An Autophagy-related Long Non-coding RNA Signature for Breast Cancer

Background: The most prevalent malignant tumor in women is breast cancer (BC). Autophagic therapies have been identified for their contribution in BC cell death. Therefore, the potential prognostic role of long non-coding RNA (lncRNA) related to autophagy in patients with BC was examined. Methods: The lncRNAs expression profiles were derived from The Cancer Genome Atlas (TCGA) database. Throughout univariate Cox regression and multivariate Cox regression test, lncRNA with BC prognosis have been differentially presented. We then defined the optimal cutoff point between high and low-risk groups. The receiver operating characteristic (ROC) curves were drawn to test this signature. In order to examine possible signaling mechanisms linked to these lncRNAs, the Gene Set Enrichment Analysis (GSEA) has been carried out. Results: Based on the lncRNA expression profiles for BC, a 9 lncRNA signature associated with autophagy was developed. The optimal cutoff value for high-risk and low-risk groups was used. The high-risk group had less survival time than the low-risk group. The result of this lncRNA signature was highly sensitive and precise. GSEA study found that the gene sets have been greatly enriched in many cancer pathways. Conclusions: Our signature of 9 lncRNAs related to autophagy has prognostic value for BC, and these lncRNAs related to autophagy may play an important role in BC biology.

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Proliferation-, estrogen-, and T-cell-related metagenes to predict outcome after adjuvant/neoadjuvant chemotherapy for operable breast cancer in the ECTO trial.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.1014 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. 1014-1014 ◽

Cited By ~ 1

Author(s):

Giampaolo Bianchini ◽

Vera Cappelletti ◽

Maurizio Callari ◽

Maria Luisa Carcangiu ◽

Wolfgang Eiermann ◽

...

Keyword(s):

Breast Cancer ◽

T Cell ◽

High Risk ◽

Risk Group ◽

Molecular Subtype ◽

Expression Profiles ◽

Risk Groups ◽

Operable Breast Cancer ◽

Low Risk ◽

Phase Iii

1014 Background: Predicting recurrence in operable breast cancer (BC) despite optimal chemotherapy would be relevant to new drug development and tailored treatments. Methods: A large series (n=3,154) of public Affymetrix gene-expression profiles (GEP) was used to define prognostic/predictive metagenes in different BC subtypes. In ER+/HER2- a proliferation and an ER-related metagene were combined to predict low, intermediate and high risk of recurrence. In TN and in HER2+ a T cell metagene was used to predict low, intermediate and high risk (higher expression associated with lower risk). The metagenes were validated in patients enrolled in the phase III ECTO trial (Gianni L. JCO 2009) and treated with the same taxane-anthracycline-CMF regimen as neoadjuvant or adjuvant therapy before endocrine therapy if indicated. The outcome was distant event free survival (DEFS). Results: 283 good quality GEPs were obtained (neoadjuvant n=121; adjuvant n=162) from 464 retrospectively collected samples. Median follow-up was 8.9 years. In ER+/HER2- tumors the 10-yrs DEFS was 92.3, 81.2 and 66.6% in low, intermediate and high risk groups, respectively [high vs low HR 4.38 (1.01-19.1) p=.048] according to proliferation and ER-related metagenes. In HER2+ and TN subgroup the 10-yrs DEFS was 97.2, 75.6 and 78.8% in low, intermediate and high risk groups, respectively [high vs low HR 8.73 (1.09-69.8) p=.041]. In TN tumors, the pCR rate was 20% in the high and 61.5% in the low risk group. By combining the predicted risk group in each molecular subtype the 10-yrs DEFS was 95.3, 79.2 and 71.5% in low (24.2%), intermediate (42.7%) and high (33.1%) risk group, respectively [logrank p=0.003; high vs low HR 6.22 (1.87-20.6) p=.002]. ER, PGR, Ki67 and lymphocyte infiltration (LI) by IHC underperformed compared to genomic predictors. Conclusions: BC patients at higher risk of relapse despite optimal standard treatment can be identified who should be spared ineffective and toxic therapy and considered for investigational new strategies. In TN and HER2+, high T cell metagene and to a lesser extent LI are prognostic/predictive and associated with an extremely low risk of DEFS after chemotherapy.

