Application of peripheral hematology index as survival outcome prediction marker of young early diagnosis stage IV NSCLC patient

Abstract

The invention belongs to a new application of a peripheral hematology index, and particularly relates to an application of the peripheral hematology index as a survival outcome prediction marker of an NSCLC patient in the early diagnosis stage IV of a young person, which focuses on the potential of the peripheral hematology index in predicting the survival outcome of the NSCLC patient in the early diagnosis stage IV of the young person. A calculation formula for integrating the peripheral hematology indexes into the risk Risk is provided for young NSCLC patients in the early diagnosis stage IV; the Risk risk stratification is applied to the NSCLC column diagram prognosis model in the early diagnosis stage IV of young people, so that the prediction efficiency of the prognosis model is remarkably enhanced.

Description

Technical Field

[0001] The present invention belongs to the new use of peripheral hematological indexes, and particularly relates to the application of peripheral hematological indexes as prediction markers for the survival outcome of young newly diagnosed stage IV NSCLC patients. Background Art

[0002] Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer and the main cause of cancer-related deaths in China and globally. In addition, its incidence shows an upward trend, and the age of patients at diagnosis is gradually decreasing. Due to the lack of sensitive early diagnosis methods, a considerable number of young patients are already in the locally advanced or advanced stage at the time of diagnosis, which has become a particularly difficult problem in clinical work.

[0003] Although the development of targeted therapy and immunotherapy has improved the survival rate of advanced NSCLC patients, the overall survival status is still not ideal. Precision medicine aims to optimize clinical decisions by classifying and identifying individuals. Although the TNM staging system has been widely used and continuously revised in clinical practice, there are still many limitations, especially in adapting to individual patient differences. Therefore, in view of the current progress of medical science, it is urgent to explore more comprehensive, accurate and personalized assessment methods for prognostic evaluation and assisting in formulating treatment plans.

[0004] As important diagnostic test results reflecting blood conditions, hematological parameters provide valuable information on disease activity and treatment response. Some studies have shown that various inflammatory and coagulation markers such as NLR, LMR, PLR, and D-dimer are of great significance for predicting the survival period of advanced non-small cell lung cancer patients. However, currently, the research on the relationship between hematological indexes and survival period in young NSCLC patients with stage IV distant metastasis at the time of initial diagnosis is relatively scarce, which poses a great challenge for clinicians to accurately predict prognosis and formulate personalized treatment strategies. Therefore, it is particularly important to conduct further research on potential biomarkers and prognostic factors in this specific group. The purpose of this study is to develop a combined nomogram model based on the survival prediction value of hematological indexes, in order to achieve convenient, accurate and personalized early prediction of the survival time of newly diagnosed stage IV NSCLC young patients, so as to provide reliable evidence for clinical decision-making and prognostic evaluation. Summary of the Invention

[0005] To overcome the above technical problems, the present invention provides the application of peripheral hematological indexes as prediction markers for the survival outcome of young newly diagnosed stage IV NSCLC patients.

[0006] The present invention provides the application of peripheral hematological indexes as predictive markers for the survival outcome of newly diagnosed young patients with stage IV NSCLC. The peripheral hematological indexes as predictive factors include white blood cell count, neutrophil count, monocyte count, hemoglobin level, D-dimer, NLR, LMR, PLR, and SII.

[0007] Through multivariate regression analysis and screening, it was found that D-dimer and NLR were negatively correlated with survival, while LMR was positively correlated with survival. A calculation formula for integrating the risk Risk was constructed using these three indexes, and survival risk stratification was performed based on the value of Risk. The Risk risk stratification was applied to the nomogram prognostic model for newly diagnosed young patients with stage IV NSCLC, enhancing the predictive efficacy of the prognostic model.

[0008] Preferably, the Risk formula: Risk = 0.107 * D-dimer + 0.108 * NLR - 0.323 * LMR.

