HLA-DPA1 gene is a potential predictor with prognostic values in multiple myeloma

Bioinformatics Analysis and Experimental Validation of Differential Genes and Pathways in Bone Nonunions

Non-union fractures pose a significant clinical challenge, often leading to prolonged pain and disability. Understanding the molecular mechanisms underlying non-union fractures is crucial for developing effective therapeutic interventions. This study integrates bioinformatics analysis and experimental validation to unravel key genes and pathways associated with non-union fractures. We identified differentially expressed genes (DEGs) between non-union and fracture healing tissues using bioinformatics techniques. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to elucidate the biological processes and pathways involved. Common DEGs were identified, and a protein-protein interaction (PPI) network was constructed. Fibronectin-1 (FN1), Thrombospondin-1 (THBS1), and Biglycan (BGN) were pinpointed as critical target genes for non-union fracture treatment. Experimental validation involved alkaline phosphatase (ALP) and Alizarin Red staining to confirm osteogenic differentiation. Our analysis revealed significant alterations in pathways related to cell behavior, tissue regeneration, wound healing, infection, and immune responses in non-union fracture tissues. FN1, THBS1, and BGN were identified as key genes, with their upregulation indicating potential disruptions in the bone remodeling process. Experimental validation confirmed the induction of osteogenic differentiation. The study provides comprehensive insights into the molecular mechanisms of non-union fractures, emphasizing the pivotal roles of FN1, THBS1, and BGN in extracellular matrix dynamics and bone regeneration. The findings highlight potential therapeutic targets and pathways for further investigation. Future research should explore interactions between these genes, validate results using in vivo fracture models, and develop tailored treatment strategies for non-union fractures, promising significant advances in clinical management.

Identification of key genes and immune infiltration in multiple myeloma by bioinformatics analysis

OBJECTIVE

Multiple Myeloma (MM) is a hematologic malignant disease with unclear molecular mechanisms. This integrated bioinformatic study aimed to identify key genes, pathways and immune cell infiltration pattern in MM.

METHODS

Differentially expressed genes (DEGs) from GSE6477 and GSE16558 dataset were filtrated with R package 'limma', whose function were explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The key genes were selected from Protein-protein interaction network (PPI) and logistic regression model. The correlation between key genes and survival in MM was evaluated using the survival and survminer package. Additionally, immune filtration analysis was accomplished by CIBERSORT tools.

RESULTS

118 DEGs (92 up-regulated and 26 down-regulated) from two GSE datasets were identified, which were closely related with B cell receptor signaling pathway and Epstein-Barr virus infection. Furthermore, CD24 and PTPRC of five hub genes identified in PPI network were further screened out by the logistic regression model. Besides, CD24 and PTPRC expression were significantly correlated to the survival time in MM patients. Finally, MM might cause different infiltrating immune cell compositions, including increased infiltrations of B cells memory, Plasma cells, T cells CD4 memory resting, T cells follicular helper, Tregs, NK cells resting, Macrophages(M0/M1), Dendritic cells resting and Mast cells activating, and lower proportions of B cells naïve, T cells CD4 naïve, Macrophages M2 and Neutrophils.

CONCLUSION

Targeting CD24 and PTPRC as molecular markers of MM is valuable to MM therapy. Moreover, the immune cell infiltration will provide new insights into MM immunopathology.

HLA-DPA1 overexpression inhibits cancer progression, reduces resistance to cisplatin, and correlates with increased immune infiltration in lung adenocarcinoma

PURPOSE

Human Leukocyte Antigen-DP alpha 1 (HLA-DPA1) is a critical gene in antigen-presenting cells and plays a significant role in immune regulation. The objective of this study was to comprehensively analyze the roles of HLA-DPA1 and its association with lung adenocarcinoma (LUAD).

