A Novel Risk Model Based on Lipid Metabolism-Associated Genes Predicts Prognosis and Indicates Immune Microenvironment in Breast Cancer

BackgroundBreast cancer (BRCA) is the most common tumor in women, and lipid metabolism involvement has been demonstrated in its tumorigenesis and development. However, the role of lipid metabolism-associated genes (LMAGs) in the immune microenvironment and prognosis of BRCA remains unclear.MethodsA total of 1076 patients with BRCA were extracted from The Cancer Genome Atlas database and randomly assigned to the training cohort (n = 760) or validation cohort (n = 316). Kaplan–Meier analysis was used to assess differences in survival. Consensus clustering was performed to categorize the patients with BRCA into subtypes. Using multivariate Cox regression analysis, an LMAG-based prognostic risk model was constructed from the training cohort and validated using the validation cohort. The immune microenvironment was evaluated using the ESTIMATE and tumor immune estimation resource algorithms, CIBERSORT, and single sample gene set enrichment analyses.ResultsConsensus clustering classified the patients with BRCA into two subgroups with significantly different overall survival rates and immune microenvironments. Better prognosis was associated with high immune infiltration. The prognostic risk model, based on four LMAGs (MED10, PLA2G2D, CYP4F11, and GPS2), successfully stratified the patients into high- and low-risk groups in both the training and validation sets. High risk scores predicted poor prognosis and indicated low immune status. Subgroup analysis suggested that the risk model was an independent predictor of prognosis in BRCA.ConclusionThis study demonstrated, for the first time, that LMAG expression plays a crucial role in BRCA. The LMAG-based risk model successfully predicted the prognosis and indicated the immune microenvironment of patients with BRCA. Our study may provide inspiration for further research on BRCA pathomechanisms.

Download Full-text

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.

Download Full-text

Comprehensive Analysis and Prognosis Prediction of N6-methyladenosine-related Lncrnas in Immune Microenvironment Infiltration of Gastric Cancer

10.21203/rs.3.rs-1021150/v1 ◽

2021 ◽

Author(s):

Jianfeng Huang ◽

Wenzheng Chen ◽

Changyu Chen ◽

Tao Xiao ◽

Zhigang Jie

Keyword(s):

Gastric Cancer ◽

Risk Model ◽

Cox Regression ◽

Cytotoxic T Lymphocyte ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Rna Modification ◽

Consensus Clustering ◽

Immune Microenvironment ◽

Cox Regression Analysis

Abstract BackgroundN6-methyladenosine (m6A) RNA modification plays an important role in regulating tumor microenvironment (TME) infiltration. However, the relationship between the expression pattern of m6A-related long non-coding RNAs (lncRNAs) and the immune microenvironment of gastric cancer (GC) is unclear. MethodsIn this study, 23 m6A-related lncRNAs were identified by Pearson’s correlation analysis and univariate Cox regression analysis. According to the expression of these lncRNAs, we identified two distinct molecular clusters by consensus clustering and compared the differences of the TME and enriched pathways between the two clusters. We further constructed a prognostic risk signature and verified it using The Cancer Genome Atlas training and testing cohorts. ResultsThe results showed that cluster 1 was associated with tumor-related and immune activation-related pathways. In addition, cluster 1 was also associated with higher ImmuneScore, StromalScore, and ESTIMATEScore. The results of the stratified survival analysis and independent prognosis analysis indicated that the risk signature is an independent prognostic indicator for patients with GC. In addition, it can effectively predict survival status in patients with different clinical characteristics. Furthermore, our risk model showed that low risk scores were significantly correlated with high expression of programmed death-1 (PD-1) and cytotoxic T-lymphocyte associated protein 4 (CTLA4), as well as sensitivity to chemotherapeutic drugs (e.g., paclitaxel and oxaliplatin). ConclusionsThis evidence contributes to our understanding of the regulation of TME infiltration by m6A-related lncRNAs and my lead to more effective immunotherapy and chemotherapy for patients with GC.

