A Novel Prognostic Signature Based on Cell-In-Cell-Related Genes for Predicting Survival and Tumor Microenvironment in Pancreatic Cancer

Abstract Background: Pancreatic cancer (PC) is a highly malignant tumor featured with high intra-tumoral heterogeneity and poor prognosis. Cell-in-cell (CIC) structures have been reported in multiple tumor types, and their presence is thought to promote clonal selection and tumor evolution. Here, we aimed to establish a CIC-related gene signature for predicting the prognosis and evaluating immune microenvironment in PC. Methods: In this study, the gene expression data, as well as corresponding clinicopathological data of PC and normal pancreatic tissues were collected from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) databases. Differential gene expression analysis, random forest screening, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were performed on 101 CIC-related genes to construct a prognostic gene signature. The effectiveness and robustness of the prognostic gene signature were evaluated by receiver operating characteristic (ROC) curves, Kaplan-Meier survival analysis and establishing the nomogram model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to annotate the biological functions of the differentially expressed genes (DEGs). Quantitative real-time PCR (qRT-PCR), western blotting and immunohistochemistry (IHC) staining were validated the core gene expression in both mRNA and protein levels. Results: A 4-gene signature was constructed to stratify patients into the low-risk and high-risk groups with distinct survival outcomes, somatic mutation profiles and immune features. The high-risk group had poorer prognosis than did the low-risk group. This signature was found to be an independent prognostic factor for PC patients with favorable predictive efficiency. Functional enrichment analyses showed that numerous terms and pathways associated with invasion and metastasis were enriched in the high-risk group. Moreover, the high-risk group had a higher tumor mutation burdens and lower immune cell infiltrations. KRT7, as the most important risk gene, was significantly associated with the worse prognosis of PC. CIC formation assay performing in PC cell lines indicated that KRT7 expression was correlated with CIC frequency. Conclusions: The signature based on four CIC-related genes could be applicable for predicting the prognosis of PC, and targeting CIC processes may be a potential therapeutic option. Further studies are needed to reveal the underlying molecular mechanisms and biological implications of CIC in PC progression.

Download Full-text

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

BioMed Research International ◽

10.1155/2020/2147397 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 1

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.

Download Full-text

Gene signature for prognosis in comparison of pancreatic cancer patients with diabetes and non-diabetes

PeerJ ◽

10.7717/peerj.10297 ◽

2020 ◽

Vol 8 ◽

pp. e10297

Author(s):

Mingjun Yang ◽

Boni Song ◽

Juxiang Liu ◽

Zhitong Bing ◽

Yonggang Wang ◽

...

Keyword(s):

Pancreatic Cancer ◽

High Risk ◽

Prognostic Value ◽

Prognostic Biomarker ◽

Risk Group ◽

Gene Signature ◽

Low Risk ◽

Risk Genes ◽

Genetic Biomarkers ◽

Patients With Diabetes

Background Pancreatic cancer (PC) has much weaker prognosis, which can be divided into diabetes and non-diabetes. PC patients with diabetes mellitus will have more opportunities for physical examination due to diabetes, while pancreatic cancer patients without diabetes tend to have higher risk. Identification of prognostic markers for diabetic and non-diabetic pancreatic cancer can improve the prognosis of patients with both types of pancreatic cancer. Methods Both types of PC patients perform differently at the clinical and molecular levels. The Cancer Genome Atlas (TCGA) is employed in this study. The gene expression of the PC with diabetes and non-diabetes is used for predicting their prognosis by LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression. Furthermore, the results are validated by exchanging gene biomarker with each other and verified by the independent Gene Expression Omnibus (GEO) and the International Cancer Genome Consortium (ICGC). The prognostic index (PI) is generated by a combination of genetic biomarkers that are used to rank the patient’s risk ratio. Survival analysis is applied to test significant difference between high-risk group and low-risk group. Results An integrated gene prognostic biomarker consisted by 14 low-risk genes and six high-risk genes in PC with non-diabetes. Meanwhile, and another integrated gene prognostic biomarker consisted by five low-risk genes and three high-risk genes in PC with diabetes. Therefore, the prognostic value of gene biomarker in PC with non-diabetes and diabetes are all greater than clinical traits (HR = 1.102, P-value < 0.0001; HR = 1.212, P-value < 0.0001). Gene signature in PC with non-diabetes was validated in two independent datasets. Conclusions The conclusion of this study indicated that the prognostic value of genetic biomarkers in PCs with non-diabetes and diabetes. The gene signature was validated in two independent databases. Therefore, this study is expected to provide a novel gene biomarker for predicting prognosis of PC with non-diabetes and diabetes and improving clinical decision.

