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Zhong Y, Cao H, Li W, Deng J, Li D, Deng J. An analysis of the prognostic role of reactive oxygen species-associated genes in breast cancer. ENVIRONMENTAL TOXICOLOGY 2024; 39:3055-3148. [PMID: 38319140 DOI: 10.1002/tox.24128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 12/11/2023] [Accepted: 12/25/2023] [Indexed: 02/07/2024]
Abstract
BACKGROUND This study aimed to type breast cancer in relation to reactive oxygen species (ROS), clinical indicators, single nucleotide variant (SNV) mutations, functional differences, immune infiltration, and predictive responses to immunotherapy or chemotherapy, and constructing a prognostic model. METHODS We used uniCox analysis, ConsensusClusterPlus, and the proportion of ambiguous clustering (PAC) to analyze The Cancer Genome Atlas (TCGA) data to determine optimal groupings and obtain differentially expressed ROS-related genes. Clinical indicators were then combined with the classification results and the Chi-square test was used to assess differences. We further examined SNV mutations, and functional differences using gene set enrichment analysis (GSEA) analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, immune cell infiltration, and response to immunotherapy and chemotherapy. A prognostic model for breast cancer was constructed using these differentially expressed genes, immunotherapy or chemotherapy responses, and survival curves. RT-qPCR was used to detect the differences in the expression of LCE3D, CA1, PIRT and SMR3A in breast cancer cell lines and normal breast epithelial cell line. RESULTS We identified two distinct tumor types with significant differences in ROS-related gene expression, clinical indicators, SNV mutations, functional pathways, and immune infiltration. The response to specific chemotherapy drugs and immunotherapy treatments also documented significant differences. The prognostic model constructed with 16 genes linked to survival could efficiently divide patients into high- and low-risk groups. The high-risk group showed a poorer prognosis, higher tumor purity, distinct immune microenvironment, and lower immunotherapy response. RT-qPCR results showed that LCE3D, CA1, PIRT and SMR3A are highly expressed in breast cancer. CONCLUSION Our methodical examination presented an enhanced insight into the molecular and immunological heterogeneity of breast cancer. It can contribute to the understanding of prognosis and offer valuable insights for personalized treatment strategies. Further, the prognostic model can potentially serve as a powerful tool for risk stratification and therapeutic decision-making in clinical settings.
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Affiliation(s)
- Yangyan Zhong
- The Second Affiliated Hospital, Department of Breast and Thyroid Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Clinical Research Center for Breast and Thyroid Disease Prevention and Control in Hunan Province, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Hong Cao
- The Second Affiliated Hospital, Department of Breast and Thyroid Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Clinical Research Center for Breast and Thyroid Disease Prevention and Control in Hunan Province, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Wei Li
- The Second Affiliated Hospital, Department of Breast and Thyroid Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Clinical Research Center for Breast and Thyroid Disease Prevention and Control in Hunan Province, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Jian Deng
- The Second Affiliated Hospital, Department of Breast and Thyroid Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Clinical Research Center for Breast and Thyroid Disease Prevention and Control in Hunan Province, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Dan Li
- The Second Affiliated Hospital, Department of Breast and Thyroid Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Clinical Research Center for Breast and Thyroid Disease Prevention and Control in Hunan Province, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Junjie Deng
- The Second Affiliated Hospital, Department of Breast and Thyroid Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Clinical Research Center for Breast and Thyroid Disease Prevention and Control in Hunan Province, Hengyang Medical School, University of South China, Hengyang, Hunan, China
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Wei C, Tao T, Zhou J, Zhu X. Leveraging a Genomic Instability-Derived Signature to Predict the Prognosis and Therapy Sensitivity of Clear Cell Renal Cell Carcinoma. Clin Genitourin Cancer 2024; 22:134-148.e8. [PMID: 37919101 DOI: 10.1016/j.clgc.2023.10.004] [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: 05/18/2023] [Revised: 10/04/2023] [Accepted: 10/08/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND Kidney cancer is a significant health concern with growing treatment resistance, often linked to genomic instability. This study used datasets from 72 renal and 952 clear cell renal cell carcinoma samples to identify genomic instability-derived lncRNAs and develop a prognostic index (GILPI). METHODS The study involved differential expression analysis, weighted gene co-expression network analysis, Cox analyses to construct GILPI, and its validation through survival analysis. SNP, TMB, and MSI data were integrated, and GSEA analysis explored associated pathways. A predictive nomogram was created, and immune cell infiltration was assessed. Targeted treatments for low-GILPI patients were identified through molecular docking and network pharmacology. RESULTS GILPI proved reliable in predicting prognosis (P<0.001, AUC=0.68) and in combination with other factors. GSEA revealed distinct pathway enrichments for different GILPI subgroups. The nomogram exhibited strong predictive performance (AUC=0.902). Immune cell differences suggest potential for immunotherapy in high-GILPI patients and targeted treatment in low-GILPI patients. Lapatinib and nilotinib were identified as effective drugs for low-GILPI patients. CONCLUSION This study identified a GILPI for kidney cancer prognosis, integrating various factors for a comprehensive assessment. It highlighted potential treatment strategies based on GILPI subgroups, enhancing personalized treatment approaches.
