Identification of Immune-Related Hub Genes in Multiple Myeloma

doi:10.21203/rs.3.rs-2981597/v1

Download PDF

Article

Identification of Immune-Related Hub Genes in Multiple Myeloma

https://doi.org/10.21203/rs.3.rs-2981597/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Purpose:: Multiple myeloma(MM) is a common malignant tumor in the blood system. Despite recent advances in its treatment, its symptomatic remission rate and survival rate are still not optimistic. In the future, it is necessary to continue to search for different treatment targets and new treatment methods in order to improve the quality of life and survival time of patients with MM. The study aims to explore the potential immune related pivotal genes and immune infiltration patterns in MM.

Methods: The study included peripheral blood samples from patients with MM who our hospital from October 2020 to April 2022. Obtain a gene chip for research from a comprehensive gene expression database, perform differential expression analysis on the processed gene dataset, and then perform functional enrichment analysis, weighted gene co expression network analysis, GSEA immune infiltration analysis, and LASSO regression analysis on the obtained differential expression genes to obtain the hub genes. Finally, the hub gene TNFSF14 (LIGHT) was validated by qRT-PCR.

Results: In the study, three immune-related hub genes (ADAM8, CR2, and TNFSF14) and three main types of peripheral immune cells (activated CD8 T cells, macrophages, and plasma cell like dendritic cells) were obtained, which are closely related to the pathogenesis of MM. Then, by collecting peripheral blood samples from some patients in our hospital and conducting real-time fluorescence quantitative polymerase chain reaction, it was confirmed that the hub gene TNFSF14 (LIGHT) mined in this study was highly expressed in peripheral blood samples from patients with MM, which may indicate that it plays a pathogenic role in MM.

Conclusion: The study found that immune-related hub genes (ADAM8, CR2, and TNFSF14) are closely related to the pathogenesis of MM, and should be further researched.

Health sciences/Diseases/Cancer

Health sciences/Diseases/Cancer/Haematological cancer

Multiple Myeloma

TNFSF14

Immune cells

Immune related genes

Multiple myeloma (MM) is a malignant tumor derived from plasma cells¹², which is believed to evolve from the asymptomatic precancerous stage of clonal proliferation of plasma cells, known as monoclonal gammopathy of undetermined significance (MGUS) of unknown significance⁸. In addition, it can be found that in some patients, an intermediate asymptomatic but more advanced precancerous stage, known as smoldering multiple myeloma (SMM)⁷. Although MM is still considered a single disease, it is actually a collection of several different cytogenetically different plasma cell malignant tumors, which is a widespread disease of monoclonal gamma globulin disease. The clinical manifestations are often hypercalcemia, anemia, renal insufficiency, and pathological fractures¹⁵. In past research and treatment, the application of new therapies such as autologous hematopoietic stem cell transplantation, proteasome inhibitors, targeted monoclonal antibodies, and immunomodulatory drugs has significantly improved the quality of life and lifespan of patients with MM². However, due to the fact that the disease progression is not fully understood, MM is still incurable. Different types of tumors have different tumor microenvironments, which include not only stromal cells, fibroblasts, and endothelial cells, but also innate and adaptive immune cells¹⁸. Currently, tumor immunotherapy is gradually playing an increasingly important role in the field of cancer treatment. Immunotherapy is a promising new type of cancer treatment⁹. With the application of immune checkpoint therapy, the clinical symptom remission rate and overall survival rate of patients with MM have been significantly improved⁴. Therefore, searching for new therapeutic targets and new methods of combined therapy is very important for further improving the efficacy of immunotherapy. There is increasing evidence that abnormal immune function plays a crucial role in the pathogenesis of MM, and abnormal immune cell infiltration is related to the pathogenesis of MM^{12, 9, 15}. Therefore, studying the immune environment of MM and searching for new immunotherapy schemes can bring better treatment outcomes for patients with MM. This article mainly studies the potential immune-related hub genes and immune infiltration patterns of MM, hoping to explore new therapeutic targets for MM.

