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Dis Markers. 2022; 2022: 2095696.
Published online 2022 Oct 12. doi:10.1155/2022/2095696
PMCID: PMC9581636
PMID: 36277989
Xinyan Jia,1 Xiangxin Liu,2 and Wenzhong Yang
3
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- Data Availability Statement
Abstract
Objective
The study is aimed at analyzing the predictive value of serum Ig A, Ig G, and TNF-α in the recurrence of multiple myeloma (MM).
Methods
136 patients with MM treated in our hospital from January 2010 to January 2017 were followed up for 5 years. Finally, 100 patients who met the inclusion and exclusion criteria and had the complete follow-up visit were selected as the study subjects, with the recurrence of MM as endpoint event, and the observation was taken until the occurrence of endpoint event in patients or the termination of this study. They were divided into the recurrence group (RG) and the nonrecurrence group (NRG) according to whether the endpoint event occurred. The venous blood of patients was collected at the first diagnosis and subsequent visit (at the time of recurrence or termination of the study) to measure the Ig A and Ig G using a full automatic special protein analyzer and the TNF-α level by enzyme-linked immunosorbent assay. The data obtained in this study were analyzed by univariate analysis to choose the factors with difference in statistical significance to draw the ROC curve, and the areas under the curve (AUC) were recorded to analyze the potential mechanism of Ig A, Ig G, and TNF-α in predicting the recurrence of MM.
Results
After follow-up visit, there were 62 patients with recurrence (62.0%) and 38 patients without recurrence (38.0%), with no obvious difference in gender, age, body weight, and immune classification between the two groups (P > 0.05). Compared with the NRG, the levels of soluble interleukin-2 receptor (sIL-2R) and β2-microglobulin (β2-MG) in the RG at the first diagnosis were distinctly higher (P < 0.001); the levels of Ig A, Ig G, and TNF-α in the RG at the first diagnosis were visibly higher (P < 0.05); and the levels of Ig A, Ig G, and TNF-α in the RG at the subsequent visit were clearly higher (P < 0.05). There was a correlation between Ig G, Ig A, and TNF-α and β2-MG at the first diagnosis and the subsequent visit (P < 0.05); there was a correlation between Ig G and TNF-α, and sIL-2R at the first diagnosis and the subsequent visit (P < 0.05); and there was a correlation between Ig A and sIL-2R at the subsequent visit (P < 0.05). The AUC of Ig G, Ig A, and TNF-α in predicting the MM at the first diagnosis were 0.772, 0.776, and 0.778, respectively.
Conclusion
The serum Ig A, Ig G, and TNF-α had a predictive value in the recurrence of MM, and TNF-α was correlated with sIL-2R and β2-MG, with the highest AUC and the best predictive value.
1. Introduction
Multiple myeloma (MM), as a malignant proliferative disease of bone marrow plasma cells, belongs to the category of B cell lymphoma, characterized by abnormal proliferation of plasma cells with the overproduction of monoclonal immunoglobulin or light chain (M protein) [1, 2], with no effective healing method at present. Patients still have a high possibility of recurrence after complete remission [3, 4], so that the system of recurrence prediction is very important in the prevention and treatment of MM. According to the immunoglobulin types secreted by myeloma cells, MM is divided into Ig A type, Ig G type, Ig M type, and light chain type in clinic, and further divided into κ type and λ type according to light chain type [5, 6]. There are differences in the secretion levels of immune factors and inflammatory factors in patients with different immune classifications, but patients can still find the same markers to evaluate the prognosis; for example, Dong Yi et al. have found that the expression of p53 protein, bcl-2 protein, and soluble interleukin-2 receptor (sIL-2R) in patients with MM at the first diagnosis and subsequent visit is positively correlated with β2-microglobulin (β2-MG), speculating that p53 protein, bcl-2 protein, and sIL-2R can be used to predict the recurrence of MM [7]. At present, there are a few reports to predict the recurrence of MM by immunoglobulin in academic circles, but it is known that B cells have an interaction with T cells and natural cells, affecting the results of immune response through different mechanisms [8, 9], so that the immune factors and inflammatory factors can reflect the activation, development, and differentiation of B cells. In clinical practice, the factors related to B cells can be selected to evaluate the recurrence possibility of patients, and the relationship between Ig A, Ig G, and TNF-α and B cells has been confirmed by literature. Ig A and Ig G are synthesized by plasma cells differentiated from B cells, and TNF-α can promote B cell differentiation, which plays an important role in osteolytic destruction in patients with MM. Based on this, 100 patients with MM in complete remission after treatment were followed up in this study to analyze the relationship between the levels of Ig A, Ig G, and TNF-α and the recurrence of MM to establish a good predictive mechanism in clinic.
