1
|
Zhao G, Chen Y, Gu Y, Xia X. The clinical value of nutritional and inflammatory indicators in predicting pneumonia among patients with intracerebral hemorrhage. Sci Rep 2024; 14:16171. [PMID: 39003396 DOI: 10.1038/s41598-024-67227-y] [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: 03/09/2024] [Accepted: 07/09/2024] [Indexed: 07/15/2024] Open
Abstract
Immunosuppression and malnutrition play pivotal roles in the complications of intracerebral hemorrhage (ICH) and are intricately linked to the development of stroke-associated pneumonia (SAP). Inflammatory markers, including NLR (neutrophil-to-lymphocyte ratio), SII (systemic immune inflammation index), SIRI (systemic inflammatory response index), and SIS (systemic inflammation score), along with nutritional indexes such as CONUT (controlling nutritional status) and PNI (prognostic nutritional index), are crucial indicators influencing the inflammatory state following ICH. In this study, our objective was to compare the predictive efficacy of inflammatory and nutritional indices for SAP in ICH patients, aiming to determine and explore their clinical utility in early pneumonia detection. Patients with severe ICH requiring ICU admission were screened from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The outcomes included the occurrence of SAP and in-hospital death. Receiver operating characteristic (ROC) analysis, multivariate logistic regression, smooth curve analysis, and stratified analysis were employed to investigate the relationship between the CONUT index and the clinical outcomes of patients with severe ICH. A total of 348 patients were enrolled in the study. The incidence of SAP was 21.3%, and the in-hospital mortality rate was 17.0%. Among these indicators, multiple regression analysis revealed that CONUT, PNI, and SIRI were independently associated with SAP. Further ROC curve analysis demonstrated that CONUT (AUC 0.6743, 95% CI 0.6079-0.7408) exhibited the most robust predictive ability for SAP in patients with ICH. Threshold analysis revealed that when CONUT < 6, an increase of 1 point in CONUT was associated with a 1.39 times higher risk of SAP. Similarly, our findings indicate that CONUT has the potential to predict the prognosis of patients with ICH. Among the inflammatory and nutritional markers, CONUT stands out as the most reliable predictor of SAP in patients with ICH. Additionally, it proves to be a valuable indicator for assessing the prognosis of patients with ICH.
Collapse
Affiliation(s)
- Guang Zhao
- Department of Emergency Medicine, The First People's Hospital of Kunshan, Kunshan, 215300, Jiangsu, China.
- Jiangsu University Health Science Center, Kunshan, Jiangsu, China.
| | - Yuyang Chen
- Department of Emergency Medicine, The First People's Hospital of Kunshan, Kunshan, 215300, Jiangsu, China
- Jiangsu University Health Science Center, Kunshan, Jiangsu, China
| | - Yuting Gu
- Department of Emergency Medicine, The First People's Hospital of Kunshan, Kunshan, 215300, Jiangsu, China
- Jiangsu University Health Science Center, Kunshan, Jiangsu, China
| | - Xiaohua Xia
- Department of Emergency Medicine, The First People's Hospital of Kunshan, Kunshan, 215300, Jiangsu, China.
- Jiangsu University Health Science Center, Kunshan, Jiangsu, China.
