Association of baseline plasma D-dimer and platelets with progression-free survival in patients with stage IV non-small cell lung cancer treated with anti-PD-1 antibody

Introduction

Lung cancer is a common malignancy, with approximately 80–85% of cases diagnosed as non-small cell lung cancer (NSCLC). Due to the lack of effective early detection methods, most patients are diagnosed at stage IV, which greatly limits treatment options and reduces the five-year survival rate (1). With the rapid development of immunotherapy, treatment strategies for stage IV NSCLC are evolving. Immune checkpoint inhibitors (ICIs), such as programmed cell death protein 1 (PD-1) inhibitors, have significantly transformed the treatment strategy for NSCLC, particularly in patients without epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) gene mutations. Compared to chemotherapy alone, anti-PD-1 antibody plus chemotherapy can effectively extend progression-free survival (PFS) and overall survival (OS), and improve objective response rate (ORR) (2-4). Currently, immunotherapy combined with chemotherapy has been approved as a first-line treatment for some stage IV NSCLC patients without EGFR and ALK driver gene mutations (4). Anti-PD-1 antibody plus chemotherapy may offer greater clinical benefits for NSCLC patients compared to chemotherapy alone. Combining different antitumor agents can help limit tumor cell resistance to a single therapy (5). Despite chemotherapy drugs’ cytotoxic effects on immune cells, increasing evidence shows that in NSCLC, chemotherapy can enhance tumor cell immunogenicity and the cytotoxicity of T cells and natural killer (NK) cells. It also modulates the tumor immune microenvironment by downregulating myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs), and reducing inhibitory cytokines like transforming growth factor (TGF)-β, thus boosting the antitumor effects of ICIs (2,5).

However, many lung cancer patients do not respond to ICIs, even when combined with chemotherapy. Additionally, ICIs are expensive and can cause severe immune-related adverse effects in some patients (1). Therefore, identifying biomarkers that predict the efficacy and prognosis of anti-PD-1 antibody in NSCLC patients is crucial for advancing more effective, precise, and individualized treatment plans. Currently, biomarkers such as programmed cell death ligand 1 (PD-L1) expression levels, tumor mutation burden, immune-related adverse reactions, and DNA damage repair deficiencies have been reported as predictors of treatment response and prognosis in NSCLC patients (1,6-8). However, these biomarkers have not accurately predicted responses to anti-PD-1 antibody in NSCLC patients, and their associated testing methods face significant challenges in clinical practice. Therefore, identifying simpler and more precise predictive biomarkers for NSCLC patients undergoing anti-PD-1 antibody is urgent.

Patients with cancer often exhibit abnormalities in coagulation and fibrinolysis. The interaction between cancer cells and endothelial cells could lead to coagulation-fibrinolysis disorders, the release of cancer pro-coagulants and tissue factors (TF), the production of cytokines, and an increase in D-dimer levels (9). D-dimer is a soluble degradation product of fibrin and serves as an indicator of coagulation and fibrinolysis status in the body (10). In tumor, elevated levels of D-dimer suggest a hypercoagulable state and increased secondary fibrinolysis, and these changes may reflect alterations in the tumor microenvironment (TME), such as angiogenesis and matrix remodeling. Such changes can promote tumor invasion and metastasis, increase the likelihood of developing resistance, reduce treatment effectiveness, and adversely affect PFS and OS (9,11). The coagulation and fibrinolytic systems are closely related to the progression of malignant tumors. Studies have reported that high D-dimer levels are associated with poor prognosis in various cancers, including gastric cancer, esophageal cancer, colorectal cancer, malignant melanoma, hepatocellular carcinoma, and breast cancer (9,12-16). In NSCLC, high plasma D-dimer levels have garnered attention as they indicate activation of the coagulation and fibrinolysis systems in these patients (10). Research has found that activation of the coagulation system is linked to tumor angiogenesis, invasion, and metastasis, ultimately leading to poorer prognosis (17,18). Similarly, activation of the fibrinolytic system can promote cancer cell growth and migration while reducing immune responses mediated by inflammatory cells, thereby accelerating tumor progression (19). Fibrinolytic factors are involved in many physiological functions, including cell migration, angiogenesis, inflammation, apoptosis, and fibrosis. They also participate in tumorigenesis through these pathways (20). Plasmin-treated dendritic cells not only fail to undergo maturation following phagocytosis, but also reducing their migration to lymph nodes. At the same time, it significantly increases the release of the immunosuppressive cytokine TGF-β (21). Additionally, fibrinolysin stimulates the growth and proliferation of tumor cells by activating various growth factors, including platelet-derived growth factors and fibroblast growth factor 2 (20). This literature also points out that urokinase-type plasminogen activator activates plasminogen to plasmin, which in turn activates pro-matrix metalloproteinases, leading to the breakdown of the extracellular matrix. This process facilitates the migration of tumor cells. Current research has found that higher D-dimer levels are associated with poor prognosis in NSCLC patients. Elevated plasma D-dimer levels are an independent risk factor for poor outcomes and can serve as an important indicator for assessing tumor spread, metastasis, and disease progression (17,22). D-dimer is easy to measure, cost-effective, and easy to apply, making it ideal for pre-processing inspection and applications. To some extent, this has enhanced the diagnostic and therapeutic efficiency of diseases and the predictive value of coagulation-fibrinolysis-related biomarkers. Therefore, D-dimer levels reflecting cancer activity may serve as prognostic markers for survival and treatment outcomes.