Identification of Gene Co-expression Module

In order to make the subsequent analysis more accurate, the two data sets were integrated and processed to remove batch effects. After processing the gene set using R language, 4578 genes were obtained. From the reduced dimension scatter diagram (Fig. 1A and B), it can be found that the samples from the two datasets can be processed for subsequent analysis and research. A gene co expression network was constructed from all genes in a total of 84 samples (Fig. 1C and D). According to the standard of scale-free topology, the soft threshold selection is 5. In addition, the study also incorporates modules with a module threshold of less than 0.25 and less than 80 genes into larger modules. Seven co-expression modules were ultimately identified, of which the gray module was considered invalid because it was composed of a set of genes that could not be assigned to any module, so this module was excluded from subsequent analysis. There are 212 genes in the brown module, 285 genes in the blue module, 196 genes in the yellow module, 88 genes in the red module, 581 genes in the turquoise module, 151 genes in the green module, and 29 genes in the gray module.

Calculation of Module-Trait Correlations

A correlation analysis was conducted between the obtained seven gene co-expression modules and clinical traits (MM and healthy controls), and a heat map of the characteristic gene network was constructed (Fig. 2A). Among them, the brown module had the highest correlation with the clinical trait status of MM (r = 0.62, p < 0.01), so the brown module was selected as the key module for the next analysis. The relationship between genetic characteristics and clinical phenotype was evaluated in key modules (Fig. 2B), with a correlation coefficient of 0.79 (p < 0.01).

Analysis of Immune Infiltration in Multiple Myeloma

The ssGSEA algorithm was used to calculate the infiltration abundance of 28 immune cell types between MM and healthy control samples. The results showed that there were significant differences in activated CD8 T cells, macrophages, activated dendritic cells, plasmacytoid dendritic cells, monocytes, and natural killer cells in MM samples compared to healthy control samples (Fig. 2C). In the differential immune cell types, compared to the control sample, the infiltration abundance of activated dendritic cells in MM samples decreased, while the infiltration abundance of activated CD8 T cells, monocytes, macrophages, pDC, and natural killer cells in MM samples increased.

Identification and Functional Enrichment Analysis of Immune-key Genes

By crossing 782 immune related genes and key module genes (brown module), a total of 26 immune key genes were obtained, as shown in Fig. 2D. Then, GO enrichment analysis and KEGG pathway enrichment analysis were performed on immune-key genes. GO enrichment analysis shows that biological processes (Fig. 3A) are mainly concentrated in the positive regulation of cell matrix adhesion, immune response, cell surface receptor signaling pathways, cell adhesion, and co stimulation of T cells. The cellular components (Fig. 3B) are mainly concentrated in the overall composition of the plasma membrane, the lumen of the endoplasmic reticulum, immune synapses, extracellular spaces, and specific granular membranes. Molecular function (Fig. 3C) is mainly enriched in transmembrane signal receptor activity, CD4 receptor binding, receptor binding, and T cell receptor binding. Further KEGG pathway enrichment analysis shows that (Fig. 3D), these genes are mainly involved in the differentiation process of osteoclasts, B cell receptor signal transduction pathway, PD-L1 expression in tumors and PD-1 checkpoint pathway, Th1 and Th2 cell differentiation, AGE-RAGE signal pathway in diabetes complications, NF- κ B signal pathway, T cell receptor signal pathway, TNF signal pathway.

Screening of Immune-Related Hub Genes and Correlation Analysis of Hub Genes and Immune cells

In order to identify immune hub genes associated with MM, the study used the expression levels and clinical characteristics of immune-key genes in all samples (MM and healthy controls) to construct a LASSO model (Fig. 4A and B). A total of 26 immune-key genes were included in LASSO logistic regression. By setting the Lambda value of the adjustment parameter in the LASSO regression model to 0.05, a total of 8 immune-related hub genes (ADAM8, CD36, CD3E, CLEC4A, CR2, CSF1, DUSP14, TNFSF14) were identified. Then, the study conducted a correlation analysis between immune-related hub genes. The results showed that ADAM8 was positively correlated with CLEC4A, CSF1, and DUSP14, while it was negatively correlated with CD36, CD3E, and CR2; TNFSF14 (LIGHT) is negatively correlated with CSF1; CD36 was positively correlated with CD3E, while CD36 was negatively correlated with CSF1; CD3E was negatively correlated with CLEC4A and DUSP14; CSF1 is positively correlated with DUSP14 (Fig. 4C). The correlation between 8 immune-related hub genes and 6 different immune cell types was evaluated (Fig. 4D). ADAM8 has a strong correlation with plasmacytoid dendritic cells and macrophages (correlation coefficients=-0.42, -0.38), and TNFSF14 (LIGHT) has a strong correlation with activated CD8 T cells and macrophages (correlation coefficients are 0.41, 0.31, respectively). CD36 has a strong correlation with macrophages (correlation coefficient of 0.35). There is a strong correlation between CD3E and activated CD8 T cells (correlation coefficient 0.38).

