1
|
Duan W, Yang L, Liu J, Dai Z, Wang Z, Zhang H, Zhang X, Liang X, Luo P, Zhang J, Liu Z, Zhang N, Mo H, Qu C, Xia Z, Cheng Q. A TGF-β signaling-related lncRNA signature for prediction of glioma prognosis, immune microenvironment, and immunotherapy response. CNS Neurosci Ther 2024; 30:e14489. [PMID: 37850692 PMCID: PMC11017415 DOI: 10.1111/cns.14489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 07/27/2023] [Accepted: 09/24/2023] [Indexed: 10/19/2023] Open
Abstract
AIMS The dysregulation of TGF-β signaling is a crucial pathophysiological process in tumorigenesis and progression. LncRNAs have diverse biological functions and are significant participants in the regulation of tumor signaling pathways. However, the clinical value of lncRNAs related to TGF-β signaling in glioma is currently unclear. METHODS Data on glioma's RNA-seq transcriptome, somatic mutation, DNA methylation data, and clinicopathological information were derived from the CGGA and TCGA databases. A prognostic lncRNA signature was constructed by Cox and LASSO regression analyses. TIMER2.0 database was utilized to deduce immune infiltration characteristics. "ELMER v.2" was used to reconstruct TF-methylation-gene regulatory network. Immunotherapy and chemotherapy response predictions were implemented by the TIDE algorithm and GDSC database, respectively. In vitro and in vivo experiments were conducted to verify the results and clarify the regulatory mechanism of lncRNA. RESULTS In glioma, a TGF-β signaling-related 15-lncRNA signature was constructed, including AC010173.1, HOXA-AS2, AC074286.1, AL592424.1, DRAIC, HOXC13-AS, AC007938.1, AC010729.1, AC013472.3, AC093895.1, AC131097.4, AL606970.4, HOXC-AS1, AGAP2-AS1, and AC002456.1. This signature proved to be a reliable prognostic tool, with high risk indicating an unfavorable prognosis and being linked to malignant clinicopathological and genomic mutation traits. Risk levels were associated with different immune infiltration landscapes, where high risk was indicative of high levels of macrophage infiltration. In addition, high risk also suggested better immunotherapy and chemotherapy response. cg05987823 was an important methylation site in glioma progression, and AP-1 transcription factor family participated in the regulation of signature lncRNA expression. AGAP2-AS1 knockdown in in vitro and in vivo experiments inhibited the proliferation, migration, and invasion of glioma cells, as well as the growth of glioma, by downregulating the expression levels of NF-κB and ERK 1/2 in the TGF-β signaling pathway. CONCLUSIONS A prognostic lncRNA signature of TGF-β signaling was established in glioma, which can be used for prognostic judgment, immune infiltration status inference, and immunotherapy response prediction. AGAP2-AS1 plays an important role in glioma progression.
