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Herreros-Pomares A, Hervás D, Bagan-Debon L, Proaño A, Garcia D, Sandoval J, Bagan J. Oral cancers preceded by proliferative verrucous leukoplakia exhibit distinctive molecular features. Oral Dis 2024; 30:1072-1083. [PMID: 36892444 DOI: 10.1111/odi.14550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 02/17/2023] [Accepted: 02/22/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVE Proliferative verrucous leukoplakia (PVL) has high rates of malignant transformation into oral squamous cell carcinoma (OSCC), but the clinical and evolutionary pattern of OSCC from PVL (PVL-OSCC) is more favorable than that of OSCC not preceded by PVL (OSCC). Here, we aimed to explore the pathophysiologic differences between PVL-OSCC and OSCC through transcriptomic and DNA methylation analyses. MATERIALS AND METHODS In this case-control study, oral biopsies from 8 PVL-OSCC and 10 OSCC patients were obtained for global sequencing using RNAseq and a genome-wide DNA methylation analysis via the Infinium EPIC Platform (graphical abstract). RESULTS One hundred and thirty-three differentially expressed genes (DEGs) were detected, 94 of them upregulated in OSCC. Most of these genes were previously described in cancer and associated with prognosis. The integrative analysis revealed 26 DEGs, corresponding to 37 CpGs, whose promoters were regulated by DNA methylation. Twenty-nine of the CpGs were found as hypermethylated in PVL-OSCC. Only 5 of the genes that were aberrantly methylated and differentially expressed were upregulated in PVL-OSCC patients, whereas 21 were underexpressed. CONCLUSIONS PVL-OSCC patients presented lower expression of cancer-related genes. Hypermethylation of the promoter region of many genes was also noticed, indicating that DNA methylation could be a regulatory mechanism.
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Affiliation(s)
- Alejandro Herreros-Pomares
- Department of Biotechnology, Universitat Politècnica de València, Valencia, Spain
- Centro de Investigación Biomédica en Red Cáncer, CIBERONC, Madrid, Spain
| | - David Hervás
- Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain
| | - Leticia Bagan-Debon
- Medicina Oral Unit, Stomatology Department, Valencia University, Valencia, Spain
| | - Alex Proaño
- Medicina Oral Unit, Stomatology Department, Valencia University, Valencia, Spain
| | - Diana Garcia
- Epigenomics Unit, Health Research Institute La Fe, Valencia, Spain
| | - Juan Sandoval
- Epigenomics Unit, Health Research Institute La Fe, Valencia, Spain
- Biomarkers and Precision Medicine Unit, Health Research Institute La Fe, Valencia, Spain
| | - Jose Bagan
- Centro de Investigación Biomédica en Red Cáncer, CIBERONC, Madrid, Spain
- Medicina Oral Unit, Stomatology Department, Valencia University, Valencia, Spain
- Department of Stomatology and Maxillofacial Surgery, Hospital General Universitario de Valencia, Valencia, Spain
- Precancer and oral cancer research group of Valencia University, Valencia, Spain
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2
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Ahmad M, Irfan MA, Sadique U, Haq IU, Jan A, Khattak MI, Ghadi YY, Aljuaid H. Multi-Method Analysis of Histopathological Image for Early Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning and Hybrid Techniques. Cancers (Basel) 2023; 15:5247. [PMID: 37958422 PMCID: PMC10650156 DOI: 10.3390/cancers15215247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/22/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient's chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.
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Affiliation(s)
- Mehran Ahmad
- Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan; (M.A.); (A.J.); (M.I.K.)
- AIH, Intelligent Information Processing Lab (NCAI), University of Engineering and Technology, Peshawar 25000, Pakistan; (U.S.); (I.u.H.)
| | - Muhammad Abeer Irfan
- Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan;
| | - Umar Sadique
- AIH, Intelligent Information Processing Lab (NCAI), University of Engineering and Technology, Peshawar 25000, Pakistan; (U.S.); (I.u.H.)
- Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan;
| | - Ihtisham ul Haq
- AIH, Intelligent Information Processing Lab (NCAI), University of Engineering and Technology, Peshawar 25000, Pakistan; (U.S.); (I.u.H.)
- Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
| | - Atif Jan
- Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan; (M.A.); (A.J.); (M.I.K.)
| | - Muhammad Irfan Khattak
- Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan; (M.A.); (A.J.); (M.I.K.)
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Al Ain 15551, United Arab Emirates;
| | - Hanan Aljuaid
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), Riyadh 11671, Saudi Arabia
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Ai J, Tan Y, Liu B, Song Y, Wang Y, Xia X, Fu Q. Systematic establishment and verification of an epithelial-mesenchymal transition gene signature for predicting prognosis of oral squamous cell carcinoma. Front Genet 2023; 14:1113137. [PMID: 37636263 PMCID: PMC10447895 DOI: 10.3389/fgene.2023.1113137] [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: 12/01/2022] [Accepted: 07/11/2023] [Indexed: 08/29/2023] Open
Abstract
Objective: Epithelial-mesenchymal transition (EMT) is linked to an unfavorable prognosis in oral squamous cell carcinoma (OSCC). Here, we aimed to develop an EMT gene signature for OSCC prognosis. Methods: In TCGA dataset, prognosis-related EMT genes with p < 0.05 were screened in OSCC. An EMT gene signature was then conducted with LASSO method. The efficacy of this signature in predicting prognosis was externally verified in the GSE41613 dataset. Correlations between this signature and stromal/immune scores and immune cell infiltration were assessed by ESTIMATE and CIBERSORT algorithms. GSEA was applied for exploring significant signaling pathways activated in high- and low-risk phenotypes. Expression of each gene was validated in 40 paired OSCC and normal tissues via RT-qPCR. Results: A prognostic 9-EMT gene signature was constructed in OSCC. High risk score predicted poorer clinical outcomes than low risk score. ROCs confirmed the well performance on predicting 1-, 3- and 5-year survival. Multivariate cox analysis revealed that this signature was independently predictive of OSCC prognosis. The well predictive efficacy was validated in the GSE41613 dataset. Furthermore, this signature was distinctly related to stromal/immune scores and immune cell infiltration in OSCC. Distinct pathways were activated in two subgroups. After validation, AREG, COL5A3, DKK1, GAS1, GPX7 and PLOD2 were distinctly upregulated and SFRP1 was downregulated in OSCC than normal tissues. Conclusion: Our data identified and verified a robust EMT gene signature in OSCC, which provided a novel clinical tool for predicting prognosis and several targets against OSCC therapy.
