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Sathipati SY, Tsai MJ, Aimalla N, Moat L, Shukla S, Allaire P, Hebbring S, Beheshti A, Sharma R, Ho SY. An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction. NAR Genom Bioinform 2024; 6:lqae022. [PMID: 38406797 PMCID: PMC10894035 DOI: 10.1093/nargab/lqae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/11/2024] [Accepted: 02/13/2024] [Indexed: 02/27/2024] Open
Abstract
Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.
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Affiliation(s)
| | - Ming-Ju Tsai
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew Senior Life, Boston, MA 02131, USA
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02131, USA
| | - Nikhila Aimalla
- Department of Internal Medicine-Pediatrics, Marshfield Clinic Health System, Marshfield, WI 54449, USA
| | - Luke Moat
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
| | - Sanjay K Shukla
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
| | - Patrick Allaire
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
| | - Scott Hebbring
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
| | - Afshin Beheshti
- Blue Marble Space Institute of Science, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA94035, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Rohit Sharma
- Department of Surgical Oncology, Marshfield Clinic Health System, Marshfield, WI 54449, USA
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- College of Health Sciences, Kaohsiung Medical University, Kaohsiung 807378, Taiwan
- Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
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Bae HL, Jeong K, Yang S, Jun H, Kim K, Chai YJ. Expression Profiles of Hypoxia-Related Genes of Cancers Originating from Anatomically Similar Locations Using TCGA Database Analysis. Medicines (Basel) 2023; 11:2. [PMID: 38248716 PMCID: PMC10819830 DOI: 10.3390/medicines11010002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 11/15/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024]
Abstract
Background: Hypoxia is a well-recognized characteristic of the tumor microenvironment of solid cancers. This study aimed to analyze hypoxia-related genes shared by groups based on tumor location. Methods: A total of 9 hypoxia-related pathways from the Kyoto Encyclopedia of Genes and Genomes database or the Reactome database were selected, and 850 hypoxia-related genes were analyzed. Based on their anatomical locations, 14 tumor types were categorized into 6 groups. The group-specific genetic risk score was classified as high- or low-risk based on mRNA expression, and survival outcomes were evaluated. Results: The risk scores in the Female Reproductive group and the Lung group were internally and externally validated. In the Female Reproductive group, CDKN2A, FN1, and ITGA5 were identified as hub genes associated with poor prognosis, while IL2RB and LEF1 were associated with favorable prognosis. In the Lung group, ITGB1 and LDHA were associated with poor prognosis, and GLS2 was associated with favorable prognosis. Functional enrichment analysis showed that the Female Reproductive group was enriched in relation to cilia and skin, while the Lung group was enriched in relation to cytokines and defense. Conclusions: This analysis may lead to better understanding of the mechanisms of cancer progression and facilitate establishing new biomarkers for prognosis prediction.
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Affiliation(s)
- Hye Lim Bae
- Department of Surgery, Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
| | - Kyeonghun Jeong
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea;
| | - Suna Yang
- Department of Clinical Medical Science, Seoul National University, Seoul 08826, Republic of Korea;
| | - Hyeji Jun
- Seoul National University Hospital Biomedical Research Institute, Seoul 03080, Republic of Korea;
| | - Kwangsoo Kim
- Department of Transdisciplinary Department of Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul 03080, Republic of Korea
- Department of Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Young Jun Chai
- Department of Surgery, Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
- Department of Transdisciplinary Department of Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul 03080, Republic of Korea
- Department of Surgery, Seoul Metropolitan Government—Seoul National University Boramae Medical Center, Seoul 07061, Republic of Korea
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Yerukala Sathipati S, Aimalla N, Tsai MJ, Carter T, Jeong S, Wen Z, Shukla SK, Sharma R, Ho SY. Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma. Carcinogenesis 2023; 44:650-661. [PMID: 37701974 DOI: 10.1093/carcin/bgad062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/01/2023] [Accepted: 09/08/2023] [Indexed: 09/14/2023] Open
Abstract
OBJECTIVE Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC. METHODS The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression. RESULTS HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ± 0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis. CONCLUSIONS Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.