ROC Curve Analysis of Immune-Related Hub Genes

The study compared the expression levels of immune-related hub genes in 84 samples, including 54 MM and 30 healthy control samples. Compared with the control group, the expression levels of ADAM8, CD36, CSF1, and TNFSF14 (LIGHT) in MM samples were significantly upregulated, while the expression levels of CD3E, CLEC4A, CR2, and DUSP14 were significantly downregulated (Fig. 5A). Perform ROC curve analysis on immune-related hub genes to distinguish MM patients from healthy control samples. As shown in Fig. 5B, the area under curve (AUC) value for the eight gene combination models is 0.98.

Validation of Immune-Related Hub Genes

Identification of expression levels of immune related hub genes in gene datasets GSE6651 and GSE47552. Figure 5C shows that the expressions of CD3E and CR2 are significantly downregulated in patients with MM, while the expressions of ADAM8, CD36, and TNFSF14 (LIGHT) are significantly upregulated. There were differences in the expression of CLEC4A, CSF1, and DUSP14, but there was no statistical significance (P > 0.05). Then, we analyzed and studied the differences in the expression of immune-related hub genes between MGUS, SMM, and MM and healthy controls. Figure 5D shows that ADAM8 and TNFSF14 (LIGHT) are the lowest expressed in healthy donor samples, and CR2 expression is significantly upregulated. The expression of CD36 and CLE4A was the lowest in patients with MM, and there was no significant difference in DUSP14 between the four samples. There was a difference in the expression of CD3E and CSF1, but there was no statistical significance (P > 0.05).

The peripheral blood samples from MM and normal peripheral blood samples were evaluated by qRT-PCR to verify whether TNFSF14 (LIGHT) was differentially expressed in the samples. The expression of TNFSF14 (LIGHT) and internal reference GAPDH in 30 samples was detected by qPCR. The expression level of TNFSF14 (LIGHT) in peripheral blood samples from patients with MM was significantly higher than that in peripheral blood samples from healthy donors (Fig. 6A). After treatment, the expression level of TNFSF14 (LIGHT) in peripheral blood samples from patients with MM decreased (Fig. 6B).

Although MM is a rare disease, it is the second most common malignant tumor of the blood system⁶. In the past few decades, treatment options for MM have continuously increased and improved, starting with the initial use of autologous stem cell transplantation, and developing to the application of new treatment options such as immunomodulatory drugs and proteasome inhibitors. Currently, in the treatment of MM, immunotherapy is routinely used. Immunomodulatory drugs such as lenalidomide, thalidomide, and later pomadolide have been shown to be effective in treating MM and significantly improve survival. MM can downregulate the recognition of cellular immune antigens and regulate the bone marrow microenvironment, thereby promoting the proliferation, anti-apoptotic ability, and immune escape ability of tumor cells, further inhibiting the anti-tumor immune ability. M. Swamydas et al. believe that immunomodulatory drugs, proteasome inhibitors, monoclonal antibodies (mAbs), checkpoint inhibitors, NK and T cell therapy have the potential to reverse immunosuppression and restore effective immune monitoring¹. Through bioinformatics analysis, the study explored the potential immune-related hub genes and immune infiltration patterns in patients with MM, providing new biomarkers for the treatment and evaluation of therapeutic effectiveness of MM.

Two data sets GSE24870 and GSE146649 were included in the study. Seven different co-expression modules were established through WGCNA, with brown modules significantly associated with the pathogenesis of MM. The GO and KEGG enrichment analysis of immune-key genes showed that immune-key genes were mainly enriched in immune response, cell surface receptor signaling pathways, T cell co-stimulation, immune synapses, transmembrane signal receptor activity, CD4 receptor binding, and T cell receptor binding. Pathway enrichment is mainly concentrated in osteoclast differentiation, immune cell related receptor signaling pathways, malignant tumor immune checkpoint pathways, and signal transduction pathways.

