Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

The shared biomarkers and immune landscape in psoriatic arthritis and rheumatoid arthritis: Findings based on bioinformatics, machine learning and single-cell analysis

OBJECTIVE

Psoriatic arthritis (PsA) and rheumatoid arthritis (RA) are the most common types of inflammatory musculoskeletal disorders that share overlapping clinical features and complications. The aim of this study was to identify shared marker genes and mechanistic similarities between PsA and RA.

METHODS

We utilized datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) and perform functional enrichment analyses. To identify the marker genes, we applied two machine learning algorithms: the least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE). Subsequently, we assessed the diagnostic capacity of the identified marker genes using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). A transcription factor (TF) network was constructed using data from JASPAR, HumanTFDB, and GTRD. We then employed CIBERSORT to analyze the abundance of immune infiltrates in PsA and RA, assessing the relationship between marker genes and immune cells. Additionally, cellular subpopulations were identified by analyzing single-cell sequencing data from RA, with T cells examined for trajectory and cellular communication using Monocle and CellChat, thereby exploring their linkage to marker genes.

RESULTS

A total of seven overlapping DEGs were identified between PsA and RA. Gene enrichment analysis revealed that these genes were associated with mitochondrial respiratory chain complex IV, Toll-like receptors, and NF-κB signaling pathways. Both machine learning algorithms identified Ribosomal Protein L22-like 1 (RPL22L1) and Lymphocyte Antigen 96 (LY96) as potential diagnostic markers for PsA and RA. These markers were validated using test sets and experimental approaches. Furthermore, GSEA analysis indicated that gap junctions may play a crucial role in the pathogenesis of both conditions. The TF network suggested a potential association between marker genes and core enrichment genes related to gap junctions. The application of CIBERSORT and single-cell RNA sequencing provided a comprehensive understanding of the role of marker genes in immune cell function. Our results indicated that RPL22L1 and LY96 are involved in T cell development and are associated with T cell communication with NK cells and monocytes. Notably, high expression of both RPL22L1 and LY96 was linked to enhanced VEGF signaling in T cells.

CONCLUSION

Our study identified RPL22L1 and LY96 as key biomarkers for PsA and RA. Further investigations demonstrated that these two marker genes are closely associated with gap junction function, T cell infiltration, differentiation, and VEGF signaling. Collectively, these findings provide new insights into the diagnosis and treatment of PsA and RA.

Identification of immune microenvironment subtypes and clinical risk biomarkers for osteoarthritis based on a machine learning model

Background

Osteoarthritis (OA) is a degenerative disease with a high incidence worldwide. Most affected patients do not exhibit obvious discomfort symptoms or imaging findings until OA progresses, leading to irreversible destruction of articular cartilage and bone. Therefore, developing new diagnostic biomarkers that can reflect articular cartilage injury is crucial for the early diagnosis of OA. This study aims to explore biomarkers related to the immune microenvironment of OA, providing a new research direction for the early diagnosis and identification of risk factors for OA.

Methods

We screened and downloaded relevant data from the Gene Expression Omnibus (GEO) database, and the immune microenvironment-related genes (Imr-DEGs) were identified using the ImmPort data set by combining weighted coexpression analysis (WGCNA). Functional enrichment of GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to explore the correlation of Imr-DEGs. A random forest machine learning model was constructed to analyze the characteristic genes of OA, and the diagnostic significance was determined by the Receiver Operating Characteristic Curve (ROC) curve, with external datasets used to verify the diagnostic ability. Different immune subtypes of OA were identified by unsupervised clustering, and the function of these subtypes was analyzed by gene set enrichment analysis (GSVA). The Drug-Gene Interaction Database was used to explore the relationship between characteristic genes and drugs.

