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Qichang Gao,1 Yiming Ma,1 Tuo Shao,1 Xiaoxuan Tao,2 Xiansheng Yang,1 Song Li,1 Jiaao Gu,1 Zhange Yu1 1Department of Spinal Surgery, The 1st Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, People’s Republic of China; 2Department of Radiotherapy, The 3st Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, People’s Republic of ChinaCorrespondence: Zhange Yu, Email yuzhange1967@163.comBackground: Progress in research on expression profiles in osteoarthritis (OA) has been limited to individual tissues within the joint, such as the synovium, cartilage, or meniscus. This study aimed to comprehensively analyze the common gene expression characteristics of various structures in OA and construct a diagnostic model.Methods: Three datasets were selected: synovium, meniscus, and knee joint cartilage. Modular clustering and differential analysis of genes were used for further functional analyses and the construction of protein networks. Signature genes with the highest diagnostic potential were identified and verified using external gene datasets. The expression of these genes was validated in clinical samples by Real-time (RT)-qPCR and immunohistochemistry (IHC) staining. This study investigated the status of immune cells in OA by examining their infiltration.Results: The merged OA dataset included 438 DEGs clustered into seven modules using WGCNA. The intersection of these DEGs with WGCNA modules identified 190 genes. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest algorithms, nine signature genes were identified (CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3), each demonstrating substantial diagnostic potential (areas under the curve from 0.701 to 0.925). Furthermore, dysregulation of various immune cells has also been observed.Conclusion: CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3 demonstrated significant diagnostic efficacy in OA and are involved in immune cell infiltration.Keywords: osteoarthritis, machine learning, immune cells infiltration, diagnostic model

Jianmin Gong,1,2,* Bo Shi,1,3,* Ping Yang,1 Adeel Khan,4 Tao Xiong,2 Zhiyang Li1 1Department of Clinical Laboratory, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210000, People’s Republic of China; 2College of Life Science, Yangtze University, Jingzhou, 434025, People’s Republic of China; 3Department of Clinical Laboratory, Nanjing Jiangning Hospital of Chinese Medicine (CM), Nanjing, 211100, People’s Republic of China; 4Department of Biotechnology, University of Science and Technology Bannu, Bannu, 28100, Islamic Republic of Pakistan*These authors contributed equally to this workCorrespondence: Tao Xiong, College of Life Science, Yangtze University, Jingzhou, 434025, People’s Republic of China, Tel +8613872387410, Email xiongtao@yangtzeu.edu.cn Zhiyang Li, Department of Clinical Laboratory, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, People’s Republic of China, Tel +8618951703765, Email lizhiyang@nju.edu.cnBackground: Hypertrophic cardiomyopathy (HCM) is a dominantly inherited disease associated with sudden immune cell associations that remain unclear. The aim of this study was to comprehensively screen candidate markers associated with HCM and immune cells and explore potential pathogenic pathways.Methods: First, download the GSE32453 dataset to identify differentially expressed genes (DEGs) and perform Gene Ontology and pathway enrichment analysis using DAVID and GSEA. Next, construct protein-protein interaction (PPI) networks using String and Cytoscape to identify hub genes. Afterward, use CIBERSORT to determine the proportion of immune cells attributed to key genes in HCM and conduct ROC analysis based on the external dataset GSE36961 to evaluate their diagnostic value. Finally, validate the expression of key genes in the hypertrophic cardiomyocyte model through qRT-PCR using data from the HPA database.Results: Comprehensive analysis revealed that there were 254 upregulated genes and 181 downregulated genes in HCM. The enrichment study underscored pathways of inflammatory signaling, including MAPK and PI3K-Akt pathways. Pathways abundant in genes associated with HCM encompassed myocardial contraction and NADH dehydrogenase activity. Additionally, the analysis of immune infiltration revealed a notable increase in macrophages, NK cells, and monocytes in the HCM group, showing statistically significant variances in CD4 memory resting T cell infiltration when compared to the healthy control group. Within the validation dataset GSE36961, the Area Under the Curve (AUC) scores for eight crucial genes (FOS, CD86, CD68, BDNF, PIK3R1, PLEK, RAC2, CCL2) each exceeded 0.8. The HPA database revealed the positioning traits and paths of these eight crucial genes in smooth muscle cells, myocardial cells, and fibroblasts. The outcomes of the qRT-PCR were aligned with the sequencing findings.Conclusion: Bioinformatics analysis unveiled pivotal genes, pathways, and immune involvement, illuminating the molecular underpinnings of HCM. These findings suggest promising therapeutic targets for clinical applications.Keywords: hypertrophic cardiomyopathy, bioinformatic, immune cells infiltration, biomarker

Shengyi Long,1,&ast; Fang Peng,1,&ast; Baohui Song,1,&ast; Liang Wang,2 Jun Chen,2 Bingbing Shang1 1The Second Hospital of Dalian Medical University, Dalian, Liaoning Province, People’s Republic of China; 2Laboratory Animal Center, Dalian Medical University, Dalian, Liaoning Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jun Chen Email chenjun@dmu.edu.cnBingbing Shang Email shangbingbing@dmu.edu.cnBackground: Hepatocellular carcinoma (HCC) is one of the most serious malignancies. The main features of HCC are vascular invasion and drug resistance. Ferroptosis is a novel cell program that is involved in several diseases, such as cancer. Heat shock protein beta 1 (HSPB1) is a major component of heat shock proteins. A recent study showed that HSPB1 could be a new therapeutic target for colorectal cancer with 5-fluorouracil-acquired resistance. However, the functional role of HSPB1 in HCC remains unclear.Aim: The aim of this study is to clarify HSPB1 expression in HCC and its potential therapeutic and prognostic value.Methods: We collected data on HSPB1 expression levels in HCC and normal liver tissues from The Cancer Genome Atlas and Gene Expression Omnibus databases. We then validated it using immunohistochemistry (IHC). Receiver operating characteristic and Kaplan–Meier survival curves were used to investigate the role of HSPB1 in the prognosis analysis of HCC. Further, we used the online Search Tool for the Retrieval of Interacting Genes/Proteins website, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes to conduct enrichment analysis and identify the predictive signaling pathways. Meanwhile, we used the TIMER and GSVA package of R (v3.6.3) to analyze the association between HSPB1 and immunocyte infiltration.Results: Compared to normal tissues, there was differential expression of HSPB1 in pan-cancers. HSPB1 expression was higher in HCC tissues than in normal tissues (p< 0.05). There was an evident significant difference between HSPB1 mRNA levels and histologic grade, vascular invasion, and alpha-fetoprotein level (all p values< 0.05). Univariate analysis indicated that HCC patients with high HSPB1 levels had shorter overall survival rates than those with low HSPB1 levels (p< 0.05). MAPK14, HSPA8, MAPKAPK3, MAPKAPK5, and MAPKAPK2 are essential proteins that interact with HSPB1. There was a significant correlation between HSPB1 expression levels and immune cell infiltration, including CD4+ T cells (r=0.203, p< 0.05).Conclusion: High HSPB1 expression is closely associated with a worse prognosis in HCC patients, and HSPB1 may be a target of immunotherapy in HCC.Keywords: hepatocellular carcinoma, ferroptosis, HSPB1, immune cells infiltration, bioinformatics analysis