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Long Q, Li Z, Yang W, Huang K, Du G. Necroptosis-related lncRNA-based novel signature to predict the prognosis and immune landscape in soft tissue sarcomas. J Cancer Res Clin Oncol 2024; 150:203. [PMID: 38635069 PMCID: PMC11026213 DOI: 10.1007/s00432-024-05682-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 03/04/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND Necroptosis-related long noncoding RNAs (lncRNAs) play crucial roles in cancer initiation and progression. Nevertheless, the role and mechanism of necroptosis-related lncRNAs in soft tissue sarcomas (STS) is so far unknown and needs to be explored further. METHODS Clinical and genomic data were obtained from the UCSC Xena database. All STS patients' subclusters were performed by unsupervised consensus clustering method based on the prognosis-specific lncRNAs, and then assessed their survival advantage and immune infiltrates. In addition, we explored the pathways and biological processes in subclusters through gene set enrichment analysis. At last, we established the necroptosis-related lncRNA-based risk signature (NRLncSig) using the least absolute shrinkage and selection operator (LASSO) method, and explored the prediction performance and immune microenvironment of this signature in STS. RESULTS A total of 911 normal soft tissue samples and 259 STS patients were included in current study. 39 prognosis-specific necroptosis-related lncRNAs were selected. Cluster 2 had a worse survival than the cluster 1 and characterized by different immune landscape in STS. A worse outcome in the high-risk group was observed by survival analysis and indicated an immunosuppressive microenvironment. The ROC curve analyses illustrated that the NRLncSig performing competitively in prediction of prognosis for STS patients. In addition, the nomogram presents excellent performance in predicting prognosis, which may be more beneficial towards STS patients' treatment. CONCLUSIONS Our result indicated that the NRLncSig could be a good independent predictor of prognosis, and significantly connected with immune microenvironment, thereby providing new insights into the roles of necroptosis-related lncRNAs in STS.
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Affiliation(s)
- Qiuzhong Long
- Department of Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Zhengtian Li
- Department of Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Wenkang Yang
- Guangxi Medical University, Nanning, Guangxi, China
| | - Ke Huang
- Wuming Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China.
| | - Gang Du
- Department of Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China.
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Liu J, Lu J, Wang G, Gu L, Li W. Prognostic characteristics of a six-gene signature based on ssGSEA in sarcoma. Aging (Albany NY) 2024; 16:1536-1554. [PMID: 38240704 PMCID: PMC10866427 DOI: 10.18632/aging.205443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 12/07/2023] [Indexed: 02/06/2024]
Abstract
BACKGROUND Sarcoma is a rare malignant tumor originating of the interstitial or connective tissue with a poor prognosis. Next-generation sequencing technology offers new opportunities for accurate diagnosis and treatment of sarcomas. There is an urgent need for new gene signature to predict prognosis and evaluate treatment outcomes. METHODS We used transcriptome data from the Cancer Genome Atlas (TCGA) database and single sample gene set enrichment analysis (ssGSEA) to explore the cancer hallmarks most associated with prognosis in sarcoma patients. Then, weighted gene coexpression network analysis, univariate COX regression analysis and random forest algorithm were used to construct prognostic gene characteristics. Finally, the prognostic value of gene markers was validated in the TCGA and Integrated Gene Expression (GEO) (GSE17118) datasets, respectively. RESULTS MYC targets V1 and V2 are the main cancer hallmarks affecting the overall survival (OS) of sarcoma patients. A six-gene signature including VEGFA, HMGB3, FASN, RCC1, NETO2 and BIRC5 were constructed. Kaplan-Meier analysis suggested that higher risk scores based on the six-gene signature associated with poorer OS (P < 0.001). The receiver Operating characteristic curve showed that the risk score based on the six-gene signature was a good predictor of sarcoma, with an area under the curve (AUC) greater than 0.73. In addition, the prognostic value of the six-gene signature was validated in GSE17118 with an AUC greater than 0.72. CONCLUSION This six-gene signature is an independent prognostic factor in patients with sarcoma and is expected to be a potential therapeutic target for sarcoma.