Collapse
Affiliation(s)
- Wei‐Wei Duan
- Department of Neurosurgery, Xiangya HospitalCentral South UniversityChangshaHunanChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaHunanChina
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaHunanChina
| | - Li‐Ting Yang
- Department of Neurosurgery, Xiangya HospitalCentral South UniversityChangshaHunanChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaHunanChina
| | - Jian Liu
- Experiment Center of Medical InnovationThe First Hospital of Hunan University of Chinese MedicineChangshaHunanChina
| | - Zi‐Yu Dai
- Department of Neurosurgery, Xiangya HospitalCentral South UniversityChangshaHunanChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaHunanChina
| | - Ze‐Yu Wang
- Department of Neurosurgery, Xiangya HospitalCentral South UniversityChangshaHunanChina
- MRC Centre for Regenerative Medicine, Institute for Regeneration and RepairUniversity of EdinburghEdinburghUK
| | - Hao Zhang
- Department of Neurosurgery, Xiangya HospitalCentral South UniversityChangshaHunanChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaHunanChina
| | - Xun Zhang
- Department of Neurosurgery, Xiangya HospitalCentral South UniversityChangshaHunanChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaHunanChina
| | - Xi‐Song Liang
- Department of Neurosurgery, Xiangya HospitalCentral South UniversityChangshaHunanChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaHunanChina
| | - Peng Luo
- Department of Oncology, Zhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Jian Zhang
- Department of Oncology, Zhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Zao‐Qu Liu
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Nan Zhang
- One‐third Lab, College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbinHei LongjiangChina
| | - Hao‐Yang Mo
- Department of Neurosurgery, Xiangya HospitalCentral South UniversityChangshaHunanChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaHunanChina
| | - Chun‐Run Qu
- Department of Neurosurgery, Xiangya HospitalCentral South UniversityChangshaHunanChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaHunanChina
| | - Zhi‐Wei Xia
- Department of NeurologyHunan Aerospace HospitalChangshaHunanChina
| | - Quan Cheng
- Department of Neurosurgery, Xiangya HospitalCentral South UniversityChangshaHunanChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaHunanChina
| |
Collapse
|
2
|
Shi Y, Sheng P, Guo M, Chen K, Zhou H, Wu M, Li W, Li B. Cuproptosis-related lncRNAs predict prognosis and immune response of thyroid carcinoma. Front Genet 2023; 14:1100909. [PMID: 37470034 PMCID: PMC10352785 DOI: 10.3389/fgene.2023.1100909] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 06/26/2023] [Indexed: 07/21/2023] Open
Abstract
Objective: To estimate the survival and prognosis of patients with thyroid carcinoma (THCA) based on the Long non-coding RNA (lncRNA) traits linked to cuproptosis and to investigate the connection between the immunological spectrum of THCA and medication sensitivity. Methods: RNA-Seq data and clinical information for THCA were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We built a risk prognosis model by identifying and excluding lncRNAs associated with cuproptosis using Cox regression and LASSO methods. Both possible biological and immune infiltration functions were investigated using Principal Component Analysis (PCA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and immunoassays. The sensitivity of the immune response to possible THCA medicines was assessed using ratings for tumor immune dysfunction and exclusion (TIDE) and tumor mutational burden (TMB). Results: Seven cuproptosis-related lncRNAs were used to construct our prognostic prediction model: AC108704.1, DIO3OS, AL157388.1, AL138767.3, STARD13-AS, AC008532.1, and PLBD1-AS1. Using data from TCGA's training, testing, and all groups, Kaplan-Meier and ROC curves demonstrated this feature's adequate predictive validity. Different clinical characteristics have varying effects on cuproptosis-related lncRNA risk models. Further analysis of immune cell infiltration and single sample Gene Set Enrichment Analysis (ssGSEA) supported the possibility that cuproptosis-associated lncRNAs and THCA tumor immunity were closely connected. Significantly, individuals with THCA showed a considerable decline in survival owing to the superposition effect of patients in the high-risk category and high TMB. Additionally, the low-risk group had a higher TIDE score compared with the high-risk group, indicating that these patients had suboptimal immune checkpoint blocking responses. To ensure the accuracy and reliability of our results, we further verified them using several GEO databases. Conclusion: The clinical and risk aspects of cuproptosis-related lncRNAs may aid in determining the prognosis of patients with THCA and improving therapeutic choices.