2. Materials and Methods
2.1. Study Design and Case Selection
As a retrospective study, 100 patients who met the requirements of experimental design were selected as the study subjects finally to analyze the predictive value of serum Ig A, Ig G, and TNF-α in the recurrence of MM (see the technical route in Figure 1). The inclusion criteria are the following: (1) Patients were in line with the diagnostic criteria of Chinese guideline for diagnosis and treatment of multiple myeloma (2013) [10]. (2) Patients were treated for the first time and had a complete remission after treatment. (3) Patients were treated in the hospital in the whole process, with complete clinic information. (4) The age of patients exceeded 18 years old. The exclusion criteria are the following: (1) patients with the hearing impairment, language disorders, unconsciousness, and mental illness, and patients who cannot communicate with others; (2) patients who withdrew the treatment halfway; (3) patients with no complete remission after treatment [11]; (4) patients with the bacterial and viral infection at the first diagnosis; (5) patients with the dysfunction of vital organs such as heart, brain, liver and kidney; (6) patients with other organic diseases; (7) patients with incomplete clinic information; and (8) patients with no complete follow-up visit in the whole process.
Figure 1
Technical route.
2.2. Moral Consideration
This study met the principles of Declaration of Helsinki (2013) [12], and patients and their families who were aware of the purpose, significance, content, and confidentiality of the study signed informed consent.
2.3. Methods and Observation Indices
100 patients with MM in complete remission after treatment who were treated in our hospital from January 2010 to January 2017 were selected to collect the data of social demography and clinical manifestation at the first diagnosis. The data of social demography included gender, age, and body weight of patients, and the data of clinical manifestation included immune classification, clinical stage, and the levels of serum sIL-2R, β2-MG, Ig A, Ig G, and TNF-α. The fasting venous blood of patients (3 ml) was taken in the morning at the first diagnosis to obtain the serum after centrifugation to determine the levels of serum sIL-2R and TNF-α by enzyme-linked immunosorbent assay (Beijing Kewei Clinical Diagnostic Reagent Inc.; NMPA approval No.: S20060028), the β2-MG level by radioimmunoassay method (Tianjin Xiehe Pharmaceutical Science and Technology Co., Ltd., NMPA approval No.: S20083085), and the levels of Ig A and Ig G by a full automatic special protein analyzer (Bio-Rad Laboratories, Inc.; original matching reagent; NMPA (I) 20182220158).
All patients were followed up for 5 years, mainly outpatient follow-up, supplemented by telephone follow-up, with the recurrence of MM as the endpoint event (recurrence referred to the recurrence after complete remission of primary treatment), and the observation was taken until the occurrence of endpoint event in patients or the termination of study. They were divided into the recurrence group (RG) and the non-recurrence group (NRG) according to whether the endpoint event occurred. The fasting venous blood of patients was taken again at the subsequent visit (at the time of recurrence or termination of the study) in the morning to determine the levels of Ig A, Ig G, and TNF-α, and then, the univariate analysis was performed on the levels of Ig A, Ig G, and TNF-α at the first diagnosis and subsequent visit. For the factors with difference in statistical significance, the occurrence of endpoint event was assigned to 1, and no occurrence of endpoint event was assigned to 0. The ROC curve was drawn by SPSS20.0 to record the areas under the curve (AUC) to analyze the value of Ig A, Ig G, and TNF-α in predicting the recurrence of MM.