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Affiliation(s)
| | - Nikhila Aimalla
- Department of Internal Medicine-Pediatrics, Marshfield Clinic Health System, Marshfield, WI 54449, USA
| | - Ming-Ju Tsai
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew Senior Life, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Tonia Carter
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
| | - Sohyun Jeong
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew Senior Life, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Zhi Wen
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
| | - Sanjay K Shukla
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
| | - Rohit Sharma
- Department of Surgical Oncology, Marshfield Clinic Health System, Marshfield, WI 54449, USA
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
- Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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Yerukala Sathipati S, Tsai MJ, Shukla SK, Ho SY. Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction. HGG Adv 2023; 4:100190. [PMID: 37124139 PMCID: PMC10130501 DOI: 10.1016/j.xhgg.2023.100190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 03/30/2023] [Indexed: 05/02/2023] Open
Abstract
The ability to detect cancer at an early stage in patients who would benefit from effective therapy is a key factor in increasing survivability. This work proposes an evolutionary supervised learning method called CancerSig to identify cancer stage-specific microRNA (miRNA) signatures for early cancer predictions. CancerSig established a compact panel of miRNA signatures as potential markers from 4,667 patients with 15 different types of cancers for the cancer stage prediction, and achieved a mean performance: 10-fold cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 84.27% ± 6.31%, 0.81 ± 0.12, 0.80 ± 0.10, and 0.80 ± 0.06, respectively. The pan-cancer analysis of miRNA signatures suggested that three miRNAs, hsa-let-7i-3p, hsa-miR-362-3p, and hsa-miR-3651, contributed significantly toward stage prediction across 8 cancers, and each of the 67 miRNAs of the panel was a biomarker of stage prediction in more than one cancer. CancerSig may serve as the basis for cancer screening and therapeutic selection..
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Affiliation(s)
- Srinivasulu Yerukala Sathipati
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
- Corresponding author
| | - Ming-Ju Tsai
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew Senior Life, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Sanjay K. Shukla
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Corresponding author
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Yerukala Sathipati S, Ho SY. Survival associated miRNA signature in patients with head and neck carcinomas. Heliyon 2023; 9:e17218. [PMID: 37360084 PMCID: PMC10285236 DOI: 10.1016/j.heliyon.2023.e17218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/07/2023] [Accepted: 06/10/2023] [Indexed: 06/28/2023] Open
Abstract
Head and neck carcinoma (HNSC) is often diagnosed at advanced stage, incurring poor patient outcome. Despite of advances in chemoradiation and surgery approaches, limited improvements in survival rates of HNSC have been observed over the last decade. Accumulating evidences have demonstrated the importance of microRNAs (miRNAs) in carcinogenesis. In this context, we sought to identify a miRNA signature associated with the survival time in patients with HNSC. This study proposed a survival estimation method called HNSC-Sig that identified a miRNA signature consists of 25 miRNAs associated with the survival in 133 patients with HNSC. HNSC-Sig achieved 10-fold cross validation a mean correlation coefficient and a mean absolute error of 0.85 ± 0.01 and 0.46 ± 0.02 years, respectively, between actual and estimated survival times. The survival analysis revealed that five miRNAs, hsa-miR-3605-3p, hsa-miR-629-3p, hsa-miR-3127-5p, hsa-miR-497-5p, and hsa-miR-374a-5p, were significantly associated with prognosis in patients with HNSC. Comparing the relative expression difference of top 10 prioritized miRNAs, eight miRNAs, hsa-miR-629-3p, hsa-miR-3127-5p, hsa-miR-221-3p, hsa-miR-501-5p, hsa-miR-491-5p, hsa-miR-149-3p, hsa-miR-3934-5p, and hsa-miR-3170, were significantly expressed between cancer and normal groups. In addition, biological relevance, disease association, and target interactions of the miRNA signature were discussed. Our results suggest that identified miRNA signature have potential to serve as biomarker for diagnosis and clinical practice in HNSC.
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Affiliation(s)
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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