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Han X, Chen Y, Xie J, Wang Y. Characteristics of m 6A-related LncRNAs in breast cancer as prognostic biomarkers and immunotherapy. J Cancer 2023; 14:2919-2930. [PMID: 37781080 PMCID: PMC10539557 DOI: 10.7150/jca.87079] [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: 06/12/2023] [Accepted: 08/31/2023] [Indexed: 10/03/2023] Open
Abstract
N6-methyladenosine (m6A) is a common RNA modification in coding and non-coding RNAs and plays an important role in the occurrence and development of breast cancer (BC). However, the role of m6A-related lncRNAs in breast cancer prognosis is unclear. This study aimed to help verify the biological function of m6A-related lncRNAs in breast cancer prognosis through bio-informatics techniques. First, we screened 18 m6A-related lncRNAs from the TCGA database: AL137847.1, AC137932.2, OTUD6B-AS1, MORF4L2-AS1, AC078846.1, AC012442.1, AL118556.1, AL138955.1, AC009754.1, AC024257.4, AL391095.1, AC024270.3, AC087392.1, LINC02649, AC090948.2, AL158212.1, ITGA6-AS1, AL133243.2 and constructed a risk-prognosis model based on this. Based on the model's median risk score, BC patients were divided into high-risk and low-risk groups. Then, the predictive value of the model was verified by Cox regression, Lasso regression, Kaplan-Meier curve and ROC curve analysis, and biological differences between the two groups were verified by GO enrichment analysis, tumor mutation burden, immune indications and in vitro tests. Importantly, the risk score of this prognostic model is an excellent independent prognostic factor, and m6A regulators are differentially expressed in patients with different risks. In addition, based on patients' different sensitivities to drugs, some drug candidates for different risk populations are screened to provide targets for breast cancer treatment. The difference in immune function between high-risk and low-risk patients also affected the sensitivity to immunotherapy. In the validation of clinical samples, we analyzed the expression of relevant lncRNAs in different risk groups and speculated the possible impact on the prognosis of breast cancer patients. The risk assessment tool built based on the full analysis of these m6A-related genes and m6A-related lncRNA libraries, as well as the m6A-related lncRNAs, has a high prognostic prediction ability, which may provide a supplementary screening method for accurately judging the prognosis of BC and a new perspective for personalized treatment of breast cancer patients.
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Affiliation(s)
- Xinwei Han
- Tai Zhou Central Hospital (Taizhou University Hospital), No.999 Donghai Road, Jiaojiang District, Taizhou, Zhejiang, 318000, China
- Cytotherapy Laboratory, Shenzhen People's Hospital, 1017, Dongmen North Road, Luohu, Shenzhen, 518020, China
| | - Yu Chen
- Tai Zhou Central Hospital (Taizhou University Hospital), No.999 Donghai Road, Jiaojiang District, Taizhou, Zhejiang, 318000, China
| | - Jiaogui Xie
- Tai Zhou Central Hospital (Taizhou University Hospital), No.999 Donghai Road, Jiaojiang District, Taizhou, Zhejiang, 318000, China
| | - Yichao Wang
- Tai Zhou Central Hospital (Taizhou University Hospital), No.999 Donghai Road, Jiaojiang District, Taizhou, Zhejiang, 318000, China
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Ma C, Luo H. A more novel and robust gene signature predicts outcome in patients with esophageal squamous cell carcinoma. Clin Res Hepatol Gastroenterol 2022; 46:102033. [PMID: 36265781 DOI: 10.1016/j.clinre.2022.102033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/24/2022] [Accepted: 10/10/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND Esophageal squamous cell carcinoma (ESCC) is a life-threatening thoracic tumor with a poor prognosis. The tumor microenvironment (TME) mainly comprises tumor cells and tumor-infiltrating immune cells mixed with stromal components. The latest research has displayed that tumor immune cell infiltration (ICI) is closely connected with the ESCC patients' clinical prognosis. This study was designed to construct a gene signature based on the ICI of ESCC to predict prognosis. METHODS Based on the selection criteria we set, the eligible ESCC cases from the GSE53625 and TCGA-ESCA datasets were chosen for the training cohort and the validation cohort, respectively. Unsupervised clustering detailed grouped ESCC cases of the training cohort based on the ICI profile. We determined the differential expression genes (DEGs) between the ICI clusters, and, subsequently, we adopted the univariate Cox analysis to recognize DEGs with prognostic potential. These screened DEGs underwent a Lasso regression, which then generated a gene signature. The harvested signature's predictive ability was further examined by the Kaplan-Meier analysis, Cox analysis, ROC, IAUC, and IBS. More importantly, we listed similar studies in the most recent year and compared theirs with ours. We performed the functional annotation, immune relevant signature correlation analysis, and immune infiltrating analysis to thoroughly understand the functional mechanism of the signature and the immune cells' roles in the gene signature's predicting capacity. RESULTS A sixteen-gene signature (ARSD, BCAT1, BIK, CLDN11, DLEU7-AS1, GGH, IGFBP2, LINC01037, LINC01446, LINC01497, M1AP, PCSK2, PCSK5, PPP2R2A, TIGD7, and TMSB4X) was generated from the Lasso model. We then confirmed the signature as having solid and stable prognostic capacity by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GSEA uncovered the specifically mechanism of action related to the gene signature. Two immune relevant signatures, including GZMA and LAG3 were identified associating with our signature. The immune-infiltrating analysis identified crucial roles of resting mast cells, which potentially support the sixteen-gene signature's prognosis ability. CONCLUSIONS We discovered a robust sixteen-gene signature that can accurately predict ESCC prognosis. The immune relevant signatures, GZMA and LAG3, and resting mast cells infiltrating were closely linked to the sixteen-gene signature's ability.