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Affiliation(s)
- Chuzhong Wei
- Kidney Department, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, China; Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
| | - Tao Tao
- Department of Gastroenterology, Clinical Research Center, Zibo Central Hospital, Zibo, China
| | - Jiajun Zhou
- Kidney Department, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, China.
| | - Xiao Zhu
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China; Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou Medical College, Hangzhou, China.
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Zhu HX, Zheng WC, Chen H, Chen JY, Lin F, Chen SH, Xue XY, Zheng QS, Liang M, Xu N, Chen DN, Sun XL. Exploring Novel Genome Instability-associated lncRNAs and their Potential Function in Pan-Renal Cell Carcinoma. Comb Chem High Throughput Screen 2024; 27:1788-1807. [PMID: 37957851 DOI: 10.2174/0113862073258779231020052115] [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: 06/01/2023] [Revised: 09/14/2023] [Accepted: 09/21/2023] [Indexed: 11/15/2023]
Abstract
OBJECTIVE Genomic instability can drive clonal evolution, continuous modification of tumor genomes, and tumor genomic heterogeneity. The molecular mechanism of genomic instability still needs further investigation. This study aims to identify novel genome instabilityassociated lncRNAs (GI-lncRNAs) and investigate the role of genome instability in pan-Renal cell carcinoma (RCC). MATERIALS AND METHODS A mutator hypothesis was employed, combining the TCGA database of somatic mutation (SM) information, to identify GI-lncRNAs. Subsequently, a training cohort (n = 442) and a testing cohort (n = 439) were formed by randomly dividing all RCC patients. Based on the training cohort dataset, a multivariate Cox regression analysis lncRNAs risk model was created. Further validations were performed in the testing cohort, TCGA cohort, and different RCC subtypes. To confirm the relative expression levels of lncRNAs in HK-2, 786-O, and 769-P cells, qPCR was carried out. Functional pathway enrichment analyses were performed for further investigation. RESULTS A total of 170 novel GI-lncRNAs were identified. The lncRNA prognostic risk model was constructed based on LINC00460, AC073218.1, AC010789.1, and COLCA1. This risk model successfully differentiated patients into distinct risk groups with significantly different clinical outcomes. The model was further validated in multiple independent patient cohorts. Additionally, functional and pathway enrichment analyses revealed that GI-lncRNAs play a crucial role in GI. Furthermore, the assessments of immune response, drug sensitivity, and cancer stemness revealed a significant relationship between GI-lncRNAs and tumor microenvironment infiltration, mutational burden, microsatellite instability, and drug resistance. CONCLUSIONS In this study, we discovered four novel GI-lncRNAs and developed a novel signature that effectively predicted clinical outcomes in pan-RCC. The findings provide valuable insights for pan-RCC immunotherapy and shed light on potential underlying mechanisms.
