Prognostic value of fatty acid metabolism-related signature and integrated analysis of the immune microenvironment in multiple myeloma

Prognostic value of fatty acid metabolism-related signature and integrated analysis of the immune microenvironment in multiple myeloma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic value of fatty acid metabolism-related signature and integrated analysis of the immune microenvironment in multiple myeloma Yang Yu, Feifei Che This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6904083/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Nov, 2025 Read the published version in BMC Cancer → Version 1 posted 10 You are reading this latest preprint version Abstract Objective : Exploring fatty acid metabolism-related genes and features to predict survival outcomes in patients with multiple myeloma. Methods : Transcriptional, survival, and clinicopathologic data of MM patients were downloaded from the GEO and MMRF dataset. Fatty acid-related genes were screened by WGCNA, and one-way cox analysis was performed to identify genes associated with survival. Lasso regression analysis was then performed to construct fatty acid metabolism-related gene characteristics and risk scores. In addition, a nomogram model containing risk scores was constructed to guide clinical decision-making. We also performed immune infiltration analysis and functional analysis to deeply explore the differences between high and low risk groups. Meanwhile, qPCR was conducted on BMMCs from 10 newly diagnosed MM patients and 10 healthy controls to validate the expression of CCNA2 , KIF11 , and NUSAP1 . Results : In total, 37 prognosis-related FMGs genes were identified. Among them, 16 genes were used to construct lasso regression models. KM analysis showed that high-risk patients had poorer prognosis (training set: P < 0.001; test set: P < 0.05). The area under the ROC curve was 0.787. Immunoscape analysis showed that high-risk patients had an immunosuppressive microenvironment. Functional enrichment studies confirmed that high-risk patients had increased abnormalities in cell cycle, aging and metabolic processes. The qPCR analysis revealed CCNA2 , KIF11 , and NUSAP1 up-regulated in MM patients. Conclusion : We identified 37 survival-associated FMGs in MM patients. Our results also suggest that survival-associated traits based on these genes are potentially robust prognostic biomarkers for MM patients. Multiple myeloma Fatty acid metabolism Prognosis Immune infiltration Biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Multiple myeloma (MM) is a plasma cell tumor with heterogeneous prognostic outcomes[ 1 ]. Recent therapeutic advances, such as the introduction of immunomodulatory drugs (IMiD), proteasome inhibitors (PIs), and anti-CD38 antibodies, have dramatically improved the prognosis of MM patients[ 2 ]. However, approximately 10–20% of patients still die prematurely within 2–3 years of diagnosis; these cases are usually defined as high-risk MM[ 3 ]. Due to the marked heterogeneity in the pathogenesis, clinical manifestations and prognosis of MM patients, this emphasizes the need to identify new molecules to provide prognostic biomarkers and/or therapeutic targets. One of the factors affecting the progression and prognosis of MM is the metabolic reprogramming of tumor cells and surrounding stromal cells, especially bone marrow adipocytes (BMAds)[ 4 – 6 ]. BMAds are the most abundant cells in the bone marrow, accounting for 70% of the cell volume[ 7 ]. Bone marrow adipocytes secrete various adipokines and cytokines, regulate the immune system, and provide metabolic substrates such as free fatty acids, thereby supporting MM growth and survival[ 8 ]. Free fatty acids are the main energy source for MM cells, and their uptake and oxidation are regulated by the expression of fatty acid transporters (FATPs) and fatty acid binding proteins (FABPs) in the plasma membrane and cytoplasm of MM cells, respectively[ 9 ]. The levels of FATPs and FABPs in MM cells vary according to the stage of the disease, genetic alterations and microenvironmental cues[ 10 ]. In addition, the effects of FFAs on MM cells are dose-dependent, stimulating cell proliferation at low concentrations and inducing lipotoxicity and cell death at high concentrations[ 4 ]. Thus, fatty acid metabolism (FAM) plays an important role in MM progression. Since it is not clear how FATP mediates FA uptake and which FATPs have FA uptake and acyl-coenzyme A synthetase activity, more research on the role of FAM in cancer is needed. Here, we performed comprehensive bioinformatics analyses, including RNA sequencing, expression analyses, survival analyses, immune microenvironment analyses, and functional interactions analyses. Based on these analyses, we constructed a prognostic signature associated with FAM in MM. Based on the signature, MM patients were categorized into high-risk and low-risk groups. Survival analysis based on risk grouping is a good method to assess the prognosis of MM patients. The analysis of the immune microenvironment may provide a reference for understanding the immune mechanism of MM. Overall, our study may provide new insights into therapeutic strategies for MM. Methods Data preprocessing MM (GSE4581, GSE136337) gene expression data and clinical characteristics were obtained from Gene Expression Omnibus (GEO). We obtained 786 MM high-throughput sequencing data with corresponding prognostic data from the MMRF CoMMpass database ( https://portal.gdc.cancer.gov/ ). Of these, GSE136337 contains 426 samples as a training set to model prognostic risk scores. GSE4581 contains 162 samples and MMRF cohort as a validation cohort to assess the accuracy of risk scores on MM prognosis. Acquisition of genes related to fatty acid metabolism The ssGSEA[ 11 ] is commonly used to calculate the enrichment score for a specific gene set in each sample, which represents the absolute enrichment of that gene set in each sample. In this study, ssGSEA was used to calculate enrichment scores for fatty acid metabolism in each sample. Weighted gene co-expression network analysis (WGCNA)[ 12 ] is a systems biology approach for characterizing patterns of gene association between different samples. It can be used to identify highly covariant sets of genes and to identify candidate biomarker genes based on the interconnectivity of the gene sets as well as the association between the gene sets and the phenotype. The enriched fraction of fatty acid metabolism is used as a phenotype to identify the gene set most associated with it. Construction and evaluation of prognostic models Univariate COX analysis was performed to identify fatty acid metabolism-related genes associated with prognosis ( P < 0.05). Subsequently, LASSO regression and tenfold cross-validation were used to further identify key genes affecting patient outcomes. Finally, prognostic models were constructed based on these genes and their coefficients. According to the predictive model, the risk score could be calculated using the following formula: $$\:\text{r}\text{i}\text{s}\text{k}\:\text{s}\text{c}\text{o}\text{r}\text{e}=\sum\:\beta\:i\ast\:Ei$$ βi indicates the relative regression coefficients, and Ei means the expression level of each gene. Model coefficients are shown in Table S1 . Patients in all cohorts were categorized into high-risk and low-risk groups based on median values. The validation set was grouped based on the same scoring. Survival analysis was performed on both groups and the accuracy of the model was assessed. Survival curves for high- and low-risk patients in the training set, test set, and total sample were plotted according to the R package "survminer". Time-dependent subject operating characteristic curves (ROC) were used to assess the predictive power of the model. Independent prognostic analysis and construction of Nomogram Univariate and multivariate COX regression analyses were performed to assess whether clinicopathologic characteristics (age, sex, LDH, albumin (ALB), β2-microglobulin (B2M), ISS staging, R-ISS staging) and genetic risk scores were independent prognostic factors in MM patients. All identified independent prognostic factors were included and a column-line graph was constructed to predict the probability of survival. Calibration curves, ROC curves, and decision curves were used to assess the discriminatory power of the column-line diagrams. Functional pathway enrichment analysis For differential genes in high and low risk groups, R package ‘clusterProfiler’ [ 13 ]was utilized to investigate