2.4. Statistical Treatment
In this study, the data processing software was SPSS20.0, and the GraphPad Prism 7 (GraphPad Software, San Diego, USA) was used to draw the pictures. The items included in the study were enumeration data and measurement data tested by X2 test and t test. P < 0.05 indicated that the difference was statistically significant.
3. Results
3.1. Comparison of Baseline Data in Patients
Except for the level values of sIL-2R and β2-MG at the first diagnosis, there was no significant difference in other baseline data between the two groups (P > 0.05), as shown in Table 1.
Table 1
Comparison of baseline data between the two groups.
| Items | RG (n = 62) | NRG (n = 38) | x2/t | P |
|---|---|---|---|---|
| Gender | 0.020 | 0.887 | ||
| Male | 35 (56.45) | 22 (57.89) | ||
| Female | 27 (4355) | 16 (42.11) | ||
| Age (, years) | 55.37 ± 5.19 | 55.76 ± 5.29 | 0.427 | 0.670 |
| Body mass (, kg) | 66.21 ± 5.32 | 66.75 ± 5.24 | 1.061 | 0.282 |
| Immune classifications | ||||
| Ig A type | 14 (22.58) | 11 (28.95) | 0.509 | 0.475 |
| Ig G type | 38 (61.29) | 22 (57.89) | 0.113 | 0.737 |
| Ig M type | 2 (3.23) | 1 (2.63) | 0.029 | 0.866 |
| Light chain type | 8 (12.90) | 4 (10.53) | 0.126 | 0.723 |
| Clinical stage | ||||
| Stage I | 1 (1.61) | 3 (7.89) | 2.421 | 0.120 |
| Stage II | 18 (29.03) | 13 (34.21) | 0.295 | 0.587 |
| Stage III | 43 (69.35) | 22 (57.89) | 1.360 | 0.244 |
| sIL-2R at the first diagnosis (, ng/L) | 0.80 ± 0.07 | 0.56 ± 0.08 | 6.119 | <0.001 |
| β2-MG at the subsequent visit (, mg/L) | 6.22 ± 1.07 | 4.94 ± 0.99 | 15.084 | <0.001 |
| Complications | ||||
| Hypertension | 13 (20.97) | 7 (18.42) | 0.096 | 0.757 |
| Diabetes mellitus | 6 (9.68) | 3 (7.89) | 0.124 | 0.725 |
| Treatment methods | ||||
| Chemotherapy | 21 (33.87) | 14 (36.84) | 0.091 | 0.762 |
| Immunomodulator | 14 (22.58) | 9 (23.68) | 0.016 | 0.899 |
| Radiotherapy | 25 (40.32) | 12 (31.58) | 0.773 | 0.379 |
| Others | 2 (3.23) | 3 (7.89) | 1.081 | 0.298 |
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3.2. Comparison of Levels of Ig A, Ig G, and TNF-α in Patients at the First Diagnosis and Subsequent Visit
Compared with the NRG, the levels of Ig G, Ig A, and TNF-α in the RG at the first diagnosis were visibly higher (48.18 ± 34.95 vs 33.71 ± 25.46, 22.98 ± 28.01 vs 10.60 ± 16.01, 6.50 ± 1.13 vs 5.30 ± 0.93, P < 0.05), and the levels of Ig G, Ig A, and TNF-α at the subsequent visit in the RG were clearly higher (51.60 ± 35.57 vs 33.31 ± 25.74, 27.65 ± 32.84 vs 9.81 ± 14.91, 7.97 ± 0.95 vs 4.14 ± 0.42, P < 0.05). See the levels of Ig A, Ig G, and TNF-α in patients with different immune classifications at the first diagnosis and subsequent visit in Table 2.