Further immune infiltration analysis revealed that there were 6 different types of infiltrating immune cells between MM and healthy control samples: activated CD8 T cells, activated dendritic cells, monocytes, macrophages, plasmacytoid dendritic cells, and natural killer cells. By using LASSO logistic regression analysis and identifying the expression levels of immune-related hub genes in the gene dataset GSE6651 and GSE47552, the study considers ADAM8, CR2, and TNFSF14 to be the most valuable.

Research has found that⁷ the ADAM family is involved in mediating the degradation of extracellular matrix in cartilage in osteoarthritis. ADAM8 is one of the ADAM proteases, also known as CD156, which is expressed in macrophages, neutrophils, and NK cells^{12, 2}. M.D. Zack et al. believe that⁹ ADAM8 is a fibronectin in human osteoarthritis chondrocytes and an important mediator in cartilage catabolism. In addition, ADAM8 has also been identified as a regulator of osteoclast formation and bone erosion associated with rheumatoid arthritis^{3, 5}. CR2, also known as CD21, is a membrane glycoprotein that is one of numerous cell surface proteins that bind to the activation and processing fragments of the complement system^{6 Suppl}. CD21 plays an important role in the selection of high affinity B cells and the development and maintenance of B cell memory¹⁰. K. Thorarinsdottir et al. believe that² low expression or deletion of CD21 can mediate joint destruction in patients with rheumatoid arthritis. In addition, a study on B-cell chronic lymphoblastic leukemia reported that³² low expression of CD21 can predict a decrease in overall survival in patients with B-cell chronic lymphoblastic leukemia, and is significantly associated with adverse clinical outcomes. TNFSF14(LIGHT) is a protein that is mainly expressed on activated T cells, activated natural killer cells, and immature dendritic cells¹². The expression of LIGHT in tumors has a profound impact on the host's immune response to tumors and the remodeling of tumor microenvironment. Some studies have shown that²⁴ LIGHT can cause the development of MM osteopathy through direct osteoclast promotion and indirect inhibition of osteogenic reactions. G. Brunetti and R Research by Rizzi, G reported tha high LIGHT levels produced by immune cells contribute to osteoclast formation and bone destruction in MM related bone diseases.

CD8 T cells are important participants in innate and adaptive immune defense mechanisms, and are the body's defense line against various harmful factors². In a study targeting various cancers, it was found that¹¹ the abundance of CD8 T cells in tumors is the best predictor of response to anti PD-1/PD-L1 therapy. The use of immune checkpoint inhibitors can increase the effector function of CD8 T cells in patients with MM and control the progression of MM¹⁶.

Macrophages are a ubiquitous cellular component that exists in all tissues under stable physiological conditions¹². The interaction between immune cells and osteoclasts in the bone marrow or joint cavity is the basis of bone immunity, and the macrophage osteoclast axis plays a crucial role. Fusion and multinucleation of macrophages are key to the formation of osteoclasts to control bone balance¹⁷. Y. Zheng et al. found that¹⁷ macrophages can help suppress anti-tumor immunity, enable survival of myeloma cells, and increase resistance to chemotherapy in MM.

Plasmacytoid dendritic cells are a unique cell lineage derived from bone marrow, mainly present in the blood and lymphatic organs. It is reported that⁴ plasmacytoid dendritic cells accumulate in the bone marrow of patients with MM. These plasmacytoid dendritic cells not only stimulate the proliferation of T cells, but also promote the growth and survival of myeloma cells. Research by A. Ray, Z. Tian et al. found that⁸ the number of plasmacytoid dendritic cells in the bone marrow microenvironment of MM increased, which not only contributes to the immune dysfunction of MM, but also promotes the progression and drug resistance of myeloma cells.

