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Wang Y, Zuo J, Duan C, Peng H, Huang J, Zhao L, Zhang L, Dong Z. Large language models assisted multi-effect variants mining on cerebral cavernous malformation familial whole genome sequencing. Comput Struct Biotechnol J 2024; 23:843-858. [PMID: 38352937 PMCID: PMC10861960 DOI: 10.1016/j.csbj.2024.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/04/2024] [Accepted: 01/19/2024] [Indexed: 02/16/2024] Open
Abstract
Cerebral cavernous malformation (CCM) is a polygenic disease with intricate genetic interactions contributing to quantitative pathogenesis across multiple factors. The principal pathogenic genes of CCM, specifically KRIT1, CCM2, and PDCD10, have been reported, accompanied by a growing wealth of genetic data related to mutations. Furthermore, numerous other molecules associated with CCM have been unearthed. However, tackling such massive volumes of unstructured data remains challenging until the advent of advanced large language models. In this study, we developed an automated analytical pipeline specialized in single nucleotide variants (SNVs) related biomedical text analysis called BRLM. To facilitate this, BioBERT was employed to vectorize the rich information of SNVs, while a deep residue network was used to discriminate the classes of the SNVs. BRLM was initially constructed on mutations from 12 different types of TCGA cancers, achieving an accuracy exceeding 99%. It was further examined for CCM mutations in familial sequencing data analysis, highlighting an upstream master regulator gene fibroblast growth factor 1 (FGF1). With multi-omics characterization and validation in biological function, FGF1 demonstrated to play a significant role in the development of CCMs, which proved the effectiveness of our model. The BRLM web server is available at http://1.117.230.196.
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Affiliation(s)
- Yiqi Wang
- College of Biomedicine and Health, College of Life Science and Technology, Huazhong Agricultural University, No.1, Shizishan Street, Wuhan 430070, Hubei, China
- Center for Neurological Disease Research, Taihe Hospital, Hubei University of Medicine, No.32, Renmin South Road, Shiyan 442000, Hubei, China
- Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, No. 32, Renmin South Road, Shiyan 442000, Hubei, China
| | - Jinmei Zuo
- Physical Examination Center, Taihe Hospital, Hubei University of Medicine, No. 32, Renmin South Road, Shiyan 442000, Hubei, China
| | - Chao Duan
- College of Biomedicine and Health, College of Life Science and Technology, Huazhong Agricultural University, No.1, Shizishan Street, Wuhan 430070, Hubei, China
- Center for Neurological Disease Research, Taihe Hospital, Hubei University of Medicine, No.32, Renmin South Road, Shiyan 442000, Hubei, China
| | - Hao Peng
- Center for Neurological Disease Research, Taihe Hospital, Hubei University of Medicine, No.32, Renmin South Road, Shiyan 442000, Hubei, China
- Department of Neurosurgery, Taihe Hospital, Hubei University of Medicine, No.32, Renmin South Road, Shiyan 442000, Hubei, China
| | - Jia Huang
- The Second Clinical Medical College, Lanzhou University, No. 222, South Tianshui Road, Lanzhou 730030, Gansu, China
| | - Liang Zhao
- Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, No. 32, Renmin South Road, Shiyan 442000, Hubei, China
| | - Li Zhang
- Center for Neurological Disease Research, Taihe Hospital, Hubei University of Medicine, No.32, Renmin South Road, Shiyan 442000, Hubei, China
- Department of Neurosurgery, Taihe Hospital, Hubei University of Medicine, No.32, Renmin South Road, Shiyan 442000, Hubei, China
| | - Zhiqiang Dong
- College of Biomedicine and Health, College of Life Science and Technology, Huazhong Agricultural University, No.1, Shizishan Street, Wuhan 430070, Hubei, China
- Center for Neurological Disease Research, Taihe Hospital, Hubei University of Medicine, No.32, Renmin South Road, Shiyan 442000, Hubei, China
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Xu H, Li C, Zhang L, Ding Z, Lu T, Hu H. Immunotherapy efficacy prediction through a feature re-calibrated 2.5D neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 249:108135. [PMID: 38569256 DOI: 10.1016/j.cmpb.2024.108135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/05/2024]
Abstract