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Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma

PeerJ ◽

10.7717/peerj.8128 ◽

2019 ◽

Vol 7 ◽

pp. e8128 ◽

Cited By ~ 12

Author(s):

Cheng Yue ◽

Hongtao Ma ◽

Yubai Zhou

Keyword(s):

High Risk ◽

Lung Adenocarcinoma ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Gene Signature ◽

Low Risk ◽

Differentially Expressed ◽

Lasso Regression

Background Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient’s outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

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A Novel Risk Classification Model Predicts Overall Survival and Locoregional Surgery Benefit in Colorectal Patients with Distant Metastasis at the Initial Diagnosis

10.21203/rs.3.rs-117490/v1 ◽

2020 ◽

Author(s):

Mo Chen ◽

Tian-en Li ◽

Pei-zhun Du ◽

Junjie Pan ◽

Zheng Wang ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Distant Metastasis ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

Risk Classification ◽

Low Risk ◽

Classification Model ◽

Intermediate Risk

Abstract Background and aims: In this research, we aimed to construct a risk classification model to predict overall survival (OS) and locoregional surgery benefit in colorectal cancer (CRC) patients with distant metastasis.Methods: We selected a cohort consisting of 12741 CRC patients diagnosed with distant metastasis between 2010 and 2014, from the Surveillance, Epidemiology and End Results (SEER) database. Patients were randomly assigned into training group and validation group at the ratio of 2:1. Univariable and multivariable Cox regression models were applied to screen independent prognostic factors. A nomogram was constructed and assessed by the Harrell’s concordance index (C-index) and calibration plots. A novel risk classification model was further established based on the nomogram.Results: Ultimately 12 independent risk factors including race, age, marriage, tumor site, tumor size, grade, T stage, N stage, bone metastasis, brain metastasis, lung metastasis and liver metastasis were identified and adopted in the nomogram. The C-indexes of training and validation groups were 0.77 (95% confidence interval [CI] 0.73-0.81) and 0.75 (95% CI 0.72-0.78), respectively. The risk classification model stratified patients into three risk groups (low-, intermediate- and high-risk) with divergent median OS (low-risk: 36.0 months, 95% CI 34.1-37.9; intermediate-risk: 18.0 months, 95% CI 17.4-18.6; high-risk: 6.0 months, 95% CI 5.3-6.7). Locoregional therapies including surgery and radiotherapy could prognostically benefit patients in the low-risk group (surgery: hazard ratio [HR] 0.59, 95% CI 0.50-0.71; radiotherapy: HR 0.84, 95% CI 0.72-0.98) and intermediate risk group (surgery: HR 0.61, 95% CI 0.54-0.68; radiotherapy: HR 0.86, 95% CI 0.77-0.95), but not in the high-risk group (surgery: HR 1.03, 95% CI 0.82-1.29; radiotherapy: HR 1.03, 95% CI 0.81-1.31). And all risk groups could benefit from systemic therapy (low-risk: HR 0.68, 95% CI 0.58-0.80; intermediate-risk: HR 0.50, 95% CI 0.47-0.54; high-risk: HR 0.46, 95% CI 0.40-0.53).Conclusion: A novel risk classification model predicting prognosis and locoregional surgery benefit of CRC patients with distant metastasis was established and validated. This predictive model could be further utilized by physicians and be of great significance for medical practice.

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Computational identification of mutator-derived lncRNA signatures of genome instability for improving the clinical outcome of cancers: a case study in breast cancer

Briefings in Bioinformatics ◽

10.1093/bib/bbz118 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1742-1755 ◽

Cited By ~ 26

Author(s):

Siqi Bao ◽

Hengqiang Zhao ◽

Jian Yuan ◽

Dandan Fan ◽

Zicheng Zhang ◽

...