METHODS

We utilized bioinformatics and conducted a meta-analysis to examine the roles of HLA-DPA1 expression on the progression and immunity of LUAD. We also performed CCK-8, wound healing, and Transwell assays to validate the functions of HLA-DPA1 in LUAD.

RESULTS

HLA-DPA1 expression is downregulated in LUAD tissues and is associated with gender, race, age, smoking history, clinical stage, histological type, lymph node metastasis, and prognosis of patients with LUAD. HLA-DPA1 is involved in immune responses, leukocyte cell-cell adhesion, and antigen processing and presentation. Overexpression of HLA-DPA1 inhibits cancer cell proliferation, migration, and invasion while promoting cell sensitivity to cisplatin in A549 and A549/DDP cells. Additionally, overexpression of HLA-DPA1 correlates with tumor purity, stromal, immune, and ESTIMATE scores, the abundance of immune cells (B cells, CD8+ T cells, CD4+ T cells, macrophages, dendritic cells, and neutrophils), and immune cell markers (programmed cell death 1, cytotoxic T-lymphocyte-associated protein 4, and cluster of differentiation 8A).

CONCLUSIONS

Decreased HLA-DPA1 expression is associated with poor prognosis and immune infiltration in LUAD while HLA-DPA1 overexpression inhibits cancer cell proliferation and progression. Therefore, HLA-DPA1 shows potential as a prognostic biomarker and a therapeutic target for LUAD.

Single Nucleotide Polymorphism rs9277336 Controls the Nuclear Alpha Actinin 4-Human Leukocyte Antigen-DPA1 Axis and Pulmonary Endothelial Pathophenotypes in Pulmonary Arterial Hypertension

Background Pulmonary arterial hypertension (PAH) is a complex, fatal disease where disease severity has been associated with the single nucleotide polymorphism (SNP) rs2856830, located near the human leukocyte antigen DPA1 (HLA-DPA1) gene. We aimed to define the genetic architecture of functional variants associated with PAH disease severity by identifying allele-specific binding transcription factors and downstream targets that control endothelial pathophenotypes and PAH. Methods and Results Electrophoretic mobility shift assays of oligonucleotides containing SNP rs2856830 and 8 SNPs in linkage disequilibrium revealed functional SNPs via allele-imbalanced binding to human pulmonary arterial endothelial cell nuclear proteins. DNA pulldown proteomics identified SNP-binding proteins. SNP genotyping and clinical correlation analysis were performed in 84 patients with PAH at University of Pittsburgh Medical Center and in 679 patients with PAH in the All of Us database. SNP rs9277336 was identified as a functional SNP in linkage disequilibrium (r2>0.8) defined by rs2856830, and the minor allele was associated with decreased hospitalizations and improved cardiac output in patients with PAH, an index of disease severity. SNP pulldown proteomics showed allele-specific binding of nuclear ACTN4 (alpha actinin 4) protein to rs9277336 minor allele. Both ACTN4 and HLA-DPA1 were downregulated in pulmonary endothelium in human patients and rodent models of PAH. Via transcriptomic and phenotypic analyses, knockdown of HLA-DPA1 phenocopied knockdown of ACTN4, both similarly controlling cell structure pathways, immune pathways, and endothelial dysfunction. Conclusions We defined the pathogenic activity of functional SNP rs9277336, entailing the allele-specific binding of ACTN4 and controlling expression of the neighboring HLA-DPA1 gene. Through inflammatory or genetic means, downregulation of this ACTN4-HLA-DPA1 regulatory axis promotes endothelial pathophenotypes, providing a mechanistic explanation for the association between this SNP and PAH outcomes.

Comprehensive analysis of thirteen-gene panel with prognosis value in Multiple Myeloma

BACKGROUND

Although there are many treatments for Multiple myeloma (MM), patients with MM still unable to escape the recurrence and aggravation of the disease.

OBJECTIVE

We constructed a risk model based on genes closely associated with MM prognosis to predict its prognostic value.