Results

Single sample gene set enrichment analysis (ssGSEA) revealed that 16 of 28 immune cell subsets in the dataset significantly differed between OA and normal groups. There were 26 Imr-DEGs identified by WGCNA, showing that functional enrichment was related to immune response. Using the random forest machine learning model algorithm, nine characteristic genes were obtained: BLNK (AUC = 0.809), CCL18 (AUC = 0.692), CD74 (AUC = 0.794), CSF1R (AUC = 0.835), RAC2 (AUC = 0.792), INSR (AUC = 0.765), IL11 (AUC = 0.662), IL18 (AUC = 0.699), and TLR7 (AUC = 0.807). A nomogram was constructed to predict the occurrence and development of OA, and the calibration curve confirmed the accuracy of these 9 genes in OA diagnosis.

Conclusion

This study identified characteristic genes related to the immune microenvironment in OA, providing new insight into the risk factors of OA.

Bioinformatics and systems biology approaches to identify potential common pathogeneses for sarcopenia and osteoarthritis

Sarcopenia, a geriatric syndrome characterized by progressive loss of muscle mass and strength, and osteoarthritis, a common degenerative joint disease, are both prevalent in elderly individuals. However, the relationship and molecular mechanisms underlying these two diseases have not been fully elucidated. In this study, we screened microarray data from the Gene Expression Omnibus to identify associations between sarcopenia and osteoarthritis. We employed multiple statistical methods and bioinformatics tools to analyze the shared DEGs (differentially expressed genes). Additionally, we identified 8 hub genes through functional enrichment analysis, protein-protein interaction analysis, transcription factor-gene interaction network analysis, and TF-miRNA coregulatory network analysis. We also discovered potential shared pathways between the two diseases, such as transcriptional misregulation in cancer, the FOXO signalling pathway, and endometrial cancer. Furthermore, based on common DEGs, we found that strophanthidin may be an optimal drug for treating sarcopenia and osteoarthritis, as indicated by the Drug Signatures database. Immune infiltration analysis was also performed on the sarcopenia and osteoarthritis datasets. Finally, receiver operating characteristic (ROC) curves were plotted to verify the reliability of our results. Our findings provide a theoretical foundation for future research on the potential common pathogenesis and molecular mechanisms of sarcopenia and osteoarthritis.

Potential prognostic biomarkers of hepatocellular carcinoma based on 4D label-free quantitative proteomics analysis pilot investigation

BACKGROUND

Hepatocellular carcinoma carries a poor prognosis and poses a serious threat to global health. Currently, there are few potential prognostic biomarkers available for the prognosis of hepatocellular carcinoma.

METHODS

This pilot study used 4D label-free quantitative proteomics to compare the proteomes of hepatocellular carcinoma and adjacent non-tumor tissue. A total of 66,075 peptides, 6363 identified proteins, and 772 differentially expressed proteins were identified in specimens from three hepatocellular carcinoma patients. Through functional enrichment analysis of differentially expressed proteins by Gene Ontology, KEGG pathway, and protein domain, we identified proteins with similar functions.

RESULTS

Twelve differentially expressed proteins (RPL17, RPL27, RPL27A, RPS5, RPS16, RSL1D1, DDX18, RRP12, TARS2, YARS2, MARS2, and NARS1) were selected for identification and validation by parallel reaction monitoring. Subsequent Western blotting confirmed overexpression of RPL27, RPS16, and TARS2 in hepatocellular carcinoma compared to non-tumor tissue in 16 pairs of clinical samples. Analysis of The Cancer Genome Atlas datasets associated the increased expression of these proteins with poor prognosis. Tissue microarray revealed a negative association between high expression of RPL27 and TARS2 and the prognosis of hepatocellular carcinoma patients, although RPS16 was not significant.

CONCLUSIONS

These data suggest that RPL27 and TARS2 play an important role in hepatocellular carcinoma progression and may be potential prognostic biomarkers of overall survival in hepatocellular carcinoma patients.

