Background
Multiple myeloma (MM) is a hematologic neoplasm caused by the malignant proliferation of clonal plasma cells. In 2019, more than 155,688 people were diagnosed with multiple myeloma worldwide, and approximately 100,000 deaths are attributed to MM per year [
1]. Various pharmacological strategies have been developed against MM, including proteasome inhibitors, immunomodulatory agents, and alkylating agents, which have successfully increased patient survivorship [
2]. However, most patients still experience relapse and resultant mortality due to drug resistance; therefore, the 5-year survival rate of MM patients in high-risk populations remains 50% or lower [
3]. Hence, it is imperative to find novel therapeutic targets for the development of new anti-multiple myeloma agents.
Proteins are versatile biologically active compounds involved in the regulation of multiple cellular and physiological functions. A new generation of proteomics technologies has enabled the identification of ectopic protein expressions and further exploration of potential biomarkers and therapeutic targets for cancer [
4]. For example, by employing a quantitative proteomics approach, Chen et al. recognized proteinase inhibitor 9 (SERPINB9) as a promising novel therapeutic target for bortezomib-resistant recurrent and relapsed MM [
5]. More recently, a proteomic profiling analysis revealed that cyclin-dependent kinase 6 (CDK6) upregulation is a targetable resistance mechanism for lenalidomide, highlighting the expanding importance of proteomic research in MM [
6]. However, most such studies were restricted to small sample sizes and/or limited protein species. And it is important to note that the causal relevance of associations from these nonrandomized observational studies remains largely unresolved due to their susceptibility to confounders or reverse causation.
Mendelian randomization (MR) is a popular approach for causal inferences by using genetic variants as instrumental variables (IVs) that mimic a lifetime randomized controlled trial [
7]. It exploits the natural random allocation of genetic variants at conception, so results from MR are much less likely to be biased by reverse causation or residual confounding. With the development of genome-wide association studies (GWASs) on human plasma proteome, an optimization framework by integrating genomic and proteomic databases for biomarker discovery has emerged [
8]. In particular, MR studies leveraging protein quantitative trait loci from variants have contributed to elucidating novel targets for breast cancer [
9], lung cancer [
10], and ovarian cancer [
11], which suggests that the analytical method is empirically validated and reliable.
We therefore applied a proteome-wide MR analysis by combing the high-throughput proteomes with genetic data to assess the causal effects of the circulating proteins on the risk of MM. Furthermore, to explore the clinical utility of these proteins, we branch out the existing research and give out a three-step parallel approach: (i) revealing their roles in the etiology of MM; (ii) disentangling the prioritization of these proteins; and (iii) evaluating the druggability of potential target proteins.
Discussion
In the present study, a pipeline composed of analytical techniques was utilized to analyze 2994 circulating proteins in relation to MM. The primary two-sample MR analysis revealed that 13 proteins were causally correlated with MM risk, with 6 showing positive associations (NAMPT, TIE1, CBR1, PDE4D, PAFAH1B2, and NCF2) and 7 showing inverse associations (FSTL1, PTPN4, SOCS3, GZMB, GPC1, C1QC, and FCGR3B). These included association with MM has been implicated elsewhere, such as NAMPT [
39], PDE4D [
40], PAFAH1B2 [
41], SOCS3 [
42], and GZMB [
43]. The next step was the KEGG enrichment analysis, which showed that three of the causally associated proteins, SOCS3, FCGR3B, and NCF2, were enriched in the osteoclast differentiation pathway. Subsequently, MR-BMA analysis indicated that NAMPT, GPC1, and NCF2 ranked among the top three MM-associated proteins. At last, a list of 4 in-development protein-targeted drugs and 5 druggable proteins supported the incorporation of genomics and proteomics in the drug development programs again. Taken together, these findings exemplify the utility of genetic analysis in identifying both known and novel loci and pathways with causal implications for MM.
