1
|
Nies HW, Mohamad MS, Zakaria Z, Chan WH, Remli MA, Nies YH. Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1232. [PMID: 34573857 PMCID: PMC8472068 DOI: 10.3390/e23091232] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/14/2021] [Accepted: 09/16/2021] [Indexed: 12/12/2022]
Abstract
Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.
Collapse
Affiliation(s)
- Hui Wen Nies
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia; (Z.Z.); (W.H.C.)
| | - Mohd Saberi Mohamad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain 17666, United Arab Emirates;
| | - Zalmiyah Zakaria
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia; (Z.Z.); (W.H.C.)
| | - Weng Howe Chan
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia; (Z.Z.); (W.H.C.)
| | - Muhammad Akmal Remli
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Kota Bharu 16100, Malaysia;
| | - Yong Hui Nies
- Department of Anatomy, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur 56000, Malaysia;
| |
Collapse
|
2
|
Identification of Monotonically Differentially Expressed Genes across Pathologic Stages for Cancers. JOURNAL OF ONCOLOGY 2020; 2020:8458190. [PMID: 33273919 PMCID: PMC7676961 DOI: 10.1155/2020/8458190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 10/17/2020] [Accepted: 10/28/2020] [Indexed: 12/09/2022]
Abstract
Given the fact that cancer is a multistage progression process resulting from genetic sequence mutations, the genes whose expression values increase or decrease monotonically across pathologic stages are potentially involved in tumor progression. This may provide insightful clues about how human cancers advance, thereby facilitating more personalized treatments. By replacing the expression values of genes with their GeneRanks, we propose a procedure capable of identifying monotonically differentially expressed genes (MEGs) as the disease advances. Using three real-world gene expression data that cover three distinct cancer types-colon, esophageal, and lung cancers-the proposed procedure has demonstrated excellent performance in detecting the potential MEGs. To conclude, the proposed procedure can detect MEGs across pathologic stages of cancers very efficiently and is thus highly recommended.
Collapse
|
3
|
Lippmann C, Ultsch A, Lötsch J. Computational functional genomics-based reduction of disease-related gene sets to their key components. Bioinformatics 2020; 35:2362-2370. [PMID: 30500872 DOI: 10.1093/bioinformatics/bty986] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 09/05/2018] [Accepted: 11/29/2018] [Indexed: 01/21/2023] Open
Abstract
MOTIVATION The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number of candidates. Although expression based methods of gene set reduction are applied to laboratory-derived genetic data, the analysis of topical sets of genes gathered from knowledge bases requires a modified approach as no quantitative information about gene expression is available. RESULTS We propose a computational functional genomics-based approach at reducing sets of genes to the most relevant items based on the importance of the gene within the polyhierarchy of biological processes characterizing the disease. Knowledge bases about the biological roles of genes can provide a valid description of traits or diseases represented as a directed acyclic graph (DAG) picturing the polyhierarchy of disease relevant biological processes. The proposed method uses a gene importance score derived from the location of the gene-related biological processes in the DAG. It attempts to recreate the DAG and thereby, the roles of the original gene set, with the least number of genes in descending order of importance. This obtained precision and recall of over 70% to recreate the components of the DAG charactering the biological functions of n=540 genes relevant to pain with a subset of only the k=29 best-scoring genes. CONCLUSIONS A new method for reduction of gene sets is shown that is able to reproduce the biological processes in which the full gene set is involved by over 70%; however, by using only ∼5% of the original genes. AVAILABILITY AND IMPLEMENTATION The necessary numerical parameters for the calculation of gene importance are implemented in the R package dbtORA at https://github.com/IME-TMP-FFM/dbtORA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Catharina Lippmann
- Fraunhofer Institute of Molecular Biology and Applied Ecology - Project Group Translational Medicine and Pharmacology (IME-TMP), Frankfurt am Main, Germany
| | - Alfred Ultsch
- DataBionics Research Group, University of Marburg, Marburg, Germany
| | - Jörn Lötsch
- Fraunhofer Institute of Molecular Biology and Applied Ecology - Project Group Translational Medicine and Pharmacology (IME-TMP), Frankfurt am Main, Germany.,Goethe-University, Institute of Clinical Pharmacology, Frankfurt am Main, Germany
| |
Collapse
|
4
|