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Affiliation(s)
- Jun Ai
- Department of Stomatology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Yaqin Tan
- Department of Stomatology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Bo Liu
- Department of Urology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Yuhong Song
- Department of Stomatology, Shenzhen Longhua District Central Hospital, Shenzhen, China
| | - Yanqin Wang
- Department of Stomatology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Xin Xia
- Department of Stomatology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Qicheng Fu
- Department of Stomatology, Shenzhen Longhua District Central Hospital, Shenzhen, China
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Zhou Z, Tang J, Lu Y, Jia J, Luo T, Su K, Dai X, Zhang H, Liu O. Prognosis-related molecular subtyping in head and neck squamous cell carcinoma patients based on glycolytic/cholesterogenic gene data. Cancer Cell Int 2023; 23:37. [PMID: 36841765 PMCID: PMC9960414 DOI: 10.1186/s12935-023-02880-3] [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: 07/29/2022] [Accepted: 02/19/2023] [Indexed: 02/27/2023] Open
Abstract
BACKGROUND Head and neck squamous cell carcinoma (HNSCC) remains an unmet medical challenge. Metabolic reprogramming is a hallmark of diverse cancers, including HNSCC. METHODS We investigated the metabolic profile in HNSCC by using The Cancer Genome Atlas (TCGA) (n = 481) and Gene Expression Omnibus (GEO) (n = 97) databases. The metabolic stratification of HNSCC samples was identified by using unsupervised k-means clustering. We analyzed the correlations of the metabolic subtypes in HNSCC with featured genomic alterations and known HNSCC subtypes. We further validated the metabolism-related subtypes based on features of ENO1, PFKFB3, NSDHL and SQLE expression in HNSCC by Immunohistochemistry. In addition, genomic characteristics of tumor metabolism that varied among different cancer types were confirmed. RESULTS Based on the median expression of coexpressed cholesterogenic and glycolytic genes, HNSCC subtypes were identified, including glycolytic, cholesterogenic, quiescent and mixed subtypes. The quiescent subtype was associated with the longest survival and was distributed in stage I and G1 HNSCC. Mutation analysis of HNSCC genes indicated that TP53 has the highest mutation frequency. The CDKN2A mutation frequency has the most significant differences amongst these four subtypes. There is good overlap between our metabolic subtypes and the HNSCC subtype. CONCLUSION The four metabolic subtypes were successfully determined in HNSCC. Compared to the quiescent subtype, glycolytic, cholesterogenic and mixed subtypes had significantly worse outcome, which might offer guidelines for developing a novel treatment strategy for HNSCC.
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Affiliation(s)
- Zekun Zhou
- grid.216417.70000 0001 0379 7164Hunan Key Laboratory of Oral Health Research & Hunan 3D Printing Engineering Research Center of Oral Care & Hunan Clinical Research Center of Oral Major Diseases and Oral Health & Academician Workstation for Oral-maxilofacial and Regenerative Medicine & Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University, Changsha, 410008 Hunan China
| | - Jianfei Tang
- grid.216417.70000 0001 0379 7164Hunan Key Laboratory of Oral Health Research & Hunan 3D Printing Engineering Research Center of Oral Care & Hunan Clinical Research Center of Oral Major Diseases and Oral Health & Academician Workstation for Oral-maxilofacial and Regenerative Medicine & Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University, Changsha, 410008 Hunan China
| | - Yixuan Lu
- grid.216417.70000 0001 0379 7164Hunan Key Laboratory of Oral Health Research & Hunan 3D Printing Engineering Research Center of Oral Care & Hunan Clinical Research Center of Oral Major Diseases and Oral Health & Academician Workstation for Oral-maxilofacial and Regenerative Medicine & Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University, Changsha, 410008 Hunan China
| | - Jia Jia
- grid.216417.70000 0001 0379 7164Hunan Key Laboratory of Oral Health Research & Hunan 3D Printing Engineering Research Center of Oral Care & Hunan Clinical Research Center of Oral Major Diseases and Oral Health & Academician Workstation for Oral-maxilofacial and Regenerative Medicine & Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University, Changsha, 410008 Hunan China
| | - Tiao Luo
- grid.216417.70000 0001 0379 7164Hunan Key Laboratory of Oral Health Research & Hunan 3D Printing Engineering Research Center of Oral Care & Hunan Clinical Research Center of Oral Major Diseases and Oral Health & Academician Workstation for Oral-maxilofacial and Regenerative Medicine & Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University, Changsha, 410008 Hunan China
| | - Kaixin Su
- grid.216417.70000 0001 0379 7164Hunan Key Laboratory of Oral Health Research & Hunan 3D Printing Engineering Research Center of Oral Care & Hunan Clinical Research Center of Oral Major Diseases and Oral Health & Academician Workstation for Oral-maxilofacial and Regenerative Medicine & Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University, Changsha, 410008 Hunan China
| | - Xiaohan Dai
- Hunan Key Laboratory of Oral Health Research & Hunan 3D Printing Engineering Research Center of Oral Care & Hunan Clinical Research Center of Oral Major Diseases and Oral Health & Academician Workstation for Oral-maxilofacial and Regenerative Medicine & Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University, Changsha, 410008, Hunan, China.
| | - Haixia Zhang
- The Oncology Department of Xiangya Second Hospital, Central South University, Changsha, 410011, Hunan, China.
| | - Ousheng Liu
- Hunan Key Laboratory of Oral Health Research & Hunan 3D Printing Engineering Research Center of Oral Care & Hunan Clinical Research Center of Oral Major Diseases and Oral Health & Academician Workstation for Oral-maxilofacial and Regenerative Medicine & Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University, Changsha, 410008, Hunan, China.
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Noh JK, Woo SR, Kong M, Lee MK, Lee JW, Lee YC, Ko S, Eun Y. Gene signature predicting recurrence in oral squamous cell carcinoma is characterized by increased oxidative phosphorylation. Mol Oncol 2022; 17:134-149. [PMID: 36271693 PMCID: PMC9812830 DOI: 10.1002/1878-0261.13328] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 10/09/2022] [Accepted: 10/21/2022] [Indexed: 02/03/2023] Open
Abstract
Although numerous studies have used systemic approaches to identify prognostic predictors in oral squamous cell carcinoma (OSCC), the effectiveness of these approaches has not been assessed clinically. Further, the mechanism underlying malignant behaviors in OSCC is poorly characterized. This study aimed to develop and verify accurate prognostic predictors for OSCC patients and assess the associated biology. We identified an OSCC-recurrence-related gene signature (ORGS) using a Cox regression analysis. Functional enrichment analysis was used to identify enriched pathways and biological processes to reveal the underlying mechanism of OSCC malignant behavior. The ORGS successfully divided OSCC patients into low- and high-risk groups with significantly different overall survivals. Pathway analysis revealed oxidative phosphorylation (OXPHOS) as a signaling pathway associated with the ORGS in OSCC. Interestingly, high OXPHOS status was strongly associated with poor overall survival in OSCC patients. Mediator complex subunit 30 (MED30) was a predicted upstream regulator of OXPHOS, and knockdown of MED30 reduced histone acetylation. We identified that the ORGS was strongly correlated with OXPHOS regulatory processes, suggesting OXPHOS as a key mechanism leading to poor prognosis in OSCC.