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Affiliation(s)
- Hui-Xin Zhu
- Department of Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
| | - Wen-Cai Zheng
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Hang Chen
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Jia-Yin Chen
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Fei Lin
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Shao-Hao Chen
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Xue-Yi Xue
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
| | - Qing-Shui Zheng
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Min Liang
- Department of Anesthesiology, Anesthesiology Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China
| | - Ning Xu
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
| | - Dong-Ning Chen
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Xiong-Lin Sun
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
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LIANG HONGYUAN, LI YANQIU, QU YONGGANG, ZHANG LINGYUN. Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay. Oncol Res 2023; 32:393-407. [PMID: 38186574 PMCID: PMC10765134 DOI: 10.32604/or.2023.031134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 09/06/2023] [Indexed: 01/09/2024] Open
Abstract
Advanced LUAD shows limited response to treatment including immune therapy. With the development of sequencing omics, it is urgent to combine high-throughput multi-omics data to identify new immune checkpoint therapeutic response markers. Using GSE72094 (n = 386) and GSE31210 (n = 226) gene expression profile data in the GEO database, we identified genes associated with lung adenocarcinoma (LUAD) death using tools such as "edgeR" and "maftools" and visualized the characteristics of these genes using the "circlize" R package. We constructed a prognostic model based on death-related genes and optimized the model using LASSO-Cox regression methods. By calculating the cell death index (CDI) of each individual, we divided LUAD patients into high and low CDI groups and examined the relationship between CDI and overall survival time by principal component analysis (PCA) and Kaplan-Meier analysis. We also used the "ConsensusClusterPlus" tool for unsupervised clustering of LUAD subtypes based on model genes. In addition, we collected data on the expression of immunomodulatory genes and model genes for each cohort and performed tumor microenvironment analyses. We also used the TIDE algorithm to predict immunotherapy responses in the CDI cohort. Finally, we studied the effect of PRKCD on the proliferation and migration of LUAD cells through cell culture experiments. The study utilized the TCGA-LUAD cohort (n = 493) and identified 2,901 genes that are differentially expressed in patients with LUAD. Through KEGG and GO enrichment analysis, these genes were found to be involved in a wide range of biological pathways. The study also used univariate Cox regression models and LASSO regression analyses to identify 17 candidate genes that were best associated with mortality prognostic risk scores. By comparing the overall survival (OS) outcomes of patients with different CDI values, it was found that increased CDI levels were significantly associated with lower OS rates. In addition, the study used unsupervised cluster analysis to divide 115 LUAD patients into two distinct clusters with significant differences in OS timing. Finally, a prognostic indicator called CDI was established and its feasibility as an independent prognostic indicator was evaluated by Cox proportional risk regression analysis. The immunotherapy efficacy was more sensitive in the group with high expression of programmed cell death models. Relationship between programmed cell death (PCD) signature models and drug reactivity. After evaluating the median inhibitory concentration (IC50) of various drugs in LUAD samples, statistically significant differences in IC50 values were found in cohorts with high and low CDI status. Specifically, Gefitinib and Lapatinib had higher IC50 values in the high-CDI cohort, while Olaparib, Oxaliplatin, SB216763, and Axitinib had lower values. These results suggest that individuals with high CDI levels are sensitive to tyrosine kinase inhibitors and may be resistant to conventional chemotherapy. Therefore, this study constructed a gene model that can evaluate patient immunotherapy by using programmed cell death-related genes based on muti-omics. The CDI index composed of these programmed cell death-related genes reveals the heterogeneity of lung adenocarcinoma tumors and serves as a prognostic indicator for patients.
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Affiliation(s)
- HONGYUAN LIANG
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - YANQIU LI
- Department of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - YONGGANG QU
- Department of Clinical Medicine, China Medical University, Shenyang, China
| | - LINGYUN ZHANG
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
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Xiong J, Chen J, Sun X, Zhao R, Gao K. Prognostic role of long non-coding RNA USP30-AS1 in ovarian cancer: insights into immune cell infiltration in the tumor microenvironment. Aging (Albany NY) 2023; 15:13776-13798. [PMID: 38054797 PMCID: PMC10756134 DOI: 10.18632/aging.205262] [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: 06/13/2023] [Accepted: 10/16/2023] [Indexed: 12/07/2023]
Abstract
Ovarian cancer represents a formidable gynecologic malignancy bearing a dismal prognosis owing to the dearth of reliable early detection approaches and a high recurrence rate. Long non-coding RNAs (lncRNAs) have garnered immense attention as key orchestrators involved in diverse biological processes and take part in cancer initiation and progression. The present study investigated the potential significance of lncRNA USP30-AS1 in ovarian cancer prognosis, as well as its putative association with immune cell infiltration in tumor immune microenvironment (TIME). By analyzing publicly available datasets, we identified six lncRNAs with prognostic prediction ability, including USP30-AS1. The results revealed a significant positive correlation of USP30-AS1 expression with the infiltration of immune cells such as Th1 cells, TFH, CD8 T cells, B cells, antigen-presenting dendritic cells (aDC), and plasmacytoid dendritic cells (pDC) in ovarian cancer specimens. These findings provide compelling evidence of the potential involvement of lncRNA in the regulation of the TME in ovarian carcinoma. The outcomes from this study underscore the potential of USP30-AS1 as a promising prognostic biomarker for ovarian cancer. Additionally, the findings offer significant insights into the plausible role of lncRNAs in modulating immune activities, thus adding to our understanding of the disease biology. Additional investigations are necessary to unravel the molecular mechanisms underpinning these connections and validate the results seen in independent cohorts and experimental models.