Platelets, a key component of the blood system, are also an important part of the TME in solid tumors. It is believed that there are direct or indirect interactions between platelets and tumor cells, maintaining tumor cell proliferation, metastasis, and immune evasion. For example, platelets can bind to the surface of tumor cells to form microaggregates, creating a physical barrier that protects tumor cells from immune cell attacks. Stimulating factors secreted by tumor cells, such as platelet-activating adenosine diphosphate, immunoglobulin (Ig) G, or functional proteins, can significantly alter the proteomic and transcriptomic profiles of platelets, enhancing their pro-coagulant, pro-angiogenic, and pro-metastatic characteristics (23). Other mediators released by cancer cells that can activate platelets include cancer pro-coagulant factors, which are proteases that activate factor X independently of TF. Additionally, the enzyme heparinase, which enhances TF activity found in platelets. The release of heparinase induces a positive feedback loop that increases TF activation, thereby activating platelets and leading to increased release of heparinase. The pro-coagulant activity mediated by heparinase has been found to be elevated in patients with NSCLC (24). Platelets, along with their stored growth factors, proteases, and small molecules, contribute to tumor growth, invasion, and angiogenesis. They play a crucial role in promoting an immunosuppressive T-cell phenotype, weakening the cytotoxic function of NK cells, and providing a physical barrier that helps tumor cells evade the immune system and facilitates metastasis to other organs (25-27). Additionally, tumor cells can directly interact with platelets in the TME and transfer PD-L1 to the platelets. High expression of platelet PD-L1 is associated with an immunosuppressive TME and plays a significant role in immune evasion in NSCLC, thereby reducing OS and PFS in these patients (26,28). On top of that, in patients with ovarian cancer and gastric cancer, there is a higher incidence of thrombocytosis associated with hematogenous metastasis and recurrence (29,30). Other studies indicate that elevated platelet counts are associated with an increased risk of lung cancer and higher levels at the initial diagnosis are linked to shorter PFS and OS in NSCLC. The findings also highlight the potential therapeutic strategy of combining antiplatelet treatment with lung cancer therapy (31-33). These are all explained that an increase in platelet count may serve as a predictive factor for certain cancers and a method for monitoring tumor progression.

