13RA1, SDC1, AK3, NUP43, SDC3) correlated with glycolysis were shown to be strongly linked to overall survival. Depending on the 7-gene-signature, 1222 BC patients were classified into subgroups of high/low-risk. Certain variables have not impaired the prognostic potential of the seven-gene signature. A seven-gene signature correlated with cellular glycolysis was developed to predict the survival of BC patients. The results include insight into cellular glycolysis mechanisms and the detection of patients with poor BC prognosis.

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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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