The etiology of MM is intricate, encompassing the dysfunction of multiple genes and signaling pathways as well as the abnormal regulation of cellular processes. Multiple lines of evidence have underscored a bidirectional prosurvival regulatory loop exists between osteoclasts (OCs) and MM cells in the bone marrow microenvironment [
44]. On one hand, OCs exert immunomodulatory effects via upregulating various inhibitory checkpoint molecules and immune-suppressive cytokines, contributing to the immunosuppressive microenvironment in MM [
45]. On the other hand, MM cells drive OCs formation and activation while hampering OCs generation and function. This cascade leads to bone resorption, impedes osteoblast activity, ultimately resulting in bone destruction and osteoporosis [
46]. Therefore, numerous studies are exploring signaling molecules in OCs differentiation as potential therapeutic avenues for MM treatment, with denosumab serving as an illustrative example due to its effective capability in delaying and mitigating bone-related events [
47,
48]. Our results consistently point to an enrichment of proteins associated with OCs differentiation, specifically SOCS3, FCGR3B, and NCF2, in MM. SOCS3, as a member of the suppressor family of cytokine signaling, acts to inhibit the activation of the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway. By negatively regulating the central JAK-STAT pathway, SOCS3 can further orchestrate bone cell growth, differentiation, and maintenance [
49,
50]. Similarly, FCGR3B is the only inhibitory member of the FcγR immunomodulator family. Recent evidence suggests that cross-regulation of immunoreceptor tyrosine-based activation motif and Fc-γ receptors could promote the suppression of spleen tyrosine kinase activation, thus leading to the inhibition of osteoclast differentiation [
51]. Finally, the gene encoding NCF2 also encodes the niacinamide adenine dinucleotide phosphate oxidase complex, thereby indirectly inducing osteoclast differentiation [
52]. Collectively, our results lend support to the causal roles of these proteins and corroborate the significance of osteoclast differentiation in the etiology of MM.
Furthermore, given the interrelated nature of protein characteristics, a MR-BMA analysis was conducted to identify the priority causal proteins. It should be noted that the primary objective of this methodology is to detect causal risk factors among a high-dimensional set of candidates rather than to unbiasedly estimate the magnitude of their causative effects [
34]. So, our results highlight the need for prioritization of NAMPT, GPC1, and NCF2 as they may be more proximal to the occurrence of MM. In detail, NAMPT serves as a rate-limiting enzyme in the salvage pathway of nicotinic acid dinucleotide synthesis [
53]. In line with our findings, a recent study found that OT-82 exhibits a potent effect on MM, which can be attributed to its ability to induce cell death through the inhibition of NAMPT [
54]. In addition, NAMPT is currently in clinical trials for hematological malignancies such as lymphoma, non-Hodgkin’s lymphoma, and acute myeloid leukemia. For NCF2, early studies specified that NCF2 is overexpressed in gastric cancer and promotes the progression of gastric cancer by activating the NF-kB signaling pathway [
55]. Recently, there is emerging evidence that high expression of NCF2 is associated with poor prognosis in patients suffering from acute myeloid leukemia [
56]. So, with its involvement in osteoclast differentiation as mentioned above, NCF2 is poised to play a vital role in the underlying mechanisms of MM. For GPC1, the available literature provides conflicting information on the role of GPC1. The previous perception of GPC1 as a biomarker for prostate cancer has been challenged by recent findings that reveal its complex, paradoxical role in the regulation of prostate cancer cell proliferation and migration [
57]. Thus, despite being considered as a potential target for cancer therapy in some solid tumors, the actual application of targeting GPC1 has not been realized. Furthermore, new evidence suggests that GPC1 expression in bone marrow-derived stromal cells exerts inhibitory effects on cancer cells, making GPC1 a promising target for the development of anti-cancer therapies targeting fibroblast cells [
58]. Nonetheless, the involvement of these proteins in MM may be substantial and merits further research attention.