It is well known that platelets and D-dimer are the most fundamental hematological biomarkers reflecting the coagulation and fibrinolytic status of patients’ physiological systems. Tumor cells can promote platelet activation through various pathways. Tumor cells can secrete soluble pro-coagulant factors, such as thrombin and extracellular vesicles carrying TF, which can enter the bloodstream in a receptor-dependent manner to activate platelets (23). Additionally, high-mobility group box 1 protein released by tumor cells can activate platelets by interacting with Toll-like receptor 4 expressed on platelets (34). The C-type lectin-like receptor 2 expressed on platelets can bind to the surface ligand podoplanin expressed by tumor cells, which plays a crucial role in inducing platelet activation, ultimately leading to cancer-associated thrombosis (23,35). Studies have reported on baseline plasma D-dimer levels predicting treatment efficacy and prognosis in lung cancer patients (17-19,36,37). However, most researches analyzed D-dimer levels based on tumor stage (I–IV) and histological subtypes (NSCLC and SCLC), with relatively limited data specifically in stage IV NSCLC patients without driver gene mutations (17-19,36,37). The predictive value of baseline plasma D-dimer for anti-PD-1 antibody in stage IV NSCLC patients without driver gene mutations remains unclear. While some evidence links platelet counts to cancer prognosis, most studies focus on platelets’ relationship with NSCLC metastasis (38) or their predictive value in operable NSCLC patients (39). Although some research has explored platelet count’s impact on prognosis in initially treated, unresectable stage III/IV NSCLC patients (40), studies on platelet count’s correlation with efficacy and prognosis in stage IV NSCLC patients without driver gene mutations receiving anti-PD-1 antibody are scarce. Therefore, this study focuses on investigating the correlation between baseline plasma D-dimer levels and platelet count and the prognosis and treatment efficacy in stage IV NSCLC patients. Notably, it explores the impact of these markers before first-line anti-PD-1 antibody on prognosis and efficacy. Given that stage IV NSCLC patients are often treated with anti-PD-1 antibody combined chemotherapy, this study includes a control group of patients treated with chemotherapy alone to compare with the anti-PD-1 plus chemotherapy group, aiming to validate the relationship between baseline plasma D-dimer and platelet levels and prognosis and treatment efficacy in stage IV NSCLC patients receiving anti-PD-1 antibody. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-763/rc).

Methods

Study design and data collection

This study included 150 stage IV NSCLC patients diagnosed at Tianjin Medical University Cancer Institute from January 1, 2018, to May 19, 2024, for a retrospective analysis. Given that stage IV NSCLC patients are often treated with anti-PD-1 antibody combined chemotherapy, this study included a control group of patients treated with chemotherapy alone to compare with the anti-PD-1 plus chemotherapy group, aiming to validate the relationship between baseline plasma D-dimer and platelet levels and prognosis and treatment efficacy in stage IV NSCLC patients without driver gene mutations receiving anti-PD-1 antibody. The study divided 150 stage IV NSCLC patients into two groups: 100 in the anti-PD-1 plus chemotherapy group and 50 in the chemotherapy group. Besides collecting the patients’ demographic and pathological characteristics (including sex, age, smoking history, histological type, metastatic sites, clinical stage, ECOG performance status, PD-L1 expression, baseline plasma D-dimer, and platelet levels), in the anti-PD-1 antibody plus chemotherapy group, this study also collected specific information on the anti-PD-1 antibodies used and the cycles of application during first-line treatment. In the chemotherapy-only group, this study collected the number of patients who received immunotherapy after first-line chemotherapy. Disease evaluations followed the Response Evaluation Criteria in Solid Tumors (RECIST) Version 1.1, categorized as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD) and so on. According to the Eastern Cooperative Oncology Group (ECOG) physical status score, the overall activity level and daily living activities of cancer patients are quantified. The primary endpoint was PFS, defined as the time from first immunotherapy or chemotherapy to disease progression or death, whichever occurs first. The secondary endpoint was ORR, defined as the proportion of patients with tumor volume reduction to a predefined level maintained for a specified duration. Demographic, clinical, and laboratory data were collected, and patients were followed electronic medical records and telephone and the disease assessment for patients was mostly conducted by the radiology department of our hospital. For some patients who had completed imaging examinations at other hospitals, our hospital reassessed their conditions and made corresponding judgments based on the imaging data provided by the patients. The follow-up deadline was on May 19, 2024.

Patients

All patients in this study met the following inclusion criteria and did not meet any of the exclusion criteria. Inclusion criteria were: (I) the tumor pathology type is NSCLC; (II) basic and clinical information can be fully obtained; (III) the stage IV according to the 8th edition of the TNM classification for NSCLC; (IV) without driver gene mutations such as EGFR or ALK.; (V) patients in the first-line treatment group receiving anti-PD-1 antibody plus chemotherapy received at least four weeks and one effective disease assessment; (VI) first-line chemotherapy-only patients received at least four weeks of treatment and one effective disease assessment; (VII) blood samples for D-dimer and platelet levels were taken within two weeks before starting chemotherapy (for example, pemetrexed, paclitaxel, carboplatin, cisplatin) or anti-PD-1 antibodies (for example, pembrolizumab, sintilimab, tislelizumab, Camrelizumab). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Institutional Review Committee of Tianjin Medical University Cancer Hospital (No. bc2020198) and individual consent for this retrospective analysis was waived.

