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Patrick MT, Sreeskandarajan S, Shefler A, Wasikowski R, Sarkar MK, Chen J, Qin T, Billi AC, Kahlenberg JM, Prens E, Hovnanian A, Weidinger S, Elder JT, Kuo CC, Gudjonsson JE, Tsoi LC. Large-scale functional inference for skin-expressing lncRNAs using expression and sequence information. JCI Insight 2023; 8:e172956. [PMID: 38131377 PMCID: PMC10807743 DOI: 10.1172/jci.insight.172956] [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: 06/08/2023] [Accepted: 11/08/2023] [Indexed: 12/23/2023] Open
Abstract
Long noncoding RNAs (lncRNAs) regulate the expression of protein-coding genes and have been shown to play important roles in inflammatory skin diseases. However, we still have limited understanding of the functional impact of lncRNAs in skin, partly due to their tissue specificity and lower expression levels compared with protein-coding genes. We compiled a comprehensive list of 18,517 lncRNAs from different sources and studied their expression profiles in 834 RNA-Seq samples from multiple inflammatory skin conditions and cytokine-stimulated keratinocytes. Applying a balanced random forest to predict involvement in biological functions, we achieved a median AUROC of 0.79 in 10-fold cross-validation, identifying significant DNA binding domains (DBDs) for 39 lncRNAs. G18244, a skin-expressing lncRNA predicted for IL-4/IL-13 signaling in keratinocytes, was highly correlated in expression with F13A1, a protein-coding gene involved in macrophage regulation, and we further identified a significant DBD in F13A1 for G18244. Reflecting clinical implications, AC090198.1 (predicted for IL-17 pathway) and AC005332.6 (predicted for IFN-γ pathway) had significant negative correlation with the SCORAD metric for atopic dermatitis. We also utilized single-cell RNA and spatial sequencing data to validate cell type specificity. Our research demonstrates lncRNAs have important immunological roles and can help prioritize their impact on inflammatory skin diseases.
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Affiliation(s)
- Matthew T. Patrick
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Sutharzan Sreeskandarajan
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - Alanna Shefler
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Rachael Wasikowski
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Mrinal K. Sarkar
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Jiahan Chen
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
- College of Sciences, North Carolina State University, Raleigh, North Carolina, USA
| | - Tingting Qin
- Department of Computational Medicine & Bioinformatics and
| | - Allison C. Billi
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - J. Michelle Kahlenberg
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Division of Rheumatology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Errol Prens
- Department of Dermatology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Alain Hovnanian
- Laboratory of Genetic Skin Diseases, Imagine Institute, Paris, France
| | - Stephan Weidinger
- Department of Dermatology and Allergy, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - James T. Elder
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Ann Arbor Veterans Affairs Hospital, Ann Arbor, Michigan, USA
| | - Chao-Chung Kuo
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | - Johann E. Gudjonsson
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Lam C. Tsoi
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Department of Computational Medicine & Bioinformatics and
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
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Dalmasso B, Ghiorzo P. Long Non-Coding RNAs and Metabolic Rewiring in Pancreatic Cancer. Cancers (Basel) 2023; 15:3486. [PMID: 37444595 DOI: 10.3390/cancers15133486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/23/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
Pancreatic adenocarcinoma is a highly aggressive disease with a poor prognosis. The reprogramming of energetic metabolism has long been implicated in pancreatic tumorigenesis and/or resistance to treatment. Considering that long non-coding RNA dysregulation has been described both in cancerogenesis and in the altered homeostasis of several metabolic pathways, metabolism-associated lncRNAs can contribute to pancreatic cancer evolution. The objective of this review is to assess the burden of lncRNA dysregulation in pancreatic cancer metabolic reprogramming, and its effect on this tumor's natural course and response to treatment. Therefore, we reviewed the available literature to assess whether metabolism-associated lncRNAs have been found to be differentially expressed in pancreatic cancer, as well as whether experimental evidence of their role in such pathways can be demonstrated. Specifically, we provide a comprehensive overview of lncRNAs that are implicated in hypoxia-related pathways, as well as in the reprogramming of autophagy, lipid metabolism, and amino acid metabolism. Our review gathers background material for further research on possible applications of metabolism-associated lncRNAs as diagnostic/prognostic biomarkers and/or as potential therapeutic targets in pancreatic adenocarcinoma.