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Affiliation(s)
- Jun Liu
- Department of Clinical Laboratory, Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan 523005, China
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou University Medical College, Shantou 515000, China
| | - Jianjun Lu
- Department of Quality Control and Evaluation, First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
| | - Gefei Wang
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou University Medical College, Shantou 515000, China
| | - Liming Gu
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou University Medical College, Shantou 515000, China
| | - Wenli Li
- Department of Clinical Laboratory, Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan 523005, China
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou University Medical College, Shantou 515000, China
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Xu N, Yu Y, Duan C, Wei J, Sun W, Jiang C, Jian B, Cao W, Jia L, Ma X. Quantitative proteomics identifies and validates urinary biomarkers of rhabdomyosarcoma in children. Clin Proteomics 2023; 20:10. [PMID: 36918772 PMCID: PMC10012572 DOI: 10.1186/s12014-023-09401-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/02/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma with poor prognosis in children. The 5-year survival rate for early RMS has improved, whereas it remains unsatisfactory for advanced patients. Urine can rapidly reflect changes in the body and identify low-abundance proteins. Early screening of tumor markers through urine in RMS allows for earlier treatment, which is associated with better outcomes. METHODS RMS patients under 18 years old, including those newly diagnosed and after surgery, were enrolled. Urine samples were collected at the time points of admission and after four cycles of chemotherapy during follow-up. Then, a two-stage workflow was established. (1) In the discovery stage, differential proteins (DPs) were initially identified in 43 RMS patients and 12 healthy controls (HCs) using a data-independent acquisition method. (2) In the verification stage, DPs were further verified as biomarkers in 54 RMS patients and 25 HCs using parallel reaction monitoring analysis. Furthermore, a receiver operating characteristic (ROC) curve was used to construct the protein panels for the diagnosis of RMS. Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA) software were used to perform bioinformatics analysis. RESULTS A total of 251 proteins were significantly altered in the discovery stage, most of which were enriched in the head, neck and urogenital tract, consistent with the most common sites of RMS. The most overrepresented biological processes from GO analysis included immunity, inflammation, tumor invasion and neuronal damage. Pathways engaging the identified proteins revealed 33 common pathways, including WNT/β-catenin signaling and PI3K/AKT signaling. Finally, 39 proteins were confirmed as urinary biomarkers for RMS, and a diagnostic panel composed of 5 candidate proteins (EPS8L2, SPARC, HLA-DRB1, ACAN, and CILP) was constructed for the early screening of RMS (AUC: 0.79, 95%CI = 0.66 ~ 0.92). CONCLUSIONS These findings provide novel biomarkers in urine that are easy to translate into clinical diagnosis of RMS and illustrate the value of global and targeted urine proteomics to identify and qualify candidate biomarkers for noninvasive molecular diagnosis.
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Affiliation(s)
- Na Xu
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, No. 56 Nalishi Road, Beijing, 100045, China.,Department of Pediatrics, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Yuncui Yu
- Clinical Research Center, Department of Pharmacy, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, No. 56 Nanlishi Road, Beijing, 100045, China
| | - Chao Duan
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, No. 56 Nalishi Road, Beijing, 100045, China
| | - Jing Wei
- Clinical Research Center, Department of Pharmacy, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, No. 56 Nanlishi Road, Beijing, 100045, China
| | - Wei Sun
- Proteomics Research Center, Core Facility of Instruments, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chiyi Jiang
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, No. 56 Nalishi Road, Beijing, 100045, China
| | - Binglin Jian
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, No. 56 Nalishi Road, Beijing, 100045, China
| | - Wang Cao
- Clinical Research Center, Department of Pharmacy, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, No. 56 Nanlishi Road, Beijing, 100045, China
| | - Lulu Jia
- Clinical Research Center, Department of Pharmacy, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, No. 56 Nanlishi Road, Beijing, 100045, China.
| | - Xiaoli Ma
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, No. 56 Nalishi Road, Beijing, 100045, China.
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Liu Y, Li S, Liu Y. Machine Learning-Driven Multiobjective Optimization: An Opportunity of Microfluidic Platforms Applied in Cancer Research. Cells 2022; 11:cells11050905. [PMID: 35269527 PMCID: PMC8909684 DOI: 10.3390/cells11050905] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/27/2022] [Accepted: 03/02/2022] [Indexed: 12/24/2022] Open
Abstract
Cancer metastasis is one of the primary reasons for cancer-related fatalities. Despite the achievements of cancer research with microfluidic platforms, understanding the interplay of multiple factors when it comes to cancer cells is still a great challenge. Crosstalk and causality of different factors in pathogenesis are two important areas in need of further research. With the assistance of machine learning, microfluidic platforms can reach a higher level of detection and classification of cancer metastasis. This article reviews the development history of microfluidics used for cancer research and summarizes how the utilization of machine learning benefits cancer studies, particularly in biomarker detection, wherein causality analysis is useful. To optimize microfluidic platforms, researchers are encouraged to use causality analysis when detecting biomarkers, analyzing tumor microenvironments, choosing materials, and designing structures.