Statistical analysis

We first compared the baseline characteristics between the anti-PD-1 antibody plus chemotherapy group and the chemotherapy-only group (including sex, age, smoking history, histological type, metastatic sites, clinical stage, ECOG performance status, PD-L1 expression, baseline plasma D-dimer, and platelet levels). Subsequently, Kaplan-Meier survival curves were compared using log-rank tests in different treatment groups (anti-PD-1 plus chemotherapy group vs. chemotherapy-only group), and univariate and multivariate Cox regression analyses identified independent risk factors for PFS. Chi-squared tests were also conducted to compare ORR differences between treatment groups (anti-PD-1 plus chemotherapy group vs. chemotherapy-only group), with univariate and multivariate logistic regression analyses to determine risk factors for ORR.

To ensure the operability of subsequent statistics, we used the upper limits of normal values for D-dimer and platelets as the cutoff values. The patients were categorized into low and high subgroups, with a baseline cutoff value of 500 ng/mL for D-dimer and a cutoff value of 350×10⁹/L for platelets before treatment. The grouping criteria are as follows: baseline D-dimer >500 ng/mL was categorized as the high D-dimer group; baseline D-dimer ≤500 ng/mL was categorized as the low D-dimer group; baseline platelet >350×10⁹/L was categorized as the high platelet group; baseline platelet ≤350×10⁹/L was categorized as the low platelet group. In the anti-PD-1 plus chemotherapy group, Kaplan-Meier survival curves for high and low D-dimer subgroups as well as high and low platelet subgroups, and validate the results using the log-rank test. Hazard ratios (HRs) were estimated with univariate and multivariate Cox regression models. Chi-squared tests, as well as univariate and multivariate logistic regression analyses, were used to examine the differences in ORR between for high and low D-dimer subgroups, as well as high and low platelet subgroups, in the anti-PD-1 plus chemotherapy group. In addition to analyzing the individual impact of baseline D-dimer and platelet levels on patient prognosis and treatment efficacy, this study also combined these two variables in stage IV NSCLC patients without driver gene mutations receiving anti-PD-1 plus chemotherapy group, analyze them pairwise to explore the combined predictive value of these two indicators for PFS and ORR. Given that D-dimer levels and platelet count are quantitative data, we planned to use restricted cubic splines to further investigate their (non-)linear relationships with the risk of PFS. Similar analyses were performed for the chemotherapy group. Statistical significance was set at P<0.05, and analyses were conducted using SPSS (version 24.0), GraphPad Prism (version 9.5), and R (version 4.1.1).

Results

Patient characteristics

The flowchart of this study is depicted in Figure 1. Table 1 details the characteristics of the 150 stage IV NSCLC patients who received first-line treatments between January 2018 and May 2024. The cohort included 121 men (80.67%) and 29 women (19.33%), with a median age of 65 years (range, 35 to 84 years). Notably, 72.67% were current or former smokers, and 40.67% had squamous cell carcinoma. The ECOG performance scores of most patients is 0 and 1 (127 of 150). In the anti-PD-1 plus chemotherapy group, the majority of patients received anti-PD-1 antibodies for 5 to 35 cycles (83 of 100). In the chemotherapy group, 26 patients received immunotherapy in subsequent treatments after progression during first-line chemotherapy. During follow-up, 112 of the 150 patients experienced disease progression or died.

Figure 1 Flowchart of inclusion and exclusion criteria. NSCLC, non-small cell lung cancer; PD-1, programmed cell death protein 1; PLT, platelet; EGFR, epidermal growth factor receptor; ALK, anaplastic lymphoma kinase.

The analysis of patients with stage IV NSCLC based on different treatment groups

We first generated survival curves for the 150 stage IV NSCLC patients using the Kaplan-Meier method to compare PFS between the anti-PD-1 plus chemotherapy group and the chemotherapy group, with validation using the log-rank test. The median PFS was 8.5 months for the combination therapy group and 5.5 months for the chemotherapy group (P<0.001). The PFS curves are shown in Figure 2.

