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Feng J, Yu Y, Yin W, Qian S. Development and verification of a 7-lncRNA prognostic model based on tumor immunity for patients with ovarian cancer. J Ovarian Res 2023; 16:31. [PMID: 36739404 PMCID: PMC9898952 DOI: 10.1186/s13048-023-01099-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/11/2023] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Both immune-reaction and lncRNAs play significant roles in the proliferation, invasion, and metastasis of ovarian cancer (OC). In this study, we aimed to construct an immune-related lncRNA risk model for patients with OC. METHOD Single sample GSEA (ssGSEA) algorithm was used to analyze the proportion of immune cells in The Cancer Genome Atlas (TCGA) and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells for OC patients. The stromal and immune scores were computed utilizing the ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) analyses were utilized to detect immune cluster-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression was conducted for lncRNA selection. The selected lncRNAs were used to construct a prognosis-related risk model, which was then validated in Gene Expression Omnibus (GEO) database and in vitro validation. RESULTS We identify two subtypes based on the ssGSEA analysis, high immunity cluster (immunity_H) and low immunity cluster (immunity_L). The proportion of patients in immunity_H cluster was significantly higher than that in immunity_L cluster. The ESTIMATE related scores are relative high in immunity_H group. Through WGCNA and LASSO analyses, we identified 141 immune cluster-related lncRNAs and found that these genes were mainly enriched in autophagy. A signature consisting of 7 lncRNAs, including AL391832.3, LINC00892, LINC02207, LINC02416, PSMB8.AS1, AC078788.1 and AC104971.3, were selected as the basis for classifying patients into high- and low-risk groups. Survival analysis and area under the ROC curve (AUC) of the signature pointed out that this risk model had high accuracy in predicting the prognosis of patients with OC. We also conducted the drug sensitive prediction and found that rapamycin outperformed in patient with high risk score. In vitro experiments also confirmed our prediction. CONCLUSIONS We identified 7 immune-related prognostic lncRNAs that effectively predicted survival in OC patients. These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for these patients.
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Affiliation(s)
- Jing Feng
- grid.452270.60000 0004 0614 4777Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province 061000 China
| | - Yiping Yu
- grid.452270.60000 0004 0614 4777Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province 061000 China
| | - Wen Yin
- grid.452270.60000 0004 0614 4777Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province 061000 China
| | - Sumin Qian
- grid.452270.60000 0004 0614 4777Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province 061000 China
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Yang J, Xu H, Li C, Li Z, Hu Z. An explorative study for leveraging transcriptomic data of embryonic stem cells in mining cancer stemness genes, regulators, and networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:13949-13966. [PMID: 36654075 DOI: 10.3934/mbe.2022650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Due to the exquisite ability of cancer stemness to facilitate tumor initiation, metastasis, and cancer therapy resistance, targeting cancer stemness is expected to have clinical implications for cancer treatment. Genes are fundamental for forming and maintaining stemness. Considering shared genetic programs and pathways between embryonic stem cells and cancer stem cells, we conducted a study analyzing transcriptomic data of embryonic stem cells for mining potential cancer stemness genes. Firstly, we integrated co-expression and regression models and predicted 820 stemness genes. Results of gene enrichment analysis confirmed the good prediction performance for enriched signatures in cancer stem cells. Secondly, we provided an application case using the predicted stemness genes to construct a breast cancer stemness network. Mining on the network identified CD44, SOX2, TWIST1, and DLG4 as potential regulators of breast cancer stemness. Thirdly, using the signature of 31,028 chemical perturbations and their correlation with stemness marker genes, we predicted 67 stemness inhibitors with reasonable accuracy of 78%. Two drugs, namely Rigosertib and Proscillaridin A, were first identified as potential stemness inhibitors for melanoma and colon cancer, respectively. Overall, mining embryonic stem cell data provides a valuable way to identify cancer stemness regulators.