their function and pathway enrichment by gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The cutoff criterion was a false discovery rate (FDR) < 0.05. Gene variant set analysis (GSVA) was used to compare differences in KEGG pathways between high and low risk groups. Analysis of tumor immune microenvironment CIBERSORT[ 14 ] was applied to calculate the abundance of different immune cell infiltrates in all MM samples in GSE136337. The abundance of different immunomodulatory pathways in all MM samples was calculated using ssGSEA. the Wilcoxon rank sum test was used to assess the differences in immune cell infiltration and pathways between high and low risk groups. Bone Marrow Mononuclear Cell (BMMCs) Collection and qRT-PCR Bone marrow aspirates were collected from 10 healthy volunteers and 10 recently diagnosed MM patients in Sichuan Provincial People’s Hospital from 2023 to 2024. Written informed consent was obtained from each study patient or healthy volunteer. Fasting 2–3 mL bone marrow aspirates were collected from patients and healthy controls and BMMCs were extracted using Ficoll-Paque PLUS (Cytiva). Total RNA was extracted from cells using Trizol (Invitrogen) and quantified using Nanodrop 2000 (ThermoFisher Scientific). PrimeScript™ RT kit and gDNA Eraser (TaKaRa Bio Inc., Kusatsu, Japan) were used for reverse transcription. Reverse transcription was performed according to the manufacturer's Instructions. Reaction mixtures were prepared using the SYBR® Premix Ex Taq™ kit (TaKaRa Bio Inc.) and quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed. The 2 −ΔΔCt method was used to analyze the relative expression of the hub genes in the two groups and set at 1.0 in the healthy control group. Primer information uploaded to Table S2 . The study was approved in writing by the Ethics Committee of Sichuan Provincial People's Hospital and Sichuan Academy of Medical Sciences (2023 − 577). Statistical analysis All raw data were processed and analyzed by the R program version 4.2.2. Differences between groups were analyzed by T test or Wilcoxon test. The Kaplan-Meier method and log-rank test were used to establish the survival curves and survival differences between the two groups. p-value < 0.05 was considered statistically significant. Results WGCNA screening of fatty acid metabolism-related genes in MM patients According to the steps in the method, a scale-free network was constructed by WGCNA with the soft threshold set to 10 (Fig. 1 A), and a total of 9 core modules were identified (Fig. 1 B). ssGSEA algorithm calculated fatty acid metabolism scores and performed correlation analysis with the 9 core modules, and an ultra-high correlation between the green module and fatty acid metabolism was identified (R = 0.61; Fig. 1 C) and the module genes were considered as fatty acid metabolism-related genes. A total of 417 key genes were obtained by screening through two indicators, module membership > 0.7 and gene significance > 0.5 (Fig. 1 D). Further univariate cox analysis screened 37 FMGs associated with prognosis for subsequent analysis (Fig. 1 E). Construction and validation of prognostic signatures for FMGs By LASSO (minimizing lambda) to establish the prognostic characteristics of 16 FMGs (PPID, CENPC, DBF4, SUB1, TWISTNB, TMF1, RRP15, MFSD8, RALA, FAM114A2, RBBP8, FMR1, YWHAQ, EPS15, MRPL39, and TRMT5) (Fig. 2 A). Risk scores were calculated for each MM patient using model coefficients. BC patients were categorized into high- and low-risk groups based on median values. The PCA clustering distributions of the high- and low-risk groups were significantly different (Fig. 2 B). The OS was significantly worse in the high-risk group ( P < 0.05; Fig. 2 C), which was also validated in the GSE4581 cohort ( P < 0.05; Fig. 2 D) and in the MMRF cohort ( P < 0.05; Fig. 2 E). 1-, 3-, and 5-year ROC areas under the curve of 0.729, 0.721, and 0.787, respectively, showed good predictive ability in train set (Fig. 2 F). There was also good diagnostic performance in two independent validation sets (Fig. 2 G-H), where GSE4581 could only be assessed for 1-year diagnostic performance due to the short follow-up period. Association analysis of pathologic markers with risk scores To determine whether the model we constructed could serve as an independent prognostic factor for MM patients. We performed Cox regression analysis for clinicopathologic characteristics and risk scores. As shown in Fig. 3 A-B, univariate Cox regression analysis showed significant correlations between OS and age, Albumin, B2M, LDH, ISS, R-ISS, and risk scores in MM patients ( P < 0.05). However, after multivariate regression analysis, we found that only age, ISS, R-ISS and risk score were independent prognostic factors significantly associated with OS ( P < 0.05). Further, we assessed the correlation between risk scores and clinicopathologic characteristics. Risk scores were correlated with gender, ISS stage, and R-ISS stage of MM patients ( P < 0.05; Fig. 3 D-F), but not with characteristics such as age (Fig. 3 C). ROC curves showed that risk scores were more accurate compared to other clinicopathologic features in the 5-year prognosis of patients diagnosed with MM (Fig. 3 G). Nomogram construction and clinical value To support clinical applications, we created column-line plots based on age, Albumin, B2M, LDH, ISS, R-ISS, and risk scores to predict 1-, 3-, and 5-year survival in MM patients (Fig. 4 A). The calibration curve showed that the survival predicted by the plot was consistent with the observed survival (Fig. 4 B). The column-line graph modeled a 5-year AUC value of 0.802, which was superior to the risk score alone (Fig. 4 C). Similarly, decision curve analysis found that patients benefited most from clinical interventions with the column-line graph model compared with the risk score ( Fig. 4 D). Risk Score-Related Immune Pathways and Functional Enrichment Analysis The immune microenvironment plays an important role in patient prognosis. The high-risk group had higher proportions of Tregs and macrophage M2 and a lower proportion of plasma cells (Fig. 5 A). In addition, we found lower levels of HLA and T-cell co-stimulatory processes in the high-risk group (Fig. 5 B). This suggests that high-risk patients have a higher immunosuppressive microenvironment. We performed differential analysis of high and low risk groups to identify significantly different physiological processes. 95 differential genes were significantly enriched for biological processes such as nuclear division, chromosome segregation (Fig. 6 A). Similarly, KEGG enrichment analysis suggested association with pathways such as Cell cycle, Oocyte meiosis, Cellular senescence, etc. (Fig. 6 B). GSVA analysis indicated that cell cycle, genomic DNA replication, oxidative phosphorylation were significantly active regulatory mechanisms in high-risk patients (Fig. 6 C). Regulatory molecular networks in high- and low-risk patients To further explore the regulatory mechanisms among risk groups, we entered 95 differential genes into the String database for protein interaction network construction (Fig. 7 A). The top ten core genes in the network were further calculated by the cytohubba plug-in in cytoscape software, which uses a centroid metric to highlight key modulators in the network. (Fig. 7 B). NUSAP1 , CCNA2 , KIF11 were used as the top three core genes for the subsequent expression of prognostic value verification. The expression of NUSAP1 , CCNA2 , and KIF11 was increased in high-risk patients ( P < 0.05; Fig. 7 C) and was associated with poorer prognosis in MM patients ( P < 0.001; Fig. 7 D). Clinical correlation analysis showed that high expression of these three genes was associated with more severe R-ISS staging (Fig. 7 E). Subsequent quantitative PCR (qPCR) analysis of BMMCs confirmed significant differential expression of NUSAP1 , CCNA2 , and KIF11 . ( P < 0.05; Fig. 7 F). Discussion MM is known to be a highly heterogeneous cancer. The prognosis and treatment response of patients with different molecular profiles vary widely[ 15 , 16 ]. Here, we established a risk signature of 16 FAM-related genes to predict the prognosis of MM patients. First, our signature successfully distinguished patients with different prognoses and its predictive efficiency were generalizable across cohorts. Second, patients in the high-risk group had a higher R-ISS classification and a higher level of immunosuppressive microenvironment. Third, cell cycle, aging and metabolic process abnormalities were increased among risk patients. An external