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Clinical Characteristics in the Prediction of Posttreatment Survival of Patients with Ovarian Cancer. DISEASE MARKERS 2022; 2022:3321014. [PMID: 35571616 PMCID: PMC9098309 DOI: 10.1155/2022/3321014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/15/2022] [Indexed: 12/14/2022]
Abstract
Objective To determine the efficacy of clinical characteristics in the prediction of prognosis in patients with ovarian cancer. Methods Clinical data were collected from 3 datasets from TCGA database, including 1680 cases of ovarian serous cystadenocarcinoma, and were analyzed. Patients with ovarian cancer admitted to our hospital in 2016 were retrieved and followed up for prognosis analysis. Results From the datasets, for patients > 75 years old at the time of diagnosis, histologic grade and mutation count were good predictors for disease-free survival, while for patients > 50 years old at the time of diagnosis, histologic grade, race, fraction genome altered, and mutation count were good predictors for overall survival. In the patients (n = 38) retrieved from our hospital, the longest dimension of lesion (cm) and body weight at admission were good predictors for overall survival. Conclusions Those clinical factors, together with the two predictive equations, could be used to comprehensively predict the long-term prognosis of patients with ovarian cancer.
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Zheng M, Mullikin H, Hester A, Czogalla B, Heidegger H, Vilsmaier T, Vattai A, Chelariu-Raicu A, Jeschke U, Trillsch F, Mahner S, Kaltofen T. Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile. Int J Mol Sci 2020; 21:E9169. [PMID: 33271935 PMCID: PMC7731240 DOI: 10.3390/ijms21239169] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 11/06/2020] [Accepted: 11/27/2020] [Indexed: 02/06/2023] Open
Abstract
(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.
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Affiliation(s)
- Mingjun Zheng
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Heather Mullikin
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Anna Hester
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Bastian Czogalla
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Helene Heidegger
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Theresa Vilsmaier
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Aurelia Vattai
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Anca Chelariu-Raicu
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Udo Jeschke
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
- Department of Obstetrics and Gynecology, University Hospital Augsburg, Stenglinstrasse 2, 86156 Augsburg, Germany
| | - Fabian Trillsch
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Sven Mahner
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Till Kaltofen
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
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Yang M, Song B, Liu J, Bing Z, Wang Y, Yu L. Gene signature for prognosis in comparison of pancreatic cancer patients with diabetes and non-diabetes. PeerJ 2020; 8:e10297. [PMID: 33240632 PMCID: PMC7666560 DOI: 10.7717/peerj.10297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 10/13/2020] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Pancreatic cancer (PC) has much weaker prognosis, which can be divided into diabetes and non-diabetes. PC patients with diabetes mellitus will have more opportunities for physical examination due to diabetes, while pancreatic cancer patients without diabetes tend to have higher risk. Identification of prognostic markers for diabetic and non-diabetic pancreatic cancer can improve the prognosis of patients with both types of pancreatic cancer. METHODS Both types of PC patients perform differently at the clinical and molecular levels. The Cancer Genome Atlas (TCGA) is employed in this study. The gene expression of the PC with diabetes and non-diabetes is used for predicting their prognosis by LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression. Furthermore, the results are validated by exchanging gene biomarker with each other and verified by the independent Gene Expression Omnibus (GEO) and the International Cancer Genome Consortium (ICGC). The prognostic index (PI) is generated by a combination of genetic biomarkers that are used to rank the patient's risk ratio. Survival analysis is applied to test significant difference between high-risk group and low-risk group. RESULTS An integrated gene prognostic biomarker consisted by 14 low-risk genes and six high-risk genes in PC with non-diabetes. Meanwhile, and another integrated gene prognostic biomarker consisted by five low-risk genes and three high-risk genes in PC with diabetes. Therefore, the prognostic value of gene biomarker in PC with non-diabetes and diabetes are all greater than clinical traits (HR = 1.102, P-value < 0.0001; HR = 1.212, P-value < 0.0001). Gene signature in PC with non-diabetes was validated in two independent datasets. CONCLUSIONS The conclusion of this study indicated that the prognostic value of genetic biomarkers in PCs with non-diabetes and diabetes. The gene signature was validated in two independent databases. Therefore, this study is expected to provide a novel gene biomarker for predicting prognosis of PC with non-diabetes and diabetes and improving clinical decision.