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Affiliation(s)
- Bruna Dalmasso
- IRCCS Ospedale Policlinico San Martino, Genetics of Rare Cancers, 16132 Genoa, Italy
| | - Paola Ghiorzo
- IRCCS Ospedale Policlinico San Martino, Genetics of Rare Cancers, 16132 Genoa, Italy
- Department of Internal Medicine and Medical Specialties, University of Genoa, 16132 Genoa, Italy
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Identification of Wnt/β-Catenin- and Autophagy-Related lncRNA Signature for Predicting Immune Efficacy in Pancreatic Adenocarcinoma. BIOLOGY 2023; 12:biology12020319. [PMID: 36829596 PMCID: PMC9952986 DOI: 10.3390/biology12020319] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/09/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023]
Abstract
Pancreatic cancer is one of the tumors with a poor prognosis. Therefore, it is significant and urgent to explore effective biomarkers for risk stratification and prognosis prediction to promote individualized treatment and prolong the survival of patients with PAAD. In this study, we identified Wnt/β-catenin- and autophagy-related long non-coding RNAs (lncRNAs) and demonstrated their role in predicting immune efficacy for PAAD patients. The univariate and multivariate Cox proportional hazards analyses were used to construct a prognostic risk model based on six autophagy- and Wnt/β-catenin-related lncRNAs (warlncRNAs): LINC01347, CASC8, C8orf31, LINC00612, UCA1, and GUSBP11. The high-risk patients were significantly associated with poor overall survival (OS). The receiver operating characteristic (ROC) curve analysis was used to assess the predictive accuracy of the prognostic risk model. The prediction efficiency was supported by the results of an independent validation cohort. Subsequently, a prognostic nomogram combining warlncRNAs with clinical indicators was constructed and showed a good predictive efficiency for survival risk stratification. Furthermore, functional enrichment analysis demonstrated that the signature according to warlncRNAs is closely linked to malignancy-associated immunoregulatory pathways. Correlation analysis uncovered that warlncRNAs' signature was considerably associated with immunocyte infiltration, immune efficacy, tumor microenvironment score, and drug resistance.
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Peng Y, Wu G, Qiu X, Luo Y, Zou Y, Wei X, Li A. Construction and validation of a necroptosis-related lncRNAs prognosis signature of hepatocellular carcinoma. Front Genet 2022; 13:916024. [PMID: 36110223 PMCID: PMC9468751 DOI: 10.3389/fgene.2022.916024] [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: 04/08/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Immunotherapy has achieved remarkable success in treating advanced liver cancer. Current evidence shows that most of the available immune checkpoint inhibitor (ICB) treatments are suboptimal, and specific markers are needed for patients regarded as good candidates for immunotherapy. Necroptosis, a type of programmed cell death, plays an important role in hepatocellular carcinoma (HCC) progression and outcome. However, studies on the necroptosis-related lncRNA in HCC are scarce. In this view, the present study investigates the link among necroptosis-related lncRNA, prognosis, immune microenvironment, and immunotherapy response.Methods: Gene transcriptome and clinical data were retrieved from The Cancer Genome Atlas database. Pearson correlation analysis of necroptosis-related genes was performed to identify necroptosis-related lncRNAs. The Wilcoxon method was used to detect differentially expressed genes, and prognostic relevant lncRNAs were obtained by univariate Cox regression analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were utilized to perform functional enrichment analysis. Lasso–Cox stepwise regression analysis was employed to calculate risk score, which was involved in analyzing immune cells infiltration, immune checkpoints expression, and predicting immunotherapeutic efficacy. Quantitative RT-PCR (qRT-PCR) was performed to detect the expression pattern of lncRNA in cell lines.Results: The 10 lncRNAs generated in this study were used to create a prognostic risk model for HCC and group patients into groups based on risk. High-risk patients with HCC have a significantly lower OS rate than low-risk patients. Multivariate Cox regression analysis showed that risk score is an independent risk factor for HCC with high accuracy. Patients in the high-risk group exhibited a weaker immune surveillance and higher expression level of immune checkpoint molecules. In terms of drug resistance, patients in the low-risk group were more sensitive to sorafenib. The OS-related nomogram was constructed to verify the accuracy of our model. Finally, quantitative RT-PCR experiments were used to verify the expression patterns of candidate genes.Conclusion: The lncRNA signature established herein, encompassing 10 necroptosis-related lncRNAs, is valuable for survival prediction and holds promise as prognostic markers for HCC.