Collapse
Affiliation(s)
- Yinli Shi
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
| | - Pei Sheng
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
| | - Ming Guo
- Zhongda Hospital Southeast University, Southeast University, Nanjing, China
| | - Kai Chen
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
| | - Hongguang Zhou
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing University of Chinese Medicine, Nanjing, China
| | - Mianhua Wu
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing University of Chinese Medicine, Nanjing, China
| | - Wenting Li
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing University of Chinese Medicine, Nanjing, China
| | - Bo Li
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing University of Chinese Medicine, Nanjing, China
| |
Collapse
|
3
|
Xu X, Liang Y, Gareev I, Liang Y, Liu R, Wang N, Yang G. LncRNA as potential biomarker and therapeutic target in glioma. Mol Biol Rep 2023; 50:841-851. [PMID: 36331751 DOI: 10.1007/s11033-022-08056-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 03/22/2022] [Indexed: 11/06/2022]
Abstract
Glioma is the most frequent type of malignant tumor in the central nervous system, accounting for about 80% of primary malignant brain tumors, usually with a poor prognosis. A number of studies have been conducted on the molecular abnormalities in glioma to further understand its pathogenesis, and it has been found that lncRNAs (long non-coding RNA) play a key role in angiogenesis, tumor growth, infiltration and metastasis of glioma. Since specific lncRNAs have an aberrant expression in brain tissue, cerebrospinal fluid as well as peripheral circulation of glioma patients, they are considered to be potential biomarkers. This review focuses on the biological characteristics of lncRNA and its value as a biomarker for glioma diagnosis and prognosis. Moreover, in view of the role of lncRNAs in glioma proliferation and chemoradiotherapy resistance, we discussed the feasibility for lncRNAs as therapeutic targets. Finally, the persisting deficiencies and future prospects of using lncRNAs as clinical biomarkers and therapeutic targets were concluded.
Collapse
Affiliation(s)
- Xun Xu
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Youzheng Street 23, Nangang District, Harbin, 150001, Heilongjiang, China
- Institute of Brain Science, Harbin Medical University, Harbin, China
| | - Yuan Liang
- Department of Neurosurgery, Xuzhou Third People's Hospital, Xuzhou, China
| | - Ilgiz Gareev
- Bashkir State Medical University, Ufa, Russia, 450008
| | - Yanchao Liang
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Youzheng Street 23, Nangang District, Harbin, 150001, Heilongjiang, China
- Institute of Brain Science, Harbin Medical University, Harbin, China
| | - Rui Liu
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Youzheng Street 23, Nangang District, Harbin, 150001, Heilongjiang, China
- Institute of Brain Science, Harbin Medical University, Harbin, China
| | - Ning Wang
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Youzheng Street 23, Nangang District, Harbin, 150001, Heilongjiang, China.
- Institute of Brain Science, Harbin Medical University, Harbin, China.
| | - Guang Yang
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Youzheng Street 23, Nangang District, Harbin, 150001, Heilongjiang, China.
- Institute of Brain Science, Harbin Medical University, Harbin, China.
| |
Collapse
|
4
|
Lu Z, Feng Y. Foreboding lncRNA markers of low-grade gliomas dependent on metabolism. Medicine (Baltimore) 2022; 101:e31302. [PMID: 36343057 PMCID: PMC9646492 DOI: 10.1097/md.0000000000031302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
At present, there is no systematic study on the signature of long-chain noncoding RNAs (lncRNAs) involved in metabolism that can fully predict the prognosis in patients with low-grade gliomas (LGGs). Therefore, consistent metabolic-related lncRNA signatures need to be established. The Cancer Genome Atlas (TCGA) was used to identify the expression profile of lncRNAs containing 529 LGGs samples. LncRNAs and genes related to metabolism are used to establish a network in the form of coexpression to screen lncRNAs related to metabolism. LncRNA was more clearly described by univariate Cox regression. Moreover, lncRNA signatures were explored by multivariate Cox regression and lasso regression. The risk score was established according to the signature and it was an unattached prognostic marker according to Cox regression analysis. Functional enrichment of lncRNAs was shown by employing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Univariate Cox retrospective analysis showed that 543 metabolism-related lncRNAs were independent prognostic factors of LGG, and multivariate Cox regression analysis confirmed that 19 metabolism-related lncRNAs were prognostic genes of LGG. In the risk model, the low-risk group had a higher Overall survival (OS) than the high-risk group (P < .001). Univariate Cox regression analysis of risk score and clinical factors showed that risk score was an independent prognostic factor (P < .001, HR = 1.047, 95% CI: 1.038-1.056). Multivariate Cox results showed that risk score could predict the prognosis of LGG (P < .001, HR = 1.036, 95% CI: 1.026-1.045). ROC curve analysis showed that risk score could predict the prognosis of LGG. The areas of 1-year, 3-years, and 5 years are 0.891, 0.904 and 0.832. GO and KEGG analysis showed that metabolism-related lncRNAs was mainly concentrated in the pathways related to tumor metabolism. In order to find a more stable and reliable target for the treatment of LGG, we established 19 metabolic-related lncRNAs prognostic model, and determined that it can predict the prognosis of LGG patients. This provides a new solution approach to the poor prognosis of patients with LGG and may reverse the trend of LGG's transformation to high-grade gliomas.