clinical cohort validated the results of the bioinformatics analysis. Altered cellular metabolism is a common feature of cancers, including myeloma. Interactions between MM cells and BM stromal cells are likely to affect and be affected by metabolic changes in myeloma and stromal cells. BMA provides a unique stromal cell type for myeloma cells that can interact with myeloma cells and can produce FA from their triglyceride stores, thereby providing nutrients to neighboring myeloma or other tumor cells with nutrients[ 10 ]. Therefore, targeting fatty acid metabolism has great potential to limit MM progression. Screening 16 of these FMGs by machine learning methods to construct prognostic risk signatures proved to be a reliable prognostic model for MM patients. First, using KM analysis, the survival value of the signature was confirmed in both the training and test sets. Our results revealed significant statistical associations (training set: P < 0.001; test set: P < 0.05). Secondly, six clinical indicators with significant survival value were identified in MM patients, and the risk scores of signatures were positively correlated in three of these clinical indicators. Further multifactorial cox analysis confirmed that risk score was an independent prognostic indicator for the prognosis of MM patients. Thirdly, ROC analysis showed that the AUC value of the signed risk score was as high as 0.787. For further application in clinical guidance, we plotted a column-line graph of the clinical indicators and the risk score, and its AUC increased to 0.802, which could effectively provide prognostic guidance. FISH is widely used to identify cytogenetic abnormalities in MM. It helps to categorize patients into different risk categories based on the presence of the aforementioned high-risk markers. Many studies have shown that the addition of more FISH markers or the use of SNP arrays can improve prognosis [ 17 , 18 ]. However, new predictive scores have so far lacked prospective validation and broad applicability. Consequently, there is a great deal of variability regarding which culture conditions and FISH probes should be used to characterize different chromosomal abnormalities [ 19 ]. Moreover, this variation stems from the standard practices of each center as well as national and international guidelines, which may vary slightly. In conclusion, there is still a lack of internationally standardized criteria for determining the prognosis of patients with MM. Clinical practice has been relatively slow to embrace diagnostic techniques such as gene chips or high-throughput sequencing, which could complement standard approaches based on morphology, FISH, and flow cytometry. Currently, tools such as the GEP70, EMC-92 classifier, and the UAMS 70 gene model are used to classify patients into different risk categories. The inherent heterogeneity of MM and the diversity of treatment options likely impede the rapid identification of new prognostic and predictive markers, and there is currently no consensus as to whether and how gene expression profiling should be utilized to redefine high-risk diseases. It is now well established that gene expression profiling provides a comprehensive understanding of the genetic characteristics of MM and helps to predict patient prognosis and response to treatment more accurately than traditional methods. Our risk score, confirmed by multivariate Cox regression analysis, can be used as an independent prognostic factor for MM, providing additional value beyond traditional metrics such as age and ISS staging, and furthermore, our model combines gene expression data with clinical features to improve the accuracy of risk stratification and prognostic judgment. With increasing research evidence of the role of fatty acids in the development of MM in patients, it was necessary to analyze fatty acid genes in this study. We performed a series of analyses to identify these genes. Sixteen FAMs were identified that were strongly associated with the prognosis of MM patients. Consistent with these results, the analysis by Xinguo Wang et al. also suggested that DBF4 may be a potential therapeutic biomarker for MM[ 20 ]. FMR1 was identified as a potential prognostic biomarker for MM[ 21 ]. High RBBP8 expression is associated with poorer survival and relapse in plasma cell myeloma. RBBP8 can be considered as an independent prognostic factor for MM[ 22 ]. RALA, also known as Ras-related protein Ral-A, is a small GTPase that regulates various cellular processes such as vesicle trafficking, cytoskeleton dynamics, and gene expression. Inhibition of Ral results in impaired SDF-1-induced migration of B cells and MM cells[ 23 ]. Targeting the YWHAQ complex inhibits cell proliferation[ 24 ] and contributes to proteasome inhibitor sensitivity in multiple myeloma[ 25 ]. Cyclin A2 belongs to the highly conserved family of cell cycle proteins and functions as a regulator of CDK kinase. Cyclin A2 binds and activates CDC2 or CDK2 kinase to promote the G1/S and G2/M transitions of the cell cycle. During G2/M, cyclin A2 is phosphorylated. It has been shown that CCNA2 is significantly downregulated when pan BCL2 inhibitors and dexamethasone (LDA) are combined [ 26 ]. This suggests that CCNA2 may serve as a potential therapeutic target for MM. The KIF11 gene encodes Eg5, a positively oriented microtubule motor, which is an important mitotic kinesin that plays a critical role in the formation and maintenance of the spindle. Eg5 has been implicated in tumorigenesis and has been found to be overexpressed in a variety of cancer tissues. The current study has demonstrated that Filanesib, a KIF11 /Eg5 inhibitor, upregulates Hsp70 through the phosphatidylinositol 3-kinase/Akt pathway in multiple myeloma cells [ 27 ]. Clinical studies have shown that Filanesib can be used to treat multiple myeloma and enhance the activity of pomalidomide and dexamethasone in multiple myeloma [ 28 – 30 ]. NUSAP1 has not been reported in MM, but has been shown to promote cancer progression, metastasis and drug resistance in several studies [ 31 – 33 ]. Therefore, our results build a theoretical foundation for the study of NUSAP1 in MM. Metabolic reprogramming usually alters immunocompetence and immune components in cancer tissues[ 34 ]. Interdisciplinary studies examining metabolic reprogramming and immune function are increasingly performed in cancer research. Using CIBERSORT, we found significant differences in immune composition between low-risk and high-risk scoring groups. In particular, we found that the high-risk scoring group had an intense immunosuppressive microenvironment, such as a significantly higher proportion of Treg as well as macrophage M2 than the low-risk scoring group. Treg cells are a type of immune cells that can suppress the activity of other immune cells and prevent autoimmune reactions. They are also involved in the regulation of anti-tumor immunity, and their number and function can be altered in various cancers, including MM. Several studies have shown that patients with MM have increased Treg cells in the bone marrow compared to healthy individuals or patients with other plasma cell disorders. Increased Treg cells may suppress the anti-MM immune response and may be associated with disease stage, risk level, and prognosis in patients with MM. Some studies also suggest that Treg cells may be involved in the development and progression of MM because they can express a gene called Foxp3, which is also overexpressed in MM cells[ 35 ]. Macrophage M2 promotes the growth and survival of myeloma cells and inhibits their apoptosis. Macrophage M2 also inhibits anti-tumor immune responses in the tumor microenvironment and promotes angiogenesis[ 36 ]. Therefore, macrophage M2 is considered a potential therapeutic target for multiple myeloma. Our results suggest potential connections and provide new directions for future research. Abbreviations MM : Multiple Myeloma WGCNA : Weighted Gene Co-expression Network Analysis ssGSEA : Single-Sample Gene Set Enrichment Analysis KM : Kaplan-Meier ROC : Receiver Operating Characteristic qPCR : Quantitative Polymerase Chain Reaction ISS : International Staging System R-ISS : Revised International Staging System Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Sichuan Provincial People's Hospital and Sichuan Academy of Medical Sciences (2023-577). Written informed consent was obtained from all patients. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Science &Technology Department of Sichuan Province, clinical research on the treatment of multiple myeloma with sequential administration of Daratumumab after autologous hematopoietic stem cell transplantation (Grant ID: 2022YFS0367). Authors' contributions Conceptualization: Feifei Che; Formal analysis: Yang Yu; Supervision: Feifei Che; Writing–original draft: Yang Yu; Writing–review and editing: Feifei Che. Acknowledgments Not applicable. Author information Authors and Affiliations Department of General Medicine, Affiliated Hospital of Weifang Medical University, Weifang, Shandong, China. 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J Exp Clin Cancer Res 2017, 36 (1):127. Xia L, Oyang L, Lin J, Tan S, Han Y, Wu N, Yi P, Tang L, Pan Q, Rao S et al : The cancer metabolic reprogramming and immune response . Mol Cancer 2021, 20 (1):28. Braga WM, Atanackovic D, Colleoni GW: The role of regulatory T cells and TH17 cells in multiple myeloma . Clin Dev Immunol 2012, 2012 :293479. Opperman KS, Vandyke K, Psaltis PJ, Noll JE, Zannettino ACW: Macrophages in multiple myeloma: key roles and therapeutic strategies . Cancer Metastasis Rev 2021, 40 (1):273-284. Additional Declarations No competing interests reported. Supplementary Files supplementaryinformationchefeifei.docx Cite Share Download PDF Status: Published Journal Publication published 08 Nov, 2025 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 21 Jul, 2025 Reviews received at journal 17 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviews received at journal 25 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers invited by journal 25 Jun, 2025 Editor assigned by journal 22 Jun, 2025 Editor invited by journal 20 Jun, 2025 Submission checks completed at journal 19 Jun, 2025 First submitted to journal 19 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6904083","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476533968,"identity":"d5f1bc17-8f8b-4a06-a9d2-4fd7e48bf994","order_by":0,"name":"Yang Yu","email":"","orcid":"","institution":"Affiliated Hospital of Weifang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yu","suffix":""},{"id":476533969,"identity":"d2754109-ae7f-40a4-93ae-e4ac4d8e0337","order_by":1,"name":"Feifei Che","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIie3PsQqCQBzH8b8cXMvZbWEE+gp/cWjpYZSgSajRLcVwCloNX6Kx8URo8gGCGgpf4KKlwSGai7y2hvvM/y8//gCa9o8sAAloAyVECBmpJUYO6AHt0aDMa+UEPADOvMpcKRS8SMtYLtDuEyaFGYPDB6Jj5HwIkhzRo8TcieEe3G3hf0/QCscNwzbIXolbg4+nzmR+S1vEZUbYRQSZUhIaKSD6lDAQpUpiHWduskZ0M0KxjGur+xeeT6/xo0XH2VTNvY0mDh91JG+rv51rmqZpnz0BoWFDEEa/C3AAAAAASUVORK5CYII=","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Feifei","middleName":"","lastName":"Che","suffix":""}],"badges":[],"createdAt":"2025-06-16 09:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6904083/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6904083/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-025-14886-3","type":"published","date":"2025-11-08T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85754748,"identity":"7da014cb-cdb6-44a7-aef2-07b9694f930b","added_by":"auto","created_at":"2025-07-01 10:39:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79814,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Identification of FMGs-related genes by WGCNA analysis in GSE136337. \u003c/strong\u003e(A) Optimal soft threshold screening. (B) Identification of gene modules by dynamic shearing. (C) Identification of fatty acid metabolism-related modules by WGCNA analysis. (D) MM vs GS analysis to identify core FMGs. (E) Univariate cox analysis of 37 FMGs.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6904083/v1/517f73281cf84beec1fe2e95.png"},{"id":85756164,"identity":"8554a397-b21a-4895-9ba4-97de8a0067f7","added_by":"auto","created_at":"2025-07-01 10:47:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of FMGs-related model. \u003c/strong\u003e(A) Lasso regression to screen key variables in GSE136337. (B) PCA clustering of high and low risk patients. K-M survival curves showing the prognostic value of risk scores in (C) GSE136337, (D) GSE4581 and (E) MMRF cohorts. ROC curves were calculated to predict the predictive accuracy of risk score in (F) GSE136337, (G) GSE4581 and (H) MMRF cohorts.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6904083/v1/946d050263f3aa13beb38d9a.png"},{"id":85753331,"identity":"eff828f2-b80d-4da1-aca4-a20ca9579c05","added_by":"auto","created_at":"2025-07-01 10:31:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25366,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of risk scores and clinical characteristics with prognosis. \u003c/strong\u003eUnivariate cox analysis (A) and multivariate cox analysis (B) were used for independent prognostic analysis of risk scores and clinical characteristics. Correlation of risk score with (C) age, (D) sex, (E) ISS staging, (F) R-ISS staging. (G) Combined ROC curves for risk scores and clinicopathologic indicators.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6904083/v1/033a499981a55214a52a1267.png"},{"id":85753336,"identity":"49273936-3bbd-440d-bd04-6e1c0346c8c2","added_by":"auto","created_at":"2025-07-01 10:31:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50853,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of nomogram model in GSE136337. \u003c/strong\u003e(A) Nomogram containing risk scores and clinicopathologic indicators. (B) Calibration curve for nomogram. (C) ROC curve of nomogram. (D) Clinical Decision Curves for Column Line Charts and Risk Scores.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6904083/v1/4539290df4bdb581c6d26182.png"},{"id":85753345,"identity":"4b396bb5-ab94-431e-b5ce-fd27a92002b9","added_by":"auto","created_at":"2025-07-01 10:31:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":30763,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis in GSE136337. \u003c/strong\u003e(A) Differences in immune cell subsets between high and low risk groups (Immune cell proportions were estimated using CIBERSORT based on gene expression data). (B) Differences in immune signaling pathways between high and low risk groups.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6904083/v1/de436e33cc256a8be54f9c5f.png"},{"id":85753338,"identity":"38da0b53-35cb-4e85-b2ea-4a3b8f6ce119","added_by":"auto","created_at":"2025-07-01 10:31:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":59912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis in GSE136337. \u003c/strong\u003e(A) GO, and (B) KEGG enrichment analysis of differential genes in high and low risk groups. (C) GSVA analysis of high and low risk groups.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6904083/v1/33013b810dff68c9d3815ff4.png"},{"id":85753340,"identity":"255cbfc9-aac0-4d29-a0bd-4204631f1fb0","added_by":"auto","created_at":"2025-07-01 10:31:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":98098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative analysis of differences between high and low risk patients.\u003c/strong\u003e (A) PPI network map of differential genes in high and low risk groups. (B) cytohubba analysis identifies core genes in PPI networks. Green: non-Top 10 proteins according to cytohubba connectivity calculations. Non-green: Top 10 proteins according to cytohubba connectivity calculations, darker color means more important in the network. (C) Expression of \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eKIF11\u003c/em\u003e, \u003cem\u003eNUSAP1\u003c/em\u003ebetween high and low risk groups. (D) KM survival curves for \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eKIF11\u003c/em\u003e, \u003cem\u003eNUSAP1\u003c/em\u003e in MM patients. (E) Correlation of \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eKIF11\u003c/em\u003e, \u003cem\u003eNUSAP1\u003c/em\u003ewith R-ISS staging in MM patients. (F) qPCR analysis of bone marrow samples showed upregulation of \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eKIF11 \u003c/em\u003eand \u003cem\u003eNUSAP1\u003c/em\u003e in MM patients.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6904083/v1/b8f43b191229005de7d90fa3.png"},{"id":95564239,"identity":"dd471b99-60b0-41db-ad45-26b32bbbdec8","added_by":"auto","created_at":"2025-11-10 16:09:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3069912,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6904083/v1/2b8b2ac0-e51a-403f-994e-6483acc43c57.pdf"},{"id":85753335,"identity":"b662efe1-efb8-48bb-bc81-fedd47a8b4e3","added_by":"auto","created_at":"2025-07-01 10:31:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18699,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryinformationchefeifei.docx","url":"https://assets-eu.researchsquare.com/files/rs-6904083/v1/79ee936615599c887f117b98.