Wu X, Wang L, Feng F, Tian S. Weighted gene expression profiles identify diagnostic and prognostic genes for lung adenocarcinoma and squamous cell carcinoma. J Int Med Res 2019; 48:300060519893837. [PMID: 31854219 PMCID: PMC7607763 DOI: 10.1177/0300060519893837] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVE To construct a diagnostic signature to distinguish lung adenocarcinoma from lung squamous cell carcinoma and a prognostic signature to predict the risk of death for patients with nonsmall-cell lung cancer, with satisfactory predictive performances, good stabilities, small sizes and meaningful biological implications. METHODS Pathway-based feature selection methods utilize pathway information as a priori to provide insightful clues on potential biomarkers from the biological perspective, and such incorporation may be realized by adding weights to test statistics or gene expression values. In this study, weighted gene expression profiles were generated using the GeneRank method and then the LASSO method was used to identify discriminative and prognostic genes. RESULTS The five-gene diagnostic signature including keratin 5 (KRT5), mucin 1 (MUC1), triggering receptor expressed on myeloid cells 1 (TREM1), complement C3 (C3) and transmembrane serine protease 2 (TMPRSS2) achieved a predictive error of 12.8% and a Generalized Brier Score of 0.108, while the five-gene prognostic signature including alcohol dehydrogenase 1C (class I), gamma polypeptide (ADH1C), alpha-2-glycoprotein 1, zinc-binding (AZGP1), clusterin (CLU), cyclin dependent kinase 1 (CDK1) and paternally expressed 10 (PEG10) obtained a log-rank P-value of 0.03 and a C-index of 0.622 on the test set. CONCLUSIONS Besides good predictive capacity, model parsimony and stability, the identified diagnostic and prognostic genes were highly relevant to lung cancer. A large-sized prospective study to explore the utilization of these genes in a clinical setting is warranted.
Collapse
Affiliation(s)
- Xing Wu
- Department of Teaching, The First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Linlin Wang
- Department of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Fan Feng
- School of Mathematics, Jilin University, Changchun, Jilin Province, China
| | - Suyan Tian
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, Jilin Province, China
| |
Collapse
|
5
|
Identification of Monotonically Differentially Expressed Genes for IFN- β-Treated Multiple Sclerosis Patients. BIOMED RESEARCH INTERNATIONAL 2019; 2019:5647902. [PMID: 31915697 PMCID: PMC6930778 DOI: 10.1155/2019/5647902] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 10/30/2019] [Accepted: 11/06/2019] [Indexed: 01/18/2023]
Abstract
Multiple sclerosis (MS) is a common neurological disability of the central nervous system. Immune-modulatory therapy with interferon-β (IFN-β) has been used as a first-line treatment to prevent relapses in MS patients. While the therapeutic mechanism of IFN-β has not been fully elucidated, the data of microarray experiments that collected longitudinal gene expression profiles to evaluate the long-term response of IFN-β treatment have been analyzed using statistical methods that were incapable of dealing with such data. In this study, the GeneRank method was applied to generate weighted gene expression values and the monotonically expressed genes (MEGs) for both IFN-β treatment responders and nonresponders were identified. The proposed procedure identified 13 MEGs for the responders and 2 MEGs for the nonresponders, most of which are biologically relevant to MS. Our work here provides some useful insight into the mechanism of IFN-β treatment for MS patients. A full understanding of the therapeutic mechanism will enable a more personalized treatment strategy possible.
Collapse
|
6
|
Tian S. Identification of monotonically differentially expressed genes for non-small cell lung cancer. BMC Bioinformatics 2019; 20:177. [PMID: 30971213 PMCID: PMC6458730 DOI: 10.1186/s12859-019-2775-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 03/22/2019] [Indexed: 12/19/2022] Open
Abstract
Background Monotonically expressed genes (MEGs) are genes whose expression values increase or decrease monotonically as a disease advances or time proceeds. Non-small cell lung cancer (NSCLC) is a multistage progression process resulting from genetic sequences mutations, the identification of MEGs for NSCLC is important. Results With the aid of a feature selection algorithm capable of identifying MEGs – the MFSelector method – two sets of potential MEGs were selected in this study: the MEGs across the different pathologic stages and the MEGs across the risk levels of death for the NSCLC patients at early stages. For the lung adenocarcinoma (AC) subtypes no statistically significant MEGs were identified across pathologic stages, however dozens of MEGs were identified across the risk levels of death. By contrast, for the squamous cell lung carcinoma (SCC) there were no statistically significant MEGs as either stage or risk level advanced. Conclusions The pathologic stage of non-small cell lung cancer patients at early stages has no prognostic value, making the identification of prognostic gene signatures for them more meaningful and highly desirable.