Download Full-text

Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.673827 ◽

2021 ◽

Vol 9 ◽

Author(s):

Hu Qian ◽

Ting Lei ◽

Yihe Hu ◽

Pengfei Lei

Keyword(s):

Lipid Metabolism ◽

Bone Remodeling ◽

Immune Status ◽

Risk Model ◽

Cox Regression ◽

Survival Prediction ◽

Molecular Subgroups ◽

Immune Microenvironment ◽

Cox Regression Analysis ◽

Lipid Metabolism Genes

ObjectivesOsteosarcoma was the most popular primary malignant tumor in children and adolescent, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past 35 years. This study aims to explore the role of lipid metabolism in the development and diagnosis of osteosarcoma.MethodsClinical information and corresponding RNA data of osteosarcoma patients were downloaded from TRGET and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, TIMER and ssGSEA analyses were applied to determinate the tumor immune microenvironment (TIME) and immune status of the identified subgroups. Functional analyses including GO, KEGG, GSVA and GSEA analyses were conducted to elucidate the underlying mechanisms. Prognostic risk model was constructed using LASSO algorithm and multivariate Cox regression analysis.ResultsTwo molecular subgroups with significantly different survival were identified. Better prognosis was associated with high immune score, low tumor purity, high abundance of immune infiltrating cells and relatively high immune status. GO and KEGG analyses revealed that the DEGs between the two subgroups were mainly enriched in immune- and bone remodeling-associated pathways. GSVA and GSEA analyses indicated that, lipid catabolism downregulation and lipid hydroxylation upregulation may impede the bone remodeling and development of immune system. Risk model based on lipid metabolism related genes (LMRGs) showed potent potential for survival prediction in osteosarcoma. Nomogram integrating risk model and clinical characteristics could predict the prognosis of osteosarcoma patients accurately.ConclusionExpression of lipid-metabolism genes is correlated with immune microenvironment of osteosarcoma patients and could be applied to predict the prognosis of in osteosarcoma accurately.

Download Full-text

A Ferroptosis-Related lncRNAs Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.678877 ◽

2021 ◽

Vol 8 ◽

Author(s):

Kaiming Zhang ◽

Liqin Ping ◽

Tian Du ◽

Gehao Liang ◽

Yun Huang ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Prognostic Value ◽

Cox Regression ◽

Validation Cohort ◽

Risk Group ◽

High Risk Group ◽

Breast Cancer Patients ◽

Immune Microenvironment ◽

Cox Regression Analysis

Background: Ferroptosis, a regulated cell death which is driven by the iron-dependent peroxidation of lipids, plays an important role in cancer. However, studies about ferroptosis-related Long non-coding RNAs (lncRNAs) in breast cancer (BC) are limited. Besides, the prognostic role of ferroptosis-related lncRNAs and their relationship to immune microenvironment in breast cancer remain unclear. This study aimed to explore the potential prognostic value of ferroptosis-related lncRNAs and their relationship to immune microenvironment in breast cancer.Methods: RNA-sequencing data of female breast cancer patients were downloaded from TCGA database. 937 patients were randomly separated into training or validation cohort in 2:1 ratio. Ferroptosis-related lncRNAs were screened by Pearson correlation analysis with 239 reported ferroptosis-related genes. A ferroptosis-related lncRNAs signature was constructed with univariate and multivariate Cox regression analyses in the training cohort, and its prognostic value was further tested in the validation cohort.Results: An 8-ferroptosis-related-lncRNAs signature was developed by multivariate Cox regression analysis to divide patients into two risk groups. Patients in the high-risk group had worse prognosis than patients in the low-risk group. Multivariate Cox regression analysis showed the risk score was an independent prognostic indicator. Receiver operating characteristic curve (ROC) analysis proved the predictive accuracy of the signature. The area under time-dependent ROC curve (AUC) reached 0.853 at 1 year, 0.802 at 2 years, 0.740 at 5 years in the training cohort and 0.791 at 1 year, 0.778 at 2 years, 0.722 at 5 years in the validation cohort. Further analysis demonstrated that immune-related pathways were significantly enriched in the high-risk group. Analysis of the immune cell infiltration landscape showed that breast cancer in the high-risk group tended be immunologically “cold”.Conclusion: We identified a novel ferroptosis-related lncRNA signature which could precisely predict the prognosis of breast cancer patients. Ferroptosis-related lncRNAs may have a potential role in the process of anti-tumor immunity and serve as therapeutic targets for breast cancer.