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Affiliation(s)
- Joo Kyung Noh
- Department of Biomedical Science and Technology, Graduate SchoolKyung Hee UniversitySeoulKorea
| | - Seon Rang Woo
- Department of Otolaryngology‐Head & Neck Surgery, Kyung Hee University School of MedicineKyung Hee University Medical CenterSeoulKorea
| | - Moonkyoo Kong
- Department of Radiation Oncology, Kyung Hee University School of MedicineKyung Hee University Medical CenterSeoulKorea
| | - Min Kyeong Lee
- Department of Biomedical Science and Technology, Graduate SchoolKyung Hee UniversitySeoulKorea
| | - Jung Woo Lee
- Department of Oral and Maxillofacial Surgery, School of DentistryKyung Hee UniversitySeoulKorea
| | - Young Chan Lee
- Department of Otolaryngology‐Head & Neck Surgery, Kyung Hee University School of MedicineKyung Hee University Medical CenterSeoulKorea
| | - Seong‐Gyu Ko
- Department of Preventive Medicine, College of Korean MedicineKyung Hee UniversitySeoulKorea
| | - Young‐Gyu Eun
- Department of Biomedical Science and Technology, Graduate SchoolKyung Hee UniversitySeoulKorea,Department of Otolaryngology‐Head & Neck Surgery, Kyung Hee University School of MedicineKyung Hee University Medical CenterSeoulKorea
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Machine-Learning Applications in Oral Cancer: A Systematic Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115715] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Over the years, several machine-learning applications have been suggested to assist in various clinical scenarios relevant to oral cancer. We offer a systematic review to identify, assess, and summarize the evidence for reported uses in the areas of oral cancer detection and prevention, prognosis, pre-cancer, treatment, and quality of life. The main algorithms applied in the context of oral cancer applications corresponded to SVM, ANN, and LR, comprising 87.71% of the total published articles in the field. Genomic, histopathological, image, medical/clinical, spectral, and speech data were used most often to predict the four areas of application found in this review. In conclusion, our study has shown that machine-learning applications are useful for prognosis, diagnosis, and prevention of potentially malignant oral lesions (pre-cancer) and therapy. Nevertheless, we strongly recommended the application of these methods in daily clinical practice.
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Zhang M, Chen X, Chen H, Zhou M, Liu Y, Hou Y, Nie M, Liu X. Identification and validation of potential novel biomarkers for oral squamous cell carcinoma. Bioengineered 2021; 12:8845-8862. [PMID: 34606406 PMCID: PMC8806987 DOI: 10.1080/21655979.2021.1987089] [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] [Indexed: 10/27/2022] Open
Abstract
Our study aimed to explore potential new diagnostic biomarkers in patients with oral squamous cell carcinoma (OSCC) to find new target molecules involved in the progression of OSCC. Potential novel biomarkers of OSCC were identified using a protein microarray assay. Compared with the healthy control group, there were five proteins (I309, GDF15, AXL, MMP3, and CTACK) in the serum of in situ oral cancer group. However, there were four differentially expressed proteins (MCSF, I309, MMP3, and CTACK) in the serum of the OSCC group. Receiver operating characteristic (ROC) curve analysis results suggested that these six proteins (I309, GDF15, AXL, MMP3, CTACK, and MCSF) had diagnostic value for OSCC. Based on The Cancer Genome Atlas (TCGA) database, we found that only GDF15 expression was associated with the prognosis of OSCC. Subsequently, we verified the expression levels of six proteins in HSC-3 and HaCaT cells, and the results showed that the level of these six proteins was significantly higher in HSC-3 cells than in normal HaCaT cells. Similarly, in the OSCC nude mouse model, the expression levels of these proteins were significantly upregulated in OSCC tumor tissue compared to the normal tissue. GDF15, MMP3, AXL, MCSF, I309, and CTACK may be used as biomarkers for OSCC diagnosis and provide a novel study direction for the treatment of OSCC.
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Affiliation(s)
- Mengxue Zhang
- Department of Periodontics & Oral Mucosal Diseases, The Affiliated Stomatology Hospital of Southwest Medical University, Luzhou, Sichuan, China.,Oral & Maxillofacial Reconstruction and Regeneration Laboratory, Southwest Medical University, Luzhou, Sichuan, China
| | - Xiao Chen
- Department of Stomatology Technology, School of Medical Technology, Sichuan College of Traditional Medcine, Mianyang, China.,Department of Orthodontics, Mianyang Stomatological Hospital, Mianyang, China
| | - He Chen
- Department of Oral and Maxillofacial Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Minyue Zhou
- Department of Periodontics & Oral Mucosal Diseases, The Affiliated Stomatology Hospital of Southwest Medical University, Luzhou, Sichuan, China.,Oral & Maxillofacial Reconstruction and Regeneration Laboratory, Southwest Medical University, Luzhou, Sichuan, China
| | - Yaoqiang Liu
- Department of Oral and Maxillofacial Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yali Hou
- Department of Oral Pathology, School and Hospital of Stomatology, Hebei Medical University & Hebei Key Laboratory of Stomatology, Shijiazhuang, China
| | - Minhai Nie
- Department of Periodontics & Oral Mucosal Diseases, The Affiliated Stomatology Hospital of Southwest Medical University, Luzhou, Sichuan, China.,Oral & Maxillofacial Reconstruction and Regeneration Laboratory, Southwest Medical University, Luzhou, Sichuan, China
| | - Xuqian Liu
- Department of Periodontics & Oral Mucosal Diseases, The Affiliated Stomatology Hospital of Southwest Medical University, Luzhou, Sichuan, China.,Oral & Maxillofacial Reconstruction and Regeneration Laboratory, Southwest Medical University, Luzhou, Sichuan, China
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8
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Lin Y, Zhang Y, Luo L, Zhang X. Clinical effect of robot-assisted radical cystectomy in bladder cancer. Am J Transl Res 2021; 13:10545-10553. [PMID: 34650725 PMCID: PMC8506996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 06/11/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE This study aimed to determine whether robot-assisted radical cystectomy (RARC) can accelerate recovery, improve pelvic lymph node dissection effects, and reduce serum tumor marker tumor specific growth factor (TSGF) levels in patients with bladder cancer. METHODS A total of 96 patients with bladder cancer admitted to our hospital were recruited as the research participants. Among them, 43 patients who adopted radical cystectomy were enrolled in the control group (CG), and 53 patients treated with RARC were included in the research group (RG). The operation time, intraoperative blood loss, postoperative bowel recovery time, gastrointestinal function recovery, complication rate, clinical efficacy, changes of TSGF levels before and after operation, postoperative satisfaction and quality of life were observed. RESULTS Compared with the CG, patients in the RG experienced longer operation times (P<0.05), less intraoperative blood loss (P<0.05), and faster time to bowel recovery, anal exhaust, and anal defecation (P<0.05); moreover, the RG had a lower incidence rate of complications (P=0.025) and TSGF levels (P<0.05), higher effective cure rate (P=0.023) and satisfaction degree (P=0.048), as well as superior quality of life scores in six dimensions (P<0.05). CONCLUSION The application of RARC can accelerate the recovery of patients with bladder cancer, improve the pelvic lymph node dissection effects, and reduce the serum levels of tumor marker TSGF.
