Introduction
Multiple myeloma (MM) is a haematological malignant tumour in which the abnormal proliferation of monoclonal plasma cells in the bone marrow is the main clinical manifestation [ 1 ]. Hypercalcaemia, renal dysfunction, and anaemia are common clinical manifestations of multiple myeloma (MM), which is highly prevalent among the elderly and exhibits a higher incidence in men than in women [ 2 ]. The advancement of autologous hematopoietic stem cell transplantation (AHscT) technology has enhanced the therapeutic responsiveness of MM patients to some extent. However, despite improvements in survival rates, MM remains an incurable disease, with most patients ultimately experiencing disease progression or relapse, posing a significant public health burden in China [ 3 ]. Research indicates that the prognosis of cancer patients is closely linked to their nutritional status, as malnutrition compromises immune function, weakens resistance to infections, and subsequently worsens patient outcomes [ 4 ]. In recent years, some researchers have continuously included the prognostic nutritional index (PNI) into the prognostic studies of tumours [ 5 – 7 ], to assess the nutritional status of patients and predict their long-term prognosis through PNI. The nutritional status of patients with MM is closely related to disease progression and prognosis. PNI, Controlling Nutritional Status (CONUT), and Systemic Inflammation and Nutrition Index (SII) can be used to assess immune function and inflammation levels. Low PNI, high CONUT, and high SII often indicate poor prognosis. In addition, a decrease in the albumin-to-globulin ratio (AGR) and the lymphocyte-to-monocyte ratio (LMR) is also associated with shorter survival. Nutritional support and inflammation control can help improve the prognosis and quality of life of MM patients.
In this paper, the clinical, pathological and follow-up data of 245 multiple myeloma patients treated with autologous haematopoietic stem cell transplantation were collected to analyse the relationship between PNI and the clinicopathological characteristics and prognosis of these patients, and to explore the prognostic significance of PNI in autologous haematopoietic stem cell transplantation treatment. Meanwhile, a column-line diagram model for predicting the prognosis of patients with multiple myeloma treated with autologous haematopoietic stem cell transplantation was constructed, forming a systematic and complete evaluation system, which helps to improve the accuracy of prognosis prediction.
Objects and methods
Subject of the study
The present study was approved by the Ethics Committee of Zhejiang provincial people’s hospital (Ethic number: 2025-063). The patients all provided informed consent. This investigation was conducted based on the principles of the declaration of Helsinki.
245 patients with multiple myeloma who received autologous haematopoietic stem cell transplantation treatment in our hospital from July 2010 to July 2018 were selected, including 190 males and 55 females; their ages ranged from 53 to 80 years old, with a mean age of (63.2 ± 7.1) years. Inclusion criteria: (1) meeting the International Myeloma Working Group (IMWG) MM diagnostic criteria; (2) Eastern Cooperative Oncology Group (ECOG) score of 0–3; (3) complete clinical data; (4) complete follow-up data.
Full, with clear endpoint status and start and end time of follow-up. Exclusion criteria: (1) combination of primary tumours in other sites; (2) death due to non-tumour factors during follow-up; (3) other organ failure; (4) patients did not sign the informed consent.
Research methodology
Patients’ inpatient medical records and test reports were collected through the electronic medical record information system in order to analyse their clinicopathological characteristics, including gender, age, BMI, R-ISS staging, bone marrow invasion, pre-transplantation disease status, lactate dehydrogenase, serum creatinine, serum calcium,β 2microglobulin, albumin, and comorbid diabetes mellitus. PNI = serum albumin level (g/L) + 5*total number of peripheral blood lymphocytes (*10 9 /L).
Methods of follow-up
Patients’ follow-up data were initiated and clinical data were collected by telephone and outpatient clinics, and the time of starting to receive autologous hematopoietic stem cell transplantation was set as the start time of follow-up, and the cut-off time was April 2019. The overall survival time was set as the time from the receipt of autologous HSCT to death or the cut-off time of the follow-up visit. Patient death was used as the follow-up cut-off.