In addition to the above proteins, the significance of other proteins in MM should not be disregarded. According to our druggable list, there are ongoing efforts to develop drugs that specifically target TIE1 and PDE4D. TIE1 is a tyrosine kinase receptor expressed by endothelial and hematopoietic cells and is functionally involved in major vascular diseases like atherosclerosis and tumor angiogenesis [
59]. Despite the lack of precise information on clinical applications, numerous studies view TIE1 inhibitors as a potential therapeutic approach for antiangiogenic treatment [
59,
60]. PDE4D, a primary cAMP-hydrolyzing enzyme in cells, is also a promising drug target. Studies have demonstrated therapeutic benefits of PDE4D inhibitors in the treatment of Alzheimer’s disease, Huntington’s disease, schizophrenia, and depression [
61]. However, recent studies have indicated that targeting PDE4D can be used for the treatment of ER positive breast cancer [
62], prostate cancer [
63], or hepatocellular carcinoma [
64]. Future research could explore the potential of PDE4D inhibitors for the treatment of MM. Furthermore, our findings indicate that CBR1, FSTL1, C1QC, and GZMB possess potential for pharmacological and clinical utilization and may be targeted through the use of small molecules or antibodies. The association of CBR1 with cancer has been extensively studied, especially with the recent discovery of its high expression in Philadelphia-like B-line acute lymphoblastic leukemia [
65]. Likewise, it has been demonstrated that FSTL1 can suppress the proliferation of nicotine-induced lung cancer cells [
66], and C1QC has proven valuable for the diagnosis of skin cutaneous melanoma with improved overall survival [
67]. Furthermore, GZMB, as a crucial component in natural killer cells, has made a significant contribution to the treatment of MM [
43]. Finally, the limited research on PAFAH1B2 or PTPN4 hinders the acquisition of extensive knowledge on their effects on MM. Nonetheless, PAFAH1B2 expression has been reported as a prognostic marker for MM in validation analysis [
41] while PTPN4 has been found to serve as an upstream therapeutic target in the treatment of prostate cancer [
68], indicating the potential for the two proteins as future research entry points.
Our study has several advantages. We innovatively explored a prospective way to intervene with circulating proteins to lower MM risk by studying the enriched pathway, the priority of therapeutic targets, and druggability of the potential causal proteins. Benefiting from the large-scale and non-overlapped GWASs data of proteome and MM, we could incorporate more functional proteins into our study and obtain more powerful MR estimates. Further, during the actual execution, a state-of-art method MR-BMA was conducted to probe the prioritized proteins and existing databases were comprehensively searched to depict the druggability profile of target proteins. We gained an advantage by applying a suggestive genome-wide
P-value threshold (5 × 10
–6) during the selection of genetic instruments, enabling the inclusion of a broader range of analyzable candidate proteins compared to the conventional standard of 5 × 10
–8 [
24,
69]. Lastly, the bidirectional MR analysis adds strength to the robustness of our findings by indicating that reverse causation is unlikely to have influenced the observed associations.
Several limitations need to be considered when interpreting our findings. First, our study was conducted using overall MM without specifying disease subtypes characterized by the immunoglobulin. Given the etiologic and prognostic heterogeneity within each subtype symptom, it is desirable to identify subtype-specific causal proteins. However, such analyses are currently constrained by the limited availability of genetic data underlying each MM subtype. Second, the causal estimation of several proteins on MM was not fully confirmed by MR-Egger method and weighted median method. However, it is important to highlight that the direction of estimates mostly aligns with the primary MR method (i.e., IVW method), and additional assessments such as Steiger filtering, GSMR HEIDI-outlier test, MR-Egger intercept test, and Cochran’s Q test did not reveal any evidence of invalid SNPs. Consequently, despite the lack of corroboration, these collective results serve to reinforce the robustness of our findings. These results still enhance the robustness of our findings. Third, MR is not perfectly analogous to a randomized controlled trial (RCT). Therefore, effects of potential causal proteins on MM derived from MR analyses may differ in magnitude from those anticipated in an RCT, and should be interpreted as life-course effects. But that does not contradict our intention to employ MR as an expedited approach to complement clinical trials and enhance their reliability. Fourth, the non-linear effects of some proteins on MM risk cannot be excluded. It is intriguing to consider the possibility that a protein may impact MM risk at extremely low or high levels, but detecting such effects in practical clinical settings can be challenging. Fifth, it is possible that the null effects of certain proteins on MM we observed may have been a consequence of inadequate statistical power due to the power of 80% to detect an OR of at least 1.30, considering
R2 = 5%, while the proportion of variance explained by SNPs for certain proteins is less than 5% [
12]. In addition, the only protein with a difference between the FinnGen Consortium and the UK Biobank dataset, FSTL1, should also be further investigated in a larger MM cohort. Thus, replication in larger studies of MM would be worthwhile. Finally, our analysis was confined to European ethnicity, and race issues frequently lead to the underuse of treatment and unintended interruptions in MM treatment [
70]; thus, we need to be careful in generalizability of our findings to other ethnic groups.
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