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Affiliation(s)
- Yuzhu Lin
- Department of Urology, Hainan Provincial People’s HospitalHaikou 570311, Hainan Province, China
| | - Ying Zhang
- Nursing Department of Hainan Provincial People’s HospitalHaikou 570311, Hainan Province, China
| | - Liumei Luo
- Department of Urology, Hainan Provincial People’s HospitalHaikou 570311, Hainan Province, China
| | - Xin Zhang
- Department of Urology, The Second Affiliated Hospital of Shandong University of Traditional Chinese MedicineJinan 250000, Shandong Province, China
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9
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Tang M, Ge Y, Zhang Q, Zhang X, Xiao C, Li Q, Zhang X, Zhang K, Song M, Wang X, Yang M, Ruan G, Mu Y, Huang H, Cong M, Zhou F, Shi H. Near-term prognostic impact of integrated muscle mass and function in upper gastrointestinal cancer. Clin Nutr 2021; 40:5169-5179. [PMID: 34461591 DOI: 10.1016/j.clnu.2021.07.028] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/13/2021] [Accepted: 07/26/2021] [Indexed: 12/31/2022]
Abstract
BACKGROUND Despite the known association between muscle mass/function and malnutrition-related mortality in upper gastrointestinal (UGI) cancer, no comprehensive study to determine the impact of muscle mass-dominant nutritional status on cancer prognosis has been conducted. The present study aimed to investigate the prognostic significance of integrated muscle mass and function in UGI cancer. METHODS Between July 2013 and March 2018, we enrolled 2546 cancer patients with risks of malnutrition (Nutrition Risk Screening 2002, ≥3 points) from a multicenter cohort study and split 527 patients with primary UGI cancer into an internal validation group. We prospectively performed instant nutritional assessment and recorded all general clinical characteristics of the participants, such as weight loss, body mass index, anthropometric measurements of muscle mass and function, dietary intake conditions, and disease burden and/or inflammation status based on the validated tools. Prognostic analyses were performed with post-assessment overall survival (OS). RESULTS According to the entire set, UGI cancer was identified as the dominant risk factor for disease burden and inflammation criteria (hazard ratio (HR), 2.08, 95% confidence interval (Cl), 1.81-2.39, P < 0.001). Integrated muscle mass/function analysis with validated cutoff values showed that hand grip strength/weight followed by triceps skinfold thickness and maximum calf circumference are the most potent predictors. Univariate and multivariate analyses revealed that reduced muscle mass/function (74.8%) and dietary intake (66.2%) independently affect OS of patients with UGI cancer. Significant associations were found between the reduced muscle mass/reduced dietary intake and the shortest OS (HR, 4.48; 95% Cl, 3.07-6.53; P < 0.001). Appending subgroups of muscle mass/function and dietary intake to the pre-existing risk model increased the efficiency of the time-dependent receiver operating characteristic curve analysis for OS in UGI cancer, particularly within 2 years of instant nutritional assessment. CONCLUSION Impaired muscle mass/function adversely affects the near-term prognosis in patients with UGI cancer. Along with a comprehensive evaluation of dietary intake conditions, the timely nutritional assessment might be useful for risk stratification of UGI cancers with potential for enteral and parenteral nutrition interventions. REGISTRATION NUMBER ChiCTR1800020329.
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Affiliation(s)
- Meng Tang
- Department of GI Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Yizhong Ge
- Department of GI Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Qi Zhang
- Department of GI Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Xi Zhang
- Department of GI Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Chunyun Xiao
- Department of Clinical Nutrition Baylor Scott & White Institute for Rehabilitation, Dallas, TX, 75204, USA
| | - Qinqin Li
- Department of GI Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Xiaowei Zhang
- Department of GI Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Kangping Zhang
- Department of GI Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Mengmeng Song
- Department of GI Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Xin Wang
- Department of GI Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Ming Yang
- Department of GI Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Guotian Ruan
- Department of GI Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Ying Mu
- Department of Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Hongyan Huang
- Department of Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Minghua Cong
- Comprehensive Oncology Department, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Fuxiang Zhou
- Department of Radiation and Medical Oncology, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Clinical Cancer Study Center, Zhongnan Hospital, Wuhan University, Wuhan, 430071, China.
| | - Hanping Shi
- Department of GI Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.
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10
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Adeoye J, Tan JY, Choi SW, Thomson P. Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review. Int J Med Inform 2021; 154:104557. [PMID: 34455119 DOI: 10.1016/j.ijmedinf.2021.104557] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES Machine learning platforms are now being introduced into modern oncological practice for classification and prediction of patient outcomes. To determine the current status of the application of these learning models as adjunctive decision-making tools in oral cavity cancer management, this systematic review aims to summarize the accuracy of machine-learning based models for disease outcomes. METHODS Electronic databases including PubMed, Scopus, EMBASE, Cochrane Library, LILACS, SciELO, PsychINFO, and Web of Science were searched up until December 21, 2020. Pertinent articles detailing the development and accuracy of machine learning prediction models for oral cavity cancer outcomes were selected in a two-stage process. Quality assessment was conducted using the Quality in Prognosis Studies (QUIPS) tool and results of base studies were qualitatively synthesized by all authors. Outcomes of interest were malignant transformation of precancer lesions, cervical lymph node metastasis, as well as treatment response, and prognosis of oral cavity cancer. RESULTS Twenty-seven articles out of 950 citations identified from electronic and manual searching were included in this study. Five studies had low bias concerns on the QUIPS tool. Prediction of malignant transformation, cervical lymph node metastasis, treatment response, and prognosis were reported in three, six, eight, and eleven articles respectively. Accuracy of these learning models on the internal or external validation sets ranged from 0.85 to 0.97 for malignant transformation prediction, 0.78-0.91 for cervical lymph node metastasis prediction, 0.64-1.00 for treatment response prediction, and 0.71-0.99 for prognosis prediction. In general, most trained algorithms predicting these outcomes performed better than alternate methods of prediction. We also found that models including molecular markers in training data had better accuracy estimates for malignant transformation, treatment response, and prognosis prediction. CONCLUSION Machine learning algorithms have a satisfactory to excellent accuracy for predicting three of four oral cavity cancer outcomes i.e., malignant transformation, nodal metastasis, and prognosis. However, considering the training approach of many available classifiers, these models may not be streamlined enough for clinical application currently.