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic value of fatty acid metabolism-related signature and integrated analysis of the immune microenvironment in multiple myeloma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple myeloma (MM) is a plasma cell tumor with heterogeneous prognostic outcomes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recent therapeutic advances, such as the introduction of immunomodulatory drugs (IMiD), proteasome inhibitors (PIs), and anti-CD38 antibodies, have dramatically improved the prognosis of MM patients[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, approximately 10\u0026ndash;20% of patients still die prematurely within 2\u0026ndash;3 years of diagnosis; these cases are usually defined as high-risk MM[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Due to the marked heterogeneity in the pathogenesis, clinical manifestations and prognosis of MM patients, this emphasizes the need to identify new molecules to provide prognostic biomarkers and/or therapeutic targets.\u003c/p\u003e \u003cp\u003eOne of the factors affecting the progression and prognosis of MM is the metabolic reprogramming of tumor cells and surrounding stromal cells, especially bone marrow adipocytes (BMAds)[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. BMAds are the most abundant cells in the bone marrow, accounting for 70% of the cell volume[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Bone marrow adipocytes secrete various adipokines and cytokines, regulate the immune system, and provide metabolic substrates such as free fatty acids, thereby supporting MM growth and survival[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Free fatty acids are the main energy source for MM cells, and their uptake and oxidation are regulated by the expression of fatty acid transporters (FATPs) and fatty acid binding proteins (FABPs) in the plasma membrane and cytoplasm of MM cells, respectively[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The levels of FATPs and FABPs in MM cells vary according to the stage of the disease, genetic alterations and microenvironmental cues[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, the effects of FFAs on MM cells are dose-dependent, stimulating cell proliferation at low concentrations and inducing lipotoxicity and cell death at high concentrations[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, fatty acid metabolism (FAM) plays an important role in MM progression. Since it is not clear how FATP mediates FA uptake and which FATPs have FA uptake and acyl-coenzyme A synthetase activity, more research on the role of FAM in cancer is needed.\u003c/p\u003e \u003cp\u003eHere, we performed comprehensive bioinformatics analyses, including RNA sequencing, expression analyses, survival analyses, immune microenvironment analyses, and functional interactions analyses. Based on these analyses, we constructed a prognostic signature associated with FAM in MM. Based on the signature, MM patients were categorized into high-risk and low-risk groups. Survival analysis based on risk grouping is a good method to assess the prognosis of MM patients. The analysis of the immune microenvironment may provide a reference for understanding the immune mechanism of MM. Overall, our study may provide new insights into therapeutic strategies for MM.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData preprocessing\u003c/h2\u003e \u003cp\u003eMM (GSE4581, GSE136337) gene expression data and clinical characteristics were obtained from Gene Expression Omnibus (GEO). We obtained 786 MM high-throughput sequencing data with corresponding prognostic data from the MMRF CoMMpass database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Of these, GSE136337 contains 426 samples as a training set to model prognostic risk scores. GSE4581 contains 162 samples and MMRF cohort as a validation cohort to assess the accuracy of risk scores on MM prognosis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAcquisition of genes related to fatty acid metabolism\u003c/h3\u003e\n\u003cp\u003eThe ssGSEA[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] is commonly used to calculate the enrichment score for a specific gene set in each sample, which represents the absolute enrichment of that gene set in each sample. In this study, ssGSEA was used to calculate enrichment scores for fatty acid metabolism in each sample. Weighted gene co-expression network analysis (WGCNA)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] is a systems biology approach for characterizing patterns of gene association between different samples. It can be used to identify highly covariant sets of genes and to identify candidate biomarker genes based on the interconnectivity of the gene sets as well as the association between the gene sets and the phenotype. The enriched fraction of fatty acid metabolism is used as a phenotype to identify the gene set most associated with it.\u003c/p\u003e\n\u003ch3\u003eConstruction and evaluation of prognostic models\u003c/h3\u003e\n\u003cp\u003eUnivariate COX analysis was performed to identify fatty acid metabolism-related genes associated with prognosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, LASSO regression and tenfold cross-validation were used to further identify key genes affecting patient outcomes. Finally, prognostic models were constructed based on these genes and their coefficients. According to the predictive model, the risk score could be calculated using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{r}\\text{i}\\text{s}\\text{k}\\:\\text{s}\\text{c}\\text{o}\\text{r}\\text{e}=\\sum\\:\\beta\\:i\\ast\\:Ei$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eβi indicates the relative regression coefficients, and Ei means the expression level of each gene. Model coefficients are shown in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003cp\u003ePatients in all cohorts were categorized into high-risk and low-risk groups based on median values. The validation set was grouped based on the same scoring. Survival analysis was performed on both groups and the accuracy of the model was assessed. Survival curves for high- and low-risk patients in the training set, test set, and total sample were plotted according to the R package \"survminer\". Time-dependent subject operating characteristic curves (ROC) were used to assess the predictive power of the model.\u003c/p\u003e\n\u003ch3\u003eIndependent prognostic analysis and construction of Nomogram\u003c/h3\u003e\n\u003cp\u003eUnivariate and multivariate COX regression analyses were performed to assess whether clinicopathologic characteristics (age, sex, LDH, albumin (ALB), β2-microglobulin (B2M), ISS staging, R-ISS staging) and genetic risk scores were independent prognostic factors in MM patients. All identified independent prognostic factors were included and a column-line graph was constructed to predict the probability of survival. Calibration curves, ROC curves, and decision curves were used to assess the discriminatory power of the column-line diagrams.\u003c/p\u003e\n\u003ch3\u003eFunctional pathway enrichment analysis\u003c/h3\u003e\n\u003cp\u003eFor differential genes in high and low risk groups, R package \u0026lsquo;clusterProfiler\u0026rsquo; [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]was utilized to investigate their function and pathway enrichment by gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The cutoff criterion was a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Gene variant set analysis (GSVA) was used to compare differences in KEGG pathways between high and low risk groups.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of tumor immune microenvironment\u003c/h2\u003e \u003cp\u003eCIBERSORT[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] was applied to calculate the abundance of different immune cell infiltrates in all MM samples in GSE136337. The abundance of different immunomodulatory pathways in all MM samples was calculated using ssGSEA. the Wilcoxon rank sum test was used to assess the differences in immune cell infiltration and pathways between high and low risk groups.