scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Author(s):  
Yinglian Pan ◽  
LiPing Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Risk Groups ◽  
Cox Proportional Hazards ◽  
Low Risk ◽  
Patient Choice ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment

Abstract Background: Ovarian cancer (OV) is the most common type of primary female reproductive cancer. BRCA1/2 gene is an important biomarker for evaluating the risk of OV, breast cancer and other related tumors and influences patient choice of individualized treatment. A powerful signature to predict OV prognosis and improve treatment personalization is urgently needed. This study aimed to identify a novel OV-related lncRNA prognostic biomarker.Methods: A Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from The Cancer Genome Atlas (TCGA) database. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was assessed, and the sensitivity and specificity of the prediction model were determined.Results: The signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as a criterion for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The 3-year overall survival (OS) rates for the high- and low-risk groups were approximately 38% and 100%, respectively. Chemotherapy treatment survival rates indicated that high-risk groups had significantly shorter OS rates with adjuvant chemotherapy than the low-risk groups. The OS of 1-, 3- and 5- years were 100%, 40%, and 15% in the high-risk groups respectively. The survival rate of the high-risk group declined rapidly after two years of OA chemotherapy treatment. In addition, multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development.Conclusion: In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with in BRCA1/2 mutations to predict prognosis and chemotherapy efficiency.

scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yinglian Pan ◽  
Li Ping Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gynecologic Cancer ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment ◽  
Prognostic Signature

Abstract Background In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide. Methods Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined. Results A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38 and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100, 40, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after 2 years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development. Conclusion In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Author(s):  
Yinglian Pan ◽  
LiPing Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gynecologic Cancer ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment ◽  
Prognostic Signature

Abstract Background: In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide. Methods: Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined.Results: A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38% and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100%, 40%, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after two years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development. Conclusion: In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

scholarly journals Identification of an Immune-Related Risk Signature Correlates With Immunophenotype and Predicts Anti-PD-L1 Efficacy of Urothelial Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Pengju Li ◽  
Shihui Hao ◽  
Yongkang Ye ◽  
Jinhuan Wei ◽  
Yiming Tang ◽  
...  
Keyword(s):  
High Risk ◽  
Urothelial Cancer ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Related Risk ◽  
Metastatic Urothelial Cancer

Immune checkpoint inhibitor (ICI) treatment has been used to treat advanced urothelial cancer. Molecular markers might improve risk stratification and prediction of ICI benefit for urothelial cancer patients. We analyzed 406 cases of bladder urothelial cancer from The Cancer Genome Atlas (TCGA) data set and identified 161 messenger RNAs (mRNAs) as differentially expressed immunity genes (DEIGs). Using the LASSO Cox regression model, an eight-mRNA-based risk signature was built. We validated the prognostic and predictive accuracy of this immune-related risk signature in 348 metastatic urothelial cancer (mUC) samples treated with anti-PD-L1 (atezolizumab) from IMvigor210. We built an immune-related risk signature based on the eight mRNAs: ANXA1, IL22, IL9R, KLRK1, LRP1, NRG3, SEMA6D, and STAP2. The eight-mRNA-based risk signature successfully categorizes patients into high-risk and low-risk groups. Overall survival was significantly different between these groups, regardless if the initial TCGA training set, the internal TCGA testing set, all TCGA set, or the ICI treatment set. The hazard ratio (HR) of the high-risk group to the low-risk group was 3.65 (p < 0.0001), 2.56 (p < 0.0001), 3.36 (p < 0.0001), and 2.42 (p = 0.0009). The risk signature was an independent prognostic factor for prediction survival. Moreover, the risk signature was related to immunity characteristics. In different tumor mutational burden (TMB) subgroups, it successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome. Our eight-mRNA-based risk signature is a stable biomarker for urothelial cancer and might be able to predict which patients benefit from ICI treatment. It might play a role in precision individualized immunotherapy.

scholarly journals Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongjie Chen ◽  
Hui Huang ◽  
Longjun Zang ◽  
Wenzhe Gao ◽  
Hongwei Zhu ◽  
...  
Keyword(s):  
High Risk ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Risk Groups ◽  
Low Risk ◽  
Ductal Adenocarcinoma ◽  
Prognostic Signature ◽  
Score Model

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

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

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.

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

2020 ◽  
Vol 18 (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

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

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.