Download Full-text

Dynamic Nomogram Predicts Overall Survival in Patients With Occult Breast Cancer: a Population-based Analysis

10.21203/rs.3.rs-1033322/v1 ◽

2021 ◽

Author(s):

Xiaomin Yang ◽

Kunlong Li ◽

Zhangjun Song ◽

Huxia Wang ◽

Sai He ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Cox Regression ◽

Validation Cohort ◽

Strategic Decision ◽

Occult Breast Cancer ◽

Training Cohort ◽

Cox Regression Analysis ◽

Ajcc Stage ◽

Stage System

Abstract Background: Due the rarity of occult breast cancer (OBC), no precise prognostic instruments were available to assess the overall survival (OS) in patients with OBC. The aim of this study is to construct a nomogram for predicting the OS probability in patients with OBC. Methods: Patients who were enrolled in the Surveillance, Epidemiology, and End Results database between 2004 and 2015 were regarded as subjects and studied. We constructed a dynamic nomogram that can predict prognosis in patients with OBC based on crucial independent factors by using univariate and multivariate Cox regression analyses. C-index and calibration plots were chosen for validation. Net reclassification index (NRI), integrated discrimination improvement (IDI) and DCA (Ddecision Curve Analysis) were used to evaluate the nomogram’s clinical pragmatism. Results: Totally, 693 patients with OBC were included in this study. The nomogram integrated six independent prognostic factors through multivariate Cox regression analysis, such as surgical method, radiotherapy status, chemotherapy status, ER status, AJCC-stage and age. The prediction model exhibited robustness with the C-index 0.75 (95%CI: 0.72-0.77) in training cohort and 0.79 (95%CI: 0.76-0.82) in validation group. Moreover, the calibration curves presented favorably. The NRI values of 0.61 (95%CI: 0.28-0.99) for 5-year,0.53 (95%CI: 0.23-0.77) for 8-year OS prediction in the training cohort,0.75 (95%CI: 0.36-1.23) for 5-year and 0.6 (95%CI: 0.15-1.2) for 8-year OS prediction in the validation cohort,and the IDI values of 0.1 (95%CI: 0.04-0.17) for 5-year and 0.11 (95%CI: 0.03-0.19) for 8-year OS prediction in the training cohort, 0.21 (95%CI: 0.09-0.3) for 5-year and 0.22 (95%CI: 0.08-0.32) for 8-year OS prediction in the validation cohort,, indicated that the established nomogram performed significantly better than the AJCC stage system alone. Furthermore, DCA showed that the nomogram in our study was clinically useful and had better discriminative ability than the AJCC stage system. Conclusions: A nomogram was developed and validated to accurately predict the individualized probability OS for patients with occult breast cancer (OBC) and is expected to offer guidance for strategic decision.

Download Full-text

Prognostic Nomogram Incorporating Immunohistochemical Results for the Overall Survival of Patients with Bladder Cancer.

10.21203/rs.3.rs-24644/v1 ◽

2020 ◽

Author(s):

Tianwei Wang ◽

Yunyan Wang

Keyword(s):

Risk Factors ◽

Overall Survival ◽

Bladder Cancer ◽

Risk Model ◽

Cox Regression ◽

Validation Cohort ◽

Multivariable Analysis ◽

Clinical Information ◽

Internal Validation ◽

Cox Regression Analysis

Abstract Objectives: In this study, we want to combine GATA3, VEGF, EGFR and Ki67 with clinical information to develop and validate a prognostic nomogram for bladder cancer.Methods: A total of 188 patients with clinical information and immunohistochemistry were enrolled in this study, from 1996 to 2018. Univariable and multivariable cox regression analysis was applied to identify risk factors for nomogram of overall survival (OS). The calibration of the nomogram was performed and the Area Under Curve (AUC) was calculated to assess the performance of the nomogram. Internal validation was performed with the validation cohort., the calibration curve and the AUC were calculated simultaneously.Results: Univariable and multivariable analysis showed that age (HR: 2.229; 95% CI: 1.162-4.274; P=0.016), histology (HR: 0.320; 95% CI: 0.136-0.751; P=0.009), GATA3 (HR: 0.348; 95% CI: 0.171-0.709; P=0.004), VEGF (HR: 2.295; 95% CI: 1.225-4.301; P=0.010) and grade (HR: 4.938; 95% CI: 1.339-18.207; P=0.016) remained as independent risk factors for OS. The age, histology, grade, GATA3 and VEGF were included to build the nomogram. The accuracy of the risk model was further verified with the C-index. The C-index were 0.65 (95% CI, 0.58-0.72) and 0.58 (95% CI, 0.46-0.70) in the training and validation cohort respectively. Conclusions: A combination of clinical variables with immunohistochemical results based nomogram would predict the overall survival of patients with bladder cancer.