Table 2
Comparison of levels of Ig A, Ig G, and TNF-α in patients with different immune classifications at the first diagnosis and subsequent visit.
| Groups | n | Ig G (g/L) | Ig A (g/L) | TNF-α (ng/L) | |||
|---|---|---|---|---|---|---|---|
| First diagnosis | Subsequent visit | First diagnosis | Subsequent visit | First diagnosis | Subsequent visit | ||
| Ig A type | |||||||
| RG | 14 | 5.05 ± 1.23 | 6.52 ± 0.24 | 74.28 ± 8.53 | 88.32 ± 2.21 | 6.41 ± 1.02 | 8.01 ± 1.23 |
| NRG | 11 | 3.97 ± 0.42 | 3.21 ± 0.43 | 35.21 ± 5.68 | 32.68 ± 5.68 | 5.21 ± 0.98 | 4.08 ± 0.24 |
| t | 2.777 | 24.444 | 13.057 | 39.156 | 2.970 | 10.397 | |
| P | 0.011 | <0.001 | <0.001 | <0.001 | 0.007 | <0.001 | |
| Ig G type | |||||||
| RG | 38 | 75.18 ± 10.42 | 79.68 ± 5.10 | 8.32 ± 0.21 | 10.32 ± 2.21 | 6.54 ± 1.20 | 7.98 ± 0.80 |
| NRG | 22 | 55.11 ± 5.65 | 54.98 ± 5.32 | 0.60 ± 0.05 | 0.52 ± 0.08 | 5.34 ± 0.98 | 4.12 ± 0.50 |
| t | 8.333 | 17.796 | 169.107 | 20.716 | 3.980 | 20.401 | |
| P | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | |
| Ig M type | |||||||
| RG | 2 | 4.30 ± 0.04 | 5.95 ± 1.20 | 6.65 ± 0.01 | 8.38 ± 0.05 | 6.43 ± 1.21 | 7.58 ± 0.84 |
| NRG | 1 | 3.24 | 2.54 | 0.20 | 0.18 | 5.40 | 4.31 |
| t | 21.637 | 2.320 | 526.640 | 133.905 | 0.695 | 3.179 | |
| P | 0.029 | 0.259 | 0.001 | 0.005 | 0.613 | 0.194 | |
| Light chain type | |||||||
| RG | 8 | 6.40 ± 0.45 | 8.51 ± 0.65 | 6.91 ± 0.78 | 8.62 ± 0.67 | 6.48 ± 0.98 | 7.99 ± 1.01 |
| NRG | 4 | 5.42 ± 0.20 | 4.57 ± 0.65 | 0.50 ± 0.06 | 0.48 ± 0.04 | 5.31 ± 0.48 | 4.36 ± 0.24 |
| t | 4.081 | 9.898 | 16.019 | 23.695 | 2.219 | 6.932 | |
| P | 0.002 | <0.001 | <0.001 | <0.001 | 0.051 | <0.001 | |
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3.3. Correlation Analysis between Ig A, Ig G, and TNF-α, and sIL-2R and β2-MG in Patients at the First Diagnosis and Subsequent Visit
There was a correlation between Ig G, Ig A, and TNF-α, and β2-MG at the first diagnosis and the subsequent visit (P < 0.05); there was a correlation between Ig G and TNF-α, and sIL-2R at the first diagnosis and the subsequent visit (P < 0.05); and there was a correlation between Ig A and sIL-2R at the subsequent visit (P < 0.05), as shown in Table 3.