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Affiliation(s)
- Chao Ma
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Huan Luo
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and the Berlin Institute of Health, Berlin, Germany.
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Lian L, Teng SB, Xia YY, Shen XM, Zheng Y, Han SG, Wang WJ, Xu XF, Zhou C. Development and verification of a hypoxia- and immune-associated prognosis signature for esophageal squamous cell carcinoma. J Gastrointest Oncol 2022; 13:462-477. [PMID: 35557566 PMCID: PMC9086047 DOI: 10.21037/jgo-22-69] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 03/08/2022] [Indexed: 08/21/2023] Open
Abstract
BACKGROUND Esophageal cancer is one of the most common gastrointestinal malignancies worldwide, with high morbidity and mortality in China. The clinical importance of the interaction between hypoxia and immune status in the tumor microenvironment has been established in esophageal squamous cell carcinoma (ESCC). This study aims to develop a new hypoxia- and immune-based gene signature to predict the survival of ESCC patients. METHODS The RNA-sequencing and clinical data of 173 cases of ESCC and 271 normal tissues were obtained from The Cancer Genome Atlas (TCGA) data portal and the Genotype-Tissue Expression (GTEx) database. Hypoxia-related genes (HRGs) and immune-related genes (IRGs) were retrieved from publicly shared data. Differentially expressed gene (DEG) analyses were carried out by the DESeq2 method using the edgeR package in R. Based on the intersection of the DEGs and HRGs/IRGs, differentially expressed HRGs (DEHRGs) and differentially expressed IRGs (DEIRGs) were obtained. DEHRGs and DEIRGs associated with prognosis were evaluated using univariate Cox proportional hazards analysis. A prognostic risk score model was constructed according to the genes acquired through Cox regression. Univariate analysis and Cox proportional hazards analysis were used to determine the independent prognostic factors related to prognosis. A nomogram was developed to predict the 1-, 2-, and 3-year overall survival (OS) probability. RESULTS A total of 73 intersecting genes were obtained as DEHRGs and a total of 548 intersecting genes were obtained as DEIRGs. The risk score was established using 8 genes (FABP7, TLR1, SYTL1, APLN, OSM, EGFR, IL17RD, MYH9) acquired from univariate Cox analysis. Based on this 8-gene-based risk score, a risk prognosis classifier was constructed to classify the samples into high- and low-risk groups according to the median risk score. The nomogram model was constructed to predict the OS of ESCC patients. CONCLUSIONS The hypoxia- and immune-based gene signature might serve as a prognostic classifier for clinical decision-making regarding individualized management, follow-up plans, and treatment strategies for ESCC patients.
