Neuroendocrine cancer prognosis monitoring method based on urine sodium-potassium ratio

Abstract

The invention is suitable for the technical field of medical monitoring, and provides a neuroendocrine cancer prognosis monitoring method based on a urine sodium-potassium ratio. The urine sodium-potassium ratio (STP) is calculated by measuring sodium and potassium excretion levels in a random urine sample, so that the influence of electrolyte imbalance and tumors on the excretion function of the kidney is evaluated, and the survival risk of a patient is predicted. Retrospective data of 759 neuroendocrine cancer (NEC) patients are analyzed, and a Cox proportional risk model and stratified analysis are utilized, so that the significant relevance between the STP and the survival rate of the patients is revealed. Research results show that when STP is larger than or equal to 1.29, the overall and cancer specific death risks of patients are remarkably increased, specifically, the overall death risk is increased by 14% and the cancer specific death risk is increased by 16% every time when STP is increased by one unit. Therefore, STP can be regarded as an important potential biomarker for prognosis of NEC patients.

Description

Technical Field

[0001] The invention belongs to the technical field of medical monitoring, and in particular relates to a neuroendocrine cancer prognosis monitoring method based on urine sodium-potassium ratio. Background Art

[0002] Neuroendocrine tumors (NENs) are rare and highly heterogeneous tumors that originate from peptidergic neurons and neuroendocrine cells, with common sites including the lungs, stomach, intestines, and pancreas. According to the classification of the World Health Organization (WHO), NENs can be divided into neuroendocrine tumors (NETs) and neuroendocrine carcinomas (NECs), of which NETs are usually low-grade malignant, while NECs are highly malignant, with obvious differences in morphological and genetic characteristics. NEC patients often show endocrine syndromes, especially the syndrome of inappropriate antidiuretic hormone secretion (SIADH), which leads to water retention and hyponatremia, which can cause neurological and cardiovascular complications in severe cases.

[0003] In the prior art, the electrolyte status and renal function are usually assessed by monitoring the ion levels in serum and urine. However, the integrity of 24-hour urine collection is difficult to ensure, and the sodium-potassium ratio of random urine samples can more accurately reflect the renal function status and electrolyte imbalance because it is not affected by urine volume and creatinine excretion. Therefore, long-term continuous monitoring using the urine sodium-potassium ratio has great clinical value. For this reason, the present invention proposes a neuroendocrine carcinoma prognosis monitoring method based on the urine sodium-potassium ratio. Summary of the invention

[0004] The purpose of the present invention is to provide a method for monitoring the prognosis of neuroendocrine cancer based on the urine sodium-potassium ratio, aiming to solve the problems raised in the above background technology.

[0005] The purpose of the present invention is achieved through the following technical solutions: A method for monitoring the prognosis of neuroendocrine cancer based on urine sodium-potassium ratio comprises the following steps: The excretion levels of sodium and potassium in random urine samples were measured, and the urine sodium-potassium ratio, that is, the ratio of urine sodium to urine potassium, was calculated; when the urine sodium-potassium ratio was ≥ 1.29, the risk of overall death and cancer-specific death increased.

[0006] Furthermore, for every one unit increase in the urine sodium-potassium ratio, the risk of overall mortality increased by 14% and the risk of cancer-specific mortality increased by 16%.

[0007] Furthermore, the urine sodium-potassium ratio is calculated by the following formula: STP =[urine sodium concentration( mmol / L )] / [urine potassium concentration( mmol / L)].

[0008] Compared with the prior art, the present invention has the following beneficial effects: The present invention calculates the urine sodium-potassium ratio (STP) by measuring the sodium and potassium excretion levels in random urine samples to evaluate the effects of electrolyte imbalance and tumors on the excretion function of the kidney and predict the patient's survival risk. By analyzing the retrospective data of 759 patients with neuroendocrine carcinoma (NEC), and using the Cox proportional hazard model and stratified analysis, the present invention revealed a significant correlation between STP and patient survival. The results showed that when STP ≥ 1.29, the patient's overall and cancer-specific mortality risk increased significantly. Specifically, for every unit increase in STP, the overall mortality risk increased by 14% and the cancer-specific mortality risk increased by 16%. Therefore, STP can be regarded as an important potential biomarker for the prognosis of NEC patients, which not only helps to identify high-risk patient groups, but also provides a scientific basis for the formulation of individualized treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is the fitting curve of the relationship between the sodium-potassium excretion ratio in urine and the risk of total mortality.