Collapse
Affiliation(s)
- Zhuangzhuang Lu
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yugong Feng
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, China
- * Correspondence: Yugong Feng, Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, China (e-mail: )
| |
Collapse
|
5
|
A somatic mutation-derived LncRNA signatures of genomic instability predicts the prognosis and tumor microenvironment immune characters in hepatocellular carcinoma. Hepatol Int 2022; 16:1220-1233. [PMID: 35947245 DOI: 10.1007/s12072-022-10375-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/04/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is an aggressive carcinoma with genome instability. Long non-coding RNAs (LncRNAs) have been functionally associated with genomic instability in cancers. However, the identification and prognostic value of lncRNAs related to genome instability have not been explored in hepatocellular carcinoma. In this study, we aim to identify a genomic instability-related lncRNA signature for predicting prognosis and the efficacy of immunotherapy in HCC patients. METHODS According to the somatic mutation and transcript data of 364 patients with HCC, we determined differentially expressed genome instability-related lncRNAs (GInLncRNAs). Gene ontology (GO) enrichment analyses and Kyoto Encyclopedia of genes and genomes enrichment analyses revealed the potential functions of genes co-expressed with those lncRNAs involved in cancer development and immune function. We further determined a genome instability-related lncRNA signature (GInLncSig) through Cox regression analysis and LASSO regression analysis. Thereafter, we performed correlation analyses with mutations, clinical stratification analyses, and survival analyses to evaluate GInLncSig predictive function. Subsequently, we construct a nomogram model for prognostic assessments of patients with HCC. Finally, we performed Immunocytes infiltration analysis, gene set enrichment analysis (ssGSEA) of immunity circle-associated pathways, and T cell-inflamed score to explore GInLncSig's potential value in guiding immunotherapy. RESULTS We identified 11 independent prognosis-associated GInLncRNAs (AC002511.2, LINC00501, LINC02055, LINC02714, LINC01508, LOC105371967, RP11_96A15.1, RP11_305F18.1, RP11_342M1.3, RP11_432J24.3, U95743.1) to construct a GInLncSig. According to the risk score calculated by GInLncSig, the high-risk group was characterized by a higher somatic mutation count, significantly poorer clinical prognosis, higher T cell-inflamed score, and specific tumor immune infiltration status compared to the low-risk group. Furthermore, we constructed a nomogram model to improve the reliability and clinical utility of predicting the prognosis of patients with HCC. CONCLUSION Our study established a reliable prognostic prediction signature that could be a tool for prognosis prediction and a promising predictive biomarker of immunotherapy in hepatocellular carcinoma.