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Affiliation(s)
- Lian Lian
- Department of Oncology, Suzhou Xiangcheng People’s Hospital, Suzhou, China
| | - Shi-Bing Teng
- Department of Thoracic Surgery, Suzhou Xiangcheng People’s Hospital, Suzhou, China
| | - You-You Xia
- Department of Radiation Oncology, The Affiliated Lianyungang Hospital of Xuzhou Medical University (The First People’s Hospital of Lianyungang), Lianyungang, China
| | - Xiao-Ming Shen
- Department of Oncology, Suzhou Xiangcheng People’s Hospital, Suzhou, China
| | - Yan Zheng
- Department of Oncology, Suzhou Xiangcheng People’s Hospital, Suzhou, China
| | - Shu-Guang Han
- Department of General Surgery, Suzhou Xiangcheng People’s Hospital, Suzhou, China
| | - Wen-Jie Wang
- Department of Radio-Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Xue-Fei Xu
- Department of General Surgery, Suzhou Xiangcheng People’s Hospital, Suzhou, China
| | - Chong Zhou
- Department of Radiation Oncology, Xuzhou Central Hospital, Xuzhou, China
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Zheng ZJ, Li YS, Zhu JD, Zou HY, Fang WK, Cui YY, Xie JJ. Construction of the Six-lncRNA Prognosis Signature as a Novel Biomarker in Esophageal Squamous Cell Carcinoma. Front Genet 2022; 13:839589. [PMID: 35432441 PMCID: PMC9008717 DOI: 10.3389/fgene.2022.839589] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 02/14/2022] [Indexed: 02/05/2023] Open
Abstract
Esophageal squamous cell carcinoma (ESCC) is a common malignant gastrointestinal tumor threatening global human health. For patients diagnosed with ESCC, determining the prognosis is a huge challenge. Due to their important role in tumor progression, long non-coding RNAs (lncRNAs) may be putative molecular candidates in the survival prediction of ESCC patients. Here, we obtained three datasets of ESCC lncRNA expression profiles (GSE53624, GSE53622, and GSE53625) from the Gene Expression Omnibus (GEO) database. The method of statistics and machine learning including survival analysis and LASSO regression analysis were applied. We identified a six-lncRNA signature composed of AL445524.1, AC109439.2, LINC01273, AC015922.3, LINC00547, and PSPC1-AS2. Kaplan-Meier and Cox analyses were conducted, and the prognostic ability and predictive independence of the lncRNA signature were found in three ESCC datasets. In the entire set, time-dependent ROC curve analysis showed that the prediction accuracy of the lncRNA signature was remarkably greater than that of TNM stage. ROC and stratified analysis indicated that the combination of six-lncRNA signature with the TNM stage has the highest accuracy in subgrouping ESCC patients. Furthermore, experiments subsequently confirmed that one of the lncRNAs LINC01273 may play an oncogenic role in ESCC. This study suggested the six-lncRNA signature could be a valuable survival predictor for patients with ESCC and have potential to be an auxiliary biomarker of TNM stage to subdivide ESCC patients more accurately, which has important clinical significance.
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Affiliation(s)
- Ze-Jun Zheng
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
| | - Yan-Shang Li
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
- Department of Pathology, Medical College of Jiaying University, Meizhou, China
| | - Jun-De Zhu
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
| | - Hai-Ying Zou
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
| | - Wang-Kai Fang
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
| | - Yi-Yao Cui
- Department of Thoracic Surgery, Beijing Friendship Hospital, Affiliated to the Capital University of Medical Sciences, Beijing, China
| | - Jian-Jun Xie
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
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A Pleiotropic Role of Long Non-Coding RNAs in the Modulation of Wnt/β-Catenin and PI3K/Akt/mTOR Signaling Pathways in Esophageal Squamous Cell Carcinoma: Implication in Chemotherapeutic Drug Response. Curr Oncol 2022; 29:2326-2349. [PMID: 35448163 PMCID: PMC9031703 DOI: 10.3390/curroncol29040189] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/19/2022] [Accepted: 03/20/2022] [Indexed: 02/06/2023] Open
Abstract
Despite the availability of modern techniques for the treatment of esophageal squamous cell carcinoma (ESCC), tumor recurrence and metastasis are significant challenges in clinical management. Thus, ESCC possesses a poor prognosis and low five-year overall survival rate. Notably, the origin and recurrence of the cancer phenotype are under the control of complex cancer-related signaling pathways. In this review, we provide comprehensive knowledge about long non-coding RNAs (lncRNAs) related to Wnt/β-catenin and phosphatidylinositol-3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) signaling pathway in ESCC and its implications in hindering the efficacy of chemotherapeutic drugs. We observed that a pool of lncRNAs, such as HERES, TUG1, and UCA1, associated with ESCC, directly or indirectly targets various molecules of the Wnt/β-catenin pathway and facilitates the manifestation of multiple cancer phenotypes, including proliferation, metastasis, relapse, and resistance to anticancer treatment. Additionally, several lncRNAs, such as HCP5 and PTCSC1, modulate PI3K/Akt/mTOR pathways during the ESCC pathogenesis. Furthermore, a few lncRNAs, such as AFAP1-AS1 and LINC01014, block the efficiency of chemotherapeutic drugs, including cisplatin, 5-fluorouracil, paclitaxel, and gefitinib, used for ESCC treatment. Therefore, this review may help in designing a better therapeutic strategy for ESCC patients.