[0010] Figure 2 Figure 3 is the Kaplan-Meier survival curve based on the urine sodium-potassium ratio (STP) of patients with neuroendocrine carcinoma. The shaded area in the figure represents the 95% confidence interval; A is the relationship between the sodium-potassium ratio in urine and the overall survival rate of patients with neuroendocrine carcinoma, and B is the relationship between the sodium-potassium ratio in urine and the cancer-specific survival rate of patients with neuroendocrine carcinoma.

[0011] Figure 3 Figure 2 is a forest plot of the relationship between the sodium-potassium ratio in urine and the overall mortality and cancer-specific mortality of patients with neuroendocrine cancer; A is the hazard ratio of the overall mortality of different subgroups in model 1 and model 2, and B is the hazard ratio of the cancer-specific mortality of different subgroups in model 1 and model 2.

[0012] Figure 4 is the percentile distribution of sodium and potassium excretion in urine. DETAILED DESCRIPTION

[0013] In order to have a clearer understanding of the technical features, purposes and beneficial effects of the present invention, the technical solution of the present invention is now described in detail below, but it should not be construed as limiting the applicable scope of the present invention.

[0014] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0015] Example 1: Urine sample collection and STP calculation; 1) Urine sample collection: Random urine samples were collected from 759 patients diagnosed with neuroendocrine carcinoma (NEC). All urine samples were collected before the patients started any anticancer treatment to ensure the objectivity and consistency of the data. During the urine sample collection process, there was no limit on the time point of urine collection, and patients could collect urine at a convenient time to improve sampling compliance.

[0016] 2) Urine analysis: Urine samples were analyzed using the standard ion electrode method (ISE) to measure the sodium (Na + ) and potassium (K + ) concentration in mmol / L. To ensure the accuracy of the test results, all urine samples were tested within 12 hours of collection.

[0017] 3) STP calculation: STP is calculated using the following formula: STP =[urine sodium concentration( mmol / L )] / [urine potassium concentration( mmol / L )], and the STP of all patients was recorded and used for subsequent analysis.

[0018] Example 2: Prognostic assessment based on STP; 1) Collect basic information of patients; The basic information of the enrolled patients was collected and the experimental data related to this study were calculated. The baseline characteristics of the study population are shown in Table 1.

[0019] Table 1. Baseline Characteristics of the Study Population.

[0020]

[0021] In the table: positive and negative values ​​represent mean ± standard deviation. Percentages may not add up to 100 due to rounding. Cardiovascular and cerebrovascular diseases include: angina, stroke, hypertension. Salt intake: Extra salt added to food does not include salt used in cooking or salt contained in the food itself.

[0022] 2) Patient grouping; According to STP, patients were divided into two groups: STP ≥ 1.29 and STP < 1.29. 1.29 was the cutoff value. Among the 759 patients, patients with STP ≥ 1.29 were classified as high-risk group, and patients with STP < 1.29 were classified as low-risk group. Figure 1Figure 1 shows the fitted curve for the relationship between the ratio of sodium excretion in urine and the risk of total mortality. The solid line shows the fitted hazard ratio and the shaded area shows the 95% confidence interval. The reference point (Ref. point = 1.294) is marked and its hazard ratio is set to 1.0. The bars at the bottom represent the distribution of STP among the study participants. Figure 1 It can be seen that P The value is close to 0.05, indicating that there is a certain degree of nonlinear relationship. The STP values ​​of most patients are concentrated in the lower range. When STP < 1.29, the hazard ratio is lower than 1.0, which means that lower STP is associated with a lower risk of death; while when STP ≥ 1.29, the hazard ratio gradually increases, indicating that higher STP is associated with a higher risk of death.

[0023] The following conclusions can be drawn: patients with STP values ​​lower than 1.29 may be classified as a low-risk group because their risk of death is relatively low; patients with STP values ​​higher than 1.29 may be classified as a high-risk group because their risk of death increases with increasing STP. STP can be used as a potential biomarker to assess the risk of death in patients. Although the nonlinear relationship lacks statistical significance, the trend suggests that there is a certain association between STP and the risk of death.

[0024] 3) Prognostic analysis; The Cox proportional hazards regression model was used to analyze the relationship between STP and the overall and cancer-specific mortality risks of patients. The effects of each unit increase in STP on the overall and cancer-specific mortality risks were calculated, and the hazard ratio (HR) and 95% confidence interval (CI) were used for statistical description.