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Affiliation(s)
- YunZhen Peng
- Cancer Center, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - GuoJing Wu
- Cancer Center, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Xin Qiu
- Department of Urology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Yue Luo
- Cancer Center, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - YiShu Zou
- Cancer Center, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - XueYan Wei
- Cancer Center, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Aimin Li
- Cancer Center, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
- *Correspondence: Aimin Li, mailto:
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The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis. Appl Bionics Biomech 2022; 2022:1666792. [PMID: 36060561 PMCID: PMC9433249 DOI: 10.1155/2022/1666792] [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: 06/05/2022] [Revised: 06/23/2022] [Accepted: 07/02/2022] [Indexed: 11/18/2022] Open
Abstract
Objective. N7-methylguanosine modification-related lncRNAs (m7G-related lncRNAs) are involved in progression of many diseases. This study was aimed at revealing the risk correlation between N7-methylguanosine modification-related lncRNAs and survival prognosis of oral squamous cell carcinoma. Methods. In the present study, coexpression network analysis and univariate Cox analysis were used to obtained 31 m7G-related mRNAs and 399 m7G-related lncRNAs. And the prognostic risk score model of OSCC patients was evaluated and optimized through cross-validation. Results. Through the coexpression analysis and risk assessment analysis of m7G-related prognostic mRNAs and lncRNAs, it was found that six m7G-related prognostic lncRNAs (AC005332.6, AC010894.1, AC068831.5, AL035446.1, AL513550.1, and HHLA3) were high-risk lncRNAs. Three m7G-related prognostic lncRNAs (AC007114.1, HEIH, and LINC02541) were protective lncRNAs. Then, survival curves were drawn by comparing the survival differences between patients with high and low expression of each m7G-related prognostic lncRNA in the prognostic risk score model. Further, risk curves, scatter plots, and heat maps were drawn by comparing the survival differences between high-risk and low-risk OSCC patients in the prognostic model. Finally, forest maps and the ROC curve were generated to verify the predictive power of the prognostic risk score model. Our results will help to find early and accurate prognostic risk markers for OSCC, which could be used for early prediction and early clinical intervention of survival, prognosis, and disease risk of OSCC patients in the future.
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Ding C, Yu Z, Zhu J, Li X, Dai M, QiangHe. Construction and Validation of a Necroptosis-Related Gene Signature for Predicting Prognosis and Tumor Microenvironment of Pancreatic Cancer. DISEASE MARKERS 2022; 2022:9737587. [PMID: 35756487 PMCID: PMC9214653 DOI: 10.1155/2022/9737587] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 05/26/2022] [Indexed: 01/05/2023]
Abstract
Pancreatic cancer (PC) is notorious for its parallel morbidity and mortality rates. Recently, necroptosis, a form of programmed cell necrosis, has gained popularity for its role in tumorigenesis and metastasis. In this study, we explored the expression of necroptosis-related genes in PC and normal pancreatic tissues and identified 52 differentially expressed genes (DEGs). The Cox regression analysis was applied to construct the prognostic risk model, which divided patients into high- and low-risk groups. PC patients in the low-risk group showed a significantly better overall survival (OS) than those in the high-risk group. We further validated the prognostic role in ICGC cohort. Further, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and tumor microenvironment (TME) analysis were used to explore the underlying mechanisms. Notably, based on the gene signature, we revealed that the risk score was strongly related to the sensitivity of chemotherapy. In conclusion, necroptosis-related genes serve as an important immune mediator, and the risk model could be used to predict the survival and to guide the development of precision drugs for patients with PC.