Download Full-text

Development and validation of a radiomics-based model to predict local progression-free survival after chemo-radiotherapy in patients with esophageal squamous cell cancer

Radiation Oncology ◽

10.1186/s13014-021-01925-z ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

He-San Luo ◽

Ying-Ying Chen ◽

Wei-Zhen Huang ◽

Sheng-Xi Wu ◽

Shao-Fu Huang ◽

...

Keyword(s):

Multivariate Analysis ◽

Local Recurrence ◽

Squamous Cell ◽

Cox Regression ◽

Validation Cohort ◽

Cell Cancer ◽

Clinical Factors ◽

Free Survival ◽

Training Cohort ◽

Cox Regression Analysis

Abstract Purpose To develop a nomogram model for predicting local progress-free survival (LPFS) in esophageal squamous cell carcinoma (ESCC) patients treated with concurrent chemo-radiotherapy (CCRT). Methods We collected the clinical data of ESCC patients treated with CCRT in our hospital. Eligible patients were randomly divided into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) with COX regression was performed to select optimal radiomic features to calculate Rad-score for predicting LPFS in the training cohort. The univariate and multivariate analyses were performed to identify the predictive clinical factors for developing a nomogram model. The C-index was used to assess the performance of the predictive model and calibration curve was used to evaluate the accuracy. Results A total of 221 ESCC patients were included in our study, with 155 patients in training cohort and 66 patients in validation cohort. Seventeen radiomic features were selected by LASSO COX regression analysis to calculate Rad-score for predicting LPFS. The patients with a Rad-score ≥ 0.1411 had high risk of local recurrence, and those with a Rad-score < 0.1411 had low risk of local recurrence. Multivariate analysis showed that N stage, CR status and Rad-score were independent predictive factors for LPFS. A nomogram model was built based on the result of multivariate analysis. The C-index of the nomogram was 0.745 (95% CI 0.7700–0.790) in training cohort and 0.723(95% CI 0.654–0.791) in validation cohort. The 3-year LPFS rate predicted by the nomogram model was highly consistent with the actual 3-year LPFS rate both in the training cohort and the validation cohort. Conclusion We developed and validated a prediction model based on radiomic features and clinical factors, which can be used to predict LPFS of patients after CCRT. This model is conducive to identifying the patients with ESCC benefited more from CCRT.

Download Full-text

The Prognostic Value of m6A-related LncRNAs in Patients with HNSCC

10.21203/rs.3.rs-778502/v1 ◽

2021 ◽

Author(s):

Liu-qing Zhou ◽

Jie-yu Zhou ◽

Yao Hu

Keyword(s):

Prognostic Value ◽

Risk Model ◽

Cox Regression ◽

Predictive Ability ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Cancer Genome Atlas

Abstract Background: N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis. m6A modifications are known to modulate RNAs, including mRNAs and lncRNAs. However, the prognostic role of m6A-related lncRNAs in head and neck squamous cell carcinoma (HNSCC) is poorly understood.Methods: Based on LASSO Cox regression, enrichment analysis, univariate and multivariate Cox regression analysis, a risk prognostic model, and consensus clustering analysis, we analyzed the 12 m6A-related lncRNAs in HNSCC samples data using the data from The Cancer Genome Atlas (TCGA) database.Results: We found twelve m6A-related lncRNAs in the training cohort and validated in all cohorts by Kaplan-Meier and Cox regression analyses, and revealing their independent prognostic value in HNSCC. Moreover, ROC analysis was conducted, confirming the strong predictive ability of this signature for HNSCC prognosis. GSEA and detailed immune infiltration analyses revealed specific pathways associated with m6A-related lncRNAs.Conclusions: In this study, a novel risk model including twelve genes (SAP30L-AS1, AC022098.1, LINC01475, AC090587.2, AC008115.3, AC015911.3, AL122035.2, AC010226.1, AL513190.1, ZNF32-AS1, AL035587.1 and AL031716.1) was built. It could accurately predict HNSCC prognosis and provide potential prediction outcome and new therapeutic target for HNSCC patients.