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBone Marrow Mononuclear Cell (BMMCs) Collection and qRT-PCR\u003c/h3\u003e\n\u003cp\u003eBone marrow aspirates were collected from 10 healthy volunteers and 10 recently diagnosed MM patients in Sichuan Provincial People\u0026rsquo;s Hospital from 2023 to 2024. Written informed consent was obtained from each study patient or healthy volunteer. Fasting 2\u0026ndash;3 mL bone marrow aspirates were collected from patients and healthy controls and BMMCs were extracted using Ficoll-Paque PLUS (Cytiva). Total RNA was extracted from cells using Trizol (Invitrogen) and quantified using Nanodrop 2000 (ThermoFisher Scientific). PrimeScript\u0026trade; RT kit and gDNA Eraser (TaKaRa Bio Inc., Kusatsu, Japan) were used for reverse transcription. Reverse transcription was performed according to the manufacturer's Instructions. Reaction mixtures were prepared using the SYBR\u0026reg; Premix Ex Taq\u0026trade; kit (TaKaRa Bio Inc.) and quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed. The 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method was used to analyze the relative expression of the hub genes in the two groups and set at 1.0 in the healthy control group. Primer information uploaded to \u003cb\u003eTable S2\u003c/b\u003e. The study was approved in writing by the Ethics Committee of Sichuan Provincial People's Hospital and Sichuan Academy of Medical Sciences (2023\u0026thinsp;\u0026minus;\u0026thinsp;577).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll raw data were processed and analyzed by the R program version 4.2.2. Differences between groups were analyzed by T test or Wilcoxon test. The Kaplan-Meier method and log-rank test were used to establish the survival curves and survival differences between the two groups. p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWGCNA screening of fatty acid metabolism-related genes in MM patients\u003c/h2\u003e \u003cp\u003eAccording to the steps in the method, a scale-free network was constructed by WGCNA with the soft threshold set to 10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), and a total of 9 core modules were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). ssGSEA algorithm calculated fatty acid metabolism scores and performed correlation analysis with the 9 core modules, and an ultra-high correlation between the green module and fatty acid metabolism was identified (R\u0026thinsp;=\u0026thinsp;0.61; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) and the module genes were considered as fatty acid metabolism-related genes. A total of 417 key genes were obtained by screening through two indicators, module membership\u0026thinsp;\u0026gt;\u0026thinsp;0.7 and gene significance\u0026thinsp;\u0026gt;\u0026thinsp;0.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Further univariate cox analysis screened 37 FMGs associated with prognosis for subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of prognostic signatures for FMGs\u003c/h2\u003e \u003cp\u003eBy LASSO (minimizing lambda) to establish the prognostic characteristics of 16 FMGs (PPID, CENPC, DBF4, SUB1, TWISTNB, TMF1, RRP15, MFSD8, RALA, FAM114A2, RBBP8, FMR1, YWHAQ, EPS15, MRPL39, and TRMT5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Risk scores were calculated for each MM patient using model coefficients. BC patients were categorized into high- and low-risk groups based on median values. The PCA clustering distributions of the high- and low-risk groups were significantly different (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The OS was significantly worse in the high-risk group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), which was also validated in the GSE4581 cohort (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) and in the MMRF cohort (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). 1-, 3-, and 5-year ROC areas under the curve of 0.729, 0.721, and 0.787, respectively, showed good predictive ability in train set (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). There was also good diagnostic performance in two independent validation sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-H), where GSE4581 could only be assessed for 1-year diagnostic performance due to the short follow-up period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociation analysis of pathologic markers with risk scores\u003c/h2\u003e \u003cp\u003eTo determine whether the model we constructed could serve as an independent prognostic factor for MM patients. We performed Cox regression analysis for clinicopathologic characteristics and risk scores. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B, univariate Cox regression analysis showed significant correlations between OS and age, Albumin, B2M, LDH, ISS, R-ISS, and risk scores in MM patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, after multivariate regression analysis, we found that only age, ISS, R-ISS and risk score were independent prognostic factors significantly associated with OS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Further, we assessed the correlation between risk scores and clinicopathologic characteristics. Risk scores were correlated with gender, ISS stage, and R-ISS stage of MM patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F), but not with characteristics such as age (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). ROC curves showed that risk scores were more accurate compared to other clinicopathologic features in the 5-year prognosis of patients diagnosed with MM (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNomogram construction and clinical value\u003c/h2\u003e \u003cp\u003eTo support clinical applications, we created column-line plots based on age, Albumin, B2M, LDH, ISS, R-ISS, and risk scores to predict 1-, 3-, and 5-year survival in MM patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The calibration curve showed that the survival predicted by the plot was consistent with the observed survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The column-line graph modeled a 5-year AUC value of 0.802, which was superior to the risk score alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Similarly, decision curve analysis found that patients benefited most from clinical interventions with the column-line graph model compared with the risk score \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRisk Score-Related Immune Pathways and Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe immune microenvironment plays an important role in patient prognosis. The high-risk group had higher proportions of Tregs and macrophage M2 and a lower proportion of plasma cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In addition, we found lower levels of HLA and T-cell co-stimulatory processes in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This suggests that high-risk patients have a higher immunosuppressive microenvironment. We performed differential analysis of high and low risk groups to identify significantly different physiological processes. 95 differential genes were significantly enriched for biological processes such as nuclear division, chromosome segregation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Similarly, KEGG enrichment analysis suggested association with pathways such as Cell cycle, Oocyte meiosis, Cellular senescence, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). GSVA analysis indicated that cell cycle, genomic DNA replication, oxidative phosphorylation were significantly active regulatory mechanisms in high-risk patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRegulatory molecular networks in high- and low-risk patients\u003c/h2\u003e \u003cp\u003eTo further explore the regulatory mechanisms among risk groups, we entered 95 differential genes into the String database for protein interaction network construction (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The top ten core genes in the network were further calculated by the cytohubba plug-in in cytoscape software, which uses a centroid metric to highlight key modulators in the network. (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). \u003cem\u003eNUSAP1\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eKIF11\u003c/em\u003e were used as the top three core genes for the subsequent expression of prognostic value verification. The expression of \u003cem\u003eNUSAP1\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, and \u003cem\u003eKIF11\u003c/em\u003e was increased in high-risk patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) and was associated with poorer prognosis in MM patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Clinical correlation analysis showed that high expression of these three genes was associated with more severe R-ISS staging (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Subsequent quantitative PCR (qPCR) analysis of BMMCs confirmed significant differential expression of \u003cem\u003eNUSAP1\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, and \u003cem\u003eKIF11\u003c/em\u003e. (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMM is known to be a highly heterogeneous cancer. The prognosis and treatment response of patients with different molecular profiles vary widely[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Here, we established a risk signature of 16 FAM-related genes to predict the prognosis of MM patients. First, our signature successfully distinguished patients with different prognoses and its predictive efficiency were generalizable across cohorts. Second, patients in the high-risk group had a higher R-ISS classification and a higher level of immunosuppressive microenvironment. Third, cell cycle, aging and metabolic process abnormalities were increased among risk patients. An external clinical cohort validated the results of the bioinformatics analysis.