Figure 2 Kaplan-Meier survival analysis of different treatments in patients with stage IV non-small cell lung cancer (log-rank test). PD-1, programmed cell death protein 1.

Subsequently, univariate Cox regression analysis identified that differences in treatment regimens [HR: 0.458, 95% confidence interval (CI): 0.306–0.686, P<0.001], platelet count (HR: 1.799, 95% CI: 1.170–2.766, P=0.007), and baseline D-dimer levels (HR: 2.002, 95% CI: 1.307–3.066, P=0.001) were associated with PFS in stage IV NSCLC patients without driver gene mutations. Multivariate Cox regression analysis confirmed that first-line chemotherapy regimen (HR: 0.473, 95% CI: 0.316–0.711, P<0.001), high baseline D-dimer levels (HR: 1.937, 95% CI: 1.262–2.973, P=0.002), and high platelet count (HR: 1.576, 95% CI: 1.021–2.432, P=0.04) were independently associated with shorter PFS in these patients (Table 2).

We compared the ORR between the anti-PD-1 plus chemotherapy group and the chemotherapy group using Pearson’s Chi-squared test. The ORR was 46.00% for the anti-PD-1 plus chemotherapy group and 14.00% for the chemotherapy group, with a statistically significant difference (P<0.001). Univariate logistic regression analysis revealed that only the treatment regimen (HR: 5.233, 95% CI: 2.148–12.747, P<0.001) are significantly associated with ORR in stage IV NSCLC patients, however, baseline D-dimer (HR: 1.305, 95% CI: 0.637–2.677, P=0.47) and platelet (HR: 0.541, 95% CI: 0.224–1.307, P=0.17) levels did not significantly affect ORR.

Analysis of the anti-PD-1 antibody plus chemotherapy group

Survival and efficacy analysis according to baseline D-dimer and platelet levels: single variable analysis

Using the Kaplan-Meier method, survival curves were plotted and analyzed for high and low D-dimer subgroups as well as high and low platelet subgroups in the anti-PD-1 plus chemotherapy group, with log-rank tests used for validation. The median PFS for the high and low D-dimer subgroups was 8.0 and 13.0 months, respectively. For the high and low platelet subgroups, the median PFS was 6.5 and 9.5 months, respectively. The statistical analysis showed that: patients with high baseline D-dimer levels had significantly shorter PFS compared to those with low D-dimer levels (P=0.005) (Figure 3A). Patients with high baseline platelet levels also had significantly shorter PFS compared to those with low platelet levels (P=0.005) (Figure 3B).

Figure 3 Kaplan-Meier survival analysis of different baseline (A) D-dimer levels and (B) platelet count in the programmed cell death protein 1 antibody plus chemotherapy group (log-rank test).

Pearson’s Chi-squared test was used to analyze ORR differences between the high and low D-dimer subgroups in this group. The ORR was 48.44% in the high D-dimer group and 41.76% in the low D-dimer group (P=0.51). Similarly, the ORR was 47.62% in the high platelet group and 45.57% in the low platelet group, without significant difference (P=0.87).

Survival and efficacy analysis according to baseline D-dimer and platelet levels: variable combination analysis

Based on the above statistical results, we further analyzed the combined effect of baseline D-dimer and platelet levels by dividing 100 patients into four groups: D-dimer_High PLT_High (n=14), D-dimer_High PLT_Low (n=50), D-dimer_Low PLT_High (n=7), and D-dimer_Low PLT_Low (n=29). The median PFS for the four groups was 6.5, 8.0, 7.0, and 20.0 months, respectively. Pairwise comparisons revealed that the PFS differences in the D-dimer_Low PLT_Low group were statistically significant compared to the D-dimer_High PLT_High, D-dimer_High PLT_Low, D-dimer_Low PLT_High groups (P values were 0.003, 0.007 and 0.02, respectively). However, the PFS differences between the D-dimer_High PLT_High and D-dimer _High PLT_Low groups, D-dimer_High PLT_High and D-dimer_Low PLT_High groups, D-dimer_High PLT_Low and D-dimer_Low PLT_High groups were not statistically significant (P values were 0.27, 0.90 and 0.27, respectively). Therefore, we defined the D-dimer_Low PLT_Low group as the normal group and the remaining three groups as the abnormal group for PFS analysis. The median PFS for the normal and abnormal groups was 20.0 and 7.5 months, respectively (P<0.001) (Figure 4).