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Affiliation(s)
- Jian Xiong
- Department of Obstetrics and Gynecology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Junyan Chen
- China Medical University, Shenyang 110122, China
| | - Xiang Sun
- Department of Obstetrics and Gynecology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Rui Zhao
- Department of Obstetrics and Gynecology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Kefei Gao
- Department of Obstetrics and Gynecology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, China
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Salido-Guadarrama I, Romero-Cordoba SL, Rueda-Zarazua B. Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer. Int J Mol Sci 2023; 24:16600. [PMID: 38068923 PMCID: PMC10706612 DOI: 10.3390/ijms242316600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023] Open
Abstract
In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.
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Affiliation(s)
- Ivan Salido-Guadarrama
- Departamento de Bioinformatìca y Análisis Estadísticos, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico
| | - Sandra L. Romero-Cordoba
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
- Biochemistry Department, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City 14080, Mexico
| | - Bertha Rueda-Zarazua
- Posgrado en Ciencias Biológicas, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
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Zhong S, Chen S, Lin H, Luo Y, He J. Selection of M7G-related lncRNAs in kidney renal clear cell carcinoma and their putative diagnostic and prognostic role. BMC Urol 2023; 23:186. [PMID: 37968670 PMCID: PMC10652602 DOI: 10.1186/s12894-023-01357-9] [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: 06/20/2023] [Accepted: 11/01/2023] [Indexed: 11/17/2023] Open
Abstract
BACKGROUND Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. This study aims to develop new biomarkers for KIRC and explore the impact of biomarkers on the immunotherapeutic efficacy for KIRC, providing a theoretical basis for the treatment of KIRC patients. METHODS Transcriptome data for KIRC was obtained from the The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Weighted gene co-expression network analysis identified KIRC-related modules of long noncoding RNAs (lncRNAs). Intersection analysis was performed differentially expressed lncRNAs between KIRC and normal control samples, and lncRNAs associated with N(7)-methylguanosine (m7G), resulting in differentially expressed m7G-associated lncRNAs in KIRC patients (DE-m7G-lncRNAs). Machine Learning was employed to select biomarkers for KIRC. The prognostic value of biomarkers and clinical features was evaluated using Kaplan-Meier (K-M) survival analysis, univariate and multivariate Cox regression analysis. A nomogram was constructed based on biomarkers and clinical features, and its efficacy was evaluated using calibration curves and decision curves. Functional enrichment analysis was performed to investigate the functional enrichment of biomarkers. Correlation analysis was conducted to explore the relationship between biomarkers and immune cell infiltration levels and common immune checkpoint in KIRC samples. RESULTS By intersecting 575 KIRC-related module lncRNAs, 1773 differentially expressed lncRNAs, and 62 m7G-related lncRNAs, we identified 42 DE-m7G-lncRNAs. Using XGBoost and Boruta algorithms, 8 biomarkers for KIRC were selected. Kaplan-Meier survival analysis showed significant survival differences in KIRC patients with high and low expression of the PTCSC3 and RP11-321G12.1. Univariate and multivariate Cox regression analyses showed that AP000696.2, PTCSC3 and clinical characteristics were independent prognostic factors for patients with KIRC. A nomogram based on these prognostic factors accurately predicted the prognosis of KIRC patients. The biomarkers showed associations with clinical features of KIRC patients, mainly localized in the cytoplasm and related to cytokine-mediated immune response. Furthermore, immune feature analysis demonstrated a significant decrease in immune cell infiltration levels in KIRC samples compared to normal samples, with a negative correlation observed between the biomarkers and most differentially infiltrating immune cells and common immune checkpoints. CONCLUSION In summary, this study discovered eight prognostic biomarkers associated with KIRC patients. These biomarkers showed significant correlations with clinical features, immune cell infiltration, and immune checkpoint expression in KIRC patients, laying a theoretical foundation for the diagnosis and treatment of KIRC.