BACKGROUND AND OBJECTIVE Lung cancer continues to be a leading cause of cancer-related mortality worldwide, with immunotherapy emerging as a promising therapeutic strategy for advanced non-small cell lung cancer (NSCLC). Despite its potential, not all patients experience benefits from immunotherapy, and the current biomarkers used for treatment selection possess inherent limitations. As a result, the implementation of imaging-based biomarkers to predict the efficacy of lung cancer treatments offers a promising avenue for improving therapeutic outcomes. METHODS This study presents an automatic system for immunotherapy efficacy prediction on the subjects with lung cancer, facilitating significant clinical implications. Our model employs an advanced 2.5D neural network that incorporates 2D intra-slice feature extraction and 3D inter-slice feature aggregation. We further present a lesion-focused prior to guide the re-calibration for intra-slice features, and a attention-based re-calibration for the inter-slice features. Finally, we design an accumulated back-propagation strategy to optimize network parameters in a memory-efficient fashion. RESULTS We demonstrate that the proposed method achieves impressive performance on an in-house clinical dataset, surpassing existing state-of-the-art models. Furthermore, the proposed model exhibits increased efficiency in inference for each subject on average. To further validate the effectiveness of our model and its components, we conducted comprehensive and in-depth ablation experiments and discussions. CONCLUSION The proposed model showcases the potential to enhance physicians' diagnostic performance due to its impressive performance in predicting immunotherapy efficacy, thereby offering significant clinical application value. Moreover, we conduct adequate comparison experiments of the proposed methods and existing advanced models. These findings contribute to our understanding of the proposed model's effectiveness and serve as motivation for future work in immunotherapy efficacy prediction.
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Affiliation(s)
- Haipeng Xu
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian 350014, China.
| | - Chenxin Li
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong 999077, SAR, China.
| | - Longfeng Zhang
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian 350014, China.
| | - Zhiyuan Ding
- School of Informatics, Xiamen University, Fujian 350014, China.
| | - Tao Lu
- Department of Radiology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fujian 350014, China.
| | - Huihua Hu
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian 350014, China.
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Zhou Q, Ye W, Yu X, Bao YJ. A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108077. [PMID: 38382307 DOI: 10.1016/j.cmpb.2024.108077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/14/2024] [Accepted: 02/10/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND The pathway-based strategy has been recently proposed for identifying biomarkers with the advantages of higher biological interpretability and cross-data robustness than the conventional gene-based strategy. However, its utility in clinical applications has been limited due to the high computational complexity and ill-defined performance. OBJECTIVE The current study presents a machine learning-based computational framework using multi-omics data for identifying a new modal of biomarkers, called pathway-derived core biomarkers, which have the advantages of both gene-based and pathway-based biomarkers. METHODS Machine-learning methods and gene-pathway network were integrated to select the pathway-derived core biomarkers. Multiple machine-learning algorithms were used to construct and validate the diagnostic models of the biomarkers based on more than 1400 multi-omics clinical samples of esophageal squamous cell carcinoma (ESCC). RESULTS The results showed that the classifier models based on the new modal biomarkers achieved superior performance in the training datasets with an average AUC/accuracy of 0.98/0.95 and 0.89/0.81 for mRNAs and miRNA, respectively, higher than the currently known classifier models based on the conventional gene-based strategy and pathway-based strategy. In the testing cohorts, the AUC/accuracy increased by 6.1 %/7.3 % than the models based on the native gene-based biomarkers. The improved performance was further confirmed in independent validation cohorts. Specifically, the sensitivity/specificity increased by ∼3 % and the variance significantly decreased by ∼69 % compared with that of the native gene-based biomarkers. Importantly, the pathway-derived core biomarkers also recovered 45 % more previously reported biomarkers than the gene-based biomarkers and are more functionally relevant to the ESCC etiology (involved in 14 versus 7 pathways related with ESCC or other cancer), highlighting the cross-data robustness of this new modal of biomarkers via enhanced functional relevance. CONCLUSIONS The results demonstrated that the new modal of biomarkers not only have improved predicting performance and robustness, but also exhibit higher functional interpretability thus leading to the potential application in cancer diagnosis.