Identifying functional subtypes of IgA nephropathy based on three machine learning algorithms and WGCNA

BACKGROUND

IgA nephropathy (IgAN) is one of the most common primary glomerulonephritis, which is a significant cause of renal failure. At present, the classification of IgAN is often limited to pathology, and its molecular mechanism has not been established. Therefore we aim to identify subtypes of IgAN at the molecular level and explore the heterogeneity of subtypes in terms of immune cell infiltration, functional level.

METHODS

Two microarray datasets (GSE116626 and GSE115857) were downloaded from GEO. Differential expression genes (DEGs) for IgAN were screened with limma. Three unsupervised clustering algorithms (hclust, PAM, and ConsensusClusterPlus) were combined to develop a single-sample subtype random forest classifier (SSRC). Functional subtypes of IgAN were defined based on functional analysis and current IgAN findings. Then the correlation between IgAN subtypes and clinical features such as eGFR and proteinuria was evaluated by using Pearson method. Subsequently, subtype heterogeneity was verified by subtype-specific modules identification based on weighted gene co-expression network analysis(WGCNA) and immune cell infiltration analysis based on CIBERSORT algorithm.

RESULTS

We identified 102 DEGs as marker genes for IgAN and three functional subtypes namely: viral-hormonal, bacterial-immune and mixed type. We screened seventeen genes specific to viral hormonal type (ATF3, JUN and FOS etc.), and seven genes specific to bacterial immune type (LIF, C19orf51 and SLPI etc.). The subtype-specific genes showed significantly high correlation with proteinuria and eGFR. The WGCNA modules were in keeping with functions of the IgAN subtypes where the MEcyan module was specific to the viral-hormonal type and the MElightgreen module was specific to the bacterial-immune type. The results of immune cell infiltration revealed subtype-specific cell heterogeneity which included significant differences in T follicular helper cells, resting NK cells between viral-hormone type and control group; significant differences in eosinophils, monocytes, macrophages, mast cells and other cells between bacterial-immune type and control.

CONCLUSION

In this study, we identified three functional subtypes of IgAN for the first time and specific expressed genes for each subtype. Then we constructed a subtype classifier and classify IgAN patients into specific subtypes, which may be benefit for the precise treatment of IgAN patients in future.

Effect of RPL27 knockdown on the proliferation and apoptosis of human liver cancer cells

RPL27 is linked to the development of various diseases including malignant tumors. RPL27 may play an oncogenic function in hepatocellular carcinoma (HCC), but this is unknown. So, the aim of this study was to investigate how the human liver cancer cell lines SNU449 and HepG2 responded to RPL27 knockdown in terms of proliferation and apoptosis. SNU449 and HepG2 were cultured and infected with shCon and shRPL27 lentiviral particles to induce RPL27 knockdown, and then RPL27 expression was detected using qPCR and Western blot. Cell proliferation was measured using CCK8, cell cloning, cell scraping, and transwell migration and invasion, while apoptosis was measured using flow cytometry (FCM). The qPCR revealed that mRNA expression of RPL27 decreased after knocking down RPL27 in cells. The CCK8 and cell cloning assay confirmed that knocking down RPL27 significantly reduced cell viability. The cell scratch assay and transwell assays showed that the proliferation rate decreased after knocking down RPL27. A substantial increase in apoptotic cells was discovered by FCM. According to WB, RPL27 knockdown increased the expression of Bax and Caspase-3 while decreasing the expression of bcl-2. The findings showed that RPL27 knockdown inhibited cell proliferation in SNU449 and HepG2 via inducing apoptosis, proving that RPL27 is a novel gene linked with HCC and is crucial for both proliferation and apoptosis. These outcomes imply that RPL27 may be a potential target for liver cancer diagnosis and therapy.