Statistical methods
SPSS 22.0 software and R software version 4.0.0 were used for statistical analyses, and the measurement data were expressed as ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{x}$$\end{document} ± s); count data were expressed as number of cases and percentage, and comparisons between the two groups were made using the χ 2 test. The optimal cut-off value of PNI was calculated by plotting the Receiver Operating Characteristic (ROC) curve, the cumulative survival rate of patients was calculated by Kaplan-Meier method and the survival curve was plotted, and the cumulative survival rate of the high PNI value group and the low PNI value group was compared by Log-rank test. Multifactorial logistic regression analysis was used to explore the influencing factors of PNI, and Cox regression model was used for univariate and multivariate survival to analyse the influencing factors of the prognosis of autologous haematopoietic stem cell transplantation for multiple myeloma. The predictive model was established by using the “rms” package in R software, and the calibration curve was plotted for validation. The difference was considered statistically significant at P < 0.05.
Results
ROC curves for PNI to predict survival outcomes in patients with multiple myeloma treated with autologous haematopoietic stem cell transplantation
Taking the survival outcome of patients with multiple myeloma treated by autologous HSCT as a variable, the ROC curve of PNI for predicting the survival outcome of patients with multiple myeloma treated by autologous HSCT was plotted, and the area under the ROC curve (AUC) was obtained to be 0.691 [95% CI (0.607, 0.776)], and the optimal cut-off value was 46.1, and the Youden index, sensitivity and specificity were 0.395, 71.7%, and 67.8% respectively (Fig. 1 ).Therefore, 46.1 was used as the cut-off value of PNI, and the patients were divided into 114 cases in the group with low PNI values and 131 cases in the group with high PNI values. The total 3-years death was 130, and the median survival period was approximately five years.
Fig. 1 ROC curves for PNI predicting 3-year survival outcomes in patients with multiple myeloma treated with autologous hematopoietic stem cell transplantation
Comparison of clinicopathological characteristics between the two groups of patients
There was no statistically significant difference in the comparison of gender, age, BMI, pre-transplantation disease status, lactate dehydrogenase, serum creatinine, serum calcium, andβ 2 microglobulin between the patients in the low and high PNI value groups ( P > 0.05), and there was a statistically significant difference in the comparison of the patients in the two groups with respect to the R-ISS staging, age, bone marrow invasion, serum calcium, and albumin ( P < 0.05) (Table 1 ).