Table 3
Correlation analysis between Ig A, Ig G, and TNF-α, and sIL-2R and β2-MG in patients at first diagnosis and subsequent visit.
| Groups | Time | sIL-2R (g/L) | β2-MG (g/L) | ||
|---|---|---|---|---|---|
| r | P | r | P | ||
| Ig G (g/L) | First diagnosis | 0.225 | 0.024 | 0.213 | 0.033 |
| Subsequent visit | 0.242 | 0.016 | 0.209 | 0.037 | |
| Ig A (g/L) | First diagnosis | 0.167 | 0.096 | 0.247 | 0.013 |
| Subsequent visit | 0.216 | 0.031 | 0.298 | 0.003 | |
| TNF-α (g/L) | First diagnosis | 0.440 | <0.001 | 0.235 | 0.019 |
| Subsequent visit | 0.823 | <0.001 | 0.501 | <0.001 | |
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3.4. Value of Ig A, Ig G, and TNF-α in Predicting the Recurrence of MM
The AUC of Ig G, Ig A, and TNF-α in predicting the MM at the first diagnosis were 0.772, 0.776, and 0.778, respectively. See variable assignment in Table 4 and the ROC curve in Figure 2.
Figure 2
ROC curve analysis of Ig A, Ig G, and TNF-α in predicting the recurrence of MM. Notes. The sources of curve in Figure 2 were Ig G (first diagnosis and subsequent visit), Ig A (first diagnosis and subsequent visit), TNF-α (first diagnosis and subsequent visit), and reference line, while the vertical axis represented the sensitivity, and the lateral axis represented 1-specificity.
Table 4
Variable assignment.
| Variables | Variable assignment | AUC |
|---|---|---|
| Endpoint events | Occurrence = 1 and no occurrence = 0 | — |
| Ig G at first diagnosis | Variable assignment | 0.788 |
| Ig G at subsequent visit | Variable assignment | 0.758 |
| Ig A at first diagnosis | Variable assignment | 0.748 |
| Ig A at subsequent visit | Variable assignment | 0.727 |
| TNF-α at first diagnosis | Variable assignment | 0.791 |
| TNF-α at subsequent visit | Variable assignment | 0.817 |
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4. Discussion
B cells are derived from pluripotent stem cells of bone marrow, which can be differentiated into plasma cells under antigen stimulation, and plasma cells can synthesize and secrete the antibody (immunoglobulin), affecting the immunoglobulin levels in patients [13], so that multiple myeloma patients with malignant proliferation of plasma cells have an obviously abnormal immunoglobulin level, suggesting that the immunoglobulin level can reflect the differentiation and activation of plasma cells [14, 15]. The study of domestic scholars Feige et al. on 66 patients with Ig G-type MM has shown that patients with Ig G ≤64 g/L are distinctly superior to patients with Ig G >64 g/L in terms of overall survival and progression-free survival [16], which further confirmed that the abnormal immunoglobulin level was an intuitive reflection of monoclonal hyperplasia degree in MM, as an indicator to evaluate the disease state and reflect the degree of tumor load. At present, there is a lack of literature in analyzing the prognosis of patients with MM from the related factors of B cells, but the potential value of immunoglobulin in predicting the recurrence of MM cannot be ignored. In this study, after five years of follow-up visit, there were 62 patients with recurrence (62.0%) and 38 patients without recurrence (38.0%), and the levels of sIL-2R, β2-MG, Ig A, Ig G, and TNF-α in the RG at the first diagnosis were signally higher than those in the NRG (P < 0.001). β2-MG, as a classical marker for evaluating the clinical stage of patients with MM in the ISS, exists on the serous membrane of all karyocytes, and it involves in the surface identification of lymphocytes and the killing of cell receptors [17, 18]. Because β2-MG can reflect the proliferation rate of myeloma cells, some studies use it as a state variable to test the value of related indicators in evaluating prognosis, and indicators that are significantly related to β2-MG can be used to test the severity of patients with MM [19, 20]. R analysis results showed that there was a correlation between Ig G, Ig A, and TNF-α, and β2-MG at the first diagnosis (P < 0.05); that is, high levels of Ig G, Ig A, and TNF-α were independently correlated with high β2-MG level. The higher the β2-MG level at the first diagnosis, the higher the levels of Ig G, Ig A, and TNF-α, and the higher the possibility of recurrence after complete remission, with a consistency of the overall trend and the results of related factors analysis. Ig G, as a classical indicator to reflect