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Affiliation(s)
- Qi Zhou
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, China
| | - Weicai Ye
- School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, and National Engineering Laboratory for Big Data Analysis and Application, Sun Yat-sen University, Guangzhou, China
| | - Xiaolan Yu
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, China; Hubei Jiangxia Laboratory, Wuhan, China
| | - Yun-Juan Bao
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, China.
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Huang Y, Liu H, Liu B, Chen X, Li D, Xue J, Li N, Zhu L, Yang L, Xiao J, Liu C. Quantified pathway mutations associate epithelial-mesenchymal transition and immune escape with poor prognosis and immunotherapy resistance of head and neck squamous cell carcinoma. BMC Med Genomics 2024; 17:49. [PMID: 38331768 PMCID: PMC10854145 DOI: 10.1186/s12920-024-01818-6] [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: 06/14/2023] [Accepted: 01/23/2024] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND Pathway mutations have been calculated to predict the poor prognosis and immunotherapy resistance in head and neck squamous cell carcinoma (HNSCC). To uncover the unique markers predicting prognosis and immune therapy response, the accurate quantification of pathway mutations are required to evaluate epithelial-mesenchymal transition (EMT) and immune escape. Yet, there is a lack of score to accurately quantify pathway mutations. MATERIAL AND METHODS Firstly, we proposed Individualized Weighted Hallmark Gene Set Mutation Burden (IWHMB, https://github.com/YuHongHuang-lab/IWHMB ) which integrated pathway structure information and eliminated the interference of global Tumor Mutation Burden to accurately quantify pathway mutations. Subsequently, to further elucidate the association of IWHMB with EMT and immune escape, support vector machine regression model was used to identify IWHMB-related transcriptomic features (IRG), while Adversarially Regularized Graph Autoencoder (ARVGA) was used to further resolve IRG network features. Finally, Random walk with restart algorithm was used to identify biomarkers for predicting ICI response. RESULTS We quantified the HNSCC pathway mutation signatures and identified pathway mutation subtypes using IWHMB. The IWHMB-related transcriptomic features (IRG) identified by support vector machine regression were divided into 5 communities by ARVGA, among which the Community 1 enriching malignant mesenchymal components promoted EMT dynamically and regulated immune patterns associated with ICI responses. Bridge Hub Gene (BHG) identified by random walk with restart was key to IWHMB in EMT and immune escape, thus, more predictive for ICI response than other 70 public signatures. CONCLUSION In summary, the novel pathway mutation scoring-IWHMB suggested that the elevated malignancy mediated by pathway mutations is a major cause of poor prognosis and immunotherapy failure in HNSCC, and is capable of identifying novel biomarkers to predict immunotherapy response.
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Affiliation(s)
- Yuhong Huang
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
- Academician Laboratory of Immunology and Oral Development & Regeneration, Dalian Medical University, Dalian, China
| | - Han Liu
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
- Academician Laboratory of Immunology and Oral Development & Regeneration, Dalian Medical University, Dalian, China
| | - Bo Liu
- Institute for Genome Engineered Animal Models of Human Diseases, Dalian Medical University, Dalian, China
| | - Xiaoyan Chen
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
| | - Danya Li
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
| | - Junyuan Xue
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
| | - Nan Li
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
- Academician Laboratory of Immunology and Oral Development & Regeneration, Dalian Medical University, Dalian, China
| | - Lei Zhu
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
- Academician Laboratory of Immunology and Oral Development & Regeneration, Dalian Medical University, Dalian, China
| | - Liu Yang
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
| | - Jing Xiao
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China.