Table 1 Comparison of clinicopathological characteristics between the low PNI value group and the high PNI value group [number of cases (%) Clinicopathological features Low PNI value group ( n = 114) High PNI value group ( n = 131) χ 2 value P -value Distinguishing between the sexes 1.216 0.270 Male 92 (80.7) 98 (74.8) Women 22 (19.3) 33 (25.2) (a person’s) age 7.178 0.007 ≤ 50 years 64 (56.1) 95 (72.5) > 50 years old 50 (43.9) 36 (27.5) BMI 2.298 0.130 ≤ 24 43 (37.7) 62 (47.3) >24 71 (62.3) 69 (52.7) R-ISS staging 6.590 0.010 I–II stage 61 (53.5) 91 (69.5) Phase III 53 (46.5) 40 (30.5) Bone marrow invasion There are 65 (57.02) 43 (32.82) Not have 49 (42.98) 88 (67.18) Pre-transplant disease status 2.188 0.335 Full remission 72 (63.2) 75 (57.3) Partial mitigation 27 (23.7) 42 (32.1) Make headway 15 (13.1) 14 (10.6) Lactate dehydrogenase 1.204 0.273 ≤ 250 U/L 46 (40.4) 62 (47.3) >250 U/L 68 (59.6) 69 (52.7) Serum creatinine 0.160 0.689 ≤ 177 μmol/L 65 (57.0) 78 (59.5) >177 μmol/L 59 (43.0) 53 (40.5) Serum calcium 4.431 0.035 ≤ 2.54 mmol/L 75 (65.8) 102 (77.9) >2.54 mmol/L 39 (34.2) 29 (22.1) β2 Microglobulin 0.16 0.689 < 3.5 g/L 25 (21.9) 26 (19.9) ≥3.5 g/L 89 (78.1) 105 (80.1) Albumin 13.637 0.000 ≥ 35 g/L 41 (36.0) 77 (58.8) <35 g/L 73 (64.0) 52 (39.7) Combined diabetes 0.094 0.759 Be 78 (68.4) 92 (70.2) Clogged 36 (31.6) 39 (29.8)
Table 1
Comparison of clinicopathological characteristics between the low PNI value group and the high PNI value group [number of cases (%)
| Clinicopathological features | Low PNI value group ( n = 114) | High PNI value group ( n = 131) | χ 2 value | P -value |
| Distinguishing between the sexes | 1.216 | 0.270 | ||
| Male | 92 (80.7) | 98 (74.8) | ||
| Women | 22 (19.3) | 33 (25.2) | ||
| (a person’s) age | 7.178 | 0.007 | ||
| ≤ 50 years | 64 (56.1) | 95 (72.5) | ||
| > 50 years old | 50 (43.9) | 36 (27.5) | ||
| BMI | 2.298 | 0.130 | ||
| ≤ 24 | 43 (37.7) | 62 (47.3) | ||
| >24 | 71 (62.3) | 69 (52.7) | ||
| R-ISS staging | 6.590 | 0.010 | ||
| I–II stage | 61 (53.5) | 91 (69.5) | ||
| Phase III | 53 (46.5) | 40 (30.5) | ||
| Bone marrow invasion | ||||
| There are | 65 (57.02) | 43 (32.82) | ||
| Not have | 49 (42.98) | 88 (67.18) | ||
| Pre-transplant disease status | 2.188 | 0.335 | ||
| Full remission | 72 (63.2) | 75 (57.3) | ||
| Partial mitigation | 27 (23.7) | 42 (32.1) | ||
| Make headway | 15 (13.1) | 14 (10.6) | ||
| Lactate dehydrogenase | 1.204 | 0.273 | ||
| ≤ 250 U/L | 46 (40.4) | 62 (47.3) | ||
| >250 U/L | 68 (59.6) | 69 (52.7) | ||
| Serum creatinine | 0.160 | 0.689 | ||
| ≤ 177 μmol/L | 65 (57.0) | 78 (59.5) | ||
| >177 μmol/L | 59 (43.0) | 53 (40.5) | ||
| Serum calcium | 4.431 | 0.035 | ||
| ≤ 2.54 mmol/L | 75 (65.8) | 102 (77.9) | ||
| >2.54 mmol/L | 39 (34.2) | 29 (22.1) | ||
| β2 Microglobulin | 0.16 | 0.689 | ||
| < 3.5 g/L | 25 (21.9) | 26 (19.9) | ||
| ≥3.5 g/L | 89 (78.1) | 105 (80.1) | ||
| Albumin | 13.637 | 0.000 | ||
| ≥ 35 g/L | 41 (36.0) | 77 (58.8) | ||
| <35 g/L | 73 (64.0) | 52 (39.7) | ||
| Combined diabetes | 0.094 | 0.759 | ||
| Be | 78 (68.4) | 92 (70.2) | ||
| Clogged | 36 (31.6) | 39 (29.8) |
Analysis of factors affecting PNI in multiple myeloma patients treated with autologous haematopoietic stem cell transplantation
Multifactorial logistic regression analysis was carried out with PNI as the dependent variable and R-ISS stage, age, bone marrow invasion, serum calcium, and albumin as the independent variables, and the assignments are shown in Table 2 , and the results showed that R-ISS stage III, age > 50 years, bone marrow invasion, and albumin were the independent risk factors for PNI < 46.1 in patients with multiple myeloma treated with autologous haematopoietic stem cell transplantation ( P < 0.05) (Table 3 ).