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Affiliation(s)
- Jihong Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- BoYu Intelligent Health Innovation Laboratory, Hangzhou 311121, China
| | - Hao Xu
- Department of Anesthesiology, Affiliated Hospital of Guangdong Medical University, Guangdong 524001, China
| | - Congshu Li
- BoYu Intelligent Health Innovation Laboratory, Hangzhou 311121, China
| | - Zhenhao Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- BoYu Intelligent Health Innovation Laboratory, Hangzhou 311121, China
| | - Zhe Hu
- Department of Anesthesiology, Affiliated Hospital of Guangdong Medical University, Guangdong 524001, China
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Yuan H, Yu Q, Pang J, Chen Y, Sheng M, Tang W. The Value of the Stemness Index in Ovarian Cancer Prognosis. Genes (Basel) 2022; 13:genes13060993. [PMID: 35741755 PMCID: PMC9222264 DOI: 10.3390/genes13060993] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/21/2022] [Accepted: 05/25/2022] [Indexed: 11/16/2022] Open
Abstract
Ovarian cancer (OC) is one of the most common gynecological malignancies. It is associated with a difficult diagnosis and poor prognosis. Our study aimed to analyze tumor stemness to determine the prognosis feature of patients with OC. At this job, we selected the gene expression and the clinical profiles of patients with OC in the TCGA database. We calculated the stemness index of each patient using the one-class logistic regression (OCLR) algorithm and performed correlation analysis with immune infiltration. We used consensus clustering methods to classify OC patients into different stemness subtypes and compared the differences in immune infiltration between them. Finally, we established a prognostic signature by Cox and LASSO regression analysis. We found a significant negative correlation between a high stemness index and immune score. Pathway analysis indicated that the differentially expressed genes (DEGs) from the low- and high-mRNAsi groups were enriched in multiple functions and pathways, such as protein digestion and absorption, the PI3K-Akt signaling pathway, and the TGF-β signaling pathway. By consensus cluster analysis, patients with OC were split into two stemness subtypes, with subtype II having a better prognosis and higher immune infiltration. Furthermore, we identified 11 key genes to construct the prognostic signature for patients with OC. Among these genes, the expression levels of nine, including SFRP2, MFAP4, CCDC80, COL16A1, DUSP1, VSTM2L, TGFBI, PXDN, and GAS1, were increased in the high-risk group. The analysis of the KM and ROC curves indicated that this prognostic signature had a great survival prediction ability and could independently predict the prognosis for patients with OC. We established a stemness index-related risk prognostic module for OC, which has prognostic-independent capabilities and is expected to improve the diagnosis and treatment of patients with OC.
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El Hadi C, Ayoub G, Bachir Y, Haykal M, Jalkh N, Kourie HR. Polygenic and Network-Based Studies in Risk Identification and Demystification of cancer. Expert Rev Mol Diagn 2022; 22:427-438. [PMID: 35400274 DOI: 10.1080/14737159.2022.2065195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer. AREAS COVERED The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress. EXPERT OPINION In the future, comprehensive profiling of all kinds of patient data (e.g., serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity.
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Affiliation(s)
| | - George Ayoub
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Yara Bachir
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Michèle Haykal
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Nadine Jalkh
- Medical Genetics Unit, Technology and Health division, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Hampig Raphael Kourie
- Department of Hematology-Oncology, Hotel Dieu de France University Hospital, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
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Qian D, Qian C, Ye B, Xu M, Wu D, Li J, Li D, Yu B, Tao Y. Development and Validation of a Novel Stemness-Index-Related Long Noncoding RNA Signature for Breast Cancer Based on Weighted Gene Co-Expression Network Analysis. Front Genet 2022; 13:760514. [PMID: 35273635 PMCID: PMC8902307 DOI: 10.3389/fgene.2022.760514] [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: 08/18/2021] [Accepted: 01/13/2022] [Indexed: 12/24/2022] Open
Abstract
Background: Breast cancer (BC) is a major leading cause of woman deaths worldwide. Increasing evidence has revealed that stemness features are related to the prognosis and progression of tumors. Nevertheless, the roles of stemness-index-related long noncoding RNAs (lncRNAs) in BC remain unclear. Methods: Differentially expressed stemness-index-related lncRNAs between BC and normal samples in The Cancer Genome Atlas database were screened based on weighted gene co-expression network analysis and differential analysis. Univariate Cox and least absolute shrinkage and selection operator regression analyses were performed to identify prognostic lncRNAs and construct a stemness-index-related lncRNA signature. Time-dependent receiver operating characteristic curves were plotted to evaluate the predictive capability of the stemness-index-related lncRNA signature. Moreover, correlation analysis and functional enrichment analyses were conducted to investigate the stemness-index-related lncRNA signature-related biological function. Finally, a quantitative real-time polymerase chain reaction was used to detect the expression levels of lncRNAs. Results: A total of 73 differentially expressed stemness-index-related lncRNAs were identified. Next, FAM83H-AS1, HID1-AS1, HOXB-AS1, RP11-1070N10.3, RP11-1100L3.8, and RP11-696F12.1 were used to construct a stemness-index-related lncRNA signature, and receiver operating characteristic curves indicated that stemness-index-related lncRNA signature could predict the prognosis of BC well. Moreover, functional enrichment analysis suggested that differentially expressed genes between the high-risk group and low-risk group were mainly involved in immune-related biological processes and pathways. Furthermore, functional enrichment analysis of lncRNA-related protein-coding genes revealed that FAM83H-AS1, HID1-AS1, HOXB-AS1, RP11-1070N10.3, RP11-1100L3.8, and RP11-696F12.1 were associated with neuroactive ligand–receptor interaction, AMPK signaling pathway, PPAR signaling pathway, and cGMP-PKG signaling pathway. Finally, quantitative real-time polymerase chain reaction revealed that FAM83H-AS1, HID1-AS1, RP11-1100L3.8, and RP11-696F12.1 might be used as the potential diagnostic biomarkers of BC. Conclusion: The stemness-index-related lncRNA signature based on FAM83H-AS1, HID1-AS1, HOXB-AS1, RP11-1070N10.3, RP11-1100L3.8, and RP11-696F12.1 could be used as an independent predictor for the survival of BC, and FAM83H-AS1, HID1-AS1, RP11-1100L3.8, and RP11-696F12.1 might be used as the diagnostic markers of BC.