\u003c/p\u003e \u003cp\u003eAltered cellular metabolism is a common feature of cancers, including myeloma. Interactions between MM cells and BM stromal cells are likely to affect and be affected by metabolic changes in myeloma and stromal cells. BMA provides a unique stromal cell type for myeloma cells that can interact with myeloma cells and can produce FA from their triglyceride stores, thereby providing nutrients to neighboring myeloma or other tumor cells with nutrients[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, targeting fatty acid metabolism has great potential to limit MM progression.\u003c/p\u003e \u003cp\u003eScreening 16 of these FMGs by machine learning methods to construct prognostic risk signatures proved to be a reliable prognostic model for MM patients. First, using KM analysis, the survival value of the signature was confirmed in both the training and test sets. Our results revealed significant statistical associations (training set: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; test set: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Secondly, six clinical indicators with significant survival value were identified in MM patients, and the risk scores of signatures were positively correlated in three of these clinical indicators. Further multifactorial cox analysis confirmed that risk score was an independent prognostic indicator for the prognosis of MM patients. Thirdly, ROC analysis showed that the AUC value of the signed risk score was as high as 0.787. For further application in clinical guidance, we plotted a column-line graph of the clinical indicators and the risk score, and its AUC increased to 0.802, which could effectively provide prognostic guidance.\u003c/p\u003e \u003cp\u003eFISH is widely used to identify cytogenetic abnormalities in MM. It helps to categorize patients into different risk categories based on the presence of the aforementioned high-risk markers. Many studies have shown that the addition of more FISH markers or the use of SNP arrays can improve prognosis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, new predictive scores have so far lacked prospective validation and broad applicability. Consequently, there is a great deal of variability regarding which culture conditions and FISH probes should be used to characterize different chromosomal abnormalities [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, this variation stems from the standard practices of each center as well as national and international guidelines, which may vary slightly. In conclusion, there is still a lack of internationally standardized criteria for determining the prognosis of patients with MM. Clinical practice has been relatively slow to embrace diagnostic techniques such as gene chips or high-throughput sequencing, which could complement standard approaches based on morphology, FISH, and flow cytometry. Currently, tools such as the GEP70, EMC-92 classifier, and the UAMS 70 gene model are used to classify patients into different risk categories. The inherent heterogeneity of MM and the diversity of treatment options likely impede the rapid identification of new prognostic and predictive markers, and there is currently no consensus as to whether and how gene expression profiling should be utilized to redefine high-risk diseases. It is now well established that gene expression profiling provides a comprehensive understanding of the genetic characteristics of MM and helps to predict patient prognosis and response to treatment more accurately than traditional methods. Our risk score, confirmed by multivariate Cox regression analysis, can be used as an independent prognostic factor for MM, providing additional value beyond traditional metrics such as age and ISS staging, and furthermore, our model combines gene expression data with clinical features to improve the accuracy of risk stratification and prognostic judgment.\u003c/p\u003e \u003cp\u003eWith increasing research evidence of the role of fatty acids in the development of MM in patients, it was necessary to analyze fatty acid genes in this study. We performed a series of analyses to identify these genes. Sixteen FAMs were identified that were strongly associated with the prognosis of MM patients. Consistent with these results, the analysis by Xinguo Wang et al. also suggested that DBF4 may be a potential therapeutic biomarker for MM[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. FMR1 was identified as a potential prognostic biomarker for MM[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. High RBBP8 expression is associated with poorer survival and relapse in plasma cell myeloma. RBBP8 can be considered as an independent prognostic factor for MM[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. RALA, also known as Ras-related protein Ral-A, is a small GTPase that regulates various cellular processes such as vesicle trafficking, cytoskeleton dynamics, and gene expression. Inhibition of Ral results in impaired SDF-1-induced migration of B cells and MM cells[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Targeting the YWHAQ complex inhibits cell proliferation[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and contributes to proteasome inhibitor sensitivity in multiple myeloma[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCyclin A2 belongs to the highly conserved family of cell cycle proteins and functions as a regulator of CDK kinase. Cyclin A2 binds and activates CDC2 or CDK2 kinase to promote the G1/S and G2/M transitions of the cell cycle. During G2/M, cyclin A2 is phosphorylated. It has been shown that \u003cem\u003eCCNA2\u003c/em\u003e is significantly downregulated when pan BCL2 inhibitors and dexamethasone (LDA) are combined [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This suggests that \u003cem\u003eCCNA2\u003c/em\u003e may serve as a potential therapeutic target for MM. The \u003cem\u003eKIF11\u003c/em\u003e gene encodes Eg5, a positively oriented microtubule motor, which is an important mitotic kinesin that plays a critical role in the formation and maintenance of the spindle. Eg5 has been implicated in tumorigenesis and has been found to be overexpressed in a variety of cancer tissues. The current study has demonstrated that Filanesib, a \u003cem\u003eKIF11\u003c/em\u003e/Eg5 inhibitor, upregulates Hsp70 through the phosphatidylinositol 3-kinase/Akt pathway in multiple myeloma cells [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Clinical studies have shown that Filanesib can be used to treat multiple myeloma and enhance the activity of pomalidomide and dexamethasone in multiple myeloma [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. \u003cem\u003eNUSAP1\u003c/em\u003e has not been reported in MM, but has been shown to promote cancer progression, metastasis and drug resistance in several studies [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, our results build a theoretical foundation for the study of \u003cem\u003eNUSAP1\u003c/em\u003e in MM.