Table 2 Variable assignment table for multifactorial logistic regression analysis of factors influencing PNI in patients with multiple myeloma treated with autologous Haematopoietic stem cell transplantation Variant Value (A person’s) age ≤ 50 years = 1, > 50 years = 2 Bone marrow invasion No = 1, Yes = 2 Serum calcium ≤ 2.54 mmol/L = 1, > 2.54 mmol/L = 2 Albumin < 35 g/L = 1, ≥ 35 g/L = 2 R-ISS staging Phase I–II = 1, Phase III = 2
Table 2
Variable assignment table for multifactorial logistic regression analysis of factors influencing PNI in patients with multiple myeloma treated with autologous Haematopoietic stem cell transplantation
| Variant | Value |
| (A person’s) age | ≤ 50 years = 1, > 50 years = 2 |
| Bone marrow invasion | No = 1, Yes = 2 |
| Serum calcium | ≤ 2.54 mmol/L = 1, > 2.54 mmol/L = 2 |
| Albumin | < 35 g/L = 1, ≥ 35 g/L = 2 |
| R-ISS staging | Phase I–II = 1, Phase III = 2 |
Table 3 Multifactorial logistic regression analysis of factors influencing PNI in patients with multiple myeloma treated with autologous Haematopoietic stem cell transplantation Variant β SE Waldχ 2 value P -value OR 95 per cent CI R-ISS staging 0.753 0.290 6.718 0.010 2.123 1.201–3.752 (A person’s) age 0.744 0.232 10.299 0.001 2.104 1.336–3.313 Bone marrow invasion 0.629 0.192 10.681 0.001 1.876 1.286–2.735 Albumin 0.609 0.284 4.606 0.032 1.839 1.054–3.208
Table 3
Multifactorial logistic regression analysis of factors influencing PNI in patients with multiple myeloma treated with autologous Haematopoietic stem cell transplantation
| Variant | β | SE | Waldχ 2 value | P -value | OR | 95 per cent CI |
| R-ISS staging | 0.753 | 0.290 | 6.718 | 0.010 | 2.123 | 1.201–3.752 |
| (A person’s) age | 0.744 | 0.232 | 10.299 | 0.001 | 2.104 | 1.336–3.313 |
| Bone marrow invasion | 0.629 | 0.192 | 10.681 | 0.001 | 1.876 | 1.286–2.735 |
| Albumin | 0.609 | 0.284 | 4.606 | 0.032 | 1.839 | 1.054–3.208 |
Analysis of cumulative survival in the low and high PNI value groups
The 1-year, 2-year and 3-year survival rates of patients with multiple myeloma treated with autologous haematopoietic stem cell transplantation were 33.3%, 15.1% and 4.3%, respectively. The 3-year survival rate of patients in the low PNI value group was 1.0%, and the 3-year survival rate of patients in the high PNI value group was 11.6%, and the 3-year survival rate of patients in the low PNI value group was lower than that of patients in the high PNI value group, and the difference was statistically significant (χ 2 = 10.843, P = 0.001) (Fig. 2 ).
Fig. 2 Analysis of cumulative survival in the low and high PNI value groups
Analysis of factors influencing the prognosis of patients with multiple myeloma treated with autologous haematopoietic stem cell transplantation
Follow-up outcomes of patients with multiple myeloma treated with autologous hematopoietic stem cell transplantation were used as the dependent variable (assigned: Death = 1, survival or loss of follow-up = 0) with clinicopathological characteristics as independent variables, and the assignment is shown in Table 4 . Unifactor analysis of Cox regression model shows that, R-ISS stage, lactate dehydrogenase, PNI, disease status before transplantation, bone marrow invasion and diabetes mellitus were the prognostic factors of autologous hematopoietic stem cell transplantation ( P < 0.05). Multivariate Cox regression model analysis showed that lactate dehydrogenase, PNI, disease status before transplantation, bone marrow invasion and diabetes mellitus were independent factors influencing the prognosis of patients with multiple myeloma treated by autologous hematopoietic stem cell transplantation ( P < 0.05). Among them, low lactate dehydrogenase, disease status below PR before transplantation, bone marrow invasion, and diabetes mellitus were risk factors, and PNI > 46.1 were protective factors (Table 5 ).