Collapse
|
6
|
Zhang Q, Liu X, Chen Z, Zhang S. Novel GIRlncRNA Signature for Predicting the Clinical Outcome and Therapeutic Response in NSCLC. Front Pharmacol 2022; 13:937531. [PMID: 35991889 PMCID: PMC9382191 DOI: 10.3389/fphar.2022.937531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/23/2022] [Indexed: 11/18/2022] Open
Abstract
Background: Non–small cell lung cancer (NSCLC) is highly malignant with driver somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) play a vital role in regulating these two aspects. However, the identification of somatic mutation-derived, genomic instability-related lncRNAs (GIRlncRNAs) and their clinical significance in NSCLC remains largely unexplored. Methods: Clinical information, gene mutation, and lncRNA expression data were extracted from TCGA database. GIRlncRNAs were screened by a mutator hypothesis-derived computational frame. Co-expression, GO, and KEGG enrichment analyses were performed to investigate the biological functions. Cox and LASSO regression analyses were performed to create a prognostic risk model based on the GIRlncRNA signature (GIRlncSig). The prediction efficiency of the model was evaluated by using correlation analyses with mutation, driver gene, immune microenvironment contexture, and therapeutic response. The prognostic performance of the model was evaluated by external datasets. A nomogram was established and validated in the testing set and TCGA dataset. Results: A total of 1446 GIRlncRNAs were selected from the screen, and the established GIRlncSig was used to classify patients into high- and low-risk groups. Enrichment analyses showed that GIRlncRNAs were mainly associated with nucleic acid metabolism and DNA damage repair pathways. Cox analyses further identified 19 GIRlncRNAs to construct a GIRlncSig-based risk score model. According to Cox regression and stratification analyses, 14 risk lncRNAs (AC023824.3, AC013287.1, AP000829.1, LINC01611, AC097451.1, AC025419.1, AC079949.2, LINC01600, AC004862.1, AC021594.1, MYRF-AS1, LINC02434, LINC02412, and LINC00337) and five protective lncRNAs (LINC01067, AC012645.1, AL512604.3, AC008278.2, and AC089998.1) were considered powerful predictors. Analyses of the model showed that these GIRlncRNAs were correlated with somatic mutation pattern, immune microenvironment infiltration, immunotherapeutic response, drug sensitivity, and survival of NSCLC patients. The GIRlncSig risk score model demonstrated good predictive performance (AUCs of ROC for 10-year survival was 0.69) and prognostic value in different NSCLC datasets. The nomogram comprising GIRlncSig and tumor stage exhibited improved robustness and feasibility for predicting NSCLC prognosis. Conclusion: The newly identified GIRlncRNAs are powerful biomarkers for clinical outcome and prognosis of NSCLC. Our study highlights that the GIRlncSig-based score model may be a useful tool for risk stratification and management of NSCLC patients, which deserves further evaluation in future prospective studies.
Collapse
Affiliation(s)
- Qiangzhe Zhang
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin, China
| | - Xicheng Liu
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Zhinan Chen
- National Translational Science Center for Molecular Medicine, Department of Cell Biology, State Key Laboratory of Cancer Biology, Fourth Military Medical University, Xi’an, China
| | - Sihe Zhang
- Department of Cell Biology, School of Medicine, Nankai University, Tianjin, China
- *Correspondence: Sihe Zhang, , https://orcid.org/0000-0002-8923-1993
| |
Collapse
|
7
|
Zhu J, Huang Q, Peng X, Luo C, Liu S, Liu Z, Wu X, Luo H. Identification of LncRNA Prognostic Signature Associated With Genomic Instability in Pancreatic Adenocarcinoma. Front Oncol 2022; 12:799475. [PMID: 35433487 PMCID: PMC9012103 DOI: 10.3389/fonc.2022.799475] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 02/28/2022] [Indexed: 12/21/2022] Open
Abstract
Background Genomic instability (GI) is a critical feature of cancer which plays a key role in the occurrence and development of pancreatic adenocarcinoma (PAAD). Long non-coding RNA (LncRNA) is an emerging prognostic biomarker because it is involved in regulating GI. Recently, researchers used such GI-related LncRNAs (GILncRNAs) to establish a prognostic signature for patients with cancer and helped in predicting the overall prognosis of the patients. However, it is evident that patients with PAAD still lack such prognostic signature constructed with GILncRNA. Methods The present study screened GILncRNAs from 83 patients with PAAD. Prognosis-related GILncRNAs were identified by univariate Cox regression analysis. The correlation coefficients of these GILncRNAs were obtained by multivariate Cox regression analysis and used to construct a signature. The signature in the present study was then assessed through survival analysis, mutation correlation analysis, independent prognostic analysis, and clinical stratification analysis in the training set and validated in the testing as well as all TCGA set. The current study performed external clinical relevance validation of the signature and validated the effect of AC108134.2 in GILncSig on PAAD using in vitro experiments. Finally, the function of GILncRNA signature (GILncSig) dependent on Gene Ontology enrichment analysis was explored and chemotherapeutic drug sensitivity analysis was also performed. Results Results of the present study found that a total of 409 GILncRNAs were identified, 5 of which constituted the prognostic risk signature in this study, namely, AC095057.3, AC108134.2, AC124798.1, AL606834.1, and AC104695.4. It was found that the signature of the present study was better than others in predicting the overall survival and applied to patients with PAAD of all ages, genders, and tumor grades. Further, it was noted that the signature of the current study in the GSE102238, was correlated with tumor length, and tumor stage of patients with PAAD. In vitro, functional experiments were used in the present study to validate that AC108134.2 is associated with PAAD genomic instability and progression. Notably, results of the pRRophetic analysis in the current study showed that the high-risk group possessed reverse characteristics and was sensitive to chemotherapy. Conclusions In conclusion, it was evident that the GILncSig used in the present study has good prognostic performance. Therefore, the signature may become a potential sensitive biological indicator of PAAD chemotherapy, which may help in clinical decision-making and management of patients with cancer.