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Affiliation(s)
- Shuangze Zhong
- Guangdong Medical University, Zhanjiang City, 524023, Guangdong Province, China
| | - Shangjin Chen
- Guangdong Medical University, Zhanjiang City, 524023, Guangdong Province, China
| | - Hansheng Lin
- Guangdong Medical University, Zhanjiang City, 524023, Guangdong Province, China
- Department of Urology, Yangjiang People's Hospital affiliated to Guangdong Medical University, Yangjiang, 42 Dongshan Road, Jiangcheng District, Guangdong Province, 529500, China
| | - Yuancheng Luo
- Guangdong Medical University, Zhanjiang City, 524023, Guangdong Province, China
| | - Jingwei He
- Department of Urology, Yangjiang People's Hospital affiliated to Guangdong Medical University, Yangjiang, 42 Dongshan Road, Jiangcheng District, Guangdong Province, 529500, China.
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He L, Gao F, Zhu J, Xu Q, Yu Q, Yang M, Huang Y. Homologous recombination deficiency serves as a prognostic biomarker in clear cell renal cell carcinoma. Exp Ther Med 2023; 26:429. [PMID: 37602311 PMCID: PMC10433413 DOI: 10.3892/etm.2023.12128] [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: 10/21/2022] [Accepted: 04/18/2023] [Indexed: 08/22/2023] Open
Abstract
Kidney renal clear cell carcinoma (KIRC) is a frequent malignant tumor characterized by a high degree of heterogeneity and genetic instability. DNA double-strand breaks generated by homologous recombination deficit (HRD) are a well-known contributor to genomic instability, which can encourage tumor development. It is not known, however, whether the molecular characteristics linked with HRD have a predictive role in KIRC. The discovery cohort comprised 501 KIRC patients from The Cancer Genome Atlas database. Genome and transcriptome data of HRD patients were used for comprehensive analysis. Single cell RNA sequencing (scRNA-seq) was used to verify the test results of bulk RNA-seq. In the present study, patients with a high HRD score had a worse prognosis compared with those with a low HRD score. The DNA damage response signaling pathways and immune-related signaling pathways were notably enriched in the HRD-positive subgroup. Further comprehensive analysis of the tumor microenvironment (TME) revealed that the signal of exhausted CD8+ T cells was enriched in the HRD-positive subgroup. Finally, scRNA-seq analyses confirmed that the immune-related signaling pathways were upregulated in HRD-positive patients. In conclusion, the present study not only demonstrated that a high HRD score is a valid prognostic biomarker in KIRC patients, but also revealed the TME in HRD-positive tumors.