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Affiliation(s)
- Cheng Ding
- Department of Hepatobiliary Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - ZhangPing Yu
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - JiQiao Zhu
- Department of Hepatobiliary Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - XianLiang Li
- Department of Hepatobiliary Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - MengHua Dai
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - QiangHe
- Department of Hepatobiliary Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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Zhu J, Huang Q, Liu S, Peng X, Xue J, Feng T, Huang W, Chen Z, Lai K, Ji Y, Wang M, Yuan R. Construction of a Novel LncRNA Signature Related to Genomic Instability to Predict the Prognosis and Immune Activity of Patients With Hepatocellular Carcinoma. Front Immunol 2022; 13:856186. [PMID: 35479067 PMCID: PMC9037030 DOI: 10.3389/fimmu.2022.856186] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 03/21/2022] [Indexed: 01/10/2023] Open
Abstract
Background Genomic instability (GI) plays a crucial role in the development of various cancers including hepatocellular carcinoma. Hence, it is meaningful for us to use long non-coding RNAs related to genomic instability to construct a prognostic signature for patients with HCC. Methods Combining the lncRNA expression profiles and somatic mutation profiles in The Cancer Genome Atlas database, we identified GI-related lncRNAs (GILncRNAs) and obtained the prognosis-related GILncRNAs through univariate regression analysis. These lncRNAs obtained risk coefficients through multivariate regression analysis for constructing GI-associated lncRNA signature (GILncSig). ROC curves were used to evaluate signature performance. The International Cancer Genomics Consortium (ICGC) cohort, and in vitro experiments were used for signature external validation. Immunotherapy efficacy, tumor microenvironments, the half-maximal inhibitory concentration (IC50), and immune infiltration were compared between the high- and low-risk groups with TIDE, ESTIMATE, pRRophetic, and ssGSEA program. Results Five GILncRNAs were used to construct a GILncSig. It was confirmed that the GILncSig has good prognostic evaluation performance for patients with HCC by drawing a time-dependent ROC curve. Patients were divided into high- and low-risk groups according to the GILncSig risk score. The prognosis of the low-risk group was significantly better than that of the high-risk group. Independent prognostic analysis showed that the GILncSig could independently predict the prognosis of patients with HCC. In addition, the GILncSig was correlated with the mutation rate of the HCC genome, indicating that it has the potential to measure the degree of genome instability. In GILncSig, LUCAT1 with the highest risk factor was further validated as a risk factor for HCC in vitro. The ESTIMATE analysis showed a significant difference in stromal scores and ESTIMATE scores between the two groups. Multiple immune checkpoints had higher expression levels in the high-risk group. The ssGSEA results showed higher levels of tumor-antagonizing immune cells in the low-risk group compared with the high-risk group. Finally, the GILncSig score was associated with chemotherapeutic drug sensitivity and immunotherapy efficacy of patients with HCC. Conclusion Our research indicates that GILncSig can be used for prognostic evaluation of patients with HCC and provide new insights for clinical decision-making and potential therapeutic strategies.
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Affiliation(s)
- Jinfeng Zhu
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Jiangxi Province Key Laboratory of Molecular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qian Huang
- Department of General Practice, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Sicheng Liu
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Jiangxi Province Key Laboratory of Molecular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xingyu Peng
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Jiangxi Province Key Laboratory of Molecular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ju Xue
- Department of Pathology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Tangbin Feng
- Department of Surgery, II, Duchang County Hospital of Traditional Chinese Medicine, Jiujiang, China
| | - Wulang Huang
- Department of General Surgery, Affiliated Hospital of Jinggangshan University, Jian, China
| | - Zhimeng Chen
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Kuiyuan Lai
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yufei Ji
- The Second Clinical Medical College of Nanchang University, Nanchang, China
| | - Miaomiao Wang
- Queen Mary College of Nanchang University, Nanchang, China
| | - Rongfa Yuan
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
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