Molecular mechanism of a novel root-end filling material containing zirconium oxide on the osteogenic/odontogenic differentiation of human osteosarcoma MG-63 cells

Although the novel root-end filling material containing zirconium oxide (NRFM-Zr) which is hydroxyapatite-based may promote osteoblast differentiation, the molecular mechanism remains unclear. The aim of this study is to investigate it underlying the osteogenic/odontogenic differentiation of human osteosarcoma MG-63 cells induced by NRFM-Zr, compared with calcium silicate-based mineral trioxide aggregate (MTA), and glass ionomer cement (GIC). Firstly, three different types of root filling materials were co-cultured with MG-63 cells, and their cell toxicity, alkaline phosphatase (ALP) activity, and calcium ion concentration were evaluated. Next, gene expression profiling microarray was employed to analyze the impact of the materials on the gene expression profile of MG-63 cells. The results of cell viability revealed that NRFM-Zr group had no significant difference compared to the negative control group. After 5 and 7 days of cultivation, both the NRFM-Zr and MTA groups exhibited significantly higher ALP activity compared to the negative control (p < 0.05). Moreover, the NRFM-Zr group had the highest calcium ion concentration, while the GIC group was the lowest (p < 0.05). Gene expression profiling microarray analysis identified 2915 (NRFM-Zr), 2254 (MTA) and 392 (GIC) differentially expressed genes, respectively. GO functional and KEGG pathway analysis revealed that differentially expressed genes of NRFM-Zr, MTA and GIC participated in 8, 6 and 0 differentiation-related pathways, respectively. Comparing the molecular mechanisms of osteogenic/odontogenic differentiation induced by hydroxyapatite-based NRFM-Zr and calcium silicate-based MTA, it was found that they shared similarities in their molecular mechanisms of promoting osteogenic differentiation. NRFM-Zr primarily promotes differentiation and inhibits cell apoptosis, thereby enhancing osteogenic/odontogenic differentiation of MG-63 cells. Furthermore, the inducing efficacy of NRFM-Zr was found to be superior to MTA.

Microenvironment modulation by key regulators of RNA N6-methyladenosine modification in respiratory allergic diseases

BACKGROUND

RNA N6-methyladenosine (m6A) regulators are considered post-transcriptional regulators that affect several biological functions, and their role in immunity, in particular, is emerging. However, the role of m6A regulators in respiratory allergic diseases remains unclear. Therefore, we aimed to investigate the role of key m6A regulators in mediating respiratory allergic diseases and immune microenvironment infiltration characteristics.

METHODS

We downloaded gene expression profiles of respiratory allergies from the Gene Expression Omnibus (GEO) database and we performed hierarchical clustering, difference analysis, and construction of predictive models to identify hub m6A regulators that affect respiratory allergies. Next, we investigate the underlying biological mechanisms of key m6A regulators by performing PPI network analysis, functional enrichment analysis, and immune microenvironment infiltration analysis. In addition, we performed a drug sensitivity analysis on the key m6A regulator, hoping to be able to provide some implications for clinical medication.

RESULTS

In this study, we identified four hub m6A regulators that affect the respiratory allergy and investigated the underlying biological mechanisms. In addition, studies on the characteristics of immune microenvironment infiltration revealed that the expression of METTL14, METTL16, and RBM15B correlated with the infiltration of the mast and Th2 cells in respiratory allergy, and METTL16 expression was found to be significantly negatively correlated with macrophages for the first time (R = -0.53, P < 0.01). Finally, a key m6A regulator, METTL14, was screened by combining multiple algorithms. In addition, by performing a drug sensitivity analysis on METTL14, we hypothesized that it may play an important role in the improvement of allergic symptoms in the upper and lower airways with topical nasal glucocorticoids.

CONCLUSIONS

Our findings suggest that m6A regulators, particularly METTL14, play a crucial role in the development of respiratory allergic diseases and the infiltration of immune cells. These results may provide insight into the mechanism of action of methylprednisolone in treating respiratory allergic diseases.