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Affiliation(s)
- Liping He
- Department of Urology, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang 310007, P.R. China
| | - Feng Gao
- Department of Urology, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang 310007, P.R. China
| | - Jingyu Zhu
- Department of Urology, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang 310007, P.R. China
| | - Qiaoping Xu
- Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, P.R. China
| | - Qiqi Yu
- Department of Urology, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang 310007, P.R. China
| | - Mei Yang
- Department of Urology, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang 310007, P.R. China
| | - Yasheng Huang
- Department of Urology, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang 310007, P.R. China
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Xie Q, Liu T, Zhang X, Ding Y, Fan X. Construction of a telomere-related gene signature to predict prognosis and immune landscape for glioma. Front Endocrinol (Lausanne) 2023; 14:1145722. [PMID: 37351101 PMCID: PMC10284135 DOI: 10.3389/fendo.2023.1145722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/23/2023] [Indexed: 06/24/2023] Open
Abstract
Background Glioma is one of the commonest malignant tumors of the brain. However, glioma present with a poor clinical prognosis. Therefore, specific detection markers and therapeutic targets need to be explored as a way to promote the survival rate of BC patients. Therefore, we need to search for quality immune checkpoints to support the efficacy of immunotherapy for glioma. Methods We first recognized differentially expressed telomere-related genes (TRGs) and accordingly developed a risk model by univariate and multivariate Cox analysis. The accuracy of the model is then verified. We evaluated the variations in immune function and looked at the expression levels of immune checkpoint genes. Finally, to assess the anti-tumor medications often used in the clinical treatment of glioma, we computed the half inhibitory concentration of pharmaceuticals. Results We finally identified nine TRGs and built a risk model. Through the validation of the model, we found good agreement between the predicted and observed values. Then, we found 633 differentially expressed genes between various risk groups to identify the various molecular pathways between different groups. The enrichment of CD4+ T cells, CD8+ T cells, fibroblasts, endothelial cells, macrophages M0, M1, and M2, mast cells, myeloid dendritic cells, and neutrophils was favorably correlated with the risk score, but the enrichment of B cells and NK cells was negatively correlated with the risk score. The expression of several immune checkpoint-related genes differed significantly across the risk groups. Finally, in order to create individualized treatment plans for diverse individuals, we searched for numerous chemotherapeutic medications for patients in various groups. Conclusion The findings of this research provide evidence that TRGs may predict a patient's prognosis for glioma, assist in identifying efficient targets for glioma immunotherapy, and provide a foundation for an efficient, customized approach to treating glioma patients.
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Affiliation(s)
- Qin Xie
- Department of Neurosurgery, Hangzhou Ninth People’s Hospital, Hangzhou, Zhejiang, China
| | - Tingting Liu
- Department of Endocrinology, Affiliated Haikou Hospital of Xiangya School of Central South University, Haikou, Hainan, China
| | - Xiaole Zhang
- Department of Neurosurgery, Hangzhou Ninth People’s Hospital, Hangzhou, Zhejiang, China
| | - Yanli Ding
- Department of Neurosurgery, Hangzhou Ninth People’s Hospital, Hangzhou, Zhejiang, China
| | - Xiaoyan Fan
- Intensive Care Unit, Hangzhou Ninth People’s Hospital, Hangzhou, Zhejiang, China
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Bai Y, He J, Ma Y, Liang H, Li M, Wu Y. Identification of DNA repair gene signature and potential molecular subtypes in hepatocellular carcinoma. Front Oncol 2023; 13:1180722. [PMID: 37260986 PMCID: PMC10227583 DOI: 10.3389/fonc.2023.1180722] [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: 03/06/2023] [Accepted: 04/20/2023] [Indexed: 06/02/2023] Open
Abstract
DNA repair is a critical factor in tumor progression as it impacts tumor mutational burden, genome stability, PD-L1 expression, immunotherapy response, and tumor-infiltrating lymphocytes (TILs). In this study, we present a prognostic model for hepatocellular carcinoma (HCC) that utilizes genes related to the DNA damage response (DDR). Patients were stratified based on their risk score, and groups with lower risk scores demonstrated better survival rates compared to those with higher risk scores. The prognostic model's accuracy in predicting 1-, 3-, and 5-year survival rates for HCC patients was analyzed using receiver operator curve analysis (ROC). Results showed good accuracy in predicting survival rates. Additionally, we evaluated the prognostic model's potential as an independent factor for HCC prognosis, along with tumor stage. Furthermore, nomogram was employed to determine the overall survival year of patients with HCC based on this independent factor. Gene set enrichment analysis (GSEA) revealed that in the high-risk group, apoptosis, cell cycle, MAPK, mTOR, and WNT cascades were highly enriched. We used training and validation datasets to identify potential molecular subtypes of HCC based on the expression of DDR genes. The two subtypes differed in terms of checkpoint receptors for immunity and immune cell filtration capacity.Collectively, our study identified potential biomarkers of HCC prognosis, providing novel insights into the molecular mechanisms underlying HCC.