| |
Collapse
|
2
|
Gupta RK, Bhushan R, Kumar S, Prasad SB. In silico analysis unveiling potential biomarkers in gallbladder carcinogenesis. Sci Rep 2024; 14:14570. [PMID: 38914609 PMCID: PMC11196699 DOI: 10.1038/s41598-024-61762-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 05/09/2024] [Indexed: 06/26/2024] Open
Abstract
Gallbladder cancer (GBC) is a rare but very aggressive most common digestive tract cancer with a high mortality rate due to delayed diagnosis at the advanced stage. Moreover, GBC progression shows asymptomatic characteristics making it impossible to detect at an early stage. In these circumstances, conventional therapy like surgery, chemotherapy, and radiotherapy becomes refractive. However, few studies reported some molecular markers like KRAS (Kirsten Rat Sarcoma) mutation, upregulation of HER2/neu, EGFR (Epidermal Growth Factor Receptor), and microRNAs in GBC. However, the absence of some specific early diagnostic and prognostic markers is the biggest hurdle for the therapy of GBC to date. The present study has been designed to identify some specific molecular markers for precise diagnosis, and prognosis, for successful treatment of the GBC. By In Silico a network-centric analysis of two microarray datasets; (GSE202479) and (GSE13222) from the Gene Expression Omnibus (GEO) database, shows 50 differentially expressed genes (DEGs) associated with GBC. Further network analysis revealed that 12 genes are highly interconnected based on the highest MCODE (Molecular Complex Detection) value, among all three genes; TRIP13 (Thyroid Receptor Interacting Protein), NEK2 (Never in Mitosis gene-A related Kinase 2), and TPX2 (Targeting Protein for Xklp2) having highest network interaction with transcription factors and miRNA suggesting critically associated with GBC. Further survival analysis data corroborate the association of these genes; TRIP13, NEK2, and TPX2 with GBC. Thus, TRIP13, NEK2, and TPX2 genes are significantly correlated with a greater risk of mortality, transforming them from mere biomarkers of the GBC for early detections and may emerge as prognostic markers for treatment.
Collapse
Affiliation(s)
- Raviranjan Kumar Gupta
- Department of Zoology, School of Life Sciences, Mahatma Gandhi Central University Bihar (MGCUB), Motihari, 845401, India
| | - Ravi Bhushan
- Department of Zoology, Munsi Singh College, Motihari, 845401, India
| | - Saket Kumar
- Department of Surgical Gastroenterology, Indira Gandhi Institute of Medical Sciences (IGIMS), Sheikhpura, Patna, India
| | - Shyam Babu Prasad
- Department of Zoology, School of Life Sciences, Mahatma Gandhi Central University Bihar (MGCUB), Motihari, 845401, India.
| |
Collapse
|
3
|
Miyata T, Hayama T, Ozawa T, Nozawa K, Misawa T, Fukagawa T. Predicting prognosis in colorectal cancer patients with curative resection using albumin, lymphocyte count and RAS mutations. Sci Rep 2024; 14:14428. [PMID: 38910183 PMCID: PMC11194255 DOI: 10.1038/s41598-024-65457-8] [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: 03/02/2024] [Accepted: 06/20/2024] [Indexed: 06/25/2024] Open
Abstract
Colorectal cancer (CRC) poses a significant global health challenge, demanding reliable prognostic tools to guide treatment decisions. This study introduces a novel prognostic scoring system, the albumin-total lymphocyte count-RAS index (ALRI), integrating serum albumin, lymphocyte count, and RAS gene mutations. A cohort of 445 stage I-III CRC patients undergoing curative resection was analyzed, revealing ALRI's association with clinicopathological factors, including age, tumor location, and invasion depth. The ALRI demonstrated superior prognostic value, with a cutoff value of 2 distinguishing high and low-risk groups. The high-ALRI group exhibited elevated rates of recurrence. Univariate and multivariate analyses identified ALRI as an independent predictor for both 5 year recurrence-free survival (RFS) and overall survival (OS). Kaplan-Meier curves illustrated significant differences in RFS and OS between high and low-ALRI groups, emphasizing ALRI's potential as a prognostic marker. Importantly, ALRI outperformed existing nutritional indices, such as controlling nutritional status and neutrophil-to-lymphocyte ratio, in predicting overall survival. The study underscores the comprehensive insight provided by ALRI, combining inflammatory, nutritional, and genetic information for robust prognostication in CRC patients. This user-friendly tool demonstrates promise for preoperative prognosis and personalized treatment strategies, emphasizing the crucial role of inflammation and nutrition in CRC outcomes.