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Affiliation(s)
- John Adeoye
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Jia Yan Tan
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region.
| | - Siu-Wai Choi
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region.
| | - Peter Thomson
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region
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11
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Zhou H, Zheng M, Shi M, Wang J, Huang Z, Zhang H, Zhou Y, Shi J. Characteristic of molecular subtypes in lung adenocarcinoma based on m6A RNA methylation modification and immune microenvironment. BMC Cancer 2021; 21:938. [PMID: 34416861 PMCID: PMC8379743 DOI: 10.1186/s12885-021-08655-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 07/10/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) is a major subtype of lung cancer and closely associated with poor prognosis. N6-methyladenosine (m6A), one of the most predominant modifications in mRNAs, is found to participate in tumorigenesis. However, the potential function of m6A RNA methylation in the tumor immune microenvironment is still murky. METHODS The gene expression profile cohort and its corresponding clinical data of LUAD patients were downloaded from TCGA database and GEO database. Based on the expression of 21 m6A regulators, we identified two distinct subgroups by consensus clustering. The single-sample gene-set enrichment analysis (ssGSEA) algorithm was conducted to quantify the relative abundance of the fraction of 28 immune cell types. The prognostic model was constructed by Lasso Cox regression. Survival analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic model. RESULT Consensus classification separated the patients into two clusters (clusters 1 and 2). Those patients in cluster 1 showed a better prognosis and were related to higher immune scores and more immune cell infiltration. Subsequently, 457 differentially expressed genes (DEGs) between the two clusters were identified, and then a seven-gene prognostic model was constricted. The survival analysis showed poor prognosis in patients with high-risk score. The ROC curve confirmed the predictive accuracy of this prognostic risk signature. Besides, further analysis indicated that there were significant differences between the high-risk and low-risk groups in stages, status, clustering subtypes, and immunoscore. Low-risk group was related to higher immune score, more immune cell infiltration, and lower clinical stages. Moreover, multivariate analysis revealed that this prognostic model might be a powerful prognostic predictor for LUAD. Ultimately, the efficacy of this prognostic model was successfully validated in several external cohorts (GSE30219, GSE50081 and GSE72094). CONCLUSION Our study provides a robust signature for predicting patients' prognosis, which might be helpful for therapeutic strategies discovery of LUAD.
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Affiliation(s)
- Hao Zhou
- Department of Thoracic Surgery, Affiliated Hospital of Nantong University and Medical School of Nantong University, Nantong, 226001, Jiangsu, China
| | - Miaosen Zheng
- Department of Pathology, Affiliated Hospital of Nantong University and Medical School of Nantong University, Nantong, 226001, Jiangsu, China
| | - Muqi Shi
- Medical School of Nantong University, Nantong, 226001, Jiangsu, China
| | - Jinjie Wang
- Department of Thoracic Surgery, Affiliated Hospital of Nantong University and Medical School of Nantong University, Nantong, 226001, Jiangsu, China
| | - Zhanghao Huang
- Department of Thoracic Surgery, Affiliated Hospital of Nantong University and Medical School of Nantong University, Nantong, 226001, Jiangsu, China
| | - Haijian Zhang
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, Jiangsu, China
| | - Youlang Zhou
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, Jiangsu, China.
| | - Jiahai Shi
- Department of Thoracic Surgery, Affiliated Hospital of Nantong University and Medical School of Nantong University, Nantong, 226001, Jiangsu, China.
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12
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Li Y, Wang S, Zhang X, Yang R, Wei X, Yan R, Jiang Y, Shen W. Expression Characteristics and Significant Prognostic Values of PGK1 in Breast Cancer. Front Mol Biosci 2021; 8:695420. [PMID: 34291087 PMCID: PMC8287903 DOI: 10.3389/fmolb.2021.695420] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/21/2021] [Indexed: 11/13/2022] Open
Abstract
It was proven that PGK1 plays a vital role in the proliferation, migration, and invasion of human breast cancer. However, the correlation of PGK1 mRNA and protein expression with clinicopathologic characteristics and prognostic values according to various kinds of breast cancer patient classifications remains unsufficient. Here, we analyzed data from the Oncomine database, Breast cancer Gene-Expression Miner v4.5, TNMplot, MuTarget, PrognoScan database, and clinical bioinformatics to investigate PGK1 expression distribution and prognostic value in breast cancer patients. Our study revealed that the mRNA and protein expression levels of PGK1 were up-regulated in various clinicopathologic types of breast cancer. Moreover, the expression of PGK1 was correlated with mutations of common tumor suppressor genes TP53 and CDH1. In addition, we found that high mRNA level of PGK1 was significantly associated with poor OS, RFS, and DMFS. Notably, Cox regression analysis showed that PGK1 could be used as an independent prognostic marker. In summary, the aforementioned findings suggested that PGK1 might be not only explored as a potential biomarker, but also combined with TP53/CDH1 for chemotherapy in breast cancer.
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Affiliation(s)
- Yanping Li
- Department of Pathology and Institute of Precision Medicine, Jining Medical University, Jining, China
| | - Shanshan Wang
- Department of Pathology and Institute of Precision Medicine, Jining Medical University, Jining, China
| | - Xiaoyuan Zhang
- Department of Pathology and Institute of Precision Medicine, Jining Medical University, Jining, China
| | - Rui Yang
- Department of Pathology and Institute of Precision Medicine, Jining Medical University, Jining, China
| | - Xiaonan Wei
- Department of Pathology and Institute of Precision Medicine, Jining Medical University, Jining, China
| | - Ruirong Yan
- Department of Pathology and Institute of Precision Medicine, Jining Medical University, Jining, China
| | - Yaru Jiang
- Department of Pathology and Institute of Precision Medicine, Jining Medical University, Jining, China
| | - Wenzhi Shen
- Department of Pathology and Institute of Precision Medicine, Jining Medical University, Jining, China.,Institute of Breast Research, Jining Medical University, Jining, China
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13
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Wang HC, Chou MC, Wu CC, Chan LP, Moi SH, Pan MR, Liu TC, Yang CH. Application of the Interaction between Tissue Immunohistochemistry Staining and Clinicopathological Factors for Evaluating the Risk of Oral Cancer Progression by Hierarchical Clustering Analysis: A Case-Control Study in a Taiwanese Population. Diagnostics (Basel) 2021; 11:diagnostics11060925. [PMID: 34063938 PMCID: PMC8224004 DOI: 10.3390/diagnostics11060925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/12/2021] [Accepted: 05/17/2021] [Indexed: 01/31/2023] Open
Abstract
The aim of this single-center case-control study is to investigate the feasibility and accuracy of oral cancer protein risk stratification (OCPRS) to analyze the risk of cancer progression. All patients diagnosed with oral cancer in Taiwan, between 2012 and 2014, and who underwent surgical intervention were selected for the study. The tissue was further processed for immunohistochemistry (IHC) for 21 target proteins. Analyses were performed using the results of IHC staining, clinicopathological characteristics, and survival outcomes. Novel stratifications with a hierarchical clustering approach and combinations were applied using the Cox proportional hazard regression model. Of the 163 participants recruited, 102 patients were analyzed, and OCPRS successfully identified patients with different progression-free survival (PFS) profiles in high-risk (53 subjects) versus low-risk (49 subjects) groups (p = 0.012). OCPRS was composed of cytoplasmic PLK1, phosphoMet, and SGK2 IHC staining. After controlling for the influence of clinicopathological features, high-risk patients were 2.33 times more likely to experience cancer progression than low-risk patients (p = 0.020). In the multivariate model, patients with extranodal extension (HR = 2.66, p = 0.045) demonstrated a significantly increased risk for disease progression. Risk stratification with OCPRS provided distinct PFS groups for patients with oral cancer after surgical intervention. OCPRS appears suitable for routine clinical use for progression and prognosis estimation.