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Affiliation(s)
- Yi Bai
- Department of Critical Care Medicine, Panjin Liaoyou Baoshihua Hospital, Liaoning, China
| | - Jinyun He
- Department of hepatobiliary surgery, Panjin Liaoyou Baoshihua Hospital, Liaoning, China
| | - Yanquan Ma
- Department of Critical Care Medicine, Panjin Liaoyou Baoshihua Hospital, Liaoning, China
| | - He Liang
- Department of integrated Chinese and Western medicine, Panjin Liaoyou Baoshihua Hospital, Liaoning, China
| | - Ming Li
- Fuxin Municipal Discipline Inspection Commission, Liaoning, China
| | - Yan Wu
- Department of rheumatology and immunology, Panjin Liaoyou Baoshihua Hospital, Liaoning, China
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11
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Li Y, Li X, Yu Z. Novel methylation-related long non-coding RNA clinical outcome prediction method: the clinical phenotype and immune infiltration research in low-grade gliomas. Front Oncol 2023; 13:1177120. [PMID: 37228500 PMCID: PMC10203515 DOI: 10.3389/fonc.2023.1177120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 04/11/2023] [Indexed: 05/27/2023] Open
Abstract
Background Recent studies have suggested that long non-coding RNAs (lncRNAs) may play crucial role in low-grade glioma; however, the underlying mechanisms linking them to epigenetic methylation remain unclear. Methods We downloaded expression level data for regulators associated with N1 methyladenosine (m1A), 5-methyladenine (m5C), and N6 methyladenosine (m6A) (M1A/M5C/M6A) methylation from the Cancer Genome Atlas-low-grade glioma (TCGA-LGG) database. We identified the expression patterns of lncRNAs, and selected methylation-related lncRNAs using Pearson correlation coefficient>0.4. Non-negative matrix dimensionality reduction was then used to determine the expression patterns of the methylation-associated lncRNAs. We constructed a weighted gene co-expression network analysis (WGCNA) network to explore the co-expression networks between the two expression patterns. Functional enrichment of the co-expression network was performed to identify biological differences between the expression patterns of different lncRNAs. We also constructed prognostic networks based on the methylation presence in lncRNAs in low-grade gliomas. Results We identified 44 regulators by literature review. Using a correlation coefficient greater than 0.4, we identified 2330 lncRNAs, among which 108 lncRNAs with independent prognostic values were further screened using univariate Cox regression at P< 0.05. Functional enrichment of the co-expression networks revealed that regulation of trans-synaptic signaling, modulation of chemical synaptic transmission, calmodulin binding, and SNARE binding were mostly enriched in the blue module. The calcium and CA2 signaling pathways were associated with different methylation-related long non-coding chains. Using the Least Absolute Shrinkage Selector Operator (LASSO) regression analysis, we analyzed a prognostic model containing four lncRNAs. The model's risk score was 1.12 *AC012063 + 0.74 * AC022382 + 0.32 * AL049712 + 0.16 * GSEC. Gene set variation analysis (GSVA) revealed significant differences in mismatch repair, cell cycle, WNT signaling pathway, NOTCH signaling pathway, Complement and Cascades, and cancer pathways at different GSEC expression levels. Thus, these results suggest that GSEC may be involved in the proliferation and invasion of low-grade glioma, making it a prognostic risk factor for low-grade glioma. Conclusion Our analysis identified methylation-related lncRNAs in low-grade gliomas, providing a foundation for further research on lncRNA methylation. We found that GSEC could serve as a candidate methylation marker and a prognostic risk factor for overall survival in low-grade glioma patients. These findings shed light on the underlying mechanisms of low-grade glioma development and may facilitate the development of new treatment strategies.
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12
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Interactome battling of lncRNA CCDC144NL-AS1: Its role in the emergence and ferocity of cancer and beyond. Int J Biol Macromol 2022; 222:1676-1687. [PMID: 36179873 DOI: 10.1016/j.ijbiomac.2022.09.209] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/23/2022] [Indexed: 11/05/2022]
Abstract
Long non-coding RNAs (lncRNAs) were, once, viewed as "noise" for transcription. Recently, many lncRNAs are functionally linked to several human disorders, including cancer. Coiled-Coil Domain Containing 144 N-Terminal-Like antisense1 (CCDC144NL-AS1) is a newly discovered cytosolic lncRNA. Aberrant CCDC144NL-AS1 expression was discovered in hepatocellular carcinoma (HCC), ovarian cancer (OC), gastric cancer (GC), non-small cell lung cancer (NSCLC), and osteosarcoma. CCDC144NL-AS1 could be a promising prognostic biological marker and therapeutic target for cancer. In this review, we will collect and highlight the available information about CCDC144NL-AS1 role in various cancers. Moreover, we will discuss the diagnostic and prognostic utility of CCDC144NL-AS1 as a new molecular biomarker for several human malignancies, besides its potential therapeutic importance.