Figure 4 Kaplan-Meier survival analysis of baseline D-dimer combined platelet (log-rank test).

We also analyzed the ORR for the four groups: D-dimer_High PLT_High (n=14), D-dimer_High PLT_Low (n=50), D-dimer_Low PLT_High (n=7), and D-dimer_Low PLT_Low (n=29). The ORR for the four groups was 21.4%, 56.0%, 28.6%, and 44.8%, respectively, and the overall ORR difference was not statistically significant (P = 0.09).

Univariate and multivariate Cox proportional hazards regression analyses of predictive classifications for PFS

Univariate Cox regression analysis revealed that high baseline plasma D-dimer (HR: 2.114, 95% CI: 1.231–3.631, P=0.007) and high platelet levels (HR: 2.136, 95% CI: 1.233–3.700, P=0.007) are significant factors affecting PFS in stage IV NSCLC patients undergoing anti-PD-1 antibody plus chemotherapy. Multivariate Cox regression analysis confirmed that high baseline plasma D-dimer levels (HR: 2.031, 95% CI: 1.181–3.492, P=0.01) and high platelet count (HR: 2.013, 95% CI: 1.163–3.485, P=0.01) are independent prognostic factors for PFS in these patients (Table 3). The study did not find significant correlations between sex, age, smoking history, histological type, stage, and patient prognosis. Additionally, statistical analysis of distant metastasis sites (lung, pleura, abdomen, and brain) did not confirm their association with PFS in stage IV NSCLC patients treated with anti-PD-1 antibodies.

(Non-)linear correlation test between baseline D-dimer, platelets, and PFS

Considering that baseline plasma D-dimer and platelet count are continuous variables, we performed restricted cubic spline regression to analyze the non-linear relationship between these variables and PFS risk (Figure 5). Nonlinear correlations are observed (for nonlinearity, P=0.01 for D-dimer, P=0.01 for platelet).

Figure 5 Non-linear association between baseline (A) D-dimer or (B) platelet and risk of PFS determined using restricted cubic spline regression. PFS, progression-free survival; HR, hazard ratio; CI, confidence interval.

Analysis of the chemotherapy group

Kaplan-Meier survival curves were generated and analyzed for high and low D-dimer subgroups as well as high and low platelet subgroups within the chemotherapy group, with log-rank tests for validation. The median PFS for the high D-dimer group was 4.5 months, compared to 7.0 months for the low D-dimer group (P=0.14) (Figure S1A). Similarly, the median PFS for the high platelet group was 4.5 months, versus 6.5 months for the low platelet group (P=0.73) (Figure S1B).

We then analyzed the differences in ORR between high and low D-dimer subgroups and high and low platelet subgroups in the chemotherapy group using Chi-squared correction tests. The ORR for the high D-dimer group and low D-dimer group were 17.14% and 6.67% (P=0.59). The ORR for the high platelet group and low platelet group were 27.27% and 10.26% (P=0.35).