[0009] The present invention provides a method for predicting the survival outcome of newly diagnosed young patients with stage IV NSCLC using peripheral hematological indexes, including: Construct the Risk formula: Risk = 0.107 * D-dimer + 0.108 * NLR - 0.323 * LMR; Perform univariate analysis on clinical variables. The results showed that BMI, gene mutation, liver metastasis, chemotherapy, and targeted therapy were influencing factors for OS. After incorporating the factors with P value < 0.05 into the multivariate Cox regression analysis, the results showed that liver metastasis and targeted therapy were independent prognostic factors affecting OS; Construct a nomogram model including liver metastasis and targeted therapy to predict OS; Optimize the nomogram model by integrating the Risk factor to obtain the OS prediction model.

[0010] The present invention provides the above-mentioned OS prediction model.

[0011] Compared with the prior art, the present invention: (1) Focuses on the potential of peripheral hematological indexes in predicting the survival outcome of newly diagnosed young patients with stage IV NSCLC; (2) Proposes a calculation formula for integrating the risk Risk of peripheral hematological indexes in the population of newly diagnosed young patients with stage IV NSCLC; (3) Applies the Risk risk stratification to the nomogram prognostic model for newly diagnosed young patients with stage IV NSCLC, thus significantly enhancing the predictive efficacy of the prognostic model. Description of the Drawings

[0012] Figure 1It is a schematic diagram of constructing risks using the least absolute shrinkage and selection operator (LASSO) model in the training cohort; Figure 2 It is a heatmap of the Cox regression risk score distribution, prognostic relationship, and binary classification data of inflammatory components in the training group (I) and validation group (II); Figure 3 It is the survival curves of different Risk score training groups (A) and validation groups (B); Figure 4 It is a nomogram for predicting the 1-, 2-, 3-, and 5-year survival probabilities of young patients with stage IV NSCLC (A nomogram constructed with only clinical factors, B nomogram integrated with Risk); Figure 5 It is a schematic diagram showing the accuracy of prognosis in the training group (A) and validation group (B) by ROC curve analysis; Figure 6 It is the calibration curves for predicting the 1-, 2-, 3-, and 5-year OS probabilities in the training groups (A, B, C, D) and validation groups (E, F, G, H); Figure 7 It is the prediction effect of the comparison of two models shown by the DCA curve. Detailed implementation manners

[0013] The embodiments of the present invention are described in detail below. Unless otherwise specified, the raw materials, equipment, etc. used can be purchased from the market or are commonly used in the art. The methods in the embodiments, unless otherwise stated, are conventional methods in the art. The following embodiments are exemplary and are only used to explain the present invention and should not be construed as a limitation of the present invention.

[0014] A total of 215 patients with newly diagnosed stage IV young non-small cell lung cancer (NSCLC) admitted to our hospital in the past 10 years were included in this study. By comprehensively analyzing the relationship between peripheral blood indexes before treatment and survival of these patients, the aim was to find more accurate and individualized survival prediction biomarkers to provide valuable guidance and reference for clinical practice.

[0015] 1. Construct the risk index Risk and verify 1.1 The patients included in the analysis were randomly divided into a training set and a validation set at a ratio of 7:3 using the bootstrapping technique. The optimal cut-off values of each inflammatory index of the training set patients were calculated and divided into high and low groups according to the cut-off values.

[0016] In the training group, white blood cell count, neutrophil count, monocyte count, hemoglobin level, D-dimer value, neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) had an impact on OS (P < 0.05) (see Table 1).

[0017] In the training group, inflammatory indicators with p < 0.05 in the univariate Cox analysis of OS were included in the LASSO-Cox regression model to establish a prognostic scoring system for analyzing the relationship between inflammatory indicators and survival ( Figure 1 、 Figure 2 ).