The study ultimately concluded that three immune hub genes (ADAM8, CR2, and TNFSF14) and three main types of peripheral immune cells (activated CD8 T cells, macrophages, and plasmacytoid dendritic cells) are closely related to the pathogenesis of MM. In addition, the study found that the expression level of TNFSF14 (LIGHT) in peripheral blood of patients with MM significantly decreased after treatment, and compared with healthy patients, TNFSF14 (LIGHT) showed a high expression state in myeloma patients. In addition, the relationship between immune hub genes and immune cells was discussed previously, suggesting that the high expression of TNFSF14 may be significantly related to the activity of CD8 T cells and macrophages.

In future research, it is necessary to focus on the mechanisms of ADAM8, CR2, and TNFSF14. The abnormal expression of these genes is closely related to the immunopathological process of MM. Further research has revealed the correlation between ADAM8, CR2, and TNFSF14 and immune cells, supporting the crucial role of these genes in MM by regulating immune infiltration, This may provide a better understanding of molecular targeted treatment strategies for improving MM.

The study uses bioinformatics analysis to identify - in MM. ADAM8, CR2, and TNFSF14 were used as candidate genes for further research. The study has discovered immune hub genes involved in the pathogenesis of MM, and is expected to serve as new therapeutic targets for MM in the future.

Data Processing

From the Gene Expression Omnibus( https://www.ncbi.nlm.nih.gov/geo/ )RNA microarray data from human MM samples were obtained from the GSE24870 and GSE146649 datasets. The GSE24870 dataset includes 20 donor samples from healthy individuals and 23 donor samples from patients with MM, and is analyzed using the GPL571 Affymetrix Human Genome U133A Plus 2.0 array on the platform. The GSE146649 dataset includes 10 donor samples from healthy individuals and 31 donor samples from patients with MM, and is analyzed using the GPL570 Affymetrix Human Genome U133A Plus 2.0 array on the platform. There are 84 samples in both datasets, including 54 samples from patients with MM and 30 samples from healthy controls. The package of R software (version 4.1.0) was used to remove batch effects on the dataset, and finally the gene expression matrix after removing batch effects was obtained.

Weighted Gene Co-expression Network Analysis

The processed gene expression matrix was subjected to WGCNA. Firstly, the study used gene expression profiles to calculate the median absolute deviation of each gene, eliminating the first 50% of genes with the smallest median absolute deviation, and using R language to eliminate outlier gene samples. The β is a soft threshold parameter, which mainly reflects the strong correlation between genes. After the soft threshold parameter is selected as 5, the adjacency is transformed into a topological overlap matrix, which can detect the connectivity of the gene network. According to TOM, highly related genes are classified into the same module, with a sensitivity of 3. Each module contains at least 60 genes. In order to further analyze the gene module, In this study, the differences in module characteristic genes were calculated, and their correlation coefficients and p-values with clinical phenotypes (MM and healthy controls) were calculated. When the p-value was less than 0.05, modules related to MM were considered key modules.

Immune Infiltration Analysis

Using the R software package "GSEA" to integrate MM and healthy control samples into immune infiltration analysis, ssGSEA analysis can be used to identify the level of immune infiltration in gene expression dataset samples⁴³. The study obtained a set of 28 immune cell related genes from the immune gene enrichment map published by P. Charoentong et al., including 782 immune related genes¹. To calculate the infiltration abundance of immune cells in MM and healthy control samples, and determine the difference between the two groups of immune cells (p < 0.05).

Functional Enrichment Analysis of Immune-Key Genes

The key module genes screened by WGCNA are intersected with immune related genes to obtain immune-key genes. Then, functional enrichment analysis (GO analysis and KEGG pathway analysis) was used to process immune-key genes to study their potential biological significance. Gene ontology analysis mainly includes three parts: biological process (BP), molecular function (MF), and cellular component (CC). The BH method is used to calculate the FDR and p value values in GO enrichment and KEGG pathway analysis by using the R language to obtain gene set enrichment results. When the p-value value is < 0.05, the results of the functional enrichment analysis are considered to have significant statistical significance.

Identification of Immune-Related Hub Genes by LASSO Regression Analysis

LASSO analysis (Least Absolute Shrinkage and Selection Operator) was used to further identify immune related hub genes in MM. The study used the "glmnet" R package to construct a LASSO model, which can distinguish MM groups from healthy control groups. The expression levels of immune-key genes and clinical traits (disease types) were used to construct LASSO logistic regression. Use the "p ROC" R package to construct the ROC curve of immune-related hub genes. In addition, the study evaluated the correlation between immune-related hub genes and immune cell types. In the study, a Lambda value of 0.0548509166438796 was set, and eight genes were ultimately obtained.