[0025] This study explored the hazard ratios and 95% confidence intervals for the association between STP and overall and cancer-specific mortality in 759 participants. Data were adjusted for variables in six different models. Variables can be grouped according to different attributes and characteristics. The following is a classification of these variables: basic demographic characteristics: age at diagnosis, sex; lifestyle factors: smoking history, sodium intake (additional salt added to food, excluding salt used in cooking or salt naturally present in food); health status and medical history: body mass index (BMI), diabetes, cardiovascular and cerebrovascular disease (including angina, stroke, hypertension), surgical history, other malignancies, use of diuretics; laboratory indicators: glucose, glycosylated hemoglobin (HbA1c), high-density lipoprotein (HDL), insulin-like growth factor 1 (IGF-1), triglycerides, cholesterol, uric acid, creatinine, C-reactive protein, urea, albumin; pathological characteristics: histology.

[0026] The six different models are as follows: Model 1: unadjusted model; Model 2: adjusted for age and sex at diagnosis; Model 3: Model 2 + body mass index, diabetes, smoking history, cardiovascular disease and cerebrovascular disease (including angina, stroke, hypertension); Model 4: Model 3 + histology, surgical history, and combined with other malignant tumors; Model 5: Model 4 + glucose + glycosylated hemoglobin + high-density lipoprotein + insulin-like growth factor 1 + triglycerides + cholesterol + uric acid + creatinine + C-reactive protein + urea + albumin; Model 6: Model 5 + diuretic use + sodium intake (extra salt added to food, excluding salt used in cooking or contained in the food itself).

[0027] The relationship between STP and overall mortality and cancer-specific mortality is shown in Table 2: Table 2. Relationship of STP with Overall Mortality and Cancer-Specific Mortality

[0028]

[0029] As can be seen in Table 2, the HR for overall mortality ranged from 1.09 to 1.16 in each model, and the HR for cancer-specific mortality ranged from 1.06 to 1.16 in each model. Among the 586 total deaths recorded, each unit increase in STP was associated with a 14% increase in the risk of death in the original unadjusted model (HR=1.14, 95% CI: 1.06~1.22). This significant association persisted in the adjusted model (HR=1.16, 95% CI: 1.05~1.28). Compared with STP < 1.29, higher STP was associated with an increased risk of death (HR=1.25, 95% CI: 1.04~1.52). Specifically, each unit increase in STP was associated with a 25% increase in the risk of overall death. Among the 549 cancer-specific deaths recorded, each unit increase in STP was associated with a 7% increased risk of cancer-specific death in the original unadjusted model (HR=1.07, 95% CI: 1.00-1.15). In the adjusted model, this association remained significant, with each unit increase in STP associated with a 16% increased risk of cancer-specific death (HR=1.16, 95% CI: 1.05-1.28). When comparing STP ≥ 1.29 with STP < 1.29, higher STP was associated with significantly higher cancer-specific death (HR=1.16, 95% CI: 0.95-1.42). Each unit increase in STP corresponded to a 16% increased risk of cancer-specific death.

[0030] In summary, this study confirmed the significant association and trend between higher STP and increased risk of overall and cancer-specific mortality through adjustment of six different models. This finding provides important reference for future research and clinical practice.

[0031] Kaplan-Meier survival curves were used to compare the survival rates of the high-risk group (STP ≥ 1.29) and the low-risk group (STP <1.29). Figure 2 As can be seen from A and B, the overall survival rate and cancer-specific survival rate of the high-risk group were significantly lower than those of the low-risk group, further confirming the effectiveness of STP as a prognostic indicator.

[0032] 3) Multi-factor adjustment analysis In the Cox proportional hazards regression model, patients' basic demographic characteristics (age, sex), lifestyle factors (smoking history, sodium intake), medical history (BMI, diabetes, cardiovascular disease) and tumor pathological characteristics (histological type, primary tumor site) were further included for adjustment.

[0033] Figure 3 Figure 2 is a forest plot of the relationship between urinary STP and overall and cancer-specific mortality in patients with neuroendocrine cancer. The forest plot shows the overall mortality of different subgroups in model 1 (unadjusted original model) and model 2 (model adjusted for body mass index, smoking history, histological type, surgical history, combined with other malignancies, and diuretic use) ( Figure 3 A) and cancer-specific mortality ( Figure 3 The hazard ratios for the risk of death in model 1 and model 2 were 1.35 and 1.41, respectively. In model 2, the hazard ratios were reduced, for example, the hazard ratios for the age group <66 years were 1.27 and 1.31, respectively. The hazard ratios for the age group <66 years were 1.27 and 1.31, respectively. PThe values ​​were all greater than 0.05, indicating that there were no significant interactions between these subgroups. The hazard ratios for cancer-related death also varied among the different subgroups. For example, in model 1, the hazard ratio for the group aged <66 years was 1.19, while the hazard ratio for the group aged ≥66 years was 1.21. In model 2, the hazard ratio for the group aged <66 years was 1.38, while the hazard ratio for the group aged ≥66 years was 1.15. Similarly, the hazard ratios for sex, age, salt intake, primary cancer site, and hypertension were not significantly different. P The values ​​were all greater than 0.05, indicating that there were no significant interactions among these subgroups.