Discussion

This study collected demographic information and clinical-pathological characteristics of stage IV NSCLC patients without driver gene mutations to explore the predictive value of baseline peripheral blood D-dimer and platelet levels as biomarkers for prognosis and efficacy of anti-PD-1 antibody. The results indicate: (I) first-line treatment regimen, baseline D-dimer and platelet levels are independent prognostic factors for stage IV NSCLC patients (P values were <0.001, 0.002 and 0.04, respectively). Patients receiving first-line anti-PD-1 antibody plus chemotherapy have better prognosis, while those with high baseline D-dimer and platelet levels have worse prognosis; (II) differences in first-line treatment regimens are associated with ORR in stage IV NSCLC patients (P<0.001). The ORR is higher in patients receiving first-line anti-PD-1 antibody plus chemotherapy, though baseline D-dimer and platelet levels do not show a significant correlation with ORR (P=0.47 for D-dimer, P=0.17 for platelet); (III) baseline D-dimer and platelet levels are independent prognostic factors for stage IV NSCLC patients receiving anti-PD-1 antibody plus chemotherapy, with high levels associated with worse PFS; (IV) in the anti-PD-1 antibody plus chemotherapy group, analysis combining baseline D-dimer and platelet levels shows that the D-dimer_Low PLT_Low group has the best prognosis, while the PFS differences in the D-dimer_Low PLT_Low group were statistically significant compared to the D-dimer_High PLT_High, D-dimer_High PLT_Low, D-dimer_Low PLT_High groups (P values were 0.003, 0.007 and 0.02, respectively). The overall difference in ORR among the four groups was not statistically significant (P=0.09); (V) baseline plasma D-dimer and platelet levels have a nonlinear correlation with PFS risk in stage IV NSCLC patients (non-linearity, P=0.01 for D-dimer, P=0.01 for platelet); (VI) in the anti-PD-1 antibody plus chemotherapy group, there are no significant differences in ORR between high and low D-dimer subgroups or high and low platelet subgroups (P=0.51 for D-dimer subgroups, P=0.87 for platelet subgroups), so the correlation between baseline plasma D-dimer and platelet levels with ORR in the anti-PD-1 plus chemotherapy group could not be confirmed; (VII) in the chemotherapy group, baseline plasma D-dimer and platelet levels have not been observed to correlate with PFS and ORR in stage IV NSCLC patients.

Patients with stage IV NSCLC often experience poor prognosis and clinical treatment outcomes due to delayed treatment for various reasons. Advancing and implementing effective, precise, and individualized treatment plans is crucial for improving the prognosis of these patients, enhancing treatment efficacy, and reducing the incidence of adverse reactions. Baseline peripheral blood D-dimer and platelet levels have been widely studied as biomarkers for NSCLC prognosis (17-19,31,36,40,41). However, data on baseline peripheral blood D-dimer and platelet levels in stage IV NSCLC patients undergoing anti-PD-1 antibody are scarce. Considering the current state of anti-PD-1 antibody combined chemotherapy for stage IV NSCLC, we established a chemotherapy group as a control to more clearly define the advantages of anti-PD-1 antibody therapy. This study included baseline D-dimer and platelet levels, and to correct the accuracy of a single biomarker in predicting prognosis and the interplay of D-dimer and platelets in the processes of coagulation, thrombosis, and fibrinolysis in stage IV NSCLC patients, we performed a statistical analysis of single factors and then combined D-dimer and platelet levels as joint indicators to provide a more accurate prognosis assessment for these patients. After completing the statistical analyses, we also explored the (non-)linear correlation between baseline D-dimer, platelet levels, and PFS risk in stage IV NSCLC patients. Since all patients included in this study had stage IV NSCLC, we recorded the sites of distant metastases and performed COX regression analysis based on different metastasis sites (presence or absence of lung, pleural, abdominal, and brain metastases).

D-dimer is a key marker of coagulation and fibrinolysis activation, which are processes closely related to angiogenesis, invasion, and metastatic spread in NSCLC. This association partially explains why elevated D-dimer levels are linked to poorer prognosis in NSCLC patients (42). Studies have confirmed that high level of peripheral blood D-dimer is an independent predictor of poor prognosis in NSCLC patients (17,43). For example, research by Wang et al. indicates that low baseline D-dimer levels are associated with better survival outcomes in early-stage lung cancer (stage I–II) patients who are eligible for surgical treatment (43). Li et al.’s retrospective analysis of advanced NSCLC patients (stage IIIB–IV) planned for anti-PD-1 or anti-PD-L1 therapy observed that high D-dimer levels prior to immunotherapy are an independent risk factor for poor prognosis (17). In NSCLC, tumor cells can elevate plasma D-dimer levels through various mechanisms. Firstly, tumor cells secrete TF that damage endothelial cells, activating thrombin and disrupting the balance between coagulation and fibrinolysis. This results in a hypercoagulable state and thrombus formation, which the body cannot effectively clear, leading to secondary activation of the fibrinolytic system and elevated D-dimer levels (10,44). Secondly, lung cancer tissue generates a strong pro-inflammatory effect while infiltrating surrounding tissues, activating the complement system. The complement component 3a (C3a) produced during this process can decrease thrombomodulin expression, promoting thrombus formation (45,46). Additionally, tumor cells secrete and release pro-inflammatory cytokines such as Interleukin (IL)-1, IL-6, and tumor necrosis factor (TNF), which act on endothelial cells, macrophages, and fibroblasts. This increases TF expression, modulates the protein C pathway, and activates platelets, thereby indirectly activating coagulation pathways and the fibrinolytic system (47-49). Additionally, during the immune response to tumor cells, the release of pro-coagulant factors further exacerbates the hypercoagulable state, disrupting the balance between coagulation and fibrinolysis. This enhanced fibrinolytic activity leads to a sharp increase in D-dimer levels (50).