Validation of Hub Genes

In order to verify the difference in the expression of immune-related hub genes between MM and healthy controls, the GSE6651 dataset was used for validation, which included a total of 12 MM patients and 13 healthy controls. In order to further understand the role of transcriptome disorders of genes in the transition from normal plasma cells to cloned plasma cells and from inert cloned plasma cells to malignant plasma cells, this study also explored the differences in the expression of immune-elated hub genes between MGUS, SMM, and MM and healthy controls. The gene expression dataset GSE47552 was selected, which included bone marrow samples from 41 MM patients, 33 SMM patients, and 20 MGUS patients, five normal bone marrow samples from healthy donor bone marrow were also included. The differential expression of immune hub genes in the GSE6651 and GSE47552 datasets is shown using a box diagram using the draw-boxplot function "tinyarray" package. The Kruskal Wallis test was used to compare the expression level of hub genes between MM and healthy control samples. The statistical significance was set at p < 0.05.

PCR Validation of Hub Genes

1) Patient and tissue samples

(P) Patient selection: MM patients who were hospitalized in the Department of Hematology, Zhongshan Hospital Affiliated to Xiamen University from August 2021 to April 2022 were included. Including 10 newly diagnosed MM patients, 10 regularly treated MM patients, and 10 healthy control donors.

(1) Interventional intervention plan: All patients in our hospital adopt a chemotherapy plan mainly consisting of lenalidomide, bortezomib, and dexamethasone.

(C) Control comparison: 10 healthy donors were selected as control group 1, 10 newly diagnosed MM patients as control group 2, and 10 blood samples from MM patients before and after standardized treatment were collected as control groups 3 and 4.

(O) – Results: Compare changes in RNA expression levels (qRT-PCR).

(T) – Time: Results will be measured after 1–3 months of standardized treatment.

In the study, 3 mL of heparin anticoagulant peripheral blood samples were collected from patients before and after treatment. The serum was isolated within 24 hours and frozen at − 80 ℃. In the study, the use of patient tissue and the acquisition of patient information were approved by the Ethics Committee of Zhongshan Hospital Affiliated to Xiamen University. (We ensured that all subjects obtained informed consent before the experiment was enrolled. The study was conducted in accordance with the principles of the declaration of Helsinki.)

2) Detection of RNA expression level by qRT-PCR

RNA extraction and quantitative polymerase chain reaction were used to isolate RNA from fresh MM tissue samples using a TRIZOL reagent (Roche). Complementary DNA (cDNA) was synthesized by reverse transcription using the HiFiScript kit (Invitrogen). Quantitative polymerase chain reaction was performed on TNFSF11 (RANKL) and GAPDH using SYBR Green qPCR Master Kit (Roche). GAPDH was selected as the internal reference gene. Collect and organize data and then draw a figure.

Acknowledgements

All authors would like to thank the patients and their families for their contribution to this study. All authors would like to thank the GEO database.

Disclosure

The author reports no conflicts of interest in this work.

Author contribution

Xinge Sheng: Conceptualization, Methodology, Data curation, Formal analysis and Writing manuscript. Xiuli Hong: Methodology, Data curation, Formal analysis and Writing manuscript. Jingyuan Lu: Data curation. Fangfang Huang: Methodology. 1,2; Huilan Deng: Formal analysis. Quanyi Lu: Revising manuscript critically for intellectual content. All authors have read and agreed to the published version of the manuscript.

Ethics Approval and Consent to Participate

The study was conducted with approval from the Ethics Committee of Zhongshan Hospital Affiliated to Xiamen University. GEO belongs to public databases. Users can download relevant data for free for research and publish relevant articles. Our study is based on open-source data and therefore has no other conflicts of interest.

Financial support

The research was funded by the Natural Science Foundation of Fujian(Youth Innovation Project) (2020J05291) and the National Natural Science Foundation of China (Youth Project) (82000171).

Patient and tissue samples

All samples were from the Department of Hematology, Zhongshan Hospital, Xiamen University.