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Affiliation(s)
- Mingjun Yang
- School of Life Science and Engineering, Lanzhou University of Technology, Lanzhou, Gansu, China
| | - Boni Song
- School of Life Science and Engineering, Lanzhou University of Technology, Lanzhou, Gansu, China
- Institute of Modern Physics of Chinese Academy of Sciences, Lanzhou, China
| | - Juxiang Liu
- Gansu Key Laboratory of Endocrine and metabolism, Department of Endocrinology, Gansu Provincial People’s Hospital, Lanzhou, Gansu, China
| | - Zhitong Bing
- Institute of Modern Physics of Chinese Academy of Sciences, Lanzhou, China
- Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University,, Lanzhou, China
| | - Yonggang Wang
- School of Life Science and Engineering, Lanzhou University of Technology, Lanzhou, Gansu, China
| | - Linmiao Yu
- School of Life Science and Engineering, Lanzhou University of Technology, Lanzhou, Gansu, China
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Bing Z, Yao Y, Xiong J, Tian J, Guo X, Li X, Zhang J, Shi X, Zhang Y, Yang K. Novel Model for Comprehensive Assessment of Robust Prognostic Gene Signature in Ovarian Cancer Across Different Independent Datasets. Front Genet 2019; 10:931. [PMID: 31681404 PMCID: PMC6798149 DOI: 10.3389/fgene.2019.00931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Accepted: 09/05/2019] [Indexed: 12/31/2022] Open
Abstract
Different analytical methods or models can often find completely different prognostic biomarkers for the same cancer. In the study of prognostic molecular biomarkers of ovarian cancer (OvCa), different studies have reported a variety of prognostic gene signatures. In the current study, based on geometric concepts, the linearity-clustering phase diagram with integrated P-value (LCP) method was used to comprehensively consider three indicators that are commonly employed to estimate the quality of a prognostic gene signature model. The three indicators, namely, concordance index, area under the curve, and level of the hazard ratio were determined via calculation of the prognostic index of various gene signatures from different datasets. As evaluation objects, we selected 13 gene signature models (Cox regression model) and 16 OvCa genomic datasets (including gene expression information and follow-up data) from published studies. The results of LCP showed that three models were universal and better than other models. In addition, combining the three models into one model showed the best performance in all datasets by LCP calculation. The combination gene signature model provides a more reliable model and could be validated in various datasets of OvCa. Thus, our method and findings can provide more accurate prognostic biomarkers and effective reference for the precise clinical treatment of OvCa.
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Affiliation(s)
- Zhitong Bing
- Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, China.,Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China.,Department of Computational Physics, Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
| | - Yuxiang Yao
- School of Physical Science and Technology, Lanzhou University, Lanzhou, China
| | - Jie Xiong
- Department of Applied Mathematics, Changsha University, Changsha, China
| | - Jinhui Tian
- Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, China.,Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China
| | - Xiangqian Guo
- Medical Bioinformatics Institute, School of Basic Medicine, Henan University, Henan, China
| | - Xiuxia Li
- Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, China.,Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China.,School of Public Health, Lanzhou University, Lanzhou, China
| | - Jingyun Zhang
- Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, China.,Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China
| | - Xiue Shi
- Institute for Evidence Based Rehabilitation Medicine of Gansu Province, Lanzhou, China
| | - Yanying Zhang
- Department of Pharmacology and Toxicology of Traditional Chinese Medicine, Gansu University of Chinese Medicine, Lanzhou, China
| | - Kehu Yang
- Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, China.,Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China.,Institute for Evidence Based Rehabilitation Medicine of Gansu Province, Lanzhou, China.,Department of Pharmacology and Toxicology of Traditional Chinese Medicine, Gansu University of Chinese Medicine, Lanzhou, China
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Yin W, Tang G, Zhou Q, Cao Y, Li H, Fu X, Wu Z, Jiang X. Expression Profile Analysis Identifies a Novel Five-Gene Signature to Improve Prognosis Prediction of Glioblastoma. Front Genet 2019; 10:419. [PMID: 31130992 PMCID: PMC6509566 DOI: 10.3389/fgene.2019.00419] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/17/2019] [Indexed: 12/12/2022] Open
Abstract