Table 4 Cox regression model variable assignment of factors influencing prognosis of patients with multiple myeloma treated with autologous Haematopoietic stem cell transplantation Variant Value Distinguishing between the sexes Male = 1, Female = 2 (A person’s) age ≤ 50 years = 1, > 50 years = 2 BMI ≤ 24 kg/m 2 = 1, >24 kg/m 2 = 2 R-ISS staging Phase I–II = 1, Phase III = 2 Bone marrow invasion No = 1, Yes = 2 Pre-transplant disease status Complete remission = 1, partial remission = 2, progression = 3 Lactate dehydrogenase ≤ 250 U/L = 1, > 250 U/L = 2 Serum creatinine ≤ 177 umol/L = 1, > 177 umol/L = 2 Serum calcium ≤ 2.54 mmol/L = 1, > 2.54 mmol/L = 2 β2 Microglobulin < 3.5 g/L = 1, ≥ 3.5 g/L = 2 Albumin < 35 g/L = 1, ≥ 35 g/L = 2 Combined diabetes No = 1, Yes = 2 PNI PNI<46.1 = 1, ≥ 46.1 = 2
Table 4
Cox regression model variable assignment of factors influencing prognosis of patients with multiple myeloma treated with autologous Haematopoietic stem cell transplantation
| Variant | Value |
| Distinguishing between the sexes | Male = 1, Female = 2 |
| (A person’s) age | ≤ 50 years = 1, > 50 years = 2 |
| BMI | ≤ 24 kg/m 2 = 1, >24 kg/m 2 = 2 |
| R-ISS staging | Phase I–II = 1, Phase III = 2 |
| Bone marrow invasion | No = 1, Yes = 2 |
| Pre-transplant disease status | Complete remission = 1, partial remission = 2, progression = 3 |
| Lactate dehydrogenase | ≤ 250 U/L = 1, > 250 U/L = 2 |
| Serum creatinine | ≤ 177 umol/L = 1, > 177 umol/L = 2 |
| Serum calcium | ≤ 2.54 mmol/L = 1, > 2.54 mmol/L = 2 |
| β2 Microglobulin | < 3.5 g/L = 1, ≥ 3.5 g/L = 2 |
| Albumin | < 35 g/L = 1, ≥ 35 g/L = 2 |
| Combined diabetes | No = 1, Yes = 2 |
| PNI | PNI<46.1 = 1, ≥ 46.1 = 2 |
Table 5 Cox regression model of factors influencing prognosis of patients with multiple myeloma treated with autologous haematopoietic stem cell transplantation Variable Single variable model Multiple variable model HR 95 per cent CI P -value HR 95 per cent CI P -value Distinguishing between the sexes 0.856 0.608–1.195 0.358 (a person’s) age 1.269 0.947–1.702 0.108 BMI 1.023 0.875–1.652 0.248 R-ISS staging 0.722 0.502–0.968 0.028 0.798 0.633–1.255 0.322 Bone marrow invasion 0.689 0.523–0.924 0.012 0.740 0.558–0.979 0.032 Pre-transplant disease status full remission 1.000 partial mitigation 1.913 0.986–3.715 0.055 1.098 0.547–2.202 0.798 make headway 3.677 1.930–7.003 0.001 2.205 1.1015–4.038 0.005 lactate dehydrogenase 1.492 1.122–1.988 0.003 1.141 1.058–1.889 0.016 serum creatinine 1.032 0.845–1.260 0.757 serum calcium 1.103 0.830–1.462 0.502 β2 Microglobulin 1.205 0.875–1.482 0.455 albumin 1.295 0.988–1.718 0.077 Combined diabetes 0.629 0.477–0.832 0.001 0.732 0.551–0.977 0.033 PNI 0.676 0.498–0.921 0.012 0.645 0.475–0.883 0.007
Table 5
Cox regression model of factors influencing prognosis of patients with multiple myeloma treated with autologous haematopoietic stem cell transplantation
| Variable | Single variable model | Multiple variable model | ||||
| HR | 95 per cent CI | P -value | HR | 95 per cent CI | P -value | |
| Distinguishing between the sexes | 0.856 | 0.608–1.195 | 0.358 | |||
| (a person’s) age | 1.269 | 0.947–1.702 | 0.108 | |||
| BMI | 1.023 | 0.875–1.652 | 0.248 | |||
| R-ISS staging | 0.722 | 0.502–0.968 | 0.028 | 0.798 | 0.633–1.255 | 0.322 |
| Bone marrow invasion | 0.689 | 0.523–0.924 | 0.012 | 0.740 | 0.558–0.979 | 0.032 |
| Pre-transplant disease status | ||||||
| full remission | 1.000 | |||||
| partial mitigation | 1.913 | 0.986–3.715 | 0.055 | 1.098 | 0.547–2.202 | 0.798 |
| make headway | 3.677 | 1.930–7.003 | 0.001 | 2.205 | 1.1015–4.038 | 0.005 |
| lactate dehydrogenase | 1.492 | 1.122–1.988 | 0.003 | 1.141 | 1.058–1.889 | 0.016 |
| serum creatinine | 1.032 | 0.845–1.260 | 0.757 | |||
| serum calcium | 1.103 | 0.830–1.462 | 0.502 | |||
| β2 Microglobulin | 1.205 | 0.875–1.482 | 0.455 | |||
| albumin | 1.295 | 0.988–1.718 | 0.077 | |||
| Combined diabetes | 0.629 | 0.477–0.832 | 0.001 | 0.732 | 0.551–0.977 | 0.033 |
| PNI | 0.676 | 0.498–0.921 | 0.012 | 0.645 | 0.475–0.883 | 0.007 |
Effectiveness of a column-line graphical model in predicting cumulative survival in patients with multiple myeloma treated with autologous haematopoietic stem cell transplantation