Data availability statement

We would like to thank the staff of the GEO database for providing open-source and freely available data. Publicly available datasets were analyzed in this study. This data can be found here: GEO data base, accession number: GSE6651; GSE47552; GSE24870 and GSE146649. (https://www.ncbi.nlm.nih.gov/geo/).

Rajkumar S. V., Dimopoulos M. A., Palumbo A., et al.International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma.Lancet Oncol.2014;15;e538-48.
Kyle R. A., Therneau T. M., Rajkumar S. V., et al.A long-term study of prognosis in monoclonal gammopathy of undetermined significance.N Engl J Med.2002;346;564-9.
Kyle R. A. and Rajkumar S. V.Management of monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM).Oncology (Williston Park).2011;25;578-86.
Greipp P. R., San Miguel J., Durie B. G., et al.International staging system for multiple myeloma.J Clin Oncol.2005;23;3412-20.
Stewart A. K., Rajkumar S. V., Dimopoulos M. A., et al.Carfilzomib, lenalidomide, and dexamethasone for relapsed multiple myeloma.N Engl J Med.2015;372;142-52.
Hinshaw D. C. and Shevde L. A.The Tumor Microenvironment Innately Modulates Cancer Progression.Cancer Res.2019;79;4557-4566.
Wang Y., Wang M., Wu H. X., et al.Advancing to the era of cancer immunotherapy.Cancer Commun (Lond).2021;41;803-829.
Sharma P., Siddiqui B. A., Anandhan S., et al.The Next Decade of Immune Checkpoint Therapy.Cancer Discov.2021;11;838-857.
Favaloro J., Liyadipitiya T., Brown R., et al.Myeloid derived suppressor cells are numerically, functionally and phenotypically different in patients with multiple myeloma.Leuk Lymphoma.2014;55;2893-900.
Kawano Y., Roccaro A. M., Ghobrial I. M., et al.Multiple Myeloma and the Immune Microenvironment.Curr Cancer Drug Targets.2017;17;806-818.
Görgün G. T., Whitehill G., Anderson J. L., et al.Tumor-promoting immune-suppressive myeloid-derived suppressor cells in the multiple myeloma microenvironment in humans.Blood.2013;121;2975-87.
Subramanian A., Tamayo P., Mootha V. K., et al.Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci U S A.2005;102;15545-50.
Charoentong P., Finotello F., Angelova M., et al.Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.Cell Rep.2017;18;248-262.
Kazandjian D.Multiple myeloma epidemiology and survival: A unique malignancy.Semin Oncol.2016;43;676-681.
Shah U. A. and Mailankody S.Emerging immunotherapies in multiple myeloma.Bmj.2020;370;m3176.
Swamydas M., Murphy E. V., Ignatz-Hoover J. J., et al.Deciphering mechanisms of immune escape to inform immunotherapeutic strategies in multiple myeloma.J Hematol Oncol.2022;15;17.
Yang C. Y., Chanalaris A. and Troeberg L.ADAMTS and ADAM metalloproteinases in osteoarthritis - looking beyond the 'usual suspects'.Osteoarthritis Cartilage.2017;25;1000-1009.
Gómez-Gaviro M., Domínguez-Luis M., Canchado J., et al.Expression and regulation of the metalloproteinase ADAM-8 during human neutrophil pathophysiological activation and its catalytic activity on L-selectin shedding.J Immunol.2007;178;8053-63.
Jaworek C., Verel-Yilmaz Y., Driesch S., et al.Cohort Analysis of ADAM8 Expression in the PDAC Tumor Stroma.J Pers Med.2021;11;
Zack M. D., Malfait A. M., Skepner A. P., et al.ADAM-8 isolated from human osteoarthritic chondrocytes cleaves fibronectin at Ala(271).Arthritis Rheum.2009;60;2704-13.
Ainola M., Li T. F., Mandelin J., et al.Involvement of a disintegrin and a metalloproteinase 8 (ADAM8) in osteoclastogenesis and pathological bone destruction.Ann Rheum Dis.2009;68;427-34.
Choi S. J., Han J. H. and Roodman G. D.ADAM8: a novel osteoclast stimulating factor.J Bone Miner Res.2001;16;814-22.