Collapse
Affiliation(s)
- Toshiya Miyata
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-ku, Tokyo, 173-0003, Japan
| | - Tamuro Hayama
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-ku, Tokyo, 173-0003, Japan.
| | - Tsuyoshi Ozawa
- Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Keijiro Nozawa
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-ku, Tokyo, 173-0003, Japan
| | - Takeyuki Misawa
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-ku, Tokyo, 173-0003, Japan
| | - Takeo Fukagawa
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-ku, Tokyo, 173-0003, Japan
| |
Collapse
|
4
|
Almotairi S, Badr E, Abdelbaky I, Elhakeem M, Abdul Salam M. Hybrid transformer-CNN model for accurate prediction of peptide hemolytic potential. Sci Rep 2024; 14:14263. [PMID: 38902287 PMCID: PMC11190137 DOI: 10.1038/s41598-024-63446-5] [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: 04/02/2024] [Accepted: 05/29/2024] [Indexed: 06/22/2024] Open
Abstract
Hemolysis is a crucial factor in various biomedical and pharmaceutical contexts, driving our interest in developing advanced computational techniques for precise prediction. Our proposed approach takes advantage of the unique capabilities of convolutional neural networks (CNNs) and transformers to detect complex patterns inherent in the data. The integration of CNN and transformers' attention mechanisms allows for the extraction of relevant information, leading to accurate predictions of hemolytic potential. The proposed method was trained on three distinct data sets of peptide sequences known as recurrent neural network-hemolytic (RNN-Hem), Hlppredfuse, and Combined. Our computational results demonstrated the superior efficacy of our models compared to existing methods. The proposed approach demonstrated impressive Matthews correlation coefficients of 0.5962, 0.9111, and 0.7788 respectively, indicating its effectiveness in predicting hemolytic activity. With its potential to guide experimental efforts in peptide design and drug development, this method holds great promise for practical applications. Integrating CNNs and transformers proves to be a powerful tool in the fields of bioinformatics and therapeutic research, highlighting their potential to drive advancement in this area.
Collapse
Affiliation(s)
- Sultan Almotairi
- Department of Computer Science, Faculty of College of Computer and Information Sciences, Majmaah University, 11952, Majmaah, Saudi Arabia
- Department of Computer Science, Faculty of Computer and Information Systems, Islamic University of Madinah, 42351, Medinah, Saudi Arabia
| | - Elsayed Badr
- Scientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt.
- The Egyptian School of Data Science (ESDS), Benha, Egypt.
| | - Ibrahim Abdelbaky
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
| | - Mohamed Elhakeem
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt.
| | - Mustafa Abdul Salam
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
- Department of Computer Science, College of Arts and Science, Wadi Addawasir, Prince Sattam Bin Abdulaziz University, 16273, Al-Kharj, Saudi Arabia
| |
Collapse
|
5
|
Qin C, Zhang J, Ma L. EMCMDA: predicting miRNA-disease associations via efficient matrix completion. Sci Rep 2024; 14:12761. [PMID: 38834687 DOI: 10.1038/s41598-024-63582-y] [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: 03/11/2024] [Accepted: 05/30/2024] [Indexed: 06/06/2024] Open
Abstract
Abundant researches have consistently illustrated the crucial role of microRNAs (miRNAs) in a wide array of essential biological processes. Furthermore, miRNAs have been validated as promising therapeutic targets for addressing complex diseases. Given the costly and time-consuming nature of traditional biological experimental validation methods, it is imperative to develop computational methods. In the work, we developed a novel approach named efficient matrix completion (EMCMDA) for predicting miRNA-disease associations. First, we calculated the similarities across multiple sources for miRNA/disease pairs and combined this information to create a holistic miRNA/disease similarity measure. Second, we utilized this biological information to create a heterogeneous network and established a target matrix derived from this network. Lastly, we framed the miRNA-disease association prediction issue as a low-rank matrix-complete issue that was addressed via minimizing matrix truncated schatten p-norm. Notably, we improved the conventional singular value contraction algorithm through using a weighted singular value contraction technique. This technique dynamically adjusts the degree of contraction based on the significance of each singular value, ensuring that the physical meaning of these singular values is fully considered. We evaluated the performance of EMCMDA by applying two distinct cross-validation experiments on two diverse databases, and the outcomes were statistically significant. In addition, we executed comprehensive case studies on two prevalent human diseases, namely lung cancer and breast cancer. Following prediction and multiple validations, it was evident that EMCMDA proficiently forecasts previously undisclosed disease-related miRNAs. These results underscore the robustness and efficacy of EMCMDA in miRNA-disease association prediction.