Identification of Immune-Related Risk Genes in Osteoarthritis Based on Bioinformatics Analysis and Machine Learning

In this research, we aimed to perform a comprehensive bioinformatic analysis of immune cell infiltration in osteoarthritic cartilage and synovium and identify potential risk genes. Datasets were downloaded from the Gene Expression Omnibus database. We integrated the datasets, removed the batch effects and analyzed immune cell infiltration along with differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to identify the positively correlated gene modules. LASSO (least absolute shrinkage and selection operator)-cox regression analysis was performed to screen the characteristic genes. The intersection of the DEGs, characteristic genes and module genes was identified as the risk genes. The WGCNA analysis demonstrates that the blue module was highly correlated and statistically significant as well as enriched in immune-related signaling pathways and biological functions in the KEGG and GO enrichment. LASSO-cox regression analysis screened 11 characteristic genes from the hub genes of the blue module. After the DEG, characteristic gene and immune-related gene datasets were intersected, three genes, PTGS1, HLA-DMB and GPR137B, were identified as the risk genes in this research. In this research, we identified three risk genes related to the immune system in osteoarthritis and provide a feasible approach to drug development in the future.

CircZSWIM6 mediates dysregulation of ECM and energy homeostasis in ageing chondrocytes through RPS14 post-translational modification

BACKGROUND

Circular RNAs (CircRNAs) are important and have different roles in disease progression. Herein, we aim to elucidate the roles of a novel CircRNA (CircZSWIM6) which is upregulated in ageing chondrocytes.

METHODS

We verified the roles of CircZSWIM6 in senescent and osteoarthritis (OA) development in vitro through CircZSWIM6 knockdown and overexpression. RNA pulldown assay and RNA binding protein immunoprecipitation were performed to identify the interaction between CircZSWIM6 and Ribosomal protein S14 (RPS14). The roles of CircZSWIM6 in ageing-related OA were also confirmed in non-traumatic and traumatic model respectively.

RESULTS

CircZSWIM6 regulates extracellular matrix (ECM) and energy metabolism in ageing chondrocyte. Mechanistically, CircZSWIM6 competitively bound to the E3 ligase STUB1 binding site on RPS14 (K125) to inhibit proteasomal degradation of RPS14 to maintain RPS14 function. CircZSWIM6-RPS14 axis is highly associated with AMPK signaling transduction, which keeps energy metabolism in chondrocyte. Furthermore, CircZSWIM6 AAV infection leads to senescent and OA phenotypes in a non-traumatic model and accelerates OA progression in a traumatic model.

CONCLUSION

Our results revealed a significant role of CircZSWIM6 in age-related OA by regulating ECM metabolism and AMPK-associated energy metabolism. We highlight the CircZSWIM6-RPS14-PCK1-AMPK axis is a potential biomarker for OA.

Role of ferroptosis-associated genes in ankylosing spondylitis and immune cell infiltration

Ankylosing spondylitis (AS) is a chronic progressive autoimmune disease with insidious onset, high rates of disability among patients, unknown pathogenesis, and no effective treatment. Ferroptosis is a novel type of regulated cell death that is associated with various cancers and diseases. However, its relation to AS is not clear. In the present study, we identified two potential therapeutic targets for AS based on genes associated with ferroptosis and explored their association with immune cells and immune cell infiltration (ICI). We studied gene expression profiles of two cohorts of patients with AS (GSE25101 and GSE41038) derived from the gene expression omnibus database, and ferroptosis-associated genes (FRGs) were obtained from the FerrDb database. LASSO regression analysis was performed to build predictive models for AS based on FRGs, and the ferroptosis level in each sample was assessed via single-sample gene set enrichment analysis. Weighted gene co-expression network and protein-protein interaction network analyses were performed for screening; two key genes, DDIT3 and HSPB1, were identified in patients with AS. The relationship between key genes and ICI levels was assessed using the CIBERSORT algorithm, followed by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Finally, DDIT3 and HSPB1 were identified as diagnostic markers and potential therapeutic targets for AS. DDIT3 was highly positively correlated with the infiltration levels of various immune cells, while HSPB1 was negatively correlated with the infiltration levels of several different types of immune cells. In conclusion, DDIT3 and HSPB1 may induce ferroptosis in the cells of patients with AS via changes in the inflammatory response in the immune microenvironment, and these genes could serve as molecular targets for AS therapy.