Collapse
Affiliation(s)
- Suyan Tian
- Division of Clinical Research, The First Hospital of Jilin University, 71 Xinmin Street, Changchun, 130021, Jilin, China.
| |
Collapse
|
7
|
Tian S, Wang C, Wang B. Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures. BIOMED RESEARCH INTERNATIONAL 2019; 2019:2497509. [PMID: 31073522 PMCID: PMC6470448 DOI: 10.1155/2019/2497509] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 03/07/2019] [Indexed: 12/29/2022]
Abstract
To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, bilevel selection, and pathway-guided gene selection. With bilevel selection methods being regarded as a special case of pathway-guided gene selection process, we discuss pathway-guided gene selection methods in detail and the importance of penalization in such methods. Last, we point out the potential utilizations of pathway-guided gene selection in one active research avenue, namely, to analyze longitudinal gene expression data. We believe this article provides valuable insights for computational biologists and biostatisticians so that they can make biology more computable.
Collapse
Affiliation(s)
- Suyan Tian
- Division of Clinical Research, The First Hospital of Jilin University, 71 Xinmin Street, Changchun, Jilin 130021, China
| | - Chi Wang
- Department of Biostatistics, Markey Cancer Center, The University of Kentucky, 800 Rose St., Lexington, KY 40536, USA
| | - Bing Wang
- School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, Jilin 130012, China
| |
Collapse
|
8
|
Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time. BIOMED RESEARCH INTERNATIONAL 2019; 2019:1724898. [PMID: 31016185 PMCID: PMC6444255 DOI: 10.1155/2019/1724898] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 02/25/2019] [Indexed: 01/02/2023]
Abstract
With the rapid evolution of high-throughput technologies, time series/longitudinal high-throughput experiments have become possible and affordable. However, the development of statistical methods dealing with gene expression profiles across time points has not kept up with the explosion of such data. The feature selection process is of critical importance for longitudinal microarray data. In this study, we proposed aggregating a gene's expression values across time into a single value using the sign average method, thereby degrading a longitudinal feature selection process into a classic one. Regularized logistic regression models with pseudogenes (i.e., the sign average of genes across time as predictors) were then optimized by either the coordinate descent method or the threshold gradient descent regularization method. By applying the proposed methods to simulated data and a traumatic injury dataset, we have demonstrated that the proposed methods, especially for the combination of sign average and threshold gradient descent regularization, outperform other competitive algorithms. To conclude, the proposed methods are highly recommended for studies with the objective of carrying out feature selection for longitudinal gene expression data.
Collapse
|
9
|
Tian S, Wang C, Chang HH. A longitudinal feature selection method identifies relevant genes to distinguish complicated injury and uncomplicated injury over time. BMC Med Inform Decis Mak 2018; 18:115. [PMID: 30526581 PMCID: PMC6284265 DOI: 10.1186/s12911-018-0685-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Feature selection and gene set analysis are of increasing interest in the field of bioinformatics. While these two approaches have been developed for different purposes, we describe how some gene set analysis methods can be utilized to conduct feature selection. METHODS We adopted a gene set analysis method, the significance analysis of microarray gene set reduction (SAMGSR) algorithm, to carry out feature selection for longitudinal gene expression data. RESULTS Using a real-world application and simulated data, it is demonstrated that the proposed SAMGSR extension outperforms other relevant methods. In this study, we illustrate that a gene's expression profiles over time can be regarded as a gene set and then a suitable gene set analysis method can be utilized directly to select relevant genes associated with the phenotype of interest over time. CONCLUSIONS We believe this work will motivate more research to bridge feature selection and gene set analysis, with the development of novel algorithms capable of carrying out feature selection for longitudinal gene expression data.
Collapse
Affiliation(s)
- Suyan Tian
- Division of Clinical Research, The First Hospital of Jilin University, 71Xinmin Street, Changchun, 130021, Jilin, China.
| | - Chi Wang
- Department of Biostatistics, Markey Cancer Center, The University of Kentucky, 800 Rose St, Lexington, KY, 40536, USA
| | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA, 30322, USA
| |
Collapse
|
10
|
Tian S, Wang C, Chang HH. To select relevant features for longitudinal gene expression data by extending a pathway analysis method. F1000Res 2018; 7:1166. [PMID: 30271585 PMCID: PMC6124382 DOI: 10.12688/f1000research.15357.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/25/2018] [Indexed: 01/30/2023] Open
Abstract
The emerging field of pathway-based feature selection that incorporates biological information conveyed by gene sets/pathways to guide the selection of relevant genes has become increasingly popular and widespread. In this study, we adapt a gene set analysis method - the significance analysis of microarray gene set reduction (SAMGSR) algorithm to carry out feature selection for longitudinal microarray data, and propose a pathway-based feature selection algorithm - the two-level SAMGSR method. By using simulated data and a real-world application, we demonstrate that a gene's expression profiles over time can be considered as a gene set. Thus a suitable gene set analysis method can be utilized or modified to execute the selection of relevant genes for longitudinal omics data. We believe this work paves the way for more research to bridge feature selection and gene set analysis with the development of novel pathway-based feature selection algorithms.
Collapse
Affiliation(s)
- Suyan Tian
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, Jilin, 130021, China
| | - Chi Wang
- Department of Biostatistics, The University of Kentuchy, Lexington, KY, 40536, USA
| | - Howard H. Chang
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA
| |
Collapse
|
11
|
Tian S. Identification of subtype-specific prognostic signatures using Cox models with redundant gene elimination. Oncol Lett 2018; 15:8545-8555. [PMID: 29805591 PMCID: PMC5950526 DOI: 10.3892/ol.2018.8418] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 03/02/2018] [Indexed: 12/14/2022] Open
Abstract
Lung cancer (LC) is a leading cause of cancer-associated mortalities worldwide. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) account for ~70% of all cases of LC. Since AC and SCC are two distinct diseases, their corresponding prognostic genes associated with patient survival time are expected to be different. To date, only a few studies have distinguished patients with good prognosis from those with poor prognosis for each specific subtype. In the present study, the Cox filter model, a feature selection algorithm that identifies subtype-specific prognostic genes to incorporate pathway information and eliminate redundant genes, was adopted. By applying the proposed model to data on non-small cell lung cancer (NSCLC), it was demonstrated that both redundant gene elimination and search space restriction can improve the predictive capacity and the model stability of resulting prognostic gene signatures. To conclude, a pre-filtering procedure that incorporates pathway information for screening likely irrelevant genes prior to complex downstream analysis is recommended. Furthermore, a feature selection algorithm that considers redundant gene elimination may be preferable to one without such a consideration.
Collapse
Affiliation(s)
- Suyan Tian
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, Jilin 130021, P.R. China
| |
Collapse
|
12
|
Zhang A, Tian S. Classification of early-stage non-small cell lung cancer by weighing gene expression profiles with connectivity information. Biom J 2017; 60:537-546. [PMID: 29206308 DOI: 10.1002/bimj.201700010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 09/10/2017] [Accepted: 10/22/2017] [Indexed: 11/11/2022]
Abstract
Pathway-based feature selection algorithms, which utilize biological information contained in pathways to guide which features/genes should be selected, have evolved quickly and become widespread in the field of bioinformatics. Based on how the pathway information is incorporated, we classify pathway-based feature selection algorithms into three major categories-penalty, stepwise forward, and weighting. Compared to the first two categories, the weighting methods have been underutilized even though they are usually the simplest ones. In this article, we constructed three different genes' connectivity information-based weights for each gene and then conducted feature selection upon the resulting weighted gene expression profiles. Using both simulations and a real-world application, we have demonstrated that when the data-driven connectivity information constructed from the data of specific disease under study is considered, the resulting weighted gene expression profiles slightly outperform the original expression profiles. In summary, a big challenge faced by the weighting method is how to estimate pathway knowledge-based weights more accurately and precisely. Only until the issue is conquered successfully will wide utilization of the weighting methods be impossible.
Collapse
Affiliation(s)
- Ao Zhang
- Intensive Care Unit (ICU), The First Hospital of Jilin University, Changchun, 130021, China
| | - Suyan Tian
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, 130021, China
| |
Collapse
|
13
|
Tian S. Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients. Oncol Lett 2017; 14:5464-5470. [PMID: 29098036 PMCID: PMC5652232 DOI: 10.3892/ol.2017.6835] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 08/04/2017] [Indexed: 01/08/2023] Open
Abstract
Non-small cell lung cancer (NSCLC) is a leading cause of cancer-associated mortality worldwide. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two primary histological subtypes of NSCLC, accounting for ~70% of lung cancer cases. Increasing evidence suggests that AC and SCC differ in the composition of genes and molecular characteristics. Previous research has focused on distinguishing AC from SCC or predicting the NSCLC patient survival rates using gene expression profiles, usually with the aid of a feature selection method. The present study conducted a pre-filtering to identify the genes that have significant expression values and a high connection with other genes in the gene network, and then used the radial coordinate visualization method to identify relevant genes. By applying the proposed procedure to NSCLC data, it was demonstrated that there is a clear segmentation between AC and SCC, however not between patients with a good prognosis and bad prognosis. The focus of discriminating AC and SCC differs from survival prediction and there are almost no overlaps between the two gene signatures. Overall, a supervised learning method is preferred and future studies aiming to identify prognostic gene signatures with an increased prediction efficiency are required.
Collapse
Affiliation(s)
- Suyan Tian
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, Jilin 130021, P.R. China.,Center for Applied Statistical Research, School of Mathematics, Jilin University, Changchun, Jilin 130012, P.R. China
| |
Collapse
|