Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer.Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets.Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines.Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

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A prognostic five immune-related long noncoding RNA signature for patients with colon cancer

10.21203/rs.3.rs-47874/v1 ◽

2020 ◽

Author(s):

Kankan Zhao ◽

Mengchuan Wang ◽

Houlong Kang ◽

Aiguo Wu

Keyword(s):

Colon Cancer ◽

Regression Analysis ◽

High Risk ◽

Noncoding Rna ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis

Abstract Background: Our study aimed to identify immune related long non-coding RNAs (LncRNAs) to serve as potential prognostic indicators and immune therapeutic targets in patients with colon cancer.Methods: The Cancer Genome Atlas (TCGA) and Molecular Signatures Databases (MSigDB) database were used to identify immune related lncRNAs in patients with colon cancer. The least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox proportional hazards regression analysis were employed to screen prognostic lncRNAs and construct immune-related multi-lncRNA signature. We used time-dependent receiver operating characteristic curve to assess the performance of this signature in colon cancer by calculating the area under the curve (AUC). Univariate and multivariate Cox regression analysis were performed to verify the independence of the prognostic value of this signature in colon cancer.Results: Five immune related lncRNAs (AC025575.2, AL161729.4, ELFN1-AS1, LINC00513, MIR210HG) were found to be significantly associated with overall survival (OS) of patients with colon cancer. Then, we developed a five immune-related lncRNA signature. According to this signature, patients were ranked into a high risk group (n = 208) and a low risk group (n = 209). Kaplan-Meier curve and log-rank method showed that patients in high risk group had worse OS than patients in low risk group (P = 5.5644e-05). AUC for predicting 3 year survival and 5 year survival was 0.776 and 0.762 respectively, which indicated good performance of this signature. Finally, this five immune-related lncRNA signature was demonstrated to be independently associated with prognosis of patients with colon cancer.Conclusion: We developed a five immune-related lncRNA signature as a prognostic biomarker for patients with colon cancer.

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Identification of a Novel Glycolysis-Related Signature to Predict the Prognosis of Patients with Breast Cancer

10.21203/rs.3.rs-720991/v1 ◽

2021 ◽

Author(s):

Menglin He ◽

Cheng Hu ◽

Jian Deng ◽

Hui Ji ◽

Weiqian Tian

Keyword(s):

Breast Cancer ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Expression Levels ◽

Incidence And Mortality

Abstract Background: Breast cancer (BC) is a kind of cancer with high incidence and mortality in female. Conventional clinical characteristics are far from accurate to predict individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods: We analyzed the data of a training cohort from the TCGA database and a validation cohort from GEO database. After the applications of GSEA and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed. The signature contains AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Then, we constructed a risk score formula to classify the patients into high and low-risk groups based on the expression levels of six-gene in patients. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Next, a nomogram was developed to predict the outcomes of patients with risk score and clinical features in 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues.Results: We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in high-risk group showed poor prognosis than that in low-risk group. The AUC values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues except AK3. Conclusion: We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate and obvious prediction and might be used to expand the prediction methods in clinical.

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Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

10.21203/rs.3.rs-17851/v1 ◽

2020 ◽

Author(s):

Li Liu ◽

She Tian ◽

Zhu Li ◽

Yongjun Gong ◽

Hao Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Principal Component ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

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An autophagy-related long non-coding RNA signature for patients with colorectal cancer

Physiology International ◽

10.1556/2060.2021.00125 ◽

2021 ◽

Author(s):