Collapse
Affiliation(s)
- Chao Qin
- School of Information Science and Engineering, Qilu Normal University, Jinan, 250200, China.
| | - Jiancheng Zhang
- School of Information Science and Engineering, Qilu Normal University, Jinan, 250200, China
| | - Lingyu Ma
- School of Control Science and Engineering, Harbin Institute of Technology, Weihai, 250200, China
| |
Collapse
|
6
|
Sulaimany S, Farahmandi K, Mafakheri A. Computational prediction of new therapeutic effects of probiotics. Sci Rep 2024; 14:11932. [PMID: 38789535 PMCID: PMC11126595 DOI: 10.1038/s41598-024-62796-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 05/21/2024] [Indexed: 05/26/2024] Open
Abstract
Probiotics are living microorganisms that provide health benefits to their hosts, potentially aiding in the treatment or prevention of various diseases, including diarrhea, irritable bowel syndrome, ulcerative colitis, and Crohn's disease. Motivated by successful applications of link prediction in medical and biological networks, we applied link prediction to the probiotic-disease network to identify unreported relations. Using data from the Probio database and International Classification of Diseases-10th Revision (ICD-10) resources, we constructed a bipartite graph focused on the relationship between probiotics and diseases. We applied customized link prediction algorithms for this bipartite network, including common neighbors, Jaccard coefficient, and Adamic/Adar ranking formulas. We evaluated the results using Area under the Curve (AUC) and precision metrics. Our analysis revealed that common neighbors outperformed the other methods, with an AUC of 0.96 and precision of 0.6, indicating that basic formulas can predict at least six out of ten probable relations correctly. To support our findings, we conducted an exact search of the top 20 predictions and found six confirming papers on Google Scholar and Science Direct. Evidence suggests that Lactobacillus jensenii may provide prophylactic and therapeutic benefits for gastrointestinal diseases and that Lactobacillus acidophilus may have potential activity against urologic and female genital illnesses. Further investigation of other predictions through additional preclinical and clinical studies is recommended. Future research may focus on deploying more powerful link prediction algorithms to achieve better and more accurate results.
Collapse
Affiliation(s)
- Sadegh Sulaimany
- Social and Biological Network Analysis Laboratory (SBNA), Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran.
| | - Kajal Farahmandi
- Department of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Aso Mafakheri
- Social and Biological Network Analysis Laboratory (SBNA), Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
| |
Collapse
|
7
|
Qin Y, Sheng Y, Ren M, Hou Z, Xiao L, Chen R. Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer. Sci Rep 2024; 14:11133. [PMID: 38750159 PMCID: PMC11096311 DOI: 10.1038/s41598-024-61849-y] [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: 01/10/2024] [Accepted: 05/10/2024] [Indexed: 05/18/2024] Open
Abstract
Ovarian cancer (OC) is one of the most prevalent and fatal malignant tumors of the female reproductive system. Our research aimed to develop a prognostic model to assist inclinical treatment decision-making.Utilizing data from The Cancer Genome Atlas (TCGA) and copy number variation (CNV) data from the University of California Santa Cruz (UCSC) database, we conducted analyses of differentially expressed genes (DEGs), gene function, and tumor microenvironment (TME) scores in various clusters of OC samples.Next, we classified participants into low-risk and high-risk groups based on the median risk score, thereby dividing both the training group and the entire group accordingly. Overall survival (OS) was significantly reduced in the high-risk group, and two independent prognostic factors were identified: age and risk score. Additionally, three genes-C-X-C Motif Chemokine Ligand 10 (CXCL10), RELB, and Caspase-3 (CASP3)-emerged as potential candidates for an independent prognostic signature with acceptable prognostic value. In Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, pathways related to immune responses and inflammatory cell chemotaxis were identified. Cellular experiments further validated the reliability and precision of our findings. In conclusion, necroptosis-related genes play critical roles in tumor immunity, and our model introduces a novel strategy for predicting the prognosis of OC patients.