Comparative performance of Breast Cancer Index (BCIN+) and CTS5 in hormone receptor-positive (HR+) lymph node-positive (N+) breast cancer patients with one to three positive nodes (N1).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 555-555
Author(s):  
Dennis Sgroi ◽  
Yi Zhang ◽  
Catherine A. Schnabel
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Endocrine Therapy ◽  
Tumor Size ◽  
Risk Groups ◽  
Cox Proportional Hazards ◽  
Low Risk ◽  
Intermediate Risk ◽  
Breast Cancer Patients

555 Background: Identification of N+ breast cancer patients with a limited risk of recurrence improves selection of those for which chemotherapy and/or extended endocrine therapy (EET) may be most appropriate to reduce overtreatment. BCIN+ integrates gene expression with tumor size and grade, and is highly prognostic for overall (0-10yr) and late (5-10yr) distant recurrence (DR) in N1 patients. Clinical Treatment Score post-5-years (CTS5) is a prognostic model based on clinicopathological factors (nodes, age, tumor size and grade) and significantly prognostic for late DR. The current analysis compares BCIN+ and CTS5 for risk of late DR in N1 patients. Methods: 349 women with HR+, N1 disease and recurrence-free for ≥5 years were included. BCIN+ results were determined blinded to clinical outcome. CTS5 was calculated as previously described (Dowsett et al, JCO 2018; 36:1941). Kaplan-Meier analysis and Cox proportional hazards regression for late DR (5-15y) were evaluated. Results: 64% of patients were > 50 years old, 34% with tumors > 2cm, 79% received adjuvant chemotherapy and 64% received up to 5 years of ET. BCIN+ stratified 23% of patients as low-risk with 1.3% risk for late DR vs those classified as high-risk with 16.1% [HR 12.4 (1.7-90.4), p = 0.0014]. CTS5 classified patients into 3 risk groups: 29% of patients as low-risk (4.2% DR), 37% as intermediate-risk (10.6% DR), and 34% as high-risk (22.1% DR) [HR intermediate vs. low: 2.3 (0.7-7.0), p = 0.16; high vs. low: 5.3 (1.8-15.5), p = 0.002]. In a subset of patients who completed 5 years of ET (N = 223), BCIN+ identified 22% of patients as low-risk with a late DR rate of 2.1%, while CTS5 identified 29% and 37% of patients as low- and intermediate-risk with late DR rates of 5.2% and 10.3%, respectively. Conclusions: BCIN+ classified N1 patients into binary risk groups and identified 20% patients with limited risk of late DR ( < 2%) that may be advised to forego EET and its attendant toxicities/side effects. In comparison, CTS5 classified patients into 3 risk groups, with low- and intermediate-risk of late DR of 4-5% and 10%, wherein the risk-benefit profile for extension of endocrine therapy is less clear.

scholarly journals Metabolic syndrome related gene signature predicts the prognosis of patients with pancreatic ductal carcinoma. A novel link between metabolic dysregulation and pancreatic ductal carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weiyang Cai ◽  
Wenming Bao ◽  
Shengwei Chen ◽  
Yan Yang ◽  
Yanyan Li
Keyword(s):  
T Cells ◽  
High Risk ◽  
Cox Regression ◽  
Ductal Carcinoma ◽  
Risk Groups ◽  
Cancer Genome ◽  
Low Risk ◽  
Pancreatic Ductal Carcinoma ◽  
Blue Module ◽  
Geo Database

Abstract Background Pancreatic cancer is one of the most common malignancies worldwide. In recent years, specific metabolic activities, which involves the development of tumor, caused wide public concern. In this study, we wish to explore the correlation between metabolism and progression of tumor. Methods A retrospective analysis including 95 patients with pancreatic ductal adenocarcinoma (PDAC) and PDAC patients from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and The Gene Expression Omnibus (GEO) database were involved in our study. Multivariate Cox regression analysis was used to construct the prognosis model. The potential connection between metabolism and immunity of PDAC was investigated through a weighted gene co-expression network analysis (WGCNA). 22 types of Tumor-infiltrating immune cells (TIICs) between high-risk and low-risk groups were estimated through CIBERSORT. Moreover, the potential immune-related signaling pathways between high-risk and low-risk groups were explored through the gene set enrichment analysis (GSEA). The role of key gene GMPS in developing pancreatic tumor was further investigated through CCK-8, colony-information, and Transwell. Results The prognostic value of the MetS factors was analyzed using the Cox regression model, and a clinical MetS-based nomogram was established. Then, we established a metabolism-related signature to predict the prognosis of PDAC patients based on the TCGA databases and was validated in the ICGC database and the GEO database to find the distinct molecular mechanism of MetS genes in PDAC. The result of WGCNA showed that the blue module was associated with risk score, and genes in the blue module were found to be enriched in the immune-related signaling pathway. Furthermore, the result of CIBERSORT demonstrated that proportions of T cells CD8, T cells Regulatory, Tregs NK cells Activated, Dendritic cells Activated, and Mast cells Resting were different between high-risk and low-risk groups. These differences are potential causes of different prognoses of PDAC patients. GSEA and the protein–protein interaction network (PPI) further revealed that our metabolism-related signature was significantly enriched in immune‐related biological processes. Moreover, knockdown of GMPS in PDAC cells suppressed proliferation, migration, and invasion of tumor cells, whereas overexpression of GMPS performed oppositely. Conclusion The results shine light on fundamental mechanisms of metabolic genes on PDAC and establish a reliable and referable signature to evaluate the prognosis of PDAC. GMPS was identified as a potential candidate oncogene with in PDAC, which can be a novel biomarker and therapeutic target for PDAC treatment.

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

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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