Download Full-text

Prognostic Value of Drug Targets Predicted using Deep Bioinformatic Analysis of M6A-associated lncRNA-based Pancreatic Cancer Model Characteristics and its Tumour Microenvironment

10.21203/rs.3.rs-1200367/v1 ◽

2021 ◽

Author(s):

Peng-wei Cao ◽

Lei Liu ◽

Zi-Han Li ◽

Feng Cao ◽

Fu-Bao Liu

Keyword(s):

Pancreatic Cancer ◽

High Risk ◽

Immune Function ◽

Immune Evasion ◽

Drug Sensitivity ◽

Risk Group ◽

Risk Groups ◽

Tumour Microenvironment ◽

High Risk Group ◽

Low Risk

Abstract Background: The role of N6-methyladenosine (m6A)-associated long-stranded non-coding RNA (lncRNA) in pancreatic cancer is unclear. Therefore, we analysed the characteristics and tumour microenvironment in pancreatic cancer and determined the value of m6A-related lncRNAs for prognosis and drug target prediction.Methods: An m6A-lncRNA co-expression network was constructed using The Cancer Genome Atlas database to screen m6A-related lncRNAs. Prognosis-related lncRNAs were screened using univariate Cox regression; patients were divided into high- and low-risk groups and randomised into training and test groups. In the training group, least absolute shrinkage and selection operator (LASSO) was used for regression analysis and to construct a prognostic model, which was validated in the test group. Tumour mutational burden (TMB), immune evasion, and immune function of risk genes were analysed using R; drug sensitivity and potential drugs were examined using the Genomics of Drug Sensitivity in Cancer database.Results: We screened 129 m6A-related lncRNAs; 17 prognosis-related m6A-related lncRNAs were obtained using multivariate analysis and three m6A-related lncRNAs (AC092171.5, MEG9, AC002091.1) were screened using LASSO regression. Survival rates were significantly higher (P < 0.05) in the low-risk than in the high-risk group. Risk score was an independent predictor affecting survival (P < 0.001), with the highest risk score being obtained by calculating the c-index. The TMB significantly differed between the high- and low-risk groups (P < 0.05). In the high- and low-risk groups, mutations were detected in 61 of 70 samples and 49 of 71 samples, respectively, with KRAS, TP53, and SMAD4 showing the highest mutation frequencies in both groups. A lower survival rate was observed in patients with a high versus low TMB. Immune function HLA, Cytolytic activity, and Inflammation-promoting, T cell co-inhibition, Check-point, and T cell co-stimulation significantly differed in different subgroups (P < 0.05). Immune evasion scores were significantly higher in the high-risk group than in the low-risk group. Eight sensitive drugs were screened: ABT.888, ATRA, AP.24534, AG.014699, ABT.263, axitinib, A.443654, and A.770041.Conclusions: We screened m6A-related lncRNAs using bioinformatics, constructed a prognosis-related model, explored TMB and immune function differences in pancreatic cancer, and identified potential therapeutic agents, providing a foundation for further studies of pancreatic cancer diagnosis and treatment.

Download Full-text

Ferroptosis-related gene signature predicts the prognosis of papillary thyroid carcinoma

Cancer Cell International ◽

10.1186/s12935-021-02389-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jinyuan Shi ◽

Pu Wu ◽

Lei Sheng ◽

Wei Sun ◽

Hao Zhang

Keyword(s):

High Risk ◽

Immune Cells ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Lasso Regression ◽

Papillary Thyroid ◽

Related Gene ◽

Normal Thyroid

Abstract Background Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC. Methods A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues. Results LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature’s genes with the influence of ferroptosis induced by sorafenib. Conclusions We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients’ prognoses and targeting ferroptosis may be an alternative for PTC’s therapy.

Download Full-text

Establishment of a novel CNV-related prognostic signature predicting prognosis in patients with breast cancer

Journal of Ovarian Research ◽

10.1186/s13048-021-00823-y ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Wei Hu ◽