Dongyan Zhao ◽

Xizhen Sun ◽

Sidan Long ◽

Shukun Yao

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Cox Regression Analysis

AbstractAimLong non-coding RNAs (lncRNAs) have been identified to regulate cancers by controlling the process of autophagy and by mediating the post-transcriptional and transcriptional regulation of autophagy-related genes. This study aimed to investigate the potential prognostic role of autophagy-associated lncRNAs in colorectal cancer (CRC) patients.MethodsLncRNA expression profiles and the corresponding clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, autophagy-related lncRNAs were identified by Pearson correlation test. Univariate Cox regression analysis and the least absolute shrinkage and selection operator analysis (LASSO) Cox regression model were performed to construct the prognostic gene signature. Gene set enrichment analysis (GSEA) was used to further clarify the underlying molecular mechanisms.ResultsWe obtained 210 autophagy-related genes from the whole dataset and found 1187 lncRNAs that were correlated with the autophagy-related genes. Using Univariate and LASSO Cox regression analyses, eight lncRNAs were screened to establish an eight-lncRNA signature, based on which patients were divided into the low-risk and high-risk group. Patients’ overall survival was found to be significantly worse in the high-risk group compared to that in the low-risk group (log-rank p = 2.731E-06). ROC analysis showed that this signature had better prognostic accuracy than TNM stage, as indicated by the area under the curve. Furthermore, GSEA demonstrated that this signature was involved in many cancer-related pathways, including TGF-β, p53, mTOR and WNT signaling pathway.ConclusionsOur study constructed a novel signature from eight autophagy-related lncRNAs to predict the overall survival of CRC, which could assistant clinicians in making individualized treatment.

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A Robust Signature Based on Autophagy-Associated LncRNAs for Predicting Prognosis in Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2020/3858373 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Mi Zhou ◽

Weihua Shao ◽

Haiyun Dai ◽

Xin Zhu

Keyword(s):

High Risk ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Risk Group ◽

Surgical Margin ◽

Training Group ◽

High Risk Group ◽

Low Risk ◽

Validation Group ◽

Cox Regression Analysis

Objective. To construct a predictive signature based on autophagy-associated lncRNAs for predicting prognosis in lung adenocarcinoma (LUAD). Materials and Methods. Differentially expressed autophagy genes (DEAGs) and differentially expressed lncRNAs (DElncRNAs) were screened between normal and LUAD samples at thresholds of ∣log2Fold Change∣>1 and P value < 0.05. Univariate Cox regression analysis was conducted to identify overall survival- (OS-) associated DElncRNAs. The total cohort was randomly divided into a training group (n=229) and a validation group (n=228) at a ratio of 1 : 1. Multivariate Cox regression analysis was used to build prognostic models in the training group that were further validated by the area under curve (AUC) values of the receiver operating characteristic (ROC) curves in both the validation and total cohorts. Results. A total of 30 DEAGs and 2997 DElncRNAs were identified between 497 LUAD tissues and 54 normal tissues; however, only 1183 DElncRNAs were related to the 30 DEAGs. A signature consisting of 13 DElncRNAs was built to predict OS in lung adenocarcinoma, and the survival analysis indicated a significant OS advantage of the low-risk group over the high-risk group in the training group, with a 5-year OS AUC of 0.854. In the validation group, survival analysis also indicated a significantly favorable OS for the low-risk group over the high-risk group, with a 5-year OS AUC of 0.737. Univariate and multivariate Cox regression analyses indicated that only positive surgical margin (vs negative surgical margin) and high-risk group (vs low-risk group) based on the predictive signature were independent risk factors predictive of overall mortality in LUAD. Conclusions. This study investigated the association between autophagy-associated lncRNAs and prognosis in LUAD and built a robust predictive signature of 13 lncRNAs to predict OS.

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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.

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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.

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A Novel Signature Based on Pyroptosis-Related Genes for Predicting Breast Cancer

10.21203/rs.3.rs-521609/v1 ◽

2021 ◽

Author(s):

Shuang Shen ◽

Xin Chen ◽

Rui Qu ◽

Youming Guo ◽

Yingying Su ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Independent Predictive Factor ◽