the B cell status, is commonly used in the studies related to immune [21, 22]. Liu et al. have believed that low Ig G level is associated with B cell dysfunction, while high Ig G level reflects that the rate of B cell differentiation into plasma cells is accelerated and the total number of plasma cells is increased [23]. Ig A accounts for 10%–20% of the total serum immunoglobulins, which has a predictive value in children with nephrotic syndrome, clostridium difficile-associated diarrhea, and other recurrent diseases. However, this study found that there was a correlation between Ig G and TNF-α and sIL-2R at the first diagnosis and the subsequent visit (P < 0.05), with no correlation between Ig A and sIL-2R at the first diagnosis. sIL-2R, as an immunosuppressive factor that reflects the tumor burden, can compete with the membrane to bind interleukin-2 (IL-2), hindering the important biological response regulated by IL-2, making the cellular immune dysfunction of body, and eventually leading to the cells with malignant clone escaping from immune surveillance to have an excessive proliferation. In this study, sIL-2R is also a state variable, which has been confirmed to be associated with the recurrence of MM. Therefore, the determination of Ig G, Ig A, and TNF-α at a specific time can be used to infer the level changes of β2-MG and sIL-2R, thus evaluating the prognosis of patients.
ROC analysis further showed that the AUC of Ig G, Ig A, and TNF-α in predicting the MM at the first diagnosis were 0.772, 0.776, and 0.778, respectively, suggesting that the predictive value of TNF-α is higher than that of Ig G and Ig A, and the relationship between TNF-α and B cells has been confirmed in the literature. This marker can promote the proliferation of B cells and lymphocytes and accelerate the rate of B cells differentiation into plasma cells [24]. Moreover, the structure outside the cell membrane of TNF receptor type I (TNF-R1) and receptor type II is hom*ologous, and the former can enter the blood circulation after the abscission of cell membrane. Yuhua et al. have found that the TNF-α level in patients with MM is signally higher than that in normal control group, and with the increase of clinical stages, the TNF-α level shows a progressive tendency in ladder type, suggesting that TNF-α is related to tumor load in patients with MM, and this marker reflects the biological parameters of tumor load [25]. Yuhua has further proposed that the growth of MM is regulated by TNF-α, and MM can be treated in clinic by inhibiting TNF-α to act on related signaling pathways. The above results showed that TNF-α was an important serum marker in the diagnosis, treatment, and prognosis of MM and had a high clinical reference value.
It is worth noting that this study only included patients with MM who had complete remission after initial treatment. Affected by practical factors, only 100 patients received the complete follow-up visit, with small study samples. Subsequent studies need to further increase the sample size to support the above conclusions, and enrich the system of immune factors and inflammatory factors related to B cells to predict the MM.
5. Conclusion
In recent years, the application of new targeted drugs like proteasome inhibitor and immunomodulator has prolonged the complete remission rate of patients with MM, but patients are still unable to cure, and most patients still relapse after systematic treatment, even ushering in a death ending. Since the recurrence mechanism of MM is not clear, it is important to establish an effective prediction mechanism of recurrence. In this study, 100 patients with MM were followed up, founding that there was an obvious difference in the levels of serum Ig A, Ig G, and TNF-α between recurrent and non-recurrent patients, and the three indicators were correlated with the classical index (β2-MG) of MM, with the AUC ≥ 0.7. Therefore, the serum Ig A, Ig G, and TNF-α have a predictive value in the recurrence of MM, and TNF-α has the highest AUC and the best predictive value. In practice, the three indicators could be combined to evaluate the prognosis of patients comprehensively.
Acknowledgments
This research was funded by a project of the Shanghai Municipal Health Commission (Clinical Study on the Effect of Sanzi Buxue Soap on Treg Cells and Th17 Cells in MDS Patients), with grant number 202040109.