[0018] Table 1 Influence of inflammatory-related factors on OS in the training group by univariate Cox analysis 1.2 Construction of the Risk formula: Risk = 0.107*D-dimer + 0.108*NLR - 0.323*LMR, and patients were divided into high-risk group (high Risk) and low-risk (low Risk) groups according to the values, and the survival differences between the high-risk group and the low-risk group were analyzed.

[0019] The results showed that in both the training set and the validation set, the survival of the high Risk group was significantly lower than that of the low Risk group. Figure 3 .

[0020] 2. Construction of a nomogram model for the survival of newly diagnosed young patients with stage IV non-small cell lung cancer 2.1 Univariate and multivariate analysis of survival Univariate analysis of clinical variables showed that BMI, gene mutation, liver metastasis, chemotherapy, and targeted therapy were influencing factors for OS. After including factors with P value < 0.05 in the multivariate Cox regression analysis, the results showed that liver metastasis and targeted therapy were independent prognostic factors for OS. Table 2.

[0021] Table 2 Univariate and multivariate Cox analysis of the survival of clinical characteristics of young patients with stage IV non-small cell lung cancer 2.2 Construction of the nomogram model for survival According to the results of the Cox regression analysis, a nomogram model including two factors, liver metastasis and targeted therapy, was constructed in the training group (P<0.05) to predict OS ( Figure 4 A).

[0022] Subsequently, the nomogram model was optimized by integrating the Risk factor ( Figure 4B). The addition of Risk significantly improved the accuracy of OS prediction, with a C-index of 0.765, while the C-index was 0.664 when using only clinical factors.

[0023] 3. Validation of the nomogram model The discrimination performance of the nomogram model was evaluated using the C-index and AUC.

[0024] The C-index of the nomogram model without inflammatory indicators (constructed only by clinicopathological factors) was 0.664 (95%CI: 0.643 - 0.685). The AUCs at 1, 2, 3, and 5 years were 0.738, 0.696, 0.703, and 0.671, respectively.

[0025] The C-index of the nomogram model after adding the risk score constructed from inflammation-related factors was 0.765 (95%CI: 0.749 - 0.781). The AUCs at 1, 2, 3, and 5 years increased to 0.852, 0.873, 0.847, and 0.774. ( Figure 5 )

[0026] The calibration curves of the two nomograms showed that the nomogram with Risk had better discrimination ( Figure 6 )

[0027] The DCA curve analysis showed that the net clinical benefit of the comprehensive nomogram with Risk was significantly better than that of the nomogram constructed only by clinicopathological factors ( Figure 7 )

[0028] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to the above embodiments without departing from the principles and purposes of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. Application of peripheral hematological indicators as predictive markers for survival outcomes in young patients with newly diagnosed stage IV NSCLC, characterized in that: Peripheral hematological indices used as predictors included white blood cell count, neutrophil count, monocyte count, hemoglobin level, D-dimer value (D-dimer), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and systemic immune inflammatory index (SII).

2. The use of peripheral hematological indicators according to claim 1 as predictive markers for survival outcomes in young newly diagnosed stage IV NSCLC patients, characterized in that: Peripheral hematological indicators were integrated into the risk calculation formula and survival risk was stratified according to the Risk value. Risk stratification was applied to the nomogram prognostic model for young people with newly diagnosed stage IV NSCLC to enhance the predictive efficacy of the prognostic model.

3. The use of peripheral hematological indicators according to claim 2 as predictive markers for survival outcomes in young newly diagnosed stage IV NSCLC patients, characterized in that: Risk formula: Risk = 0.107*D-dimer + 0.108*NLR - 0.323*LMR.

4. A method for predicting the survival outcome of young patients with newly diagnosed stage IV NSCLC using peripheral hematological indicators, characterized by: Construct the Risk formula: Risk = 0.107*D-dimer + 0.108*NLR - 0.323*LMR; A nomogram model including liver metastasis and targeted therapy was constructed to predict OS; The OS prediction model was obtained by integrating the Risk factors to optimize the nomogram model.

5. The OS prediction model according to claim 4.

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