Keyword(s):

Breast Cancer ◽

Genomic Instability ◽

Genome Instability ◽

Risk Group ◽

Expression Profiles ◽

Risk Groups ◽

Low Risk ◽

A Genome ◽

Significant Difference

Abstract Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, identification of genome instability-associated lncRNAs and their clinical significance in cancers remain largely unexplored. Here, we developed a mutator hypothesis-derived computational frame combining lncRNA expression profiles and somatic mutation profiles in a tumor genome and identified 128 novel genomic instability-associated lncRNAs in breast cancer as a case study. We then identified a genome instability-derived two lncRNA-based gene signature (GILncSig) that stratified patients into high- and low-risk groups with significantly different outcome and was further validated in multiple independent patient cohorts. Furthermore, the GILncSig correlated with genomic mutation rate in both ovarian cancer and breast cancer, indicating its potential as a measurement of the degree of genome instability. The GILncSig was able to divide TP53 wide-type patients into two risk groups, with the low-risk group showing significantly improved outcome and the high-risk group showing no significant difference compared with those with TP53 mutation. In summary, this study provided a critical approach and resource for further studies examining the role of lncRNAs in genome instability and introduced a potential new avenue for identifying genomic instability-associated cancer biomarkers.

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The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

Cell Communication and Signaling ◽

10.1186/s12964-020-00663-1 ◽

2020 ◽

Vol 18 (1) ◽

Cited By ~ 1

Author(s):

Bufu Tang ◽

Jinyu Zhu ◽

Jie Li ◽

Kai Fan ◽

Yang Gao ◽

...

Keyword(s):

High Risk ◽

Iron Metabolism ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Prognostic Models ◽

Integrated Analysis ◽

Immune Microenvironment ◽

Diagnostic Models

Abstract Background In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. Methods Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. Results Four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. Conclusions The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients. Graphical abstract

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Breast Cancer Index and risk stratification in luminal subtypes: A trans-ATAC study.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.534 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 534-534

Author(s):

Ivana Sestak ◽

Yi Zhang ◽

Catherine A. Schnabel ◽

Jack M. Cuzick ◽

Mitchell Dowsett

Keyword(s):

Breast Cancer ◽

High Risk ◽

Cox Regression ◽

Early Stage ◽

Risk Groups ◽

Low Risk ◽

Luminal A ◽

Prognostic Information ◽

Prognostic Performance ◽

Breast Cancer Index

534 Background: The Breast Cancer Index (BCI) is a gene-expression based signature that provides prognostic information for overall (0-10 years) and late (5-10 years) distant recurrence (DR) and prediction of extended endocrine benefit in hormone receptor positive (HR+) early stage breast cancer. The current analysis aims to further characterize, correlate and compare the prognostic performance of BCI in luminal subtypes based on immunohistochemical classification. Methods: 670 postmenopausal women with HR+, LN- disease from the TransATAC cohort were included in this analysis. Luminal A-like tumors (LumA) were identified as those with ER+ and/or PR+ and HER2 -, and Ki67 < 20% by IHC. All other tumors were classified as Luminal B-like (LumB) for this analysis. Primary endpoint was DR. Cox regression models were used to examine BCI prognostic performance according to luminal subtype, adjusting for the clinicopathological model Clinical Treatment Score (CTS). Results: 452 (67.5%) patients were classified as LumA and 218 (32.5%) as LumB. BCI was highly prognostic in LumA cancers (adjusted HR = 1.57 (1.23-1.96), P < 0.001, ΔLR-χ2= 14.09), but not in LumB tumors (adjusted HR = 1.20 (0.94-1.52, P = 0.14, ΔLR-χ2= 2.23). In LumA, 10-year DR risks in BCI intermediate and high risk groups were very similar (25.6% (16.4-38.6) and 25.3% (13.5-44.3), respectively) and significantly different from BCI low (3.9% (2.1-7.0); HR = 7.47 (3.50-15.96) and HR = 8.13 (3.27-20.23), respectively). In LumB, 10-year DR risks in BCI low and BCI intermediate risk groups (13.8% (6.8-26.9) and 14.6% (8.3-24.9), respectively) were very similar and significantly lower than for the BCI high (29.1% (20.0-41.1)). Lum subtyping was only prognostic in the BCI low risk group (LumA vs. LumB: HR = 4.27 (1.65-11.02)) but not in the other two BCI risk groups. Conclusions: BCI provided significant prognostic information in Lum A subtype. These results show that BCI intermediate and high risk had similar risk of DR in LumA tumors, while shared similarly low risk of DR as BCI-low in LumB tumors. Further evaluation is needed to elucidate the distinct mechanisms underlying each classification system.