The predictive effect on the cumulative survival of patients with multiple myeloma treated with autologous haematopoietic stem cell transplantation was shown linearly by a column-linear graphical model incorporating the results of multifactorial Cox regression including lactate dehydrogenase, PNI, pre-transplantation disease status, bone marrow invasion, and comorbid diabetes mellitus, to predict the survival of patients with multiple myeloma treated with autologous haematopoietic stem cell transplantation for 1, 2, and 3 years, with the consistency index (C-) index) was 0.755 (Fig. 3 ). The calibration curve showed that the actual incidence showed good agreement with the predicted incidence (Fig. 4 ).
Fig. 3 Effectiveness of the column-line graphical model in predicting 1-, 2- and 3-year survival in patients with multiple myeloma treated with autologous hematopoietic stem cell transplantation
Fig. 4 Calibration curve for survival of patients with multiple myeloma treated with autologous hematopoietic stem cell transplantation
Discussion
Autologous haematopoietic stem cell transplantation can rebuild the normal haematopoietic and immune functions of patients, significantly prolonging the disease progression-free survival and overall survival [ 8 ]. Myeloablative preconditioning during transplantation eliminates the immunosuppressive microenvironment and reduces the tumour load, thus enhancing patients’ prognosis and quality of life [ 9 ]. PNI, as an indicator for assessing nutritional status, has received increasing attention. Studies have confirmed that PNI has an important role in the prognostic assessment of malignant tumours and has been validated in a variety of malignant tumours [ 10 – 13 ]. In this study, we analysed the relationship between PNI and the clinicopathological characteristics and prognosis of multiple myeloma patients treated with hematopoietic stem cell transplantation, explored the significance of PNI in the prognosis of multiple myeloma patients treated with autologous hematopoietic stem cell transplantation and constructed a column-line graph model for predicting the prognosis of patients treated with hematopoietic stem cell transplantation for multiple myeloma, which is designed to help clinicians better assess the prognosis of patients. At first, we plotted the ROC curve of PNI for predicting the survival outcome of autologous hematopoietic stem cell transplantation in the treatment of multiple myeloma, and the best cut-off value of PNI was obtained by calculating Youden’s index, and the corresponding sensitivity and specificity were also calculated. Patients were classified into low PNI value and high PNI value groups based on the best cut-off value of PNI. Previous studies have shown that 50 points is the grouping standard of PNI, and PNI < 50 points suggests that patients are malnourished, and the lower the score suggests that the nutritional status of patients is more unsatisfactory [ 14 , 15 ]. The authors believe that the study lacks a solid scientific foundation and does not adhere to the rigorous standards expected in research. In this investigation, the optimal cut-off value for the Prognostic Nutritional Index (PNI) was determined to be 46.1 through Receiver Operating Characteristic (ROC) curve analysis, which is significantly relevant to autologous hematopoietic stem cell transplantation in patients with multiple myeloma. Furthermore, PNI serves as an indicator for assessing nutritional status; clinicians can evaluate patients’ nutritional conditions directly via their PNI scores and subsequently develop personalized nutritional intervention plans. The blood parameters in the PNI were obtained from the 7 d prior to the hematopoietic stem cell transplantation and were calculated by the formula. Relevant studies have shown that PNI has a high correlation with clinicopathological characteristics such as age, gastric cancer stage and lymph node metastasis in gastric cancer patients [ 16 , 17 ]. In this study, multifactorial logistic regression analysis revealed that R-ISS stage III, age > 50 years, bone marrow invasion, and albumin were independent risk factors for PNI < 46.1 in patients with multiple myeloma treated with autologous hematopoietic stem cell transplantation. Some studies have confirmed that reduced PNI may be an influential factor in the development of postoperative complications after autologous stem cell transplantation [ 18 ], therefore, clinical attention needs to be paid to the intervention of the factors influencing the nutritional status of multiple myeloma while focusing on the nutritional status of multiple myeloma.