Data Availability
Data to support the findings of this study is available on reasonable request from the corresponding author.
Conflicts of Interest
The authors do not have conflicts of interest to declare.
References
1. Ferguson I. D., Patiño-Escobar B., Tuomivaara S. T., et al. The surfaceome of multiple myeloma cells suggests potential immunotherapeutic strategies and protein markers of drug resistance. Nature communications. 2022;13(1):p. 4121. doi:10.1038/s41467-022-31810-6. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
2. Matthew C., Jennifer C., Xin Q. In situ conventional karyotyping and fluorescence hybridization for detection of chromosomal abnormalities in multiple myeloma. Journal of Hematology. 2022;11:87–91. doi:10.14740/jh1007. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
3. Martins C. O., Huet S., San S. Y., et al. Mass spectrometry-based method targeting Ig variable regions for assessment of minimal residual disease in multiple myeloma. The Journal of Molecular Diagnostics. 2020;22(7):901–911. doi:10.1016/j.jmoldx.2020.04.002. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
4. Wei M., Guo H., Liu S., et al. Combined immune score predicts the prognosis of newly diagnosed multiple myeloma patients in the bortezomib-based therapy era. Medicine (Baltimore). 2021;100(41, article e27521) doi:10.1097/MD.0000000000027521. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
5. Karapetians A., Bajaj T., Valdes A., Heidari A. A rare case of multiple myeloma presenting as Evan’s syndrome. Journal of Investigative Medicine High Impact Case Reports. 2019;7:p. 232470961985276. doi:10.1177/2324709619852760. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
6. Patel Urmeel H., Drabick Joseph J., Jozef M., Talamo G. Nonsecretory and light chain escape in patients with multiple myeloma. Clinical Lymphoma, Myeloma & Leukemia. 2018;18(12):e515–e519. doi:10.1016/j.clml.2018.06.028. [PubMed] [CrossRef] [Google Scholar]
7. Dong Y., Zhu T., Ruixiang X., Qingshu Z., Xuejie C. Recurrence prediction of multiple myeloma. China Medical Herald. 2009;6(27):36–37. [Google Scholar]
8. Yu Y., Teng X. J., Luo G. L., Wu L. X., Guo B. Q. Value of serum light chain in diagnosis and evaluation of efficacy for multiple myeloma. Zhongguo Shi Yan Xue Ye Xue Za Zhi. 2020;28(3):886–893. doi:10.19746/j.cnki.issn.1009-2137.2020.03.028. [PubMed] [CrossRef] [Google Scholar]
9. Ye C., Chen W., Gao Q., et al. Secondary immunodeficiency and hypogammaglobulinemia with IgG levels of <5 g/L in patients with multiple myeloma: a retrospective study between 2012 and 2020 at a university hospital in China. Medical Science Monitor. 2021;27, article e930241 doi:10.12659/MSM.930241. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
10. Callander Natalie S. NCCN guidelines® insights: multiple myeloma, version 3.2022. Journal of the National Comprehensive Cancer Network. 2022;20(1):8–19. doi:10.6004/jnccn.2022.0002. [PubMed] [CrossRef] [Google Scholar]
11. Tahiru W., Izarra Santamaria A., Hultdin J., Wu W. Y., Späth F. Progression patterns in monoclonal gammopathy of undetermined significance and multiple myeloma outcome: a cohort study in 42 patients. Experimental Hematology & Oncology. 2022;11(1):p. 8. doi:10.1186/s40164-022-00259-0. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
12. World Medical Association. World medical association declaration of Helsinki. Journal of the American Medical Association. 2013;310(20):2191–2194. doi:10.1001/jama.2013.281053. [PubMed] [CrossRef] [Google Scholar]