METHODS

Gene function enrichment and signal pathway enrichment analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, univariate and multivariate Cox regression analysis, Kaplan-Meier (KM) survival analysis and Receiver Operating Characteristic (ROC) analysis were used to identify the prognostic gene signature for MM. Finally, the prognostic gene signature was validated using the Gene Expression Omnibus (GEO) database.

RESULTS

Thirteen prognostic genes were screened by univariate Cox analysis and LASSO regression analysis. Multivariate Cox analysis revealed risk score to be an independent prognostic factor for patients with MM [Hazard Ratio (HR) = 2.564, 95% Confidence Interval (CI) = 2.223-2.958, P< 0.001]. The risk score had a high level of predictive value according to ROC analysis, with an area under the curve (AUC) of 0.744.

CONCLUSIONS

The potential prognostic signature of thirteen genes were assessed and a risk model was constructed that significantly correlated with prognosis in MM patients.

An emerging prognosis prediction model for multiple myeloma: Hypoxia-immune related microenvironmental gene signature

Multiple myeloma (MM), a hematologic malignancy, is characterized by malignant plasma cells clonal proliferation. Many evidences indicated the indirect interaction between hypoxic environment and immune state in MM tumorigenesis, but the underlying mechanism remains unclear. MM-related datasets were downloaded from the Gene Expression Omnibus (GEO) database. The R packages were applied for screening protective differentially expressed genes (DEGs) and risk DEGs. The signature was constructed based the most prognostic gene signature in the training and assessed in the validation cohorts. The immune cell infiltration, the expression of the HLA family and immune checkpoint genes inside the low- and high-risk groups were compared to determine the differences in immune infiltration and immunotherapy responses. Moreover, the expression of HLA families and immune checkpoints inside the low- and high-risk groups was markedly disordered. The results indicated hypoxia- and immune-related genes, including CHRDL1, DDIT4, DNTT, FAM133A, MYB, PRR15, QTRT1, and ZNF275, were identified and used to construct a prognostic signature. Role of DDIT4 in multiple myeloma was confirmed in vivo and in vitro. DDIT4 knockdown inhibited MM cell viability, migration and invasion potential as well as promoted myeloma cells apoptosis under hypoxia. Taken together, our study may contribute to the treatment and prognosis prediction of MM.

Hypoxia–Immune-Related Gene SLC19A1 Serves as a Potential Biomarker for Prognosis in Multiple Myeloma

Background

Multiple myeloma (MM) remains an incurable malignant tumor of plasma cells. Increasing evidence has reported that hypoxia and immune status contribute to the progression of MM. In this research, the prognostic value of the hypoxia–immune-related gene SLC19A1 in MM was evaluated by bioinformatics analysis.

Method

RNA-sequencing (RNA-seq) data along with clinical information on MM were downloaded from the Gene Expression Omnibus (GEO) database. Consistent clustering analysis and ESTIMATE algorithms were performed to establish the MM sample subgroups related to hypoxia and immune status, respectively, based on the GSE24080 dataset. The differentially expressed analysis was performed to identify the hypoxia–immune-related genes. Subsequently, a hypoxia–immune-gene risk signature for MM patients was constructed by univariate and multivariate Cox regression analyses, which was also verified in the GSE4581 dataset. Furthermore, the mRNA expression of SLC19A1 was determined using qRT-PCR in 19 MM patients, and the correlations between the genetic expression of SLC19A1 and clinical features were further analyzed.

Result

A total of 47 genes were identified as hypoxia–immune-related genes for MM. Among these genes, SLC19A1 was screened to construct a risk score model that had better predictive power for MM. The constructed prognostic signature based on SLC19A1 was verified in the GSE4581 dataset. All independent prognostic factors (age, β₂-microglobulin, LDH, albumin, MRI, and gene risk score) were used to develop a nomogram that showed a better performance for predicting the survival probability of MM patients for 1–5 years. Furthermore, SLC19A1 was highly expressed in newly diagnosed and relapsed MM patients, and high expression of SLC19A1 is correlated with higher bone marrow aspiration plasma cells and β₂-microglobulin levels in MM patients.