The results of multifactorial Cox regression model analysis in this study showed that lactate dehydrogenase, PNI, pre-transplantation disease status, bone marrow invasion, and comorbid diabetes mellitus were independent prognostic factors in patients with multiple myeloma treated with autologous hematopoietic stem cell transplantation ( P < 0.05).LDH is one of the risk factors for poor prognosis in MM. Serum LDH testing is important in the diagnosis, efficacy assessment and prognosis prediction in hematological malignancies such as non-Hodgkin’s lymphoma, chronic lymphocytic leukemia and MM. Patients with high LDH expression usually exhibit shorter median overall survival (OS) and progression-free survival (PFS) [ 19 – 21 ]. Cancer cells are dependent on glycolysis and lactate production increases even in the presence of sufficient oxygen, and cancer cells have 5–10 times more glucose metabolites than normal cells, which are mainly converted to lactate, with increased enzyme release in the blood. Among them, lactate dehydrogenase is widely present in human tissues and is involved in tumorigenesis and metabolism; therefore, LDH is considered a sensitive indicator of hypoxia, anaerobic glycolysis and malignant transformation of cellular metabolism [ 22 ]. Studies have shown that high LDH is closely associated with poor chemotherapeutic response, early death and low survival in MM patients, and is one of the poor prognostic factors, especially in elderly patients over 65 years old. LDH is considered superior to other prognostic indicators such as hemoglobin (Hb) and serum 2-microglobulin (2-MG) in the prognostic assessment system of elderly MM patients and is an important factor in extramedullary plasmacytoma, chromosomal abnormalities, and high serum free light chain (FLC) [ 23 ]. In the present study, we found that serum LDH was significantly elevated in some MM patients, suggesting that the tumour load was heavy and malignant, and that tumour load was an important determinant of survival. Multifactorial analysis showed that patients with high LDH levels had a poorer prognosis, and the results of the column line graph indicated a lower survival rate. Malnutrition is a common complication in patients with malignant tumours, increasing the risk of postoperative complications, decreasing immune function, and leading to reduced tolerance of antitumour therapy and shorter survival [ 24 ]. The results of this study showed that PNI is a potential marker in predicting the prognosis of patients with multiple myeloma (MM).PNI is calculated based on lymphocyte counts and serum albumin levels. Lymphocytes inhibit the growth and proliferation of cancer cells through the secretion of cytokines, such as tumour necrosis factor and interferon-gamma. When the number of lymphocytes decreases, the body’s immune function is weakened, and cancer cells are susceptible to immune escape which enhances their malignant behavior [ 25 ]. Albumin is a key nutritional indicator synthesized by the liver, and a decrease in its level leads to a decrease in systemic immune function, which in turn promotes the proliferation of tumour cells [ 26 ]. Therefore, low PNI values usually reflect malnutrition and immune dysfunction and are associated with poor prognosis in MM patients. Several studies have shown that the degree of disease remission before and after transplantation has a significant impact on the survival of MM patients, which is consistent with the results of the present study. Pasvolsky et al. [ 27 ] found that the PFS of patients who had achieved VGPR before transplantation was higher than that of patients who had not, and the difference was statistically significant ( P = 0.021), and