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Identification of prognostic long non-coding RNA signature with potential drugs in hepatocellular carcinoma. Aging (Albany NY) 2021; 13:18789-18805. [PMID: 34285143 PMCID: PMC8351707 DOI: 10.18632/aging.203322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 07/05/2021] [Indexed: 12/24/2022]
Abstract
Hepatocellular carcinoma (HCC) is the primary malignancy in the liver with high rate of death and recurrence. Novel prognostic model would be crucial for early diagnosis and improved clinical decision. The study aims to provide an effective lncRNA-based signature to predict survival time and tumor recurrence for HCC. Based on public database, lncRNA-based classifiers for overall survival and tumor recurrence were built with regression analysis and cross validation strategy. According to the risk-score of the classifiers, the whole cohorts were divided into groups with high and low risk. Afterwards, the efficiency of the lncRNA-based classifiers was evaluated and compared with other clinical factors. Finally, candidate small molecules for high risk groups were further screened using drug response databases to explore potential drugs for HCC treatment.
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Zhang S, Li J, Gao H, Tong Y, Li P, Wang Y, Du L, Wang C. lncRNA Profiles Enable Prognosis Prediction and Subtyping for Esophageal Squamous Cell Carcinoma. Front Cell Dev Biol 2021; 9:656554. [PMID: 34127945 PMCID: PMC8196240 DOI: 10.3389/fcell.2021.656554] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 04/08/2021] [Indexed: 12/24/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) have emerged as useful prognostic markers in many tumors. In this study, we investigated the potential application of lncRNA markers for the prognostic prediction of esophageal squamous cell carcinoma (ESCC). We identified ESCC-associated lncRNAs by comparing ESCC tissues with normal tissues. Subsequently, Kaplan–Meier (KM) method in combination with the univariate Cox proportional hazards regression (UniCox) method was used to screen prognostic lncRNAs. By combining the differential and prognostic lncRNAs, we developed a prognostic model using cox stepwise regression analysis. The obtained prognostic prediction model could effectively predict the 3- and 5-year prognosis and survival of ESCC patients by time-dependent receiver operating characteristic (ROC) curves (area under curve = 0.87 and 0.89, respectively). Besides, a lncRNA-based classification of ESCC was generated using k-mean clustering method and we obtained two clusters of ESCC patients with association with race and Barrett’s esophagus (BE) (both P < 0.001). Finally, we found that lncRNA AC007128.1 was upregulated in both ESCC cells and tissues and associated with poor prognosis of ESCC patients. Furthermore, AC007128.1 could promote epithelial-mesenchymal transition (EMT) of ESCC cells by increasing the activation of MAPK/ERK and MAPK/p38 signaling pathways. Collectively, our findings indicated the potentials of lncRNA markers in the prognosis, molecular subtyping, and EMT of ESCC.
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Affiliation(s)
- Shujun Zhang
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Juan Li
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Huiru Gao
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yao Tong
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Peilong Li
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yunshan Wang
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Lutao Du
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong Engineering & Technology Research Center for Tumor Marker Detection, Jinan, China.,Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan, China
| | - Chuanxin Wang
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong Engineering & Technology Research Center for Tumor Marker Detection, Jinan, China.,Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan, China
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Qi Y, Xin M, Zhang Y, Hao Y, Liu Q, Wang P, Guo Q. TTSurv: Exploring the Multi-Gene Prognosis in Thousands of Tumors. Front Oncol 2021; 11:691310. [PMID: 34113575 PMCID: PMC8186665 DOI: 10.3389/fonc.2021.691310] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 05/06/2021] [Indexed: 12/21/2022] Open
Abstract
Thoracic malignancies are a common type of cancer and area major global health problem. These complex diseases, including lung cancer, esophageal cancer, and breast cancer, etc. have attracted considerable attention from researchers. Potential gene-cancer associations can be explored by demonstrating the association between clinical data and gene expression data. Emerging evidence suggests that the transcriptome plays a particularly critical role as a diagnostic biomarker in pathology and histology studies. Thus, there is an urgent need to develop a platform that allows users to perform a comprehensive prognostic analysis of thoracic cancers. Here, we developed TTSurv, which aims to correlate coding and noncoding genes with cancers by combining high-throughput data with clinical prognosis. TTSurv focuses on the application of high-throughput data to detect ncRNAs, such as lncRNAs and microRNAs, as novel diagnostic and prognostic biomarkers. For a more comprehensive analysis, a large amount of public expression profile data with clinical follow-up information have been integrated into TTSurv. TTSurv also provides flexible methods such as a minimum p-value algorithm and unsupervised clustering methods that can classify thoracic cancer samples into different risk groups. TTSurv will expand our understanding of ncRNAs in thoracic malignancies and provide new insights into their application as potential prognostic/diagnostic biomarkers.
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Affiliation(s)
- Yue Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mengyu Xin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yuanfu Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yangyang Hao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Qian Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Peng Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Qiuyan Guo
- Department of Gynecology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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