Collapse
Affiliation(s)
- Yuling Qin
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixiange Road, Xicheng District, Beijing, 100053, China
| | - Yawen Sheng
- Shandong University of Traditional Chinese Medicine, Jinan, 250014, Shandong, China
| | - Mengxue Ren
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixiange Road, Xicheng District, Beijing, 100053, China
| | - Zitong Hou
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixiange Road, Xicheng District, Beijing, 100053, China
| | - Lu Xiao
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixiange Road, Xicheng District, Beijing, 100053, China
| | - Ruixue Chen
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixiange Road, Xicheng District, Beijing, 100053, China.
| |
Collapse
|
8
|
Zhou L, Wang X, Peng L, Chen M, Wen H. SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference. J Cell Mol Med 2024; 28:e18372. [PMID: 38747737 PMCID: PMC11095317 DOI: 10.1111/jcmm.18372] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 04/10/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024] Open
Abstract
Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.
Collapse
Affiliation(s)
- Liqian Zhou
- School of Life Sciences and ChemistryHunan University of TechnologyHunanChina
| | - Xiwen Wang
- School of Life Sciences and ChemistryHunan University of TechnologyHunanChina
| | - Lihong Peng
- School of Life Sciences and ChemistryHunan University of TechnologyHunanChina
| | - Min Chen
- School of Computer ScienceHunan Institute of TechnologyHengyangChina
| | - Hong Wen
- School of Computer ScienceHunan University of TechnologyHunanChina
| |
Collapse
|
9
|
Chen M, Deng Y, Li Z, Ye Y, Zeng L, He Z, Peng G. SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm. J Cell Mol Med 2024; 28:e18345. [PMID: 38693850 PMCID: PMC11063733 DOI: 10.1111/jcmm.18345] [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: 12/31/2023] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 05/03/2024] Open
Abstract
Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.
Collapse
Affiliation(s)
- Min Chen
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Yingwei Deng
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Zejun Li
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Yifan Ye
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Lijun Zeng
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Ziyi He
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Guofang Peng
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| |
Collapse
|
10
|
Oh AR, Kwon JH, Jin G, Kong SM, Lee DJ, Park J. Association between inflammation-based prognostic markers and mortality after hip replacement. Sci Rep 2024; 14:9263. [PMID: 38649407 PMCID: PMC11035583 DOI: 10.1038/s41598-024-58646-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
We aimed to evaluate the association between inflammation-based prognostic markers and mortality after hip replacement. From March 2010 to June 2020, we identified 5,369 consecutive adult patients undergoing hip replacement with C-reactive protein (CRP), albumin, and complete blood count measured within six months before surgery. Receiver operating characteristic (ROC) curves were generated to evaluate predictabilities and estimate thresholds of CRP-to-albumin ratio (CAR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). Patients were divided according to threshold, and mortality risk was compared. The primary outcome was one-year mortality, and overall mortality was also analyzed. One-year mortality was 2.9%. Receiver operating characteristics analysis revealed areas under the curve of 0.838, 0.832, 0.701, and 0.732 for CAR, NLR, PLR, and modified Glasgow Prognostic Score, respectively. The estimated thresholds were 2.10, 3.16, and 11.77 for CAR, NLR, and PLR, respectively. According to the estimated threshold, high CAR and NLR were associated with higher one-year mortality after adjustment (1.0% vs. 11.7%; HR = 2.16; 95% CI 1.32-3.52; p = 0.002 for CAR and 0.8% vs. 9.6%; HR = 2.05; 95% CI 1.24-3.39; p = 0.01 for NLR), but PLR did not show a significant mortality increase (1.4% vs. 7.4%; HR = 1.12; 95% CI 0.77-1.63; p = 0.57). Our study demonstrated associations of preoperative levels of CAR and NLR with postoperative mortality in patients undergoing hip replacement. Our findings may be helpful in predicting mortality in patients undergoing hip replacement.