the results of the present study are similar to this one, in that a better degree of remission before transplantation can effectively prolong PFS, and the effect of post-transplantation efficacy on OS and PFS can also be further observed. It has been noted that the survival of patients after HSCT is affected by various factors [ 28 ], such as age, Ann Arbor stage, International Prognostic Index score, ECOG score, degree of bone marrow infiltration, remission status prior to transplantation, timing of transplantation, and pretreatment regimen, etc., and the results of the present study are consistent with these studies. The association of diabetes mellitus (DM), a widely prevalent chronic disease, with cancer has been confirmed by several high-quality epidemiological studies [ 29 ]. Although the exact mechanism by which DM affects cancer is not clear, it is generally accepted that hyperinsulinism leads to an increase in circulating levels of insulin-like growth factor-1 (IGF-1), which activates the mTOR signaling pathway and thus promotes tumour cell proliferation [ 30 ]. Several in vitro experiments have shown that IGF-1 is associated with poor prognosis and drug resistance in MM patients [ 31 ], which provides a theoretical basis for this study.
In this study, a column-line diagram for predicting the prognosis of patients with multiple myeloma treated with autologous hematopoietic stem cell transplantation was constructed using independent prognostic factors screened by Cox regression model. The accuracy of the column-line diagram was assessed by C-index and calibration plots, and the results showed that the column-line diagram model had a good predictive effect. The column-line diagram predicts the probability of occurrence of a prognostic outcome by assigning specific scores to each influencing factor and accumulating these scores. This model can simplify complex regression equations and present linearized results. The column plot model allows healthcare professionals to rapidly assess the prognosis of patients with multiple myeloma treated with autologous HSCT, which facilitates the adoption of personalized interventions at an early stage and enhances clinical value while ultimately improving the prognosis and quality of life of patients.
Conclusion
In summary, PNI has an important predictive value for the prognosis of patients treated with autologous haematopoietic stem cell transplantation for multiple myeloma, and the prediction model of the column line graph constructed based on it is helpful for the individual analysis of the prognosis of patients in the clinic.
Limitation
This article has some limitations. The identified PNI threshold should not be interpreted as definitive, but rather as a preliminary reference point that warrants further validation. Future studies with independent cohorts are necessary to confirm the clinical utility and robustness of this finding. The sample size of this study is small, the constructed regression model may have some bias, and the external validation of the column chart model is not carried out. Therefore, further expansion of the sample size is needed to conduct in-depth studies in the future. In addition, the correlation between PNI and complications in multiple myeloma patients treated with autologous stem cell transplantation was not included in this study, and further research is needed.
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Acknowledgements
Not applicable.
Author contributions
All the authors had full access to all the data of the study, who were responsible for data integrity and accuracy. YX worked on study concept. YZZ was responsible for data acquisition. CLZ wrote the manuscript. DM participated in the design, administrative, technical, and material support. YX worked on supervision.
Funding
No.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The present study was approved by the Ethics Committee of Linyi Central Hospital. This investigation was conducted based on the principles of the declaration of Helsinki. The patients all provided informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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