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Zhu Z, Zhang C, Qian J, Feng N, Zhu W, Wang Y, Gong Y, Li X, Lin J, Zhou L. Construction and validation of a ferroptosis-related long noncoding RNA signature in clear cell renal cell carcinoma. Cancer Cell Int 2022; 22:283. [PMID: 36104748 PMCID: PMC9476564 DOI: 10.1186/s12935-022-02700-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 09/04/2022] [Indexed: 12/24/2022] Open
Abstract
Abstract
Background
Clear cell renal cell carcinoma (ccRCC) is characterized by the accumulation of lipid-reactive oxygen species. Ferroptosis, due to the lipid peroxidation, has been reported to be strongly correlated with tumorigenesis and progression. However, the functions of the ferroptosis process in ccRCC remain unclear.
Methods
After sample cleaning, data integration, and batch effect removal, we used the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases to screen out the expression and prognostic value of ferroptosis-related lncRNAs and then performed the molecular subtyping using the K-means method. Then, the functional pathway enrichment and immune microenvironment infiltration between the different clusters were carried out. The results showed a significant difference in immune cell infiltration between the two clusters and the associated marker responded to individualized differences in treatment. Then, least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish a prognostic signature based on 5 lncRNAs. This signature could accurately predicted patient prognosis and served as an independent clinical risk factor. We then combined significant clinical parameters in multivariate Cox regression and the prognostic signature to construct a clinical predictive nomogram, which provides appropriate guidance for predicting the overall survival of ccRCC patients.
Results
The prognostic differentially expressed ferroptosis-related LncRNAs (DEFRlncRNAs) were found, and 5 lncRNAs were finally used to establish the prognostic signature in the TCGA cohort, with subsequently validation in the internal and external cohorts. Moreover, we conducted the molecular subtyping and divided the patients in the TCGA cohort into two clusters showing differences in Hallmark pathways, immune infiltration, immune target expression, and drug therapies. Differences between clusters contributed to individualizing treatment. Furthermore, a nomogram was established to better predict the clinical outcomes of the ccRCC patients.
Conclusions
Our study conducted molecular subtyping and established a novel predictive signature based on the ferroptosis-related lncRNAs, which contributed to the prognostic prediction and individualizing treatment of ccRCC patients.
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Development and validation of ferroptosis-related lncRNAs prognosis signatures in kidney renal clear cell carcinoma. Cancer Cell Int 2021; 21:591. [PMID: 34736453 PMCID: PMC8567554 DOI: 10.1186/s12935-021-02284-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 10/20/2021] [Indexed: 12/14/2022] Open
Abstract
Background Ferroptosis is a recently recognised new type of cell death which may be a potential target for cancer therapy. In the present study, we aimed to screen ferroptosis-related differentially expressed long non-coding RNAs as biomarkers to predict the outcome of kidney renal clear cell carcinoma. Methods RNAseq count data and corresponding clinical information were obtained from the Cancer Genome Atlas database. Lists of ferroptosis-related genes and long non-coding RNAs were obtained from the FerrDb and GENCODE databases, respectively. The candidate prognostic signatures were screened by Cox regression analyses and least absolute shrinkage and selection operator analyses. Results Three ferroptosis-related long non-coding RNAs (DUXAP8, LINC02609, and LUCAT1) were significantly correlated with the overall survival of kidney renal clear cell carcinoma independently. Kidney renal clear cell carcinoma patients with high-risk values displayed worse OS. Meanwhile, the expression of these three ferroptosis-related long non-coding RNAs and their risk scores were significantly correlated with clinicopathological features. Principal component analyses showed that patients with kidney renal clear cell carcinoma have differential risk values were well distinguished by the three ferroptosis-related long non-coding RNAs. Conclusions The present study suggests that the risk assessment model constructed by these three ferroptosis-related long non-coding RNAs could accurately predict the outcome of kidney renal clear cell carcinoma. We also provide a novel perspective for cancer prognosis screening. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-021-02284-1.
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