Conclusion

In conclusion, our results suggest that SLC19A1 is aberrantly expressed in MM and highly expressed SLC19A1 might be a biomarker correlated with inferior prognosis. More importantly, we identified SLC19A1 as a hypoxia–immune-related gene in MM. Future functional and mechanistic studies will further clarify the roles of SLC19A1 in MM.

Factors Explaining the Coincidence of Smoldering Multiple Myeloma and Primary Biliary Cholangitis: A Case Report

To date, there have been nine reported instances of coinciding smoldering multiple myeloma (SMM) and primary biliary cholangitis (PBC). The term SMM was coined in 1980 to describe low-severity multiple myeloma cases, a hematologic neoplasia that involves the malignant proliferation of plasma cells. PBC is an autoimmune disorder targeting the intrahepatic bile ducts and is characterized by elevated anti-mitochondrial antibodies and often resulting in autoimmune liver cirrhosis. Currently, there is no plausible rationale for the coincidence of SMM and PBC in patients. This report investigates the relationship between SMM and PBC in a Hispanic 49-year-old female residing in the United States and attempts to determine the possible genetic and biochemical causes of this coincidence.

In silico identification of novel biomarkers for key players in transition from normal colon tissue to adenomatous polyps

Adenomatous polyps of the colon are the most common neoplastic polyps. Although most of adenomatous polyps do not show malign transformation, majority of colorectal carcinomas originate from neoplastic polyps. Therefore, understanding of this transformation process would help in both preventive therapies and evaluation of malignancy risks. This study uncovers alterations in gene expressions as potential biomarkers that are revealed by integration of several network-based approaches. In silico analysis performed on a unified microarray cohort, which is covering 150 normal colon and adenomatous polyp samples. Significant gene modules were obtained by a weighted gene co-expression network analysis. Gene modules with similar profiles were mapped to a colon tissue specific functional interaction network. Several clustering algorithms run on the colon-specific network and the most significant sub-modules between the clusters were identified. The biomarkers were selected by filtering differentially expressed genes which also involve in significant biological processes and pathways. Biomarkers were also validated on two independent datasets based on their differential gene expressions. To the best of our knowledge, such a cascaded network analysis pipeline was implemented for the first time on a large collection of normal colon and polyp samples. We identified significant increases in TLR4 and MSX1 expressions as well as decrease in chemokine profiles with mostly pro-tumoral activities. These biomarkers might appear as both preventive targets and biomarkers for risk evaluation. As a result, this research proposes novel molecular markers that might be alternative to endoscopic approaches for diagnosis of adenomatous polyps.

Comprehensive bioinformatics analysis reveals the hub genes and pathways associated with multiple myeloma

PURPOSE

While the prognosis of multiple myeloma (MM) has significantly improved over the last decade because of new treatment options, it remains incurable. Aetiological explanations and biological targets based on genomics may provide additional help for rational disease intervention.

MATERIALS AND METHODS

Three microarray datasets associated with MM were downloaded from the Gene Expression Omnibus (GEO) database. GSE125364 and GSE39754 were used as the training set, and GSE13591 was used as the verification set. The differentially expressed genes (DEGs) were obtained from the training set, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate their functions. The hub genes were derived from the combined results of a protein-protein interaction (PPI) network and weighted gene coexpression network analysis (WGCNA). The receiver operating characteristic (ROC) curves of hub genes were plotted to evaluate their clinical diagnostic value. Biological processes and signaling pathways associated with hub genes were explained by gene set enrichment analysis (GSEA).