Cooper N. R., Bradt B. M., Rhim J. S., et al.CR2 complement receptor.J Invest Dermatol.1990;94;112s-117s.
Carroll M. C.The complement system in regulation of adaptive immunity.Nat Immunol.2004;5;981-6.
Thorarinsdottir K., Camponeschi A., Jonsson C., et al.CD21(-/low) B cells associate with joint damage in rheumatoid arthritis patients.Scand J Immunol.2019;90;e12792.
Nichols E. M., Jones R., Watson R., et al.A CD21 low phenotype, with no evidence of autoantibodies to complement proteins, is consistent with a poor prognosis in CLL.Oncotarget.2015;6;32669-80.
Wang J., Lo J. C., Foster A., et al.The regulation of T cell homeostasis and autoimmunity by T cell-derived LIGHT.J Clin Invest.2001;108;1771-80.
Brunetti G., Rizzi R., Oranger A., et al.LIGHT/TNFSF14 increases osteoclastogenesis and decreases osteoblastogenesis in multiple myeloma-bone disease.Oncotarget.2014;5;12950-67.
Brunetti G., Rizzi R., Storlino G., et al.LIGHT/TNFSF14 as a New Biomarker of Bone Disease in Multiple Myeloma Patients Experiencing Therapeutic Regimens.Front Immunol.2018;9;2459.
Zhang N. and Bevan M. J.CD8(+) T cells: foot soldiers of the immune system.Immunity.2011;35;161-8.
Lee J. S. and Ruppin E.Multiomics Prediction of Response Rates to Therapies to Inhibit Programmed Cell Death 1 and Programmed Cell Death 1 Ligand 1.JAMA Oncol.2019;5;1614-1618.
Guillerey C., Harjunpää H., Carrié N., et al.TIGIT immune checkpoint blockade restores CD8(+) T-cell immunity against multiple myeloma.Blood.2018;132;1689-1694.
Gordon S. and Taylor P. R.Monocyte and macrophage heterogeneity.Nat Rev Immunol.2005;5;953-64.
Yao Y., Cai X., Ren F., et al.The Macrophage-Osteoclast Axis in Osteoimmunity and Osteo-Related Diseases.Front Immunol.2021;12;664871.
Gambari L., Grassi F., Roseti L., et al.Learning from Monocyte-Macrophage Fusion and Multinucleation: Potential Therapeutic Targets for Osteoporosis and Rheumatoid Arthritis.Int J Mol Sci.2020;21;
Zheng Y., Cai Z., Wang S., et al.Macrophages are an abundant component of myeloma microenvironment and protect myeloma cells from chemotherapy drug-induced apoptosis.Blood.2009;114;3625-8.
Chauhan D., Singh A. V., Brahmandam M., et al.Functional interaction of plasmacytoid dendritic cells with multiple myeloma cells: a therapeutic target.Cancer Cell.2009;16;309-23.
Ray A., Tian Z., Das D. S., et al.A novel TLR-9 agonist C792 inhibits plasmacytoid dendritic cell-induced myeloma cell growth and enhance cytotoxicity of bortezomib.Leukemia.2014;28;1716-24.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Identification of Immune-Related Hub Genes in Multiple Myeloma

Status:

Version 1

Abstract

Figures

Introduction

Results

Identification of Gene Co-expression Module

Calculation of Module-Trait Correlations

Analysis of Immune Infiltration in Multiple Myeloma

Identification and Functional Enrichment Analysis of Immune-key Genes

Screening of Immune-Related Hub Genes and Correlation Analysis of Hub Genes and Immune cells

ROC Curve Analysis of Immune-Related Hub Genes

Validation of Immune-Related Hub Genes

Discussion

Conclusion

Materials and Methods

Data Processing

Weighted Gene Co-expression Network Analysis

Immune Infiltration Analysis

Functional Enrichment Analysis of Immune-Key Genes

Identification of Immune-Related Hub Genes by LASSO Regression Analysis

Validation of Hub Genes

1) Patient and tissue samples

2) Detection of RNA expression level by qRT-PCR

Declarations

Acknowledgements

Disclosure

Author contribution

Ethics Approval and Consent to Participate

Financial support

Patient and tissue samples

Data availability statement

References

Additional Declarations

Status:

Version 1