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Affiliation(s)
- Da Qian
- Department of Burn and Plastic Surgery-Hand Surgery, Changshu Hospital Affiliated to Soochow University, Changshu, China
| | - Cheng Qian
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu, China
| | - Buyun Ye
- Second Clinical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Ming Xu
- Department of Burn and Plastic Surgery-Hand Surgery, Changshu Hospital Affiliated to Soochow University, Changshu, China
| | - Danping Wu
- Department of Breast Surgery, Changshu Hospital Affiliated to Soochow University, Changshu, China
| | - Jialu Li
- Department of Breast Surgery, Changshu Hospital Affiliated to Nanjing University of Chinese Medicine, Changshu, China
| | - Dong Li
- Department of Burn and Plastic Surgery-Hand Surgery, Changshu Hospital Affiliated to Soochow University, Changshu, China
| | - Bin Yu
- Department of Burn and Plastic Surgery-Hand Surgery, Changshu Hospital Affiliated to Soochow University, Changshu, China
| | - Yijing Tao
- Department of Cardiology, Changshu Hospital Affiliated to Soochow University, Changshu, China
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Ren Z, Hu M, Wang Z, Ge J, Zhou X, Zhang G, Zheng H. Ferroptosis-Related Genes in Lung Adenocarcinoma: Prognostic Signature and Immune, Drug Resistance, Mutation Analysis. Front Genet 2021; 12:672904. [PMID: 34434214 PMCID: PMC8381737 DOI: 10.3389/fgene.2021.672904] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/20/2021] [Indexed: 12/25/2022] Open
Abstract
It is reported that ferroptosis has close relation with tumorigenesis and drug resistance. However, the clinical significance of ferroptosis in lung adenocarcinoma (LUAD) remains elusive, and the potential targets for ferroptosis-based treatment are limited. In this study, we constructed a 15-gene prognostic signature predicting overall survival based on ferroptosis-related genes (ferroptosis driver genes VDAC2, GLS2, FLT3, TLR4, PHKG2, phosphogluconate dehydrogenase (PGD), PANX1, KRAS, PEBP1, ALOX15, and ALOX12B, and suppressor genes ACSL3, CISD1, FANCD2, and SLC3A2) in The Cancer Genome Atlas (TCGA)-LUAD cohort. The signature’s predictive ability was validated in the GSE68465 and GSE72094 cohorts by survival analysis and independent prognostic analysis with clinical features. Nomograms were provided for clinical reference. Functional analysis revealed that ferroptosis was closely related to cell cycle, cell metabolism, and immune pathways. Pan-cancer analysis comprehensively analyzed these 15 genes in 33 cancer types, indicating that the heterogeneity of 15 genes was evident across different cancer types. Besides, these genes were critical regulators modulating drug resistance, tumor microenvironment infiltration, and cancer stemness. Then, we screened 10 genes (TLR4, PHKG2, PEBP1, GLS2, FLT3, ALOX15, ACSL3, CISD1, FANCD2, and SLC3A2) as potential targets for further research because their biological functions in ferroptosis were consistent with their prognostic significance. Somatic mutation and copy number variation analysis revealed that the alteration rates of KRAS, PGD, and ALOX15 were more than 1% and significantly associated with overall survival in LUAD. Moreover, the expression of KRAS and PGD was positively related to tumor mutation burden, indicating that KRAS and PGD could serve as novel biomarkers for predicting immunotherapy response rate. Our study identified and validated a ferroptosis-related gene signature for LUAD, provided a 10-gene set for future research, and screened KRAS and PGD as potential novel immunotherapy biomarkers.
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Affiliation(s)
- Ziyuan Ren
- Basic Medical School, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Minghui Hu
- Clinical Lab, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhonglin Wang
- Department of Mathematics, University of California, Irvine, Irvine, CA, United States
| | - Junpeng Ge
- Department of Biology Engineering, Shandong Jianzhu University, Jinan, China
| | - Xiaoyan Zhou
- Clinical Lab, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Guoming Zhang
- Basic Medical School, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hongying Zheng
- Clinical Lab, The Affiliated Hospital of Qingdao University, Qingdao, China
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