Identification and Validation of Hub Genes for Predicting Treatment Targets and Immune Landscape in Rheumatoid Arthritis

Background

Rheumatoid arthritis (RA) is recognized as a chronic inflammatory disease featured by pathological synovial inflammation. Currently, the underlying pathophysiological mechanisms of RA remain unclear. In the study, we attempted to explore the underlying mechanisms of RA and provide potential targets for the therapy of RA via bioinformatics analysis.

Methods

We downloaded four microarray datasets (GSE77298, GSE55235, GSE12021, and GSE55457) from the GEO database. Firstly, GSE77298 and GSE55457 were identified DEGs by the “limma” and “sva” packages of R software. Then, we performed GO, KEGG, and GSEA enrichment analyses to further analyze the function of DEGs. Hub genes were screened using LASSO analysis and SVM-RFE analysis. To further explore the differences of the expression of hub genes in healthy control and RA patient synovial tissues, we calculated the ROC curves and AUC. The expression levels of hub genes were verified in synovial tissues of normal and RA rats by qRT-PCR and western blot. Furthermore, the CIBERSORTx was implemented to assess the differences of infiltration in 22 immune cells between normal and RA synovial tissues. We explored the association between hub genes and infiltrating immune cells.

Results

CRTAM, CXCL13, and LRRC15 were identified as RA's potential hub genes by machine learning and LASSO algorithms. In addition, we verified the expression levels of three hub genes in the synovial tissue of normal and RA rats by PCR and western blot. Moreover, immune cell infiltration analysis showed that plasma cells, T follicular helper cells, M0 macrophages, M1 macrophages, and gamma delta T cells may be engaged in the development and progression of RA.

Conclusions

In brief, our study identified and validated that three hub genes CRTAM, CXCL13, and LRRC15 might involve in the pathological development of RA, which could provide novel perspectives for the diagnosis and treatment with RA.

Whole-transcriptome sequencing and ceRNA interaction network of temporomandibular joint osteoarthritis

Purpose: The aim of this study was to conduct a comprehensive transcriptomic analysis to explore the potential biological functions of noncoding RNA (ncRNAs) in temporomandibular joint osteoarthritis (TMJOA). Methods: Whole transcriptome sequencing was performed to identify differentially expressed genes (DEGs) profiles between the TMJOA and normal groups. The functions and pathways of the DEGs were analyzed using Metascape, and a competitive endogenous RNA (ceRNA) network was constructed using Cytoscape software. Results: A total of 137 DEmRNAs, 65 DEmiRNAs, 132 DElncRNAs, and 29 DEcircRNAs were identified between the TMJOA and normal groups. Functional annotation of the DEmRNAs revealed that immune response and apoptosis are closely related to TMJOA and also suggested key signaling pathways related to TMJOA, including chronic depression and PPAR signaling pathways. We identified vital mRNAs, including Klrk1, Adipoq, Cryab, and Hspa1b. Notably, Adipoq expression in cartilage was significantly upregulated in TMJOA compared with normal groups (10-fold, p < 0.001). According to the functional analysis of DEmRNAs regulated by the ceRNA network, we found that ncRNAs are involved in the regulation of autophagy and apoptosis. In addition, significantly DEncRNAs (lncRNA-COX7A1, lncRNA-CHTOP, lncRNA-UFM1, ciRNA166 and circRNA1531) were verified, and among these, circRNA1531 (14.5-fold, p < 0.001) and lncRNA-CHTOP (14.8-fold, p < 0.001) were the most significantly downregulated ncRNAs. Conclusion: This study showed the potential of lncRNAs, circRNAs, miRNAs, and mRNAs may as clinical biomarkers and provides transcriptomic insights into their functional roles in TMJOA. This study identified the transcriptomic signatures of mRNAs associated with immunity and apoptosis and the signatures of ncRNAs associated with autophagy and apoptosis and provides insight into ncRNAs in TMJOA.