RESULTS

A total of 1759 DEGs were identified. GO and KEGG pathway analyses suggested that the DEGs were related to the process of protein metabolism. RPN1, SEC61A1, SPCS1, SRPR, SRPRB, SSR1 and TRAM1 were proven to have clinical diagnostic value for MM. The GSEA results suggested that the hub genes were widely involved in the N-glycan biosynthesis pathway.

CONCLUSION

The hub genes identified in this study can partially explain the potential molecular mechanisms of MM and serve as candidate biomarkers for disease diagnosis.

Immune classifier-based signatures provide good prognostic stratification and predict the clinical benefits of immune-based therapies for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is an aggressive cancer with a high rate of death globally. The use of bioinformatics may help to identify immune cell-related genes both as targets for potential immunotherapies and for their value associated with predicting therapy responses.

METHODS

In this study, mRNA expression profiles of HCC samples from The Cancer Genome Atlas (TCGA) database were subjected to gene enrichment, cell type abundance, immune cell infiltration, and pathway enrichment analyses to determine immune cell gene features, cell type abundance, and functional annotation characteristics. We also evaluated their prognostic values using Cox regression and Kaplan-Meier analyses and assessed potential responses to chemotherapy. Four subgroups (Groups 1-4) were identified. Group 4 was associated with advanced clinical characteristics, high immune cell enrichment scores, and the poorest outcomes.

RESULTS

Differentially expressed genes (DEGs) in the HCC samples were enriched in the following pathways: antigen binding, cell surface receptor signal transduction of the immune response, and cell surface activated receptor signal transduction of the immune response. Highly expressed genes in Group 4 were enriched in elements of the WNT signalling pathway. We identified five immune-related genes (SEMA3A, TNFRSF11B, GUCA2A, SAA1, and CALCR) that were significantly related to HCC prognosis. A prognostic model based on these five genes exhibited good predictive value, with 1-year and 5-year area under the curve (AUC) values of  >  0.66. Group 4 was also potentially more sensitive to EHT 1864, FH535, and lapatinib chemotherapies than the other groups.

CONCLUSIONS

We identified and validated four HCC subgroups based on immune system-related genes and identified five genes that may be used for an immune-based prognostic model for HCC treatment.

Identification of four methylation-driven genes as candidate biomarkers for monitoring single-walled carbon nanotube-induced malignant transformation of the lung

Long-term exposure to carbon nanotubes (CNTs) has been reported to induce malignant transformation. This study aimed to screen candidate biomarkers for monitoring occupational workers to prevent the development of lung cancer. mRNA (GSE56104) and methylation (GSE153246) profiles of lung epithelial BEAS-2B cells exposed to malignant transformation dose of single-walled CNTs or control medium were downloaded from Gene Expression Omnibus database. A total of 1513 differentially expressed genes (DEGs) and 912 differentially methylated genes (DMGs) were identified using LIMMA method. The weighted correlation network analysis identified blue and turquoise modules were associated with malignant transformation of BEAS-2B cells, 124 DMGs of which were overlapped with DEGs. The mRNA and methylation levels of four methylation-driven DEGs were validated in both lung adenocarcinoma (LUAD) and squamous cell carcinomas (LUSC) of The Cancer Genome Atlas dataset and they were associated with overall survival of LUAD patients. Downregulation of PXMP4 and MCOLN2, while upregulation of MET was confirmed in both LUSC and LUAD via Human Protein Atlas analysis. PXMP4 and MET protein levels were also supported in the proteomic analysis of LUAD. Receiver operating characteristic (ROC) curve analysis showed the combination of four genes may be the optimal biomarker for predicting lung cancer, with the area under ROC curve >0.9. Function analysis revealed BARX2 may interact with CCND1 to regulate cell cycle; MET and PXMP4/MCOLN2 may positively correlate with CCR5/IL-6 or GATA3/HLA-DPB1/HLA-DPA1 to involve immune regulation. In conclusion, these four methylation-driven genes may be candidate prognostic and diagnostic biomarkers for single-walled CNT-related lung cancer.