Mingyue Li ◽

Qi Zhang ◽

Chuan Liu ◽

Xinmei Wang ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

High Risk ◽

Risk Score ◽

Copy Number ◽

Risk Group ◽

Gene Signature ◽

Low Risk ◽

Breast Cancer Dataset ◽

Cox Analysis

Abstract Background Copy number variation (CNVs) is a key factor in breast cancer development. This study determined prognostic molecular characteristics to predict breast cancer through performing a comprehensive analysis of copy number and gene expression data. Methods Breast cancer expression profiles, CNV and complete information from The Cancer Genome Atlas (TCGA) dataset were collected. Gene Expression Omnibus (GEO) chip data sets (GSE20685 and GSE31448) containing breast cancer samples were used as external validation sets. Univariate survival COX analysis, multivariate survival COX analysis, least absolute shrinkage and selection operator (LASSO), Chi square, Kaplan-Meier (KM) survival curve and receiver operating characteristic (ROC) analysis were applied to build a gene signature model and assess its performance. Results A total of 649 CNV related-differentially expressed gene obtained from TCGA-breast cancer dataset were related to several cancer pathways and functions. A prognostic gene sets with 9 genes were developed to stratify patients into high-risk and low-risk groups, and its prognostic performance was verified in two independent patient cohorts (n = 327, 246). The result uncovered that 9-gene signature could independently predict breast cancer prognosis. Lower mutation of PIK3CA and higher mutation of TP53 and CDH1 were found in samples with high-risk score compared with samples with low-risk score. Patients in the high-risk group showed higher immune score, malignant clinical features than those in the low-risk group. The 9-gene signature developed in this study achieved a higher AUC. Conclusion The current research established a 5-CNV gene signature to evaluate prognosis of breast cancer patients, which may innovate clinical application of prognostic assessment.

Download Full-text

Identification of N6-Methyladenosine-Associated Long Non-coding RNAs for Immunotherapeutic Response and Prognosis in Patients With Pancreatic Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.748442 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xinshuang Yu ◽

Peng Dong ◽

Yu Yan ◽

Fengjun Liu ◽

Hui Wang ◽

...

Keyword(s):

Pancreatic Cancer ◽

T Cell ◽

High Risk ◽

Messenger Rna ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Risk Scores ◽

Significant Difference ◽

Selection Operator

Pancreatic cancer is a highly aggressive disease with poor prognosis. N6-methyladenosine (m6A) is critical for post-transcriptional modification of messenger RNA (mRNA) and long non-coding RNA (lncRNA). However, the m6A-associated lncRNAs (m6A-lncRNA) and their values in predicting clinical outcomes and immune microenvironmental status in pancreatic cancer patients remain largely unexplored. This study aimed to evaluate the importance of m6A-lncRNA and established a m6A-lncRNA signature for predicting immunotherapeutic response and prognosis of pancreatic cancer. The m6A-lncRNA co-expression networks were constructed using data from the TCGA and GTEx database. Based on the least absolute shrinkage and selection operator (LASSO) analysis, we constructed an 8 m6A-lncRNA signature risk model, and selection operator (LASSO) analysis, and stratified patients into the high- and low-risk groups with significant difference in overall survival (OS) (HR = 2.68, 95% CI = 1.74–4.14, P < 0.0001). Patients in the high-risk group showed significantly reduced OS compared to patients in the low-risk group (P < 0.001). The clinical characteristics and m6A-lncRNA risk scores were used to construct a nomogram which accurately predicted the OS in pancreatic cancer. TIMER 2.0 were used to investigate tumor immune infiltrating cells and its relationship with pancreatic cancer. CIBERSORT analysis revealed increased higher infiltration proportions of M0 and M2 macrophages, and lower infiltration of naive B cell, CD8+ T cell and Treg cells in the high-risk group. Compared to the low-risk group, functional annotation using ssGSEA showed that T cell infiltration and the differential immune-related check-point genes are expressed at low level in the high-risk group (P < 0.05). In summary, our study constructed a novel m6A-associated lncRNAs signature to predict immunotherapeutic responses and provided a novel nomogram for the prognosis prediction of pancreatic cancer.

Download Full-text

Development and validation of a ferroptosis-related prognostic model in pancreatic cancer

10.21203/rs.3.rs-139597/v2 ◽

2021 ◽

Author(s):

Chen-jie Qiu ◽

Xue-bing Wang ◽

Zi-ruo Zheng ◽

Chao-zhi Yang ◽

Kai Lin ◽

...

Keyword(s):

Pancreatic Cancer ◽

T Cell ◽

High Risk ◽

Risk Score ◽

Prognostic Model ◽

Immune Cell ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Immune Cell Infiltration

Abstract Background: The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas database, we established the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus datasets and tissue samples obtained from our center were utilized to validate the model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The prognosis of the low-risk group was significantly better than that of the high-risk group. In the high-risk group, patients treated with chemotherapy had a better prognosis. The nomogram showed that the model was the most important element. Gene set enrichment analysis identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

Download Full-text

A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma

Frontiers in Oncology ◽

10.3389/fonc.2021.639874 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiaoxia Tong ◽

Xiaofei Qu ◽

Mengyun Wang

Keyword(s):

Gene Expression ◽

High Risk ◽

Risk Score ◽

Cutaneous Melanoma ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Rank Test ◽

Score Model

BackgroundCutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM.MethodsGene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed via Tumor Immune Estimation Resource (TIMER) site.ResultsA total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (ADAMDEC1, GNLY, HSPA13, and TRIM29) model was developed by univariate and multivariate Cox regression analyses. The K-M plots showed that the high-risk group was associated with shortened survival than that in the low-risk group (P < 0.0001). Multivariate analysis suggested that the model was an independent prognostic factor (high-risk vs. low-risk, HR= 2.06, P < 0.001). Meanwhile, the high-risk group was prone to have larger breslow depth (P< 0.001) and ulceration (P< 0.001).ConclusionsThe four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.