Collapse
Affiliation(s)
- Jinfeng Zhu
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Jiangxi Province Key Laboratory of Molecular Medicine, Nanchang, China
| | - Qian Huang
- Department of General Practice, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Xingyu Peng
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Jiangxi Province Key Laboratory of Molecular Medicine, Nanchang, China
| | - Chen Luo
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Sicheng Liu
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zitao Liu
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xun Wu
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hongliang Luo
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| |
Collapse
|
8
|
Single cell RNA sequencing reveals differentiation related genes with drawing implications in predicting prognosis and immunotherapy response in gliomas. Sci Rep 2022; 12:1872. [PMID: 35115572 PMCID: PMC8814011 DOI: 10.1038/s41598-022-05686-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 01/17/2022] [Indexed: 11/30/2022] Open
Abstract
Differentiation states of glioma cells correlated with prognosis and tumor-immune microenvironment (TIME) in patients with gliomas. We aimed to identify differentiation related genes (DRGs) for predicting the prognosis and immunotherapy response in patients with gliomas. We identified three differentiation states and the corresponding DRGs in glioma cells through single-cell transcriptomics analysis. Based on the DRGs, we separated glioma patients into three clusters with distinct clinicopathological features in combination with bulk RNA-seq data. Weighted correlation network analysis, univariate cox regression analysis and least absolute shrinkage and selection operator analysis were involved in the construction of the prognostic model based on DRGs. Distinct clinicopathological characteristics, TIME, immunogenomic patterns and immunotherapy responses were identified across three clusters. A DRG signature composing of 12 genes were identified for predicting the survival of glioma patients and nomogram model integrating the risk score and multi-clinicopathological factors were constructed for clinical practice. Patients in high-risk group tended to get shorter overall survival and better response to immune checkpoint blockage therapy. We obtained 9 candidate drugs through comprehensive analysis of the differentially expressed genes between the low and high-risk groups in the model. Our findings indicated that the risk score may not only contribute to the determination of prognosis but also facilitate in the prediction of immunotherapy response in glioma patients.
Collapse
|
9
|
Cao Y, Zhu H, Liu W, Wang L, Yin W, Tan J, Zhou Q, Xin Z, Huang H, Xie D, Zhao M, Jiang X, Peng J, Ren C. Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma. Front Genet 2022; 12:758596. [PMID: 35069679 PMCID: PMC8766732 DOI: 10.3389/fgene.2021.758596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 11/29/2021] [Indexed: 12/21/2022] Open
Abstract
Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear. Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N6-methyladenosine (m6A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA). Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m6A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs. Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m6A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets.