Collapse
Affiliation(s)
- Ah Ran Oh
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Ji-Hye Kwon
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Gayoung Jin
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - So Myung Kong
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Dong Jae Lee
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Jungchan Park
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea.
| |
Collapse
|
11
|
Hu H, Hu L, Deng Z, Jiang Q. A prognostic nomogram for recurrence survival in post-surgical patients with varicose veins of the lower extremities. Sci Rep 2024; 14:5486. [PMID: 38448552 PMCID: PMC10918178 DOI: 10.1038/s41598-024-55812-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 02/28/2024] [Indexed: 03/08/2024] Open
Abstract
Varicose veins of the lower extremities (VVLEs) are prevalent globally. This study aims to identify prognostic factors and develop a prediction model for recurrence survival (RS) in VVLEs patients after surgery. A retrospective analysis of VVLEs patients from the Third Hospital of Nanchang was conducted between April 2017 and March 2022. A LASSO (Least Absolute Shrinkage and Selection Operator) regression model pinpointed significant recurrence predictors, culminating in a prognostic nomogram. The model's performance was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). The LASSO regression identified seven predictors for the nomogram predicting 1-, 2-, and 5-year RS. These predictors were age, body mass index (BMI), hypertension, diabetes, the Clinical Etiological Anatomical Pathophysiological (CEAP) grade, iliac vein compression syndrome (IVCS), and postoperative compression stocking duration (PCSD). The nomogram's C-index was 0.716, with AUCs (Area Under the Curve scores) of 0.705, 0.725, and 0.758 for 1-, 2-, and 5-year RS, respectively. Calibration and decision curve analyses validated the model's predictive accuracy and clinical utility. Kaplan-Meier analysis distinguished between low and high-risk groups with significant prognostic differences (P < 0.05). This study has successfully developed and validated a nomogram for predicting RS in patients with VVLEs after surgery, enhancing personalized care and informing clinical decision-making.
Collapse
Affiliation(s)
- Hai Hu
- Department of General Surgery, The Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xihu District, Nanchang, Jiangxi, China
| | - Lili Hu
- Department of pediatrics, The Third Hospital of Nanchang, Nanchang, China
| | - Ziqing Deng
- Department of General Surgery, The Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xihu District, Nanchang, Jiangxi, China
| | - Qihua Jiang
- Department of General Surgery, The Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xihu District, Nanchang, Jiangxi, China.
- Department of Breast Surgery, The Third Hospital of Nanchang, Nanchang, China.
| |
Collapse
|
12
|
Shoombuatong W, Homdee N, Schaduangrat N, Chumnanpuen P. Leveraging a meta-learning approach to advance the accuracy of Na v blocking peptides prediction. Sci Rep 2024; 14:4463. [PMID: 38396246 PMCID: PMC10891130 DOI: 10.1038/s41598-024-55160-z] [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: 12/28/2023] [Accepted: 02/21/2024] [Indexed: 02/25/2024] Open
Abstract
The voltage-gated sodium (Nav) channel is a crucial molecular component responsible for initiating and propagating action potentials. While the α subunit, forming the channel pore, plays a central role in this function, the complete physiological function of Nav channels relies on crucial interactions between the α subunit and auxiliary proteins, known as protein-protein interactions (PPI). Nav blocking peptides (NaBPs) have been recognized as a promising and alternative therapeutic agent for pain and itch. Although traditional experimental methods can precisely determine the effect and activity of NaBPs, they remain time-consuming and costly. Hence, machine learning (ML)-based methods that are capable of accurately contributing in silico prediction of NaBPs are highly desirable. In this study, we develop an innovative meta-learning-based NaBP prediction method (MetaNaBP). MetaNaBP generates new feature representations by employing a wide range of sequence-based feature descriptors that cover multiple perspectives, in combination with powerful ML algorithms. Then, these feature representations were optimized to identify informative features using a two-step feature selection method. Finally, the selected informative features were applied to develop the final meta-predictor. To the best of our knowledge, MetaNaBP is the first meta-predictor for NaBP prediction. Experimental results demonstrated that MetaNaBP achieved an accuracy of 0.948 and a Matthews correlation coefficient of 0.898 over the independent test dataset, which were 5.79% and 11.76% higher than the existing method. In addition, the discriminative power of our feature representations surpassed that of conventional feature descriptors over both the training and independent test datasets. We anticipate that MetaNaBP will be exploited for the large-scale prediction and analysis of NaBPs to narrow down the potential NaBPs.