Glioblastoma multiforme (GBM) is the most aggressive primary central nervous system malignant tumor. The median survival of GBM patients is 12–15 months, and the 5 years survival rate is less than 5%. More novel molecular biomarkers are still urgently required to elucidate the mechanisms or improve the prognosis of GBM. This study aimed to explore novel biomarkers for GBM prognosis prediction. The gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets of GBM were downloaded. A total of 2241 overlapping differentially expressed genes (DEGs) were identified from TCGA and GSE7696 datasets. By univariate COX regression survival analysis, 292 survival-related genes were found among these DEGs (p < 0.05). Functional enrichment analysis was performed based on these survival-related genes. A five-gene signature (PTPRN, RGS14, G6PC3, IGFBP2, and TIMP4) was further selected by multivariable Cox regression analysis and a prognostic model of this five-gene signature was constructed. Based on this risk score system, patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Moreover, with the assistance of GEPIA http://gepia.cancer-pku.cn/, all five genes were found to be differentially expressed in GBM tissues compared with normal brain tissues. Furthermore, the co-expression network of the five genes was constructed based on weighted gene co-expression network analysis (WGCNA). Finally, this five-gene signature was further validated in other datasets. In conclusion, our study identified five novel biomarkers that have potential in the prognosis prediction of GBM.
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Affiliation(s)
- Wen Yin
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China
| | - Guihua Tang
- Department of Clinical Laboratory, Hunan Provincial People's Hospital (First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Quanwei Zhou
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China
| | - Yudong Cao
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China
| | - Haixia Li
- Department of Operative Nursing, Xiangya Hospital of Central South University, Changsha, China
| | - Xianyong Fu
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China
| | - Zhaoping Wu
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China
| | - Xingjun Jiang
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China
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Bing Z, Tian J, Zhang J, Li X, Wang X, Yang K. An Integrative Model of miRNA and mRNA Expression Signature for Patients of Breast Invasive Carcinoma with Radiotherapy Prognosis. Cancer Biother Radiopharm 2017; 31:253-60. [PMID: 27610468 DOI: 10.1089/cbr.2016.2059] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Radiotherapy is widely used in breast cancer treatment. The radiotherapy for breast invasive carcinoma (BRCA) presents challenges with the complex clinical factors, and too many genes have been found to be associated with BRCA radiotherapy prognosis. The aim of this study was to construct an integrative model to combine the clinical data and RNA expression data (including microRNA and mRNA) to predict the survival durations of BRCA patients with radiotherapy. Also, the authors try to find the key regulation pairs between mRNA and miRNA from prediction. They collected mRNA and microRNA expression profiles and gathered the corresponding clinical data of 73 BRCA patients with radiotherapy from The Cancer Genome Atlas (TCGA). According to an integrative model from univariate Cox regression between RNA expression and patient survival, they classified the patients with radiotherapy into low-risk and high-risk groups. The results showed that nine mRNAs were considered as protective genes and five miRNAs and eight mRNAs were considered as high-risk genes. Moreover, the high-risk group has a significantly shorter survival time in comparison with the low-risk group by the log-rank test (p = 0.0039). The reliability of the gene signature was validated by an independent data set from the Gene Expression Omnibus (GEO). Furthermore, three pairs of miRNA-mRNA, closely associated to survival, were identified. These findings and method may prove valuable for improving the clinical management of BRCA patients with radiotherapy.
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Affiliation(s)
- Zhitong Bing
- 1 Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University , Lanzhou, China .,2 Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province , Lanzhou, China .,3 Institute of Modern Physics of Chinese Academy of Sciences , Lanzhou, China
| | - Jinhui Tian
- 1 Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University , Lanzhou, China .,2 Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province , Lanzhou, China
| | - Jingyun Zhang
- 1 Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University , Lanzhou, China .,2 Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province , Lanzhou, China
| | - Xiuxia Li
- 1 Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University , Lanzhou, China .,2 Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province , Lanzhou, China
| | - Xiaohu Wang
- 4 Gansu Provincial Cancer Hospital , Lanzhou, China
| | - Kehu Yang
- 1 Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University , Lanzhou, China .,2 Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province , Lanzhou, China
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