[0034] Therefore, the following conclusions can be drawn: After adjusting for multiple confounding factors, the risk ratio of model 2 is generally lower than that of model 1, indicating that confounding factors may have an impact on the risk of death. P The values ​​were all greater than 0.05, indicating that there was no significant interaction between these subgroups. There was a certain association between STP and overall mortality and cancer-specific mortality. This association did not differ significantly among different subgroups and was relatively consistent in different populations. That is, the association between adjusted STP and mortality risk was still significant.

[0035] The following is a stratified analysis: 31) Stratified by sex and age; Patients were stratified by sex (male and female) and age (< 66 years and ≥ 66 years) to explore the relationship between STP and mortality risk in different groups.

[0036] The total number of deaths among female patients was 256, with HR of 1.31 (1.03-1.68) in model 1 and 1.31 (1.01-1.69) in model 2. The total number of deaths among male patients was 330, with HR of 1.35 (1.08-1.68) in model 1 and 1.25 (0.99-1.57) in model 2. The number of cancer-related deaths among female patients was 240, with HR of 1.31 (1.02-1.69) in model 1 and 1.35 (1.03-1.77) in model 2. The number of cancer-related deaths among male patients was 309, with HR of 1.09 (0.87-1.37) in model 1 and 1.09 (0.85-1.4) in model 2.

[0037] The total number of deaths among people aged <66 years was 233, with a HR of 1.35 (1.04-1.74) in model 1 and 1.27 (0.97-1.67) in model 2. The total number of deaths among people aged ≥66 years was 352, with a HR of 1.41 (1.14-1.74) in model 1 and 1.31 (1.04-1.65) in model 2. The number of cancer-related deaths among people aged <66 years was 220, with a HR of 1.19 (0.91-1.56) in model 1 and 1.38 (1.02-1.88) in model 2. The number of cancer-related deaths among people aged ≥66 years was 329, with a HR of 1.21 (0.97-1.5) in model 1 and 1.15 (0.91-1.45) in model 2.

[0038] From the above results, it can be seen that the prognostic significance of STP in each stratification (gender and age) remains consistent, that is, high STP is associated with a higher risk of death.

[0039] 32) Stratification by primary cancer site; Patients were stratified according to the primary site of cancer (such as lung, small intestine, pancreas, bladder, etc.) to verify the universal applicability of STP.

[0040] The total number of deaths with lung cancer as the primary site was 419, with a HR of 1.41 (1.16-1.71) in model 1 and 1.29 (1.05-1.59) in model 2. The total number of deaths with other primary sites (small intestine, pancreas, bladder, etc.) was 167, with a HR of 1.09 (0.8-1.48) in model 1 and 1.19 (0.86-1.64) in model 2. The number of cancer-related deaths with lung cancer as the primary site was 391, with a HR of 1.29 (1.06-1.58) in model 1 and 1.24 (1-1.54) in model 2. The number of deaths related to cancer whose primary sites were other cancers (small intestine, pancreas, bladder, etc.) was 158. The HR in model 1 was 0.94 (0.69-1.29), and the HR in model 2 was 1.02 (0.71-1.45).

[0041] From the above results, it can be seen that STP has a significant predictive effect on the risk of death in NEC patients with different primary sites.

[0042] 33) Stratification by sodium intake; According to the patients' dietary habits, the patients were divided into three groups: never adding salt, sometimes adding salt, and usually / always adding salt.

[0043] In the group of patients who never added salt, there were 254 total deaths, with a HR of 1.45 (1.13-1.85) in model 1 and 1.4 (1.08-1.82) in model 2. There were 172 total deaths in the group who sometimes added salt, with a HR of 1.31 (0.97-1.77) in model 1 and 1.2 (0.85-1.7) in model 2. There were 160 total deaths in the group who usually / always added salt, with a HR of 1.23 (0.9-1.68) in model 1 and 1.33 (0.95-1.87) in model 2.

[0044] In the group of patients who never added salt, there were 240 cancer-related deaths, with a HR of 1.1 (0.85-1.42) in model 1 and 1.08 (0.82-1.43) in model 2. There were 160 cancer-related deaths in the group who sometimes added salt, with a HR of 1.33 (0.97-1.83) in model 1 and 1.29 (0.9-1.85) in model 2. There were 149 cancer-related deaths in the group who usually / always added salt, with a HR of 1.24 (0.89-1.72) in model 1 and 1.27 (0.89-1.8) in model 2.