Collapse
Affiliation(s)
- Yudong Cao
- Department of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hecheng Zhu
- Changsha Kexin Cancer Hospital, Changsha, China
| | - Weidong Liu
- Key Laboratory for Carcinogenesis of Chinese Ministry of Health, School of Basic Medical Science, Cancer Research Institute, Central South University, Changsha, China
| | - Lei Wang
- Key Laboratory for Carcinogenesis of Chinese Ministry of Health, School of Basic Medical Science, Cancer Research Institute, Central South University, Changsha, China
| | - Wen Yin
- Department of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jun Tan
- Department of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Quanwei Zhou
- Department of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhaoqi Xin
- Department of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hailong Huang
- Department of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Dongcheng Xie
- Department of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Ming Zhao
- Changsha Kexin Cancer Hospital, Changsha, China
| | - Xingjun Jiang
- Department of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jiahui Peng
- Department of Medical Ultrasonics, Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Caiping Ren
- Department of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Key Laboratory for Carcinogenesis of Chinese Ministry of Health, School of Basic Medical Science, Cancer Research Institute, Central South University, Changsha, China
| |
Collapse
|
10
|
Sabaie H, Mazaheri Moghaddam M, Mazaheri Moghaddam M, Amirinejad N, Asadi MR, Daneshmandpour Y, Hussen BM, Taheri M, Rezazadeh M. Long non-coding RNA-associated competing endogenous RNA axes in the olfactory epithelium in schizophrenia: a bioinformatics analysis. Sci Rep 2021; 11:24497. [PMID: 34969953 PMCID: PMC8718521 DOI: 10.1038/s41598-021-04326-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 12/21/2021] [Indexed: 12/26/2022] Open
Abstract
The etiology of schizophrenia (SCZ), as a serious mental illness, is unknown. The significance of genetics in SCZ pathophysiology is yet unknown, and newly identified mechanisms involved in the regulation of gene transcription may be helpful in determining how these changes affect SCZ development and progression. In the current work, we used a bioinformatics approach to describe the role of long non-coding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) in the olfactory epithelium (OE) samples in order to better understand the molecular regulatory processes implicated in SCZ disorders in living individuals. The Gene Expression Omnibus database was used to obtain the OE microarray dataset (GSE73129) from SCZ sufferers and control subjects, which contained information about both lncRNAs and mRNAs. The limma package of R software was used to identify the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). RNA interaction pairs were discovered using the Human MicroRNA Disease Database, DIANA-LncBase, and miRTarBase databases. In this study, the Pearson correlation coefficient was utilized to find positive correlations between DEmRNAs and DElncRNAs in the ceRNA network. Eventually, lncRNA-associated ceRNA axes were developed based on co-expression relations and DElncRNA-miRNA-DEmRNA interactions. This work found six potential DElncRNA-miRNA-DEmRNA loops in SCZ pathogenesis, including, SNTG2-AS1/hsa-miR-7-5p/SLC7A5, FLG-AS1/hsa-miR-34a-5p/FOSL1, LINC00960/hsa-miR-34a-5p/FOSL1, AQP4-AS1/hsa-miR-335-5p/FMN2, SOX2-OT/hsa-miR-24-3p/NOS3, and CASC2/hsa-miR-24-3p/NOS3. According to the findings, ceRNAs in OE might be promising research targets for studying SCZ molecular mechanisms. This could be a great opportunity to examine different aspects of neurodevelopment that may have been hampered early in SCZ patients.
Collapse
Affiliation(s)
- Hani Sabaie
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.,Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Madiheh Mazaheri Moghaddam
- Department of Genetics and Molecular Medicine, School of Medicine, Zanjan University of Medical Sciences (ZUMS), Zanjan, Iran
| | - Nazanin Amirinejad
- Department of Biology, Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Mohammad Reza Asadi
- Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Yousef Daneshmandpour
- Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Bashdar Mahmud Hussen
- Department of Pharmacognosy, College of Pharmacy, Hawler Medical University, Erbil, Kurdistan Region, Iraq
| | - Mohammad Taheri
- Men's Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Maryam Rezazadeh
- Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran. .,Clinical Research Development Unit of Tabriz Valiasr Hospital, Tabriz University of Medical Sciences, Tabriz, Iran.