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Affiliation(s)
- Hui-Ching Wang
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Division of Hematology and Oncology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
| | - Meng-Chun Chou
- Department of Nursing, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
| | - Chun-Chieh Wu
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Department of Pathology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Leong-Perng Chan
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Department of Otolaryngology-Head and Neck Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Otorhinolaryngology-Head and Neck Surgery, Kaohsiung Municipal Ta-Tung Hospital and Kaohsiung Medical University Hospital, Kaohsiung 807, Taiwan
| | - Sin-Hua Moi
- Center of Cancer Program Development, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824, Taiwan;
| | - Mei-Ren Pan
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Correspondence: (M.-R.P.); (T.-C.L.); (C.-H.Y.); Tel.: +886-7-3121101-5092-34 (M.-R.P.); +886-4-781-3888 (T.-C.L.); +886-7-381-4526 (C.-H.Y.); Fax: +886-7-3218309 (M.-R.P.)
| | - Ta-Chih Liu
- Department of Hematology-Oncology, Chang Bing Show Chwan Memorial Hospital, Changhua 505, Taiwan
- Correspondence: (M.-R.P.); (T.-C.L.); (C.-H.Y.); Tel.: +886-7-3121101-5092-34 (M.-R.P.); +886-4-781-3888 (T.-C.L.); +886-7-381-4526 (C.-H.Y.); Fax: +886-7-3218309 (M.-R.P.)
| | - Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807, Taiwan
- Ph. D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Correspondence: (M.-R.P.); (T.-C.L.); (C.-H.Y.); Tel.: +886-7-3121101-5092-34 (M.-R.P.); +886-4-781-3888 (T.-C.L.); +886-7-381-4526 (C.-H.Y.); Fax: +886-7-3218309 (M.-R.P.)
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14
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Zhang D, Zheng Y, Yang S, Li Y, Wang M, Yao J, Deng Y, Li N, Wei B, Wu Y, Zhu Y, Li H, Dai Z. Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival. Front Oncol 2021; 10:596087. [PMID: 33489894 PMCID: PMC7821871 DOI: 10.3389/fonc.2020.596087] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 11/26/2020] [Indexed: 12/11/2022] Open
Abstract
To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11-gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan–Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.
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Affiliation(s)
- Dai Zhang
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yi Zheng
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Si Yang
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yiche Li
- Breast Center Department, The Fourth Hospital of Hebei Medical University, Hebei Medical University, Shijiazhuang, China
| | - Meng Wang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jia Yao
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Yujiao Deng
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Na Li
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Bajin Wei
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Ying Wu
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yuyao Zhu
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Hongtao Li
- Department of Breast Head and Neck surgery, The 3rd Affiliated Teaching Hospital of Xinjiang Medical University (Affiliated Tumor Hospital), Urumqi, China
| | - Zhijun Dai
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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15
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Tang X, Luo L, Li Y, Wu H, Hu Q, Yue H, He X, Zou J, Min S. Therapeutic potential of targeting HSPA5 through dual regulation of two candidate prognostic biomarkers ANXA1 and PSAT1 in osteosarcoma. Aging (Albany NY) 2020; 13:1212-1235. [PMID: 33291071 PMCID: PMC7835002 DOI: 10.18632/aging.202258] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 09/03/2020] [Indexed: 12/26/2022]
Abstract
Osteosarcoma is the most common primary malignant bone tumor that mostly affects young people's health. The prognosis of patients with unresectable or recurrent osteosarcoma still remains dismal. Based on gene integration analysis from GEO and TARGET databases by R language, the differentially expressed genes of osteosarcoma patients were identified. Biological molecular function analysis indicated that these genes were importantly enriched in the process of cell adhesion molecule binding. Gene significance highly-related to clinical traits of osteosarcoma was found by weighted gene co-expression network analysis. Additionally, receiver operating characteristic curve analysis was conducted to find prognostic markers in LASSO Cox regression model. Two candidate biomarkers, ANXA1 and PSAT1, for the prognosis of osteosarcoma were detected separately on the basis of WGCNA and LASSO model. Of note, their expression profiles were interrelated with an important therapeutic target HSPA5. In vitro pharmaceutical experiments were performed to explore the biological role and prognostic benefit of candidates. Suppression of HSPA5 effectively upregulated ANXA1 and inhibited PSAT1, resulting in osteosarcoma cell proliferation arrest and apoptosis. These findings suggest that HSPA5 serves as a core molecule for osteosarcoma therapy due to its bidirectional regulation of candidate prognostic biomarkers ANXA1 and PSAT1.
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Affiliation(s)
- Xiaojun Tang
- Department of Spinal Surgery, Orthopaedic Medical Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, Guangdong Province, China.,Department of Spinal Surgery, The Second Affiliated Hospital, University of South China, Hengyang 421001, Hunan Province, China
| | - Lingli Luo
- Medical College, Hunan Polytechnic of Environment and Biology, Hengyang 421005, Hunan Province, China
| | - Yukun Li
- Hunan Province Key Laboratory of Tumor Cellular and Molecular Pathology, Cancer Research Institute, University of South China, Hengyang 421001, Hunan Province, China
| | - Hailong Wu
- Department of Spinal Surgery, Peking University Shenzhen Hospital, Shenzhen 518036, Guangdong Province, China
| | - Qing Hu
- Department of Pathology, People’s Hospital of Hunan Province, Changsha 410005, Hunan Province, China
| | - Haiyan Yue
- Department of Pathology, The Central Hospital of Shaoyang, Shaoyang 422000, Hunan Province, China
| | - Xiao He
- Department of Breast Surgery, Hunan Provincial Tumor Hospital, Changsha 410005, Hunan Province, China
| | - Juan Zou
- Hunan Province Key Laboratory of Tumor Cellular and Molecular Pathology, Cancer Research Institute, University of South China, Hengyang 421001, Hunan Province, China
| | - Shaoxiong Min
- Department of Spinal Surgery, Orthopaedic Medical Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, Guangdong Province, China.,Department of Spinal Surgery, Peking University Shenzhen Hospital, Shenzhen 518036, Guangdong Province, China
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16
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Zhang F, Liu Y, Yang Y, Yang K. Development and validation of a fourteen- innate immunity-related gene pairs signature for predicting prognosis head and neck squamous cell carcinoma. BMC Cancer 2020; 20:1015. [PMID: 33081731 PMCID: PMC7574345 DOI: 10.1186/s12885-020-07489-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/02/2020] [Indexed: 02/06/2023] Open
Abstract
Background Immune-related genes is closely related to the occurrence and prognosis of head and neck squamous cell carcinoma (HNSCC). At the same time, immune-related genes have great potential as prognostic markers in many types of cancer. The prognosis of HNSCC is still poor currently, and it may be effective to predict the clinical outcome of HNSCC by immunogenic analysis. Methods RNASeq and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), the MINiML format GSE65858 chip expression data was downloaded from NCBI, and immune-related genes was downloaded from the InnateDB database. Immune-related genes in 519 HNSC patients were integrated from TCGA dataset. By using multivariate COX analysis and Lasso regression, robust immune-related gene pairs (IRGPs) that predict clinical outcomes of HNSCC were identified. Finally, a risk prognostic model related to immune gene pair was established and verified by clinical features, test sets and GEO external validation set. Results A total of 699 IRGPs were significantly correlated with the prognosis of HNSCC patients. Fourteen robust IRGPs were finally obtained by Lasso regression and a prognostic risk prediction model was constructed. Risk score of each sample were calculated based on Risk models and divided into the high-risk group (Risk-H) and low Risk group (Risk-L). Risk models were able to stratify the risk in patients with TNM Stage, Age, gender, and smoking history, and the AUC > 0.65 in training set and test set, shows that 14-IRGPs signature in patients with HNSCC has excellent classification performance. In addition, 14-IRGPs had the highest average C index compared with the prognostic characteristics and T, N, and Age of the 3 previously reported HNSCC. Conclusion This study constructed 14-IRGPs as a novel prognostic marker for predicting survival in HNSCC patients.