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Affiliation(s)
- Yi Liu
- School of Engineering, Dali University, Dali 671000, China;
| | - Sijing Li
- School of Engineering, Dali University, Dali 671000, China;
- Correspondence: (S.L.); (Y.L.)
| | - Yaling Liu
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA 18015, USA
- Correspondence: (S.L.); (Y.L.)
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Wang Y, Lin X, Sun D. A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models? ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1597. [PMID: 34790803 PMCID: PMC8576716 DOI: 10.21037/atm-21-4733] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/02/2021] [Indexed: 12/18/2022]
Abstract
Objective To discover potential predictors and explore how to build better models by summarizing the existing prognostic prediction models of non-small cell lung cancer (NSCLC). Background Research on clinical prediction models of NSCLC has experienced explosive growth in recent years. As more predictors of prognosis are discovered, the choice of predictors to build models is particularly important, and in the background of more applications of next-generation sequencing technology, gene-related predictors are widely used. As it is more convenient to obtain samples and follow-up data, the prognostic model is preferred by researchers. Methods PubMed and the Cochrane Library were searched using the items “NSCLC”, “prognostic model”, “prognosis prediction”, and “survival prediction” from 1 January 1980 to 5 May 2021. Reference lists from articles were reviewed and relevant articles were identified. Conclusions The performance of gene-related models has not obviously improved. Relative to the innovation and diversity of predictors, it is more important to establish a highly stable model that is convenient for clinical application. Most of the prevalent models are highly biased and referring to PROBAST at the beginning of the study may be able to significantly control the bias. Existing models should be validated in a large external dataset to make a meaningful comparison.
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Affiliation(s)
- Yuhang Wang
- Graduate School, Tianjin Medical University, Tianjin, China
| | | | - Daqiang Sun
- Graduate School, Tianjin Medical University, Tianjin, China.,Department of Thoracic Surgery, Tianjin Chest Hospital of Nankai University, Tianjin, China
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Zhang L, Tang X, Wan J, Zhang X, Zheng T, Lin Z, Liu T. Construction of a Novel Signature and Prediction of the Immune Landscape in Soft Tissue Sarcomas Based on N6-Methylandenosine-Related LncRNAs. Front Mol Biosci 2021; 8:715764. [PMID: 34733885 PMCID: PMC8559337 DOI: 10.3389/fmolb.2021.715764] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 09/28/2021] [Indexed: 12/21/2022] Open
Abstract
Background: N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) are critically involved in cancer development. However, the roles and clinical significance of m6A-related lncRNAs in soft tissue sarcomas (STS) are inconclusive, thereby warranting further investigations. Methods: Transcriptome profiling data were extracted from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx). Consensus clustering was employed to divide patients into clusters and Kaplan–Meier analysis was used to explore the prognostic differences between the subgroups. Gene set enrichment analysis (GSEA) was conducted to identify the biological processes and signaling pathways associated with m6A-Related lncRNAs. Finally, patients were randomly divided into training and validation cohorts, and least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to establish the m6A-related lncRNA-based risk signature. Results: A total of 259 STS patients from TCGA-SARC dataset were enrolled in our study. Thirteen m6A-Related lncRNAs were identified to be closely related to the prognosis of STS patients. Patients were divided into two clusters, and patients in cluster 2 had a better overall survival (OS) than those in cluster 1. Patients in different clusters also showed differences in immune scores, infiltrating immune cells, and immune checkpoint expression. Patients were further classified into high-risk and low-risk subgroups according to risk scores, and high-risk patients were found to have a worse prognosis. The receiver operating characteristic (ROC) curve indicated that the risk signature displayed excellent performance at predicting the prognosis of patients with STS. Further, the risk signature was remarkably connected with the immune microenvironment and chemosensitivity in STS. Conclusion: Our study demonstrated that m6A-related lncRNAs were significantly associated with prognosis and tumor immune microenvironment and could function as independent prognosis-specific predictors in STS, thereby providing novel insights into the roles of m6A-related lncRNAs in STS.
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Affiliation(s)
- Li Zhang
- Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, China.,Department of Endocrinology, The Fifth Central Hospital of Tianjin, Tianjin, China
| | - Xianzhe Tang
- Department of Orthopedics, Chenzhou No. 1 People's Hospital, Chenzhou, China
| | - Jia Wan
- Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xianghong Zhang
- Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Tao Zheng
- Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Zhengjun Lin
- Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, China.,Xiangya School of Medicine, Central South University, Changsha, China
| | - Tang Liu
- Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, China
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