- Academician Laboratory of Immunology and Oral Development & Regeneration, Dalian Medical University, Dalian, China.
| | - Chao Liu
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China.
- Academician Laboratory of Immunology and Oral Development & Regeneration, Dalian Medical University, Dalian, China.
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Qiu J, Li X, He Y, Wang Q, Li J, Wu J, Jiang Y, Han J. Identification of comutation in signaling pathways to predict the clinical outcomes of immunotherapy. J Transl Med 2022; 20:613. [PMID: 36564823 PMCID: PMC9783967 DOI: 10.1186/s12967-022-03836-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 12/17/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Immune checkpoint blockades (ICBs) have emerged as a promising treatment for cancer. Recently, tumour mutational burden (TMB) and neoantigen load (NAL) have been proposed to be potential biomarkers to predict the efficacy of ICB; however, they were limited by difficulties in defining the cut-off values and inconsistent detection platforms. Therefore, it is critical to identify more effective predictive biomarkers for screening patients who will potentially benefit from immunotherapy. In this study, we aimed to identify comutated signaling pathways to predict the clinical outcomes of immunotherapy. METHODS Here, we comprehensively analysed the signaling pathway mutation status of 9763 samples across 33 different cancer types from The Cancer Genome Atlas (TCGA) by mapping the somatic mutations to the pathways. We then explored the comutated pathways that were associated with increased TMB and NAL by using receiver operating characteristic (ROC) curve analysis and multiple linear regressions. RESULTS Our results revealed that comutation of the Spliceosome (Sp) pathway and Hedgehog (He) signaling pathway (defined as SpHe-comut+) could be used as a predictor of increased TMB and NAL and was associated with increased levels of immune-related signatures. In seven independent immunotherapy cohorts, we validated that SpHe-comut+ patients exhibited a longer overall survival (OS) or progression-free survival (PFS) and a higher objective response rate (ORR) than SpHe-comut- patients. Moreover, a combination of SpHe-comut status with PD-L1 expression further improved the predictive value for ICB therapy. CONCLUSION Overall, SpHe-comut+ was demonstrated to be an effective predictor of immunotherapeutic benefit in seven independent immunotherapy cohorts and may serve as a potential and convenient biomarker for the clinical application of ICB therapy.
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Affiliation(s)
- Jiayue Qiu
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, 157 BaoJian Road, Harbin, 150081 People’s Republic of China
| | - Xiangmei Li
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, 157 BaoJian Road, Harbin, 150081 People’s Republic of China
| | - Yalan He
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, 157 BaoJian Road, Harbin, 150081 People’s Republic of China
| | - Qian Wang
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, 157 BaoJian Road, Harbin, 150081 People’s Republic of China
| | - Ji Li
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, 157 BaoJian Road, Harbin, 150081 People’s Republic of China
| | - Jiashuo Wu
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, 157 BaoJian Road, Harbin, 150081 People’s Republic of China
| | - Ying Jiang
- grid.412068.90000 0004 1759 8782College of Basic Medical Science, Heilongjiang University of Chinese Medicine, Harbin, 150040 People’s Republic of China
| | - Junwei Han
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, 157 BaoJian Road, Harbin, 150081 People’s Republic of China
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Li J, Qiu J, Han J, Li X, Jiang Y. Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic Pathway Signatures. Genes (Basel) 2022; 13:1976. [PMID: 36360212 PMCID: PMC9690299 DOI: 10.3390/genes13111976] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 01/07/2024] Open
Abstract
Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions.
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Affiliation(s)
- Ji Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Jiayue Qiu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xiangmei Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Ying Jiang
- College of Basic Medical Science, Heilongjiang University of Chinese Medicine, Harbin 150040, China
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