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Affiliation(s)
- Fujun Zhang
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Chongqing Medical University, No 1. Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Yu Liu
- Department of Pharmacy, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Yixin Yang
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Chongqing Medical University, No 1. Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Kai Yang
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Chongqing Medical University, No 1. Youyi Road, Yuzhong District, Chongqing, 400016, China.
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17
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Xiao K, Tan J, Yuan J, Peng G, Long W, Su J, Xiao Y, Xiao Q, Wu C, Qin C, Hu L, Liu K, Liu S, Zhou H, Ning Y, Ding X, Liu Q. Prognostic value and immune cell infiltration of hypoxic phenotype-related gene signatures in glioblastoma microenvironment. J Cell Mol Med 2020; 24:13235-13247. [PMID: 33009892 PMCID: PMC7701576 DOI: 10.1111/jcmm.15939] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/14/2020] [Accepted: 09/14/2020] [Indexed: 02/06/2023] Open
Abstract
Glioblastoma (GBM) is a malignant intracranial tumour with the highest proportion and lethality. It is characterized by invasiveness and heterogeneity. However, the currently available therapies are not curative. As an essential environmental cue that maintains glioma stem cells, hypoxia is considered the cause of tumour resistance to chemotherapy and radiation. Growing evidence shows that immunotherapy focusing on the tumour microenvironment is an effective treatment for GBM; however, the current clinicopathological features cannot predict the response to immunotherapy and provide accurate guidance for immunotherapy. Based on the ESTIMATE algorithm, GBM cases of The Cancer Genome Atlas (TCGA) data set were classified into high- and low-immune/stromal score groups, and a four-gene tumour environment-related model was constructed. This model exhibited good efficiency at forecasting short- and long-term prognosis and could also act as an independent prognostic biomarker. Additionally, this model and four of its genes (CLECL5A, SERPING1, CHI3L1 and C1R) were found to be associated with immune cell infiltration, and further study demonstrated that these four genes might drive the hypoxic phenotype of perinecrotic GBM, which affects hypoxia-induced glioma stemness. Therefore, these might be important candidates for immunotherapy of GBM and deserve further exploration.
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Affiliation(s)
- Kai Xiao
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China
| | - Jun Tan
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China.,Institute of Skull Base Surgery and Neuro-oncology at Hunan, Changsha, China
| | - Jian Yuan
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China.,Institute of Skull Base Surgery and Neuro-oncology at Hunan, Changsha, China
| | - Gang Peng
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China.,Institute of Skull Base Surgery and Neuro-oncology at Hunan, Changsha, China
| | - Wenyong Long
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China.,Institute of Skull Base Surgery and Neuro-oncology at Hunan, Changsha, China
| | - Jun Su
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China
| | - Yao Xiao
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China
| | - Qun Xiao
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China
| | - Changwu Wu
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China.,Institute of Anatomy, University of Leipzig, Leipzig, Germany
| | - Chaoying Qin
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China.,Institute of Skull Base Surgery and Neuro-oncology at Hunan, Changsha, China
| | - Lili Hu
- Medical College of Hunan Normal University, Changsha, China
| | - Kaili Liu
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, China
| | - Shunlian Liu
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, China
| | - Hao Zhou
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, China
| | - Yichong Ning
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, China
| | - Xiaofeng Ding
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, China
| | - Qing Liu
- Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China.,Institute of Skull Base Surgery and Neuro-oncology at Hunan, Changsha, China
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18
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Cao R, Zhang J, Jiang L, Wang Y, Ren X, Cheng B, Xia J. Comprehensive Analysis of Prognostic Alternative Splicing Signatures in Oral Squamous Cell Carcinoma. Front Oncol 2020; 10:1740. [PMID: 32984057 PMCID: PMC7485395 DOI: 10.3389/fonc.2020.01740] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 08/04/2020] [Indexed: 12/17/2022] Open
Abstract
Background Alternative splicing (AS) plays an essential role in tumorigenesis and progression. This study aimed to develop a novel prognostic model based on the AS events to obtain more accurate survival prediction and search for potential therapeutic targets in oral squamous cell carcinoma (OSCC). Methods Seven types of AS events in 326 OSCC patients with RNA-seq were obtained from the TCGA SpliceSeq tool and the TCGA database. Cox analysis, the least absolute shrinkage and selection operator Cox regression and random forest were employed to establish prognostic models. Genomics of Drug Sensitivity in Cancer (GDSC) was adopted to estimate the possible drug sensiticity. Prognostic splicing factor (SF)-AS network was constructed by Cytoscape. Results The final model included 12 AS events, showing satisfactory performance. The area under the curve for 3- and 5-year survival in the training cohort was 0.83 and 0.82, respectively while that in internal validation was 0.83 and 0.82 accordingly. The calibration curve also indicated a satisfactory agreement between the observation and the predictive values. Low-risk patients stratified by the final model presented higher sensitivity to three chemo drugs. Besides, the prognostic SF-AS regulatory network contained five key SFs and 62 AS events. Conclusions We developed a powerful prognostic AS signature for OSCC and deepened the understanding of SF-AS network regulatory mechanisms. Low-risk patients tended to be more sensitive to the three chemo drugs while five key SFs including CELF2, TIA1, HNRNPC, HNRNPK, and SRSF9 were identified as potential prognostic biomarkers, which may offer new prospects for effective therapies of OSCC.
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Affiliation(s)
- Ruoyan Cao
- Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.,Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Jiayu Zhang
- Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.,Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Laibo Jiang
- Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.,Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Yanting Wang
- Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.,Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Xianyue Ren
- Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.,Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Bin Cheng
- Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.,Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Juan Xia
- Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.,Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
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Zhong LK, Gan XX, Deng XY, Shen F, Feng JH, Cai WS, Liu QY, Miao JH, Zheng BX, Xu B. Potential five-mRNA signature model for the prediction of prognosis in patients with papillary thyroid carcinoma. Oncol Lett 2020; 20:2302-2310. [PMID: 32782547 PMCID: PMC7400165 DOI: 10.3892/ol.2020.11781] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 05/21/2020] [Indexed: 12/12/2022] Open
Abstract
Although the mortality rate of papillary thyroid carcinoma (PTC) is relatively low, the recurrence rates of PTC remain high. The high recurrence rates are related to the difficulties in treatment. Gene expression profiles has provided novel insights into potential therapeutic targets and molecular biomarkers of PTC. The aim of the present study was to identify mRNA signatures which may categorize PTCs into high-and low-risk subgroups and aid with the predictions for prognoses. The mRNA expression profiles of PTC and normal thyroid tissue samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs were identified using the ‘EdgeR’ software package. Gene signatures associated with the overall survival of PTC were selected, and enrichment analysis was performed to explore the biological pathways and functions of the prognostic mRNAs using the Database for Visualization, Annotation and Integration Discovery. A signature model was established to investigate a specific and robust risk stratification for PTC. A total of 1,085 differentially expressed mRNAs were identified between the PTC and normal thyroid tissue samples. Among them, 361 mRNAs were associated with overall survival (P<0.05). A 5-mRNA prognostic signature for PTC (ADRA1B, RIPPLY3, PCOLCE, TEKT1 and SALL3) was identified to classify the patients into high-and low-risk subgroups. These prognostic mRNAs were enriched in Gene Ontology terms such as ‘calcium ion binding’, ‘enzyme inhibitor activity’, ‘carbohydrate binding’, ‘transcriptional activator activity’, ‘RNA polymerase II core promoter proximal region sequence-specific binding’ and ‘glutathione transferase activity’, and Kyoto Encyclopedia of Genes and Genomes signaling pathways such as ‘pertussis’, ‘ascorbate and aldarate metabolism’, ‘systemic lupus erythematosus’, ‘drug metabolism-cytochrome P450 and ‘complement and coagulation cascades’. The 5-mRNA signature model may be useful during consultations with patients with PTC to improve the prediction of their prognosis. In addition, the prognostic signature identified in the present study may reveal novel therapeutic targets for patients with PTC.
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Affiliation(s)
- Lin-Kun Zhong
- Department of General Surgery, The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong 510630, P.R. China.,Department of General Surgery, Zhongshan City People's Hospital Affiliated to Sun Yat-sen University, Zhongshan, Guangdong 528403, P.R. China
| | - Xiao-Xiong Gan
- Department of General Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510180, P.R. China
| | - Xing-Yan Deng
- Department of General Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510180, P.R. China
| | - Fei Shen
- Department of General Surgery, The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong 510630, P.R. China.,Department of General Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510180, P.R. China
| | - Jian-Hua Feng
- Department of General Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510180, P.R. China
| | - Wen-Song Cai
- Department of General Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510180, P.R. China
| | - Qiong-Yao Liu
- Department of Oncology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510180, P.R. China
| | - Jian-Hang Miao
- Department of General Surgery, Zhongshan City People's Hospital Affiliated to Sun Yat-sen University, Zhongshan, Guangdong 528403, P.R. China
| | - Bing-Xing Zheng
- Department of General Surgery, Zhongshan City People's Hospital Affiliated to Sun Yat-sen University, Zhongshan, Guangdong 528403, P.R. China
| | - Bo Xu
- Department of General Surgery, The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong 510630, P.R. China.,Department of General Surgery, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510180, P.R. China
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Prolyl 4-hydroxylase subunit alpha 3 presents a cancer promotive function in head and neck squamous cell carcinoma via regulating epithelial-mesenchymal transition. Arch Oral Biol 2020; 113:104711. [DOI: 10.1016/j.archoralbio.2020.104711] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 03/16/2020] [Accepted: 03/17/2020] [Indexed: 12/11/2022]
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Sun J, Xie T, Jamal M, Tu Z, Li X, Wu Y, Li J, Zhang Q, Huang X. CLEC3B as a potential diagnostic and prognostic biomarker in lung cancer and association with the immune microenvironment. Cancer Cell Int 2020; 20:106. [PMID: 32265595 PMCID: PMC7110733 DOI: 10.1186/s12935-020-01183-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 03/23/2020] [Indexed: 02/06/2023] Open
Abstract
Background Lung cancer is the leading cause of cancer-related mortality globally. Discovering effective biomarkers for early diagnosis and prognosis is important to reduce the mortality rate and ensure efficient therapy for lung cancer patients. C-type lectin domain family 3 member B (CLEC3B) has been reported in various cancers, but its correlation with lung cancer remains elusive. Methods The GEO, TCGA and Oncomine databases were analyzed to examine the expression of CLEC3B in lung cancer. The CLEC3B mRNA levels in 15 patient tissue samples were detected by real-time PCR and the CLEC3B protein levels in 34 patient tissue samples were detected by immunohistochemistry. A Chi-square test was performed to analyze the correlation of CLEC3B expression and clinicopathological factors. The diagnostic value of CLEC3B was revealed by receiver operating characteristic (ROC) curves. Univariate and multivariate Cox proportional hazards regression models and Kaplan–Meier plots were used to evaluate the prognostic value of CLEC3B in lung cancer. The TIMER database was used to evaluate the correlation of CLEC3B and immune infiltration. Gene set enrichment analysis revealed tumor‐associated biological processes related to CLEC3B. Results CLEC3B is significantly downregulated in lung cancer patients compared with nontumor controls according to database analysis and patient tissue sample detection (p < 0.001). Specifically, CLEC3B is significantly downregulated in stage IA lung cancer patients (p < 0.001) and has a high diagnostic accuracy (area under the receiver operating characteristic curve > 0.9). Moreover, low expression of CLEC3B is related to poor progression-free survival (HR = 0.60, 95% CI 0.49–0.74, p = 8.3e−07) and overall survival (HR = 0.66, 95% CI 0.58–0.75, p = 2.1e−10), indicating it as a risk factor for lung cancer. Multivariate analysis value showed that low expression of CLEC3B may be an independent risk factor for disease‐free survival in lung cancer patients (HR = 0.655, 95% CI 0.430–0.996, Cox p = 0.048). In addition, we also investigated the potential role of CLEC3B in tumor-immune interactions and found that CLEC3B might be associated with the immune infiltration and immune activation of lung cancer, especially in squamous cell carcinoma. Conclusions Our findings indicate that CLEC3B expression is downregulated in lung cancer and reveal the diagnostic and prognostic potential of CLEC3B in lung cancer and its potential as an immune-related therapeutic target in lung cancer.
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Affiliation(s)
- Jiaxing Sun
- 1Department of Blood Transfusion, Zhongnan Hospital of Wuhan University, Wuhan, China.,2Department of Immunology, School of Basic Medical Science, Wuhan University, Wuhan, China
| | - Tian Xie
- 1Department of Blood Transfusion, Zhongnan Hospital of Wuhan University, Wuhan, China.,2Department of Immunology, School of Basic Medical Science, Wuhan University, Wuhan, China
| | - Muhammad Jamal
- 2Department of Immunology, School of Basic Medical Science, Wuhan University, Wuhan, China
| | - Zhenbo Tu
- 2Department of Immunology, School of Basic Medical Science, Wuhan University, Wuhan, China
| | - Xinran Li
- 3School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Yingjie Wu
- 4Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jingyuan Li
- 2Department of Immunology, School of Basic Medical Science, Wuhan University, Wuhan, China
| | - Qiuping Zhang
- 2Department of Immunology, School of Basic Medical Science, Wuhan University, Wuhan, China
| | - Xiaoxing Huang
- 1Department of Blood Transfusion, Zhongnan Hospital of Wuhan University, Wuhan, China
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