Increasing evidence suggests that the hemostatic system itself supports tumor cell survival and disease progression. Various factors within the hemostatic system may contribute to cancer cell survival, proliferation, invasion, metastasis, and tumor angiogenesis. Platelets, playing a crucial role in hemostasis, are therefore of significant interest in cancer research. Platelet aggregation can prolong the survival time of circulating tumor cells. As a protective thrombus, it may provide a barrier for tumor cells to evade immune system recognition (23), furthermore, cancer cells in contact with platelets can phagocytose entire platelets through membrane fusion-dependent or dynamin-dependent mechanisms. Meanwhile, tumor cells can execute ‘platelet mimicry’ by uptake and presentation or by utilizing platelet-derived lipids, nucleic acids, and proteins, allowing them to evade the immune system and enhance their proliferation and metastatic capabilities. The association between platelets and survival in cancer patients is also well-recognized. However, the role of platelets in primary tumor growth remains controversial in the literature. Some studies report that platelets inhibit primary tumor growth, while others suggest they promote it. In experimental lung cancer models, increased platelet activation and production within tumors have been reported to accelerate tumor growth (32). Zhu et al. found that in stage IV NSCLC patients with brain metastases, platelet count is an independent prognostic factor. High platelet count are associated with poor prognosis in these patients (51). Soyano et al.’s retrospective analysis of advanced NSCLC patients did not find a correlation between platelet count prior to anti-PD-1 antibody and PFS or OS (52). One explanation for these conflicting results could be differences in the study populations and sample sizes.

The primary reason for the cancer-associated hypercoagulable state and increased thrombotic risk in tumor patients is that tumor cells can interact with and activate platelets, although other cell types and molecules may also be involved (23). When tumor cells interact with and activate platelets, it leads to cancer-associated thrombosis. During fibrinolysis, D-dimer is released into the bloodstream. Considering the close relationship between coagulation, thrombosis, and the fibrinolytic system, this study uses baseline plasma D-dimer and platelet levels as biomarkers to predict treatment efficacy and prognosis in stage IV NSCLC patients. Yet some clinical research has also shown that elevated plasma D-dimer and platelet levels are associated with poor prognosis in NSCLC patients undergoing chemotherapy. Wang et al. found that increased plasma D-dimer levels are associated with reduced PFS in stage IV NSCLC patients receiving first-line chemotherapy (53). Li et al. found that pre-chemotherapy platelet count may contribute to hematogenous metastasis in advanced NSCLC (stage III–IV) by promoting cancer cell migration. Elevated platelet count has also been identified as an independent prognostic factor in these patients (40). However, our study on baseline D-dimer and platelet levels in the chemotherapy group did not find the aforementioned associations, possibly due to differences in stages, sample sizes, specific chemotherapy regimens, or study populations.

In this study, we compared the impact of different first-line treatment regimens on the prognosis of patients with stage IV NSCLC and found that patients receiving first-line anti-PD-1 antibody plus chemotherapy had better survival outcomes. We further conducted a Cox regression analysis, which confirmed that the differences in treatment regimens are related to the prognosis of the study population. Notably, our study found that the ORR differed only among the different first-line treatment groups. Therefore, we merely conducted univariate logistic regression analysis and found a correlation between the differences in first-line treatment and ORR in the study population. In the anti-PD-1 antibody plus chemotherapy group, high baseline D-dimer levels and platelet count were associated with shorter PFS. We also performed statistical calculations in the chemotherapy-alone group, considering the current status of combination treatments for stage IV NSCLC. And our data highlight the therapeutic advantage of anti-PD-1 antibodies in this context. However, in the ORR analysis for combined treatment group, no correlation was observed between baseline D-dimer or platelet levels and ORR, so we did not proceed with logistic regