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Wu Y, Xie Q, Wu L, Li Z, Li X, Zhang L, Zhang B. Identification of activating transcription factor 6 (ATF6) as a novel prognostic biomarker and potential target in oral squamous cell carcinoma. Gene 2024; 915:148436. [PMID: 38579904 DOI: 10.1016/j.gene.2024.148436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/08/2024] [Accepted: 04/02/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND Oral squamous cell carcinoma (OSCC) is originating from oral mucosal epithelial cells. Autophagy plays a crucial role in cancer treatment by promoting cellular self-degradation and eliminating damaged components, thereby enhancing therapeutic efficacy. In this study, we aim to identify a novel autophagy-related biomarker to improve OSCC therapy. METHODS We firstly utilized Cox and Lasso analyses to identify that ATF6 is associated with OSCC prognosis, and validated the results by Kaplan-Meier survival analysis. We further identified the downstream pathways and related genes by enrichment analysis and WGCNA analysis. Subsequently, we used short interfering RNA to investigate the effects of ATF6 knockdown on proliferation, migration, apoptosis, and autophagy in SCC-9 and SCC-15 cells through cell viability assay, transwell assay, EdU incorporation assay, flow cytometry analysis, western blot analysis and immunofluorescence analysis, etc. RESULTS: Bioinformatics analyses showed that ATF6 overexpression was associated with prognosis and detrimental to survival. In vitro studies verified that ATF6 knockdown reduced OSCC cell proliferation and migration. Mechanistically, ATF6 knockdown could promote cellular autophagy and apoptosis. CONCLUSION We propose that ATF6 holds potential as a prognostic biomarker linked to autophagy in OSCC. This study provides valuable clues for further exploration of targeted therapy against OSCC.
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Affiliation(s)
- Yan Wu
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121001, China
| | - Qiang Xie
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Lifeng Wu
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Zhijia Li
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Xiaojing Li
- CHN ENERGY Digital Intelligence Technology Development (Beijing) Co., Ltd., Beijing 100011, China
| | - Lan Zhang
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China.
| | - Bin Zhang
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121001, China.
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Zhang L, Wang J, Guo Y, Yue H, Zhang M. The construction, validation and promotion of the nomogram prognosis prediction model of UCEC, and the experimental verification of the expression and knockdown of the key gene GPX4. Heliyon 2024; 10:e24415. [PMID: 38312660 PMCID: PMC10835249 DOI: 10.1016/j.heliyon.2024.e24415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/29/2023] [Accepted: 01/08/2024] [Indexed: 02/06/2024] Open
Abstract
Background Adequate prognostic prediction of Uterine Corpus Endometrial Carcinoma (UCEC) is crucial for informing clinical decision-making. However, there is a scarcity of research on the utilization of a nomogram prognostic evaluation model that incorporates pyroptosis-related genes (PRGs) in UCEC. Methods By analyzing data from UCEC patients in the TCGA database, four PRGs associated with prognosis were identified. Subsequently, a "risk score" was developed using these four PRGs and LASSO. Ordinary and web-based dynamic nomogram prognosis prediction models were constructed. The discrimination, calibration, clinical benefit, and promotional value of the selected GPX4 were validated. The expression level of GPX4 in UCEC cell lines was subsequently verified. The effects of GPX4 knock-down on the malignant biological behavior of UCEC cells were assessed. Results Four key PRGs and a "risk score" were identified, with the "risk score" calculated as (-0.4323) * GPX4 + (0.2385) * GSDME + (0.0525) * NLRP2 + (-0.3299) * NOD2. The nomogram prognosis prediction model, incorporating the "risk score," "age," and "FIGO stage," demonstrated moderate predictive performance (AUC >0.7), good calibration, and clinical significance for 1, 3, and 5-year survival. The web-based dynamic nomogram demonstrated significant promotional value (https://shibaolu.shinyapps.io/DynamicNomogramForUCEC/). UCEC cells exhibited abnormally elevated expression of GPX4, and the knockdown of GPX4 effectively suppressed malignant biological activities, including proliferation and migration, while inducing apoptosis. The findings from tumorigenic experiments conducted on nude mice further validated the results obtained from cellular experiments. Conclusion Following validation, the nomogram prognosis prediction model, which relies on four pivotal PRGs, demonstrated a high degree of accuracy in forecasting the precise probability of prognosis for patients with UCEC. Additionally, the web-based dynamic nomogram exhibited considerable potential for promotion. Notably, the key gene GPX4 exhibited characteristics of a potential oncogene in UCEC, as it facilitated malignant biological behavior and impeded apoptosis.
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Affiliation(s)
- Lindong Zhang
- Department of Gynecology, The Third Affiliated Hospital of Zhengzhou University, 7 Rehabilitation Front Street, Zhengzhou, 450052, China
| | - Jialin Wang
- Department of Orthopedics, Xuanwu Hospital, Capital Medical University, Beijing, 100000, China
| | - Yan Guo
- Department of Oncology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, No. 7 Weiwu Street, Zhengzhou, 450003, China
| | - Haodi Yue
- Department of Center for Clinical Single Cell Biomedicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, No. 7 Weiwu Street, Zhengzhou, 450003, China
| | - Mengjun Zhang
- Department of Gynecology, The Third Affiliated Hospital of Zhengzhou University, 7 Rehabilitation Front Street, Zhengzhou, 450052, China
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Liu H, Che H, Zhang M, Lv J, Pu C, Wu J, Zhang Y, Gu Y. Developing CuS for Predicting Aggressiveness and Prognosis in Lung Adenocarcinoma. Genes (Basel) 2023; 14:genes14051055. [PMID: 37239416 DOI: 10.3390/genes14051055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/03/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
Cuproptosis is a newfound cell death form that depends on copper (Cu) ionophores to transport Cu into cancer cells. Studies on the relationship have covered most common cancer types and analyzed the links between cuproptosis-related genes (CRGs) and various aspects of tumor characteristics. In this study, we evaluated the role of cuproptosis in lung adenocarcinoma (LUAD) and constructed the cuproptosis-related score (CuS) to predict aggressiveness and prognosis in LUAD, so as to achieve precise treatment for patients. CuS had a better predictive performance than cuproptosis genes, possibly due to the synergy of SLC family genes, and patients with a high CuS had a poor prognosis. Functional enrichment analysis revealed the correlation between CuS and immune and mitochondrial pathways in multiple datasets. Furthermore, we predicted six potential drugs targeting high-CuS patients, including AZD3759, which is a targeted drug for LUAD. In conclusion, cuproptosis is involved in LUAD aggressiveness, and CuS can accurately predict the prognosis of patients. These findings provide a basis for precise treatment of patients with high CuS in LUAD.
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Affiliation(s)
- Honghao Liu
- Computational Biology Research Center, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Haijun Che
- College of Pharmacy, Chengdu Airport Campus, Southwest Minzu University, Chengdu 610041, China
| | - Mengyan Zhang
- Computational Biology Research Center, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Jinyue Lv
- Computational Biology Research Center, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Chengjie Pu
- Computational Biology Research Center, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Jiawei Wu
- State Key Laboratory of Agrobiotechnology, School of Life Science, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - Yan Zhang
- Computational Biology Research Center, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
- College of Pathology, Qiqihar Medical University, Qiqihar 161042, China
| | - Yue Gu
- Computational Biology Research Center, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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Wang J, Kong C, Pan F, Lu S. Construction and Validation of a Nomogram Clinical Prediction Model for Predicting Osteoporosis in an Asymptomatic Elderly Population in Beijing. J Clin Med 2023; 12:1292. [PMID: 36835828 PMCID: PMC9967366 DOI: 10.3390/jcm12041292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/24/2023] [Accepted: 02/01/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Based on the high prevalence and occult-onset of osteoporosis, the development of novel early screening tools was imminent. Therefore, this study attempted to construct a nomogram clinical prediction model for predicting osteoporosis. METHODS Asymptomatic elderly residents in the training (n = 438) and validation groups (n = 146) were recruited. BMD examinations were performed and clinical data were collected for the participants. Logistic regression analyses were performed. A logistic nomogram clinical prediction model and an online dynamic nomogram clinical prediction model were constructed. The nomogram model was validated by means of ROC curves, calibration curves, DCA curves, and clinical impact curves. RESULTS The nomogram clinical prediction model constructed based on gender, education level, and body weight was well generalized and had moderate predictive value (AUC > 0.7), better calibration, and better clinical benefit. An online dynamic nomogram was constructed. CONCLUSIONS The nomogram clinical prediction model was easy to generalize, and could help family physicians and primary community healthcare institutions to better screen for osteoporosis in the general elderly population and achieve early detection and diagnosis of the disease.
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Affiliation(s)
| | | | | | - Shibao Lu
- Department of Orthopedics, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Beijing 100000, China
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Liang L, Liu L, Mai S, Chen Y. A novel machine learning model based on ubiquitin-related gene pairs and clinical features to predict prognosis and treatment effect in colon adenocarcinoma. Eur J Med Res 2023; 28:41. [PMID: 36681855 PMCID: PMC9863211 DOI: 10.1186/s40001-023-00993-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 01/04/2023] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Ubiquitin and ubiquitin-like (UB/UBL) conjugations are essential post-translational modifications that contribute to cancer onset and advancement. In colon adenocarcinoma (COAD), nonetheless, the biological role, as well as the clinical value of ubiquitin-related genes (URGs), is unclear. The current study sought to design and verify a ubiquitin-related gene pairs (URGPs)-related prognostic signature for predicting COAD prognoses. METHODS Using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, URGP's predictive signature was discovered. Signatures differentiated high-risk and low-risk patients. ROC and Kaplan-Meier assessed URGPs' signature. Gene set enrichment analysis (GSEA) examined biological nomogram enrichment. Chemotherapy and tumor immune microenvironment were also studied. RESULTS The predictive signature used six URGPs. High-risk patients had a worse prognosis than low-risk patients, according to Kaplan-Meier. After adjusting for other clinical characteristics, the URGPs signature could reliably predict COAD patients. In the low-risk group, we found higher amounts of invading CD4 memory-activated T cells, follicular helper T cells, macrophages, and resting dendritic cells. Moreover, low-risk group had higher immune checkpoint-related gene expression and chemosensitivity. CONCLUSION Our research developed a nomogram and a URGPs prognostic signature to predict COAD prognosis, which may aid in patient risk stratification and offer an effective evaluation method of individualized treatment in clinical settings.
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Affiliation(s)
- Liping Liang
- grid.284723.80000 0000 8877 7471Department of Gastroenterology, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Le Liu
- grid.284723.80000 0000 8877 7471Department of Gastroenterology, Integrated Clinical Microecology Center, Shenzhen Hospital, Southern Medical University, 1333 New Lake Road, Shenzhen, 518100 China
| | - Shijie Mai
- grid.284723.80000 0000 8877 7471Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Ye Chen
- grid.284723.80000 0000 8877 7471Department of Gastroenterology, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China ,grid.284723.80000 0000 8877 7471Department of Gastroenterology, Integrated Clinical Microecology Center, Shenzhen Hospital, Southern Medical University, 1333 New Lake Road, Shenzhen, 518100 China
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Zhao Z, Feng Q, Zhang Y, Ning Z. Adaptive risk-aware sharable and individual subspace learning for cancer survival analysis with multi-modality data. Brief Bioinform 2023; 24:6847200. [PMID: 36433784 DOI: 10.1093/bib/bbac489] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/16/2022] [Accepted: 10/15/2022] [Indexed: 11/27/2022] Open
Abstract
Biomedical multi-modality data (also named multi-omics data) refer to data that span different types and derive from multiple sources in clinical practices (e.g. gene sequences, proteomics and histopathological images), which can provide comprehensive perspectives for cancers and generally improve the performance of survival models. However, the performance improvement of multi-modality survival models may be hindered by two key issues as follows: (1) how to learn and fuse modality-sharable and modality-individual representations from multi-modality data; (2) how to explore the potential risk-aware characteristics in each risk subgroup, which is beneficial to risk stratification and prognosis evaluation. Additionally, learning-based survival models generally refer to numerous hyper-parameters, which requires time-consuming parameter setting and might result in a suboptimal solution. In this paper, we propose an adaptive risk-aware sharable and individual subspace learning method for cancer survival analysis. The proposed method jointly learns sharable and individual subspaces from multi-modality data, whereas two auxiliary terms (i.e. intra-modality complementarity and inter-modality incoherence) are developed to preserve the complementary and distinctive properties of each modality. Moreover, it equips with a grouping co-expression constraint for obtaining risk-aware representation and preserving local consistency. Furthermore, an adaptive-weighted strategy is employed to efficiently estimate crucial parameters during the training stage. Experimental results on three public datasets demonstrate the superiority of our proposed model.
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Affiliation(s)
- Zhangxin Zhao
- School of Biomedical Engineering at Southern Medical University, Guangdong, China
| | - Qianjin Feng
- School of Biomedical Engineering at Southern Medical University, Guangdong, China
| | - Yu Zhang
- School of Biomedical Engineering, Southern Medical University, Guangdong, China
| | - Zhenyuan Ning
- School of Biomedical Engineering at Southern Medical University, Guangdong, China
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Wang W, Yuan H, Han J, Liu W. PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery. Comput Struct Biotechnol J 2022; 21:365-377. [PMID: 36582441 PMCID: PMC9791601 DOI: 10.1016/j.csbj.2022.12.005] [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: 09/10/2022] [Revised: 12/02/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022] Open
Abstract
Risk gene identification has attracted much attention in the past two decades. Since most genes need to be translated into proteins and cooperate with other proteins to form protein complexes to carry out cellular functions, which significantly extends the functional diversity of individual proteins, revealing the molecular mechanism of cancer from a comprehensive perspective needs to shift from identifying individual risk genes toward identifying risk protein complexes. Here, we embed protein complexes into the regularized learning framework and propose a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes. Experiments on deep proteomic data of two cancer types show that PCLassoLog yields superior predictive performance on independent datasets. More importantly, PCLassoLog identifies risk protein complexes that not only contain individual risk proteins but also incorporate close partners that synergize with them. Furthermore, selection probabilities are calculated and two other protein complex-based models are proposed to complement PCLassoLog in identifying reliable risk protein complexes. Based on PCLassoLog, a pan-cancer analysis is performed to identify risk protein complexes in 12 cancer types. Finally, PCLassoLog is used to discover risk protein complexes associated with gene mutation. We implement all protein complex-based models as an R package PCLassoReg, which may serve as an effective tool to discover risk protein complexes in various contexts.
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Affiliation(s)
- Wei Wang
- College of Science, Heilongjiang Institute of Technology, Harbin 150050, China
| | - Haiyan Yuan
- College of Science, Heilongjiang Institute of Technology, Harbin 150050, China
| | - Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China,Corresponding authors.
| | - Wei Liu
- College of Science, Heilongjiang Institute of Technology, Harbin 150050, China,Corresponding authors.
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Identification of a Prognostic Model Based on Immune Cell Signatures in Clear Cell Renal Cell Carcinoma. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:1727575. [PMID: 36052158 PMCID: PMC9427244 DOI: 10.1155/2022/1727575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 07/22/2022] [Accepted: 07/30/2022] [Indexed: 11/18/2022]
Abstract
Background. Accumulating evidence substantiated that the immune cells were intricately intertwined with the prognosis and therapy of clear cell renal cell carcinoma (ccRCC). We aimed to construct an immune cell signatures (ICS) score model to predict the prognosis of ccRCC patients and furnish guidance for finding appropriate treatment strategies. Methods. Based on The Cancer Genome Atlas (TCGA) database, the normalized enrichment score (NES) of 184 ICSf was calculated using single-sample gene set enrichment analysis (ssGSEA). An ICS score model was generated in light of univariate Cox regression and Least absolute shrinkage and selection operator (Lasso)-Cox regression, which was independently validated in ArrayExpress database. In addition, we appraised the predictive power of the model via Kaplan-Meier (K-M) curves and time-dependent receiver operating characteristic (ROC) curves. Eventually, immune infiltration, genomic alterations and immunotherapy were analyzed between high and low ICS score groups. Results. Initially, we screened 11 ICS with prognostic impact based on 515 ccRCC patients. K-M curves presented that the high ICS score group experienced a poorer prognosis (
). In parallel, ROC curves revealed a satisfactory reliability of model to predict individual survival at 1, 3, and 5 years, with area under the curves (AUCs) of 0.744, 0.713, and 0.742, respectively. In addition, we revealed that the high ICS score group was characterized by increased infiltration of immune cells, strengthened BAP1 mutation frequency, and enhanced expression of immune checkpoint genes. Conclusion. The ICS score model has higher predictive power for patients’ prognosis and can instruct ccRCC patients in seeking suitable treatment.
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Additional Postoperative Radiotherapy Prolonged the Survival of Patients with I-IIA Small Cell Lung Cancer: Analysis of the SEER Database. JOURNAL OF ONCOLOGY 2022; 2022:6280538. [PMID: 35761902 PMCID: PMC9233591 DOI: 10.1155/2022/6280538] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/20/2022] [Indexed: 12/04/2022]
Abstract
Purpose Complete resection and adjuvant chemotherapy are recommended as the standard strategy for patients with stage I-IIA small cell lung cancer (SCLC). However, the role of additional postoperative radiotherapy (PORT) in treatment remains controversial. Methods Patients with stage I-IIA SCLC undergoing surgery and adjuvant chemotherapy were extracted from the Surveillance, Epidemiology, and End Results database. Stage I-IIA, defined as T1-2N0M0, was recalculated according to the 8th AJCC TNM staging system. Propensity score matching (PSM) was conducted to identify the therapeutic impact of PORT. Univariate Cox hazards regression and least absolute shrinkage and selection operator regression were utilized for primary screening of prognostic variables for I-IIA SCLC disease. A nomogram to predict overall survival (OS) was constructed based on the multivariate Cox proportional hazards model, evaluated with area under the curve, calibration curve, and decision curve analysis, and validated with bootstrap resampling. Results Our results demonstrated that compared with no PORT, PORT significantly prolonged the median OS (8.58 vs. 5.17 years, HR = 0.61 [0.39–0.96], P = 0.032) and median cancer-specific survival (11.33 vs. 8.08, HR = 0.47 [0.27–0.82], P = 0.0086) after PSM. The 5-year OS rate was 61.56% vs. 46.60%. Five variables including age at diagnosis, gender, T stage, surgical type, and PORT were elucidated to impact on prognosis and included in a nomogram to predict 3-/5-/10-year OS probability. The area under the curve values were 0.72, 0.71, and 0.81, respectively. The nomogram also exhibited satisfactory accuracy and clinical usefulness. Conclusion PORT was verified to improve the OS of patients with T1-2N0M0 SCLC after surgery and chemotherapy. A prognostic nomogram was developed and validated for OS prediction for these patients.
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Yang M, He H, Peng T, Lu Y, Yu J. Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8598046. [PMID: 35392038 PMCID: PMC8983226 DOI: 10.1155/2022/8598046] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/01/2022] [Accepted: 03/10/2022] [Indexed: 11/17/2022]
Abstract
Background A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA). Methods From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan-Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model's independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built. Results Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability. Conclusions The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.
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Affiliation(s)
- Mian Yang
- Department of Colon Anorectal Surgery, Lihuili Hospital, Ningbo Medical Center, Ningbo, Zhejiang, China
| | - Haibin He
- Department of Gastrointestinal Surgery, Lihuili Hospital, Ningbo Medical Center, Ningbo, Zhejiang, China
| | - Tao Peng
- Department of Colon Anorectal Surgery, Lihuili Hospital, Ningbo Medical Center, Ningbo, Zhejiang, China
| | - Yi Lu
- Department of Chemoradiotherapy, Lihuili Hospital, Ningbo Medical Center, Ningbo, Zhejiang, China
| | - Jiazi Yu
- Department of Colon Anorectal Surgery, Lihuili Hospital, Ningbo Medical Center, Ningbo, Zhejiang, China
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Li L, Liu ZP. Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models. J Transl Med 2021; 19:514. [PMID: 34930307 PMCID: PMC8686664 DOI: 10.1186/s12967-021-03180-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group. METHODS Regularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, [Formula: see text]-RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts. RESULTS The nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues ([Formula: see text]). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group ([Formula: see text]) in both internal and external validations. CONCLUSIONS The proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.
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Affiliation(s)
- Lingyu Li
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, China
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
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