Download Full-text

Establishment and Validation of Prognostic Nomograms Based on Serum Copper Level for Patients With Early-Stage Triple-Negative Breast Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.770115 ◽

2021 ◽

Vol 9 ◽

Author(s):

Fangfang Duan ◽

Jianpei Li ◽

Jiajia Huang ◽

Xin Hua ◽

Chenge Song ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Triple Negative ◽

Cox Regression ◽

Early Stage ◽

Serum Copper ◽

Copper Level ◽

Serum Copper Level ◽

Training Cohort ◽

Cox Regression Analysis

Background: Altered copper levels have been observed in several cancers, but studies on the relationship between serum copper and early-stage triple-negative breast cancer (TNBC) remain scare. We sought to establish a predictive model incorporating serum copper levels for individualized survival predictions.Methods: We retrospectively analyzed clinicopathological information and baseline peripheric blood samples of patients diagnosed with early-stage TNBC between September 2005 and October 2016 at Sun Yat-sen University Cancer Center. The optimal cut-off point of serum copper level was determined using maximally selected log-rank statistics. Kaplan-Meier curves were used to estimate survival probabilities. Independent prognostic indicators associated with survival were identified using multivariate Cox regression analysis, and subsequently, prognostic nomograms were established to predict individualized disease-free survival (DFS) and overall survival (OS). The nomograms were validated in a separate cohort of 86 patients from the original randomized clinical trial SYSUCC-001 (SYSUCC-001 cohort).Results: 350 patients were eligible in this study, including 264 in the training cohort and 86 in the SYSUCC-001 cohort. An optimal cut-off value of 21.3 μmol/L of serum copper was determined to maximally divide patients into low- and high-copper groups. After a median follow-up of 87.1 months, patients with high copper levels had significantly worse DFS (p = 0.002) and OS (p < 0.001) than those with low copper levels in the training cohort. Multivariate Cox regression analysis revealed that serum copper level was an independent factor for DFS and OS. Further, prognostic models based on serum copper were established for individualized predictions. These models showed excellent discrimination [C-index for DFS: 0.689, 95% confidence interval (CI): 0.621–0.757; C-index for OS: 0.728, 95% CI: 0.654–0.802] and predictive calibration, and were validated in the SYSUCC-001 cohort.Conclusion: Serum copper level is a potential predictive biomarker for patients with early-stage TNBC. Predictive nomograms based on serum copper might be served as a practical tool for individualized prognostication.

Download Full-text

Application of an Autophagy-Related Gene Prognostic Risk Model Based on TCGA Database in Cervical Cancer

Frontiers in Genetics ◽

10.3389/fgene.2020.616998 ◽

2021 ◽

Vol 11 ◽

Author(s):

Huadi Shi ◽

Fulan Zhong ◽

Xiaoqiong Yi ◽

Zhenyi Shi ◽

Feiyan Ou ◽

...

Keyword(s):

Cervical Cancer ◽

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

Survival Prediction ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Model Based

Background: Autophagy plays an important role in the development of cancer. However, the prognostic value of autophagy-related genes (ARGs) in cervical cancer (CC) is unclear. The purpose of this study is to construct a survival model for predicting the prognosis of CC patients based on ARG signature.Methods: ARGs were obtained from the Human Autophagy Database and Molecular Signatures Database. The expression profiles of ARGs and clinical data were downloaded from the TCGA database. Differential expression analysis of CC tissues and normal tissues was performed using R software to screen out ARGs with an aberrant expression. Univariate Cox, Lasso, and multivariate Cox regression analyses were used to construct a prognostic model which was validated by using the test set and the entire set. We also performed an independent prognostic analysis of risk score and some clinicopathological factors of CC. Finally, a clinical practical nomogram was established to predict individual survival probability.Results: Compared with normal tissues, there were 63 ARGs with an aberrant expression in CC tissues. A risk model based on 3 ARGs was finally obtained by Lasso and Cox regression analysis. Patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both train set and validation set. The ROC curve validated its good performance in survival prediction, suggesting that this model has a certain extent sensitivity and specificity. Multivariate Cox analysis showed that the risk score was an independent prognostic factor. Finally, we mapped a nomogram to predict 1-, 3-, and 5-year survival for CC patients. The calibration curves indicated that the model was reliable.Conclusion: A risk prediction model based on CHMP4C, FOXO1, and RRAGB was successfully constructed, which could effectively predict the prognosis of CC patients. This model can provide a reference for CC patients to make precise treatment strategy.

Download Full-text