| |
Collapse
|
11
|
Li R, Wu S, Wu X, Zhao P, Li J, Xue K, Li J. Immune-relatedlncRNAs can predict the prognosis of acute myeloid leukemia. Cancer Med 2021; 11:888-899. [PMID: 34904791 PMCID: PMC8817083 DOI: 10.1002/cam4.4487] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/01/2021] [Accepted: 11/20/2021] [Indexed: 11/08/2022] Open
Abstract
The immune microenvironment in acute myeloid leukemia (AML) is closely related to patients' prognosis. Long noncoding RNAs (lncRNAs) are emerging as key regulators in immune systems. In this study, we established a prognostic model using an immune-related lncRNA (IRL) signature to predict AML patients' overall survival (OS) through Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis. Kaplan-Meier analysis, receiver operating characteristic (ROC) analysis, univariate Cox regression, and multivariate Cox regression analyses further illustrated the reliability of our prognostic model. An IRL signature-based nomogram consisting of other clinical features efficiently predicted the OS of AML patients. The incorporation of the IRL signature improved the ELN2017 risk stratification system's prognostic accuracy. In addition, we found that monocytes and metabolism-related pathways may play a role in AML progression. Overall, the IRL signature appears as a novel effective model for evaluating the OS of AML patients and may be implemented to contribute to the prolonged OS in AML patients.
Collapse
Affiliation(s)
- Ran Li
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shishuang Wu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaolu Wu
- Department of Children Health Care, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Ping Zhao
- Department of Biology, University of North Alabama, Florence, Alabama, USA
| | - Jingyi Li
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kai Xue
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junmin Li
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
12
|
Zhang W, Xin J, Lai J, Zhang W. LncRNA LINC00184 promotes docetaxel resistance and immune escape via miR-105-5p/PD-L1 axis in prostate cancer. Immunobiology 2021; 227:152163. [PMID: 34896914 DOI: 10.1016/j.imbio.2021.152163] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/16/2021] [Accepted: 12/02/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND Docetaxel (DTX) resistance is a common factor in metastatic prostate cancer (PC) chemotherapy that leads to treatment failure. Because lncRNA is involved in a variety of regulatory processes in tumor progression, this study aimed to explore the function and mechanism of LINC00184 in docetaxel resistance of PC. METHODS Two PC cell lines and their docetaxel resistant cell lines (DU145/DTX and PC3/DTX) were used. The expression of LINC00184 in both cell lines and PC patient samples were evaluated. SiRNA knocking down was used to test the function of LINC00184 in proliferation and colony formation. Interaction between LINC00184 and its target miR-105-5p, as well as miR-105-5p and PD-L1 was checked by luciferase reporter assay and RNA pull-down assay. PC cell line and CD8 + T cell co-culture system was established, miR-105-5p inhibitor was co-transfected with LINC00184 siRNA to investigate the underline mechanism. RESULTS LINC00184 was found to be associated with docetaxel resistance and adverse prognosis of prostate cancer. It regulated docetaxel resistance and T-cell-mediated immune response in prostate cancer cells. LINC00184 was induced by adsorption of miR-105-5p and negatively regulated it, subsequently inhibited the expression level of PD-L1. CONCLUSIONS LINC00184 promoted docetaxel resistance and immune escape in prostate cancer cells by adsorption of miR-105-5p, resulted in upregulation of the expression of PD-L1. LINC00184 could possibly be considered as a potential target for treatment in prostate cancer patients with docetaxel-resistance.
Collapse
Affiliation(s)
- Wei Zhang
- Department of Urology, Quanzhou First Hospital Affiliated to Fujian Medical University, No. 248-252 East Street, Licheng District, Quanzhou 362000, People's Republic of China
| | - Jun Xin
- Department of Urology, Quanzhou First Hospital Affiliated to Fujian Medical University, No. 248-252 East Street, Licheng District, Quanzhou 362000, People's Republic of China
| | - Jinjin Lai
- Department of Urology, Quanzhou First Hospital Affiliated to Fujian Medical University, No. 248-252 East Street, Licheng District, Quanzhou 362000, People's Republic of China
| | - Wenbin Zhang
- Department of Urology, Quanzhou First Hospital Affiliated to Fujian Medical University, No. 248-252 East Street, Licheng District, Quanzhou 362000, People's Republic of China.
| |
Collapse
|