Diagnostic and prognostic value of m5C regulatory genes in hepatocellular carcinoma

Background: A high mortality rate makes hepatocellular carcinoma (HCC) one of the most common types of cancer globally. 5-methylcytosine (m5C) is an epigenetic modification that contributes to the prognosis of several cancers, but its relevance to HCC remains unknown. We sought to determine if the m5C-related regulators had any diagnostic or prognostic value in HCC.

Methods: M5C regulatory genes were screened and compared between HCC and normal tissue from The Cancer Genome Atlas (TCGA)and Gene Expression Omnibus (GEO) databases. Least absolute shrinkage and selection operator method (LASSO) and univariate Cox regression analysis of differentially expressed genes were then performed to identify diagnostic markers. A LASSO prognostic model was constructed using M5C regulatory genes with prognostic values screened by TCGA expression data. HCC patients were stratified based on risk score, then clinical characteristics analysis and immune correlation analysis were performed for each subgroup, and the molecular functions of different subgroups were analyzed using both Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA). The prognostic model was evaluated using univariate and multivariate Cox analyses as well as a nomogram. Molecular typing was performed according to m5C regulatory genes and immune checkpoint genes expression respectively, and clinical characterization and immune correlation analysis were performed for each subgroup.

Results: M5C regulatory genes are expressed differently in HCC patients with different clinical and pathological characteristics, and mutations in these genes are frequent. Based on five m5C regulators (NOP2, NSUN2, TET1, YBX1, and DNMT3B), we constructed a prognostic model with high predictive ability. The risk score was found to be an independent prognostic indicator. Additionally, risk scores can also be applied in subgroups with different clinical characteristics as prognostic indicators.

Conclusion: The study combined data from TCGA and GEO for the first time to reveal the genetic and prognostic significance of m5C-related regulators in HCC, which provides new directions for identifying predictive biomarkers and developing molecularly targeted therapies for HCC.

CLP1 is a Prognosis-Related Biomarker and Correlates With Immune Infiltrates in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic, heterogeneous autoimmune disease with a high disability rate that seriously affects society and individuals. However, there is a lack of effective and reliable diagnostic markers and therapeutic targets. In this study, we identified diagnostic markers of RA based on RNA modification and explored its role as well as degree of immune cell infiltration. We used the gene expression profile data of three synovial tissues (GSE55235, GSE55457, GSE77298) from the Gene Expression Omnibus (GEO) database and the gene of 5 RNA modification genes (including m6A, m1A, m5C, APA, A-1), combined with cluster analysis, identified four RNA modifiers closely related to RA (YTHDC1, LRPPRC, NOP2, and CLP1) and five immune cells namely T cell CD8, CD4 memory resting, T cells regulatory (Tregs) Macrophages M0, and Neutrophils. Based on the LASSO regression algorithm, hub genes and immune cell prediction models were established respectively in RA and a nomogram based on the immune cell model was built. Around 4 key RNA modification regulator genes, miRNA-mRNA, mRNA-TF networks have been established, and GSEA-GO, KEGG-GSEA enrichment analysis has been carried out. Finally, CLP1 was established as an effective RA diagnostic marker, and was highly positively correlated with T cells follicular helper (Tfh) infiltration. On the other hand, highly negatively correlated with the expression of mast cells. In short, CLP1 may play a non-negligible role in the onset and development of RA by altering immune cell infiltration, and it is predicted to represent a novel target for RA clinical diagnosis and therapy.