\u003c/p\u003e \u003cp\u003eMetabolic reprogramming usually alters immunocompetence and immune components in cancer tissues[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Interdisciplinary studies examining metabolic reprogramming and immune function are increasingly performed in cancer research. Using CIBERSORT, we found significant differences in immune composition between low-risk and high-risk scoring groups. In particular, we found that the high-risk scoring group had an intense immunosuppressive microenvironment, such as a significantly higher proportion of Treg as well as macrophage M2 than the low-risk scoring group. Treg cells are a type of immune cells that can suppress the activity of other immune cells and prevent autoimmune reactions. They are also involved in the regulation of anti-tumor immunity, and their number and function can be altered in various cancers, including MM. Several studies have shown that patients with MM have increased Treg cells in the bone marrow compared to healthy individuals or patients with other plasma cell disorders. Increased Treg cells may suppress the anti-MM immune response and may be associated with disease stage, risk level, and prognosis in patients with MM. Some studies also suggest that Treg cells may be involved in the development and progression of MM because they can express a gene called Foxp3, which is also overexpressed in MM cells[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Macrophage M2 promotes the growth and survival of myeloma cells and inhibits their apoptosis. Macrophage M2 also inhibits anti-tumor immune responses in the tumor microenvironment and promotes angiogenesis[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, macrophage M2 is considered a potential therapeutic target for multiple myeloma. Our results suggest potential connections and provide new directions for future research.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMM\u003c/em\u003e\u003c/strong\u003e: Multiple Myeloma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWGCNA\u003c/em\u003e\u003c/strong\u003e: Weighted Gene Co-expression Network Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003essGSEA\u003c/em\u003e\u003c/strong\u003e: Single-Sample Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eKM\u003c/em\u003e\u003c/strong\u003e: Kaplan-Meier\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eROC\u003c/em\u003e\u003c/strong\u003e: Receiver Operating Characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eqPCR\u003c/em\u003e\u003c/strong\u003e: Quantitative Polymerase Chain Reaction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eISS\u003c/em\u003e\u003c/strong\u003e: International Staging System\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eR-ISS\u003c/em\u003e\u003c/strong\u003e: Revised International Staging System\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Sichuan Provincial People's Hospital and Sichuan Academy of Medical Sciences (2023-577). Written informed consent was obtained from all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Science \u0026amp;Technology Department of Sichuan Province, clinical research on the treatment of multiple myeloma with sequential administration of Daratumumab after autologous hematopoietic stem cell transplantation (Grant ID: 2022YFS0367).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Feifei Che; Formal analysis: Yang Yu; Supervision: Feifei Che; Writing–original draft: Yang Yu; Writing–review and editing: Feifei Che.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eDepartment of General Medicine, Affiliated Hospital of Weifang Medical University, Weifang, Shandong, China. Yang Yu\u003c/p\u003e\n\u003cp\u003eDepartment of Hemotology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China. Feifei Che\u003c/p\u003e\n\u003cp\u003eCorrespondence author\u003c/p\u003e\n\u003cp\u003eCorrespondence to Feifei Che.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePalumbo A, Anderson K: \u003cstrong\u003eMultiple myeloma\u003c/strong\u003e. \u003cem\u003eN Engl J Med \u003c/em\u003e2011, \u003cstrong\u003e364\u003c/strong\u003e(11):1046-1060.\u003c/li\u003e\n\u003cli\u003eRajkumar SV: \u003cstrong\u003eMultiple myeloma: 2020 update on diagnosis, risk-stratification and management\u003c/strong\u003e. \u003cem\u003eAm J Hematol \u003c/em\u003e2020, \u003cstrong\u003e95\u003c/strong\u003e(5):548-567.\u003c/li\u003e\n\u003cli\u003eSonneveld P, Avet-Loiseau H, Lonial S, Usmani S, Siegel D, Anderson KC, Chng WJ, Moreau P, Attal M, Kyle RA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eTreatment of multiple myeloma with high-risk cytogenetics: a consensus of the International Myeloma Working Group\u003c/strong\u003e. \u003cem\u003eBlood 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strategies\u003c/strong\u003e. \u003cem\u003eCancer Metastasis Rev \u003c/em\u003e2021, \u003cstrong\u003e40\u003c/strong\u003e(1):273-284.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multiple myeloma, Fatty acid metabolism, Prognosis, Immune infiltration, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-6904083/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6904083/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: Exploring fatty acid metabolism-related genes and features to predict survival outcomes in patients with multiple myeloma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Transcriptional, survival, and clinicopathologic data of MM patients were downloaded from the GEO and MMRF dataset. Fatty acid-related genes were screened by WGCNA, and one-way cox analysis was performed to identify genes associated with survival. Lasso regression analysis was then performed to construct fatty acid metabolism-related gene characteristics and risk scores. In addition, a nomogram model containing risk scores was constructed to guide clinical decision-making. We also performed immune infiltration analysis and functional analysis to deeply explore the differences between high and low risk groups. Meanwhile, qPCR was conducted on BMMCs from 10 newly diagnosed MM patients and 10 healthy controls to validate the expression of \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eKIF11\u003c/em\u003e, and \u003cem\u003eNUSAP1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: In total, 37 prognosis-related FMGs genes were identified. Among them, 16 genes were used to construct lasso regression models. KM analysis showed that high-risk patients had poorer prognosis (training set: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; test set: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). The area under the ROC curve was 0.787. Immunoscape analysis showed that high-risk patients had an immunosuppressive microenvironment. Functional enrichment studies confirmed that high-risk patients had increased abnormalities in cell cycle, aging and metabolic processes. The qPCR analysis revealed \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eKIF11\u003c/em\u003e, and \u003cem\u003eNUSAP1\u003c/em\u003eup-regulated in MM patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: We identified 37 survival-associated FMGs in MM patients. Our results also suggest that survival-associated traits based on these genes are potentially robust prognostic biomarkers for MM patients.\u003c/p\u003e","manuscriptTitle":"Prognostic value of fatty acid metabolism-related signature and integrated analysis of the immune microenvironment in multiple myeloma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 10:31:29","doi":"10.21203/rs.3.rs-6904083/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-21T08:08:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-17T20:29:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189358615959504231197546756103090917049","date":"2025-07-09T15:03:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T05:26:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129388936659524727227504974516525363955","date":"2025-06-25T05:06:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-25T04:13:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-23T00:00:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-20T16:31:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-19T10:05:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-06-19T10:02:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"458fd141-fc43-4a48-9de1-569f1ebf9e19","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:06:33+00:00","versionOfRecord":{"articleIdentity":"rs-6904083","link":"https://doi.org/10.1186/s12885-025-14886-3","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-11-08 15:57:26","publishedOnDateReadable":"November 8th, 2025"},"versionCreatedAt":"2025-07-01 10:31:29","video":"","vorDoi":"10.1186/s12885-025-14886-3","vorDoiUrl":"https://doi.org/10.1186/s12885-025-14886-3","workflowStages":[]},"version":"v1","identity":"rs-6904083","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6904083","identity":"rs-6904083","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}