13. Tadao I. Future therapeutic strategies for multiple myeloma. Rinshō Ketsueki. 2022;63:635–645. doi:10.11406/rinketsu.63.635. [PubMed] [CrossRef] [Google Scholar]
14. Schinke M., Ihorst G., Duyster J., Wäsch R., Schumacher M., Engelhardt M. Risk of disease recurrence and survival in patients with multiple myeloma: a German study group analysis using a conditional survival approach with long-term follow-up of 815 patients. Cancer. 2020;126(15):3504–3515. doi:10.1002/cncr.32978. [PubMed] [CrossRef] [Google Scholar]
15. Richardson P. G., Beksaç M., Špička I., Mikhael J. Isatuximab for the treatment of relapsed/refractory multiple myeloma. Expert Opinion on Biological Therapy. 2020;20(12):1395–1404. doi:10.1080/14712598.2021.1841747. [PubMed] [CrossRef] [Google Scholar]
16. Feige N., Feng L., Jingjing S., Yongping Z. 66 patients with IgG multiple myeloma correlation analysis of IgG level at diagnosis with curative effect and prognosis. China Experimental Hematology Journal. 2019;27(4):1159–1165. [PubMed] [Google Scholar]
17. Rahul B., Mimi L. Isatuximab, carfilzomib and dexamethasone (Isa-Kd) for the management of relapsed multiple myeloma. Future Oncology. 2021;17(35):4849–4860. doi:10.2217/fon-2021-0778. [PubMed] [CrossRef] [Google Scholar]
18. Oriol A., Larocca A., Leleu X., et al. Melflufen for relapsed and refractory multiple myeloma. Expert Opinion on Investigational Drugs. 2020;29(10):1069–1078. doi:10.1080/13543784.2020.1808884. [PubMed] [CrossRef] [Google Scholar]
19. Duan D., Wang K., Wei C., et al. The BCMA-targeted fourth-generation CAR-T cells secreting IL-7 and CCL19 for therapy of refractory/recurrent multiple myeloma. Frontiers in Immunology. 2021;12:p. 609421. doi:10.3389/fimmu.2021.609421. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
20. Maura F., Rustad E. H., Boyle E. M., Morgan G. J. Reconstructing the evolutionary history of multiple myeloma. Best Practice & Research. Clinical Haematology. 2020;33(1):p. 101145. doi:10.1016/j.beha.2020.101145. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
21. Alsouqi A., Khan M., Dhakal B., et al. KD-PACE salvage therapy for aggressive relapsed refractory multiple myeloma, plasma cell leukemia and extramedullary myeloma. Clinical Lymphoma, Myeloma & Leukemia. 2021;21(8):526–535. doi:10.1016/j.clml.2021.03.013. [PubMed] [CrossRef] [Google Scholar]
22. Hu X., Wang W., Wang Y., Liu K. Fear of cancer recurrence in patients with multiple myeloma: prevalence and predictors based on a family model analysis. Psychooncology. 2021;30(2):176–184. doi:10.1002/pon.5546. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
23. Liu H., Zhao J. Q., Ni Z. F., Liu Y. H., Jiao J. Correlation of the expression alteration of Tim-3 with the T cell and B cell dysfunction in peripheral blood of multiple myeloma patients. Zhongguo shi yan xue ye xue za zhi/Zhongguo bing li sheng li xue hui = Journal of Experimental Hematology/Chinese Association of Pathophysiology. 2020;28(3):881–885. [PubMed] [Google Scholar]
24. Esheba Noha E. Assessment of leptin and resistin levels in non-obese multiple myeloma patients and their relation with Ig level and disease stage. Journal of the Egyptian National Cancer Institute. 2014;26(2):61–66. doi:10.1016/j.jnci.2014.02.002. [PubMed] [CrossRef] [Google Scholar]
25. Yuhua Z., Wenming C., Li S., Peng Z., Shicheng X. Detection and clinical significance of tumor necrosis factor and its receptor 1 in multiple myeloma. Chinese Journal of Laboratory Medicine. 2003;1:p. 53. [Google Scholar]
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