Collapse
Affiliation(s)
- Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
| | - Nutta Homdee
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, 10900, Thailand
| |
Collapse
|
13
|
Ge X, Lei S, Wang P, Wang W, Wang W. The metabolism-related lncRNA signature predicts the prognosis of breast cancer patients. Sci Rep 2024; 14:3500. [PMID: 38347041 PMCID: PMC10861477 DOI: 10.1038/s41598-024-53716-7] [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: 01/03/2024] [Accepted: 02/04/2024] [Indexed: 02/15/2024] Open
Abstract
Long non-coding RNAs (lncRNAs) involved in metabolism are recognized as significant factors in breast cancer (BC) progression. We constructed a novel prognostic signature for BC using metabolism-related lncRNAs and investigated their underlying mechanisms. The training and validation cohorts were established from BC patients acquired from two public sources: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The prognostic signature of metabolism-related lncRNAs was constructed using the least absolute shrinkage and selection operator (LASSO) cox regression analysis. We developed and validated a new prognostic risk model for BC using the signature of metabolism-related lncRNAs (SIRLNT, SIAH2-AS1, MIR205HG, USP30-AS1, MIR200CHG, TFAP2A-AS1, AP005131.2, AL031316.1, C6orf99). The risk score obtained from this signature was proven to be an independent prognostic factor for BC patients, resulting in a poor overall survival (OS) for individuals in the high-risk group. The area under the curve (AUC) for OS at three and five years were 0.67 and 0.65 in the TCGA cohort, and 0.697 and 0.68 in the GEO validation cohort, respectively. The prognostic signature demonstrated a robust association with the immunological state of BC patients. Conventional chemotherapeutics, such as docetaxel and paclitaxel, showed greater efficacy in BC patients classified as high-risk. A nomogram with a c-index of 0.764 was developed to forecast the survival time of BC patients, considering their risk score and age. The silencing of C6orf99 markedly decreased the proliferation, migration, and invasion capacities in MCF-7 cells. Our study identified a signature of metabolism-related lncRNAs that predicts outcomes in BC patients and could assist in tailoring personalized prevention and treatment plans.
Collapse
Affiliation(s)
- Xin Ge
- Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi District, Zhengzhou, 450052, China
| | - Shu Lei
- Department of Gynecology and Obstetrics, The Third Affiliated Hospital of Zhengzhou University, No.3 Kangfu Middle Street, Erqi District, Zhengzhou, 450052, China
| | - Panliang Wang
- Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi District, Zhengzhou, 450052, China
| | - Wenkang Wang
- Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi District, Zhengzhou, 450052, China
| | - Wendong Wang
- Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi District, Zhengzhou, 450052, China.
| |
Collapse
|
14
|
Xu F, Hu H, Lin H, Lu J, Cheng F, Zhang J, Li X, Shuai J. scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks. Brief Bioinform 2024; 25:bbae091. [PMID: 38487851 PMCID: PMC10940817 DOI: 10.1093/bib/bbae091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/08/2024] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.
Collapse
Affiliation(s)
- Fei Xu
- Department of Physics, Anhui Normal University, Wuhu 241002, China
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou 325001, China
| | - Huan Hu
- Institute of Applied Genomics, Fuzhou University, Fuzhou 350108, China
| | - Hai Lin
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou 325001, China
| | - Jun Lu
- Department of Physics, Anhui Normal University, Wuhu 241002, China
- School of Medical Imageology, Wannan Medical College, Wuhu 241002, China
| | - Feng Cheng
- Department of Physics, and Fujian Provincial Key Lab for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China
| | - Jiqian Zhang
- Department of Physics, Anhui Normal University, Wuhu 241002, China
| | - Xiang Li
- Department of Physics, and Fujian Provincial Key Lab for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China
| | - Jianwei Shuai
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou 325001, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou 325001, China
| |
Collapse
|