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The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

10.21203/rs.3.rs-17980/v2 ◽

2020 ◽

Author(s):

Jiansong Ji ◽

Bufu Tang ◽

Jinyu Zhu ◽

Jie Li ◽

Kai Fan ◽

...

Keyword(s):

High Risk ◽

Iron Metabolism ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Prognostic Models ◽

Integrated Analysis ◽

Immune Microenvironment ◽

Diagnostic Models

Abstract Background : In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. Methods : Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. Results : four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. Conclusions: The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients.

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The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

10.21203/rs.3.rs-17980/v3 ◽

2020 ◽

Author(s):

Jiansong Ji ◽

Bufu Tang ◽

Jinyu Zhu ◽

Jie Li ◽

Kai Fan ◽

...

Keyword(s):

High Risk ◽

Iron Metabolism ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Prognostic Models ◽

Integrated Analysis ◽

Immune Microenvironment ◽

Diagnostic Models

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

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.796729 ◽

2021 ◽

Vol 9 ◽

Author(s):

Peng Gu ◽

Lei Zhang ◽

Ruitao Wang ◽

Wentao Ding ◽

Wei Wang ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Long Noncoding Rna ◽

Noncoding Rna ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis

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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Identification and Validation of m6A-Related lncRNA Signature as Potential Predictive Biomarkers in Breast Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.745719 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wenchang Lv ◽

Yichen Wang ◽

Chongru Zhao ◽

Yufang Tan ◽

Mingchen Xiong ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Risk Score ◽

Assessment Tool ◽

Cox Regression ◽

Screening Method ◽

Predictive Biomarkers ◽

Risk Groups ◽

Low Risk ◽

Main Challenge

The metastasis and poor prognosis are still regarded as the main challenge in the clinical treatment of breast cancer (BC). Both N6-methyladenosine (m6A) modification and lncRNAs play vital roles in the carcinogenesis and evolvement of BC. Considering the unknown association of m6A and lncRNAs in BC, this study therefore aims to discern m6A-related lncRNAs and explore their prognostic value in BC patients. Firstly, a total of 6 m6A-related lncRNAs were screened from TCGA database and accordingly constructed a prognostic-predicting model. The BC patients were then divided into high-risk and low-risk groups dependent on the median cutoff of risk score based on this model. Then, the predictive value of this model was validated by the analyses of cox regression, Kaplan-Meier curve, ROC curve, and the biological differences in the two groups were validated by PCA, KEGG, GSEA, immune status as well as in vitro assay. Finally, we accordingly constructed a risk prognostic model based on the 6 identified m6A-related lncRNAs, including Z68871.1, AL122010.1, OTUD6B-AS1, AC090948.3, AL138724.1, EGOT. Interestingly, the BC patients were divided into the low-risk and high-risk groups with different prognoses according to the risk score. Notably, the risk score of the model was an excellent independent prognostic factor. In the clinical sample validation, m6A regulatory proteins were differentially expressed in patients with different risks, and the markers of tumor-associated macrophages and m6A regulators were co-localized in high-risk BC tissues. This well-validated risk assessment tool based on the repertoire of these m6A-related genes and m6A-related lncRNAs, is of highly prognosis-predicting ability, and might provide a supplemental screening method for precisely judging BC prognosis.

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