[0045] From the above results, it can be seen that the prognostic value of STP remained stable in all groups regardless of the amount of additional sodium intake.

[0046] 34) Stratification by hypertension; Patients were stratified according to the presence or absence of hypertension.

[0047] The total number of deaths with hypertension was 179, with a HR of 1.35 (1-1.82) in model 1 and 1.2 (0.87-1.66) in model 2. The total number of deaths without hypertension was 403, with a HR of 1.35 (1.11-1.64) in model 1 and 1.32 (1.08-1.62) in model 2.

[0048] The number of cancer-related deaths with hypertension was 170, and the HR was 1.26 (0.92-1.71) in model 1 and 1.13 (0.8-1.6) in model 2. The number of cancer-related deaths without hypertension was 375, and the HR was 1.19 (0.97-1.46) in model 1 and 1.21 (0.97-1.5) in model 2.

[0049] From the above results, it can be seen that regardless of whether the patients suffer from hypertension, STP has a significant predictive effect on the risk of death.

[0050] Example 3: Prognostic assessment based on urinary sodium and potassium excretion; Cox proportional hazards regression model was used to analyze the relationship between urinary sodium and potassium excretion and the overall and cancer-specific mortality risks of patients. The effect of each unit increase in excretion on the overall and cancer-specific mortality risks was calculated, and the hazard ratio (HR) and 95% confidence interval (CI) were used for statistical description.

[0051] This study explored the hazard ratios and 95% confidence intervals between urinary sodium and potassium excretion and overall mortality and cancer-specific mortality in 759 participants, and the results were adjusted in six different models (the same six different models in Example 2). The associations of urinary sodium and potassium excretion with overall mortality and cancer-specific mortality are shown in Table 3: Table 3. Association of Urinary Sodium and Potassium Excretion with Overall and Cancer-Specific Mortality.

[0052]

[0053] As can be seen from Table 3, for overall mortality, the corresponding hazard ratio for each increase of 10mmol / L in sodium excretion ranged from 1.02 to 1.03, indicating a small but consistent increase in the risk of overall mortality. In contrast, the hazard ratio for potassium excretion ranged from 0.98 to 0.99, showing no significant association with the risk of overall mortality. For cancer-specific mortality, the corresponding hazard ratio for each increase of 10mmol / L in sodium excretion was 1.01, and the hazard ratio for potassium excretion ranged from 0.97 to 0.98, indicating no significant association with the risk of cancer-specific mortality.

[0054] Figure 4 The percentile distribution of sodium and potassium excretion in the urine of participants is shown. The percentiles shown in the figure include the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles. For each percentile, the excretion values ​​of sodium and potassium are presented to compare their distribution in the population. As can be seen from the figure, for sodium and potassium, as the percentile increases, the excretion also increases. This indicates that in the population, individuals in higher percentiles excrete more sodium and potassium; sodium excretion is higher than potassium in all percentiles. This may reflect that people usually consume more sodium than potassium in their daily diet; at lower percentiles (such as 5 and 10), the difference in sodium and potassium excretion is small, while at higher percentiles (such as 90 and 95), this difference becomes more significant; the high sodium and low potassium excretion pattern may be associated with certain health problems, such as hypertension and cardiovascular disease. This distribution may indicate the need for health interventions in people with high sodium intake and low potassium intake.

[0055] The above are only preferred embodiments of the present invention. It should be pointed out that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention. These should also be regarded as the protection scope of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A method for monitoring the prognosis of neuroendocrine cancer based on urine sodium-potassium ratio, characterized in that: The following steps are involved: The excretion levels of sodium and potassium in random urine samples were measured, and the urine sodium-potassium ratio, that is, the ratio of urine sodium to urine potassium, was calculated; when the urine sodium-potassium ratio was ≥ 1.29, the risk of overall death and cancer-specific death increased.

2. The method for monitoring the prognosis of neuroendocrine cancer based on urine sodium-potassium ratio according to claim 1, characterized in that: For every one-unit increase in the urine sodium-potassium ratio, the risk of overall death increased by 14% and the risk of cancer-specific death increased by 16%.

3. The method for monitoring the prognosis of neuroendocrine cancer based on urine sodium-potassium ratio according to claim 1, characterized in that: The urine sodium-potassium ratio is calculated by the following formula: STP =[urine sodium concentration( mmol / L )] / [urine potassium concentration( mmol / L )].

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