Download Full-text

Construction and Identification of a Novel 5-Gene Signature for Predicting the Prognosis in Breast Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.669931 ◽

2021 ◽

Vol 8 ◽

Author(s):

Lingling Guo ◽

Yu Jing

Keyword(s):

Breast Cancer ◽

High Risk ◽

Risk Model ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

M2 Macrophage ◽

Hub Genes ◽

Cox Regression Analysis

Background: Breast cancer is one of the most common malignancies in women worldwide. The purpose of this study was to identify the hub genes and construct prognostic signature that could predict the survival of patients with breast cancer (BC).Methods: We identified differentially expressed genes between the responder group and non-responder group based on the GEO cohort. Drug-resistance hub genes were identified by weighted gene co-expression network analysis, and a multigene risk model was constructed by univariate and multivariate Cox regression analysis based on the TCGA cohort. Immune cell infiltration and mutation characteristics were analyzed.Results: A 5-gene signature (GP6, MAK, DCTN2, TMEM156, and FKBP14) was constructed as a prognostic risk model. The 5-gene signature demonstrated favorable prediction performance in different cohorts, and it has been confirmed that the signature was an independent risk indicater. The nomogram comprising 5-gene signature showed better performance compared with other clinical features, Further, in the high-risk group, high M2 macrophage scores were related with bad prognosis, and the frequency of TP53 mutations was greater in the high-risk group than in the low-risk group. In the low-risk group, high CD8+ T cell scores were associated with a good prognosis, and the frequency of CDH1 mutations was greater in the low-risk group than that in the high-risk group. At the same time, patients in the low risk group have a good response to immunotherapy in terms of immunotherapy. The results of immunohistochemistry showed that MAK, GP6, and TEMEM156 were significantly highly expressed in tumor tissues, and DCTN2 was highly expressed in normal tissues.Conclusions: Our study may find potential new targets against breast cancer, and provide new insight into the underlying mechanisms.

Download Full-text

A glycolysis-related gene expression signature in predicting recurrence of breast cancer

10.21203/rs.3.rs-27021/v1 ◽

2020 ◽

Author(s):

Jianing Tang ◽

Gaosong Wu

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

High Risk ◽

Cancer Patients ◽

Risk Group ◽

Gene Signature ◽

Low Risk ◽

Individual Treatment ◽

Breast Cancer Patients ◽

Related Gene

Abstract Background Metabolic change is the hallmark of cancer. Even in the presence of oxygen, cancer cells reprogram their glucose metabolism to enhance glycolysis and reduce oxidative phosphorylation. In the present study, we aimed to develop a glycolysis-related gene signature to predict the prognosis of breast cancer patients.Methods Gene expression profiles and clinical data of breast cancer patients were obtained from the GEO database. Univariate, Lasso-penalized, and multivariate Cox analysis were performed to construct the glycolysis-related gene signature.Results A four-gene based signature (ALDH2, PRKACB, STMN1 and ZNF292) was developed to separate patients into high-risk and low-risk groups. Kaplan-Meier survival analysis demonstrated that patients in low-risk group had significantly better prognosis than those in the high-risk group. Time-dependent ROC analysis demonstrated that the glycolysis-related gene signature had excellent prognostic accuracy. We further confirmed the expression of the four prognostic genes in breast cancer and paracancerous tissues samples using qRT-PCR analysis. Expression level of PRKACB was higher in paracancerous tissues, while STMN1 and ZNF292 were overexpressed in tumor samples. No difference was found in ALDH2 expression. The same results were observed in the IHC data from the human protein atlas. Global proteome data of 105 TCGA breast cancer samples obtained from the Clinical Proteomic Tumor Analysis Consortium were used to evaluate the prognostic value of their protein levels. Consistently, high expression of PRKACB protein level was associated with better prognosis, while high ZNF292 and STMN1 protein expression levels indicated poor prognosis.Conclusions The glycolysis-related gene signature might provide an effective prognostic predictor and a new view for individual treatment of breast cancer patients.

Download Full-text