Tumour Immunity

Abstract Background: Breast cancer (BC) surpassed lung cancer as the most frequent malignant tumour in women. In recent years, pyroptosis has revealed itself as an inflammatory form of programmed cell death. However, it is unclear as to the expression of genes associated with pyroptosis in BC and its relationship to prognosis. Results: In this study, we identified 31 pyroptosis regulators that are differentially expressed between BC and normal breast. The differently expressed genes (DEG) allow BC patients to be divided into three subtypes. Through single-factor and multi-factor COX regression and the application of least absolute contraction and selection operator (LASSO) Cox regression method, the survival prognostic value of each gene related to pyroptosis in The Cancer Genome Atlas (TCGA) cohort was evaluated, and a 4-gene signature was constructed. BC patients of the TCGA cohort are divided into low-risk or high-risk groups by risk score. The survival of the low-risk group was significantly higher than the high-risk group (P <0.001). Using the median risk score from the TCGA cohort, BC patients from the Gene Expression Omnibus (GEO) cohort were divided into two risk sub-groups and similar conclusions were drawn. In combination with clinicopathological characteristics, the risk score is an independent predictive factor of OS in BC patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) indicated that the high-risk group's immune genes were enriched and immune status was reduced. Conclusions: In conclusion, pyroptosis-related genes are important for tumour immunity and can be used to predict the prognosis of BC.

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

10.21203/rs.3.rs-1049643/v1 ◽

2021 ◽

Author(s):

Congli Jia ◽

Fu Yang ◽

Ruining Li

Keyword(s):

Breast Cancer ◽

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Tumor Immune Microenvironment

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

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The Pyroptosis-Related 9 LncRNA Signature and LncRNA-miRNA-mRNA Regulatory Network in Breast Cancer: A Comprehensive Analysis Based On TCGA Database

10.21203/rs.3.rs-889312/v1 ◽

2021 ◽

Author(s):

Ye Tian ◽

Yanan Zhang ◽

Jing Dong ◽

Lin Li

Keyword(s):

Breast Cancer ◽

High Risk ◽

Regulatory Network ◽

Prognostic Model ◽

Immune Cell ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Tumor Immune Microenvironment ◽

Cox Analysis

Abstract Background: Pytoproptosis has been verified to participate in various malignancies. However, studies on pyroptosis-related lncRNAs in breast cancer and its effects on tumor immune micro-environment are still limited. Consequently, it was aimed in this study to construct a pyroptosis-related lncRNAs signature for prognostic prediction and explore the effect of the pyroptosis-related LncRNAs on tumor immune microenvironment through LncRNA-miRNA-mRNA regulatory network. Methods: The pyroptosis-related differentially expressed genes (DEGs) were discovered using differential expression analysis. The differentially expressed LncRNAs (DELncRNAs) associated with DEGs were discovered using correlation analysis. The function of DEGs was analyed using GO and KEGG analyses. The LncRNAs signature used as the prognostic model of breast cancer was constructed using univariate and multivariate Cox analysis, and the effectiveness was verified by K-M analysis and ROC curve. The risk score calculated using the prognostic model was proved as an independent factor by univariate Cox analysis, multivariate Cox analysis and PCA analysis, and used to predict patient prognosis through nomogram. The pathyways enriched in High risk group and Low risk group were analyzed by GSEA. The differences in immune cell distribution (B cell memory, T cell CD4+, T cell CD8+ among others) were analyzed using ssGSEA. The immune function (type I/II IFN response among others), immune checkpoint (ADORA2A among others) and m6A-related protein expression (FTO among others) of High risk group and Low risk group were compared. The regulatory network of pyroptosis-related LncRNA-miRNA-mRNA was constructed and the core network was extracted. The functions of the target genes of miRNA associated with DELncRNAs were explored using GO and KEGG analysis. Results: A 9 LncRNAs signature (LMNTD2-AS1, AL589765.4, AC079298.3, U62317.3, LINC02446, AL645608.7, HSD11B1-AS1, AC009119.1, AC087239.1) was constructed as the prognostic model of breast cancer. Significant differences were discovered in immune cell distribution, immune function, immune checkpointand m6A-related protein expression between High risk group and Low risk group. The regulatory network of LncRNA-miRNA-mRNA was constructed and found to participate in the crosstalk among apoptosis, pyroptosis and necroptosis of breast cancer. Conclusions: The 9 lncRNAs signature was valuable for predicting breast cancer prognosis, and the pyroptosis-related lncRNAs influenced tumor immune microenvironment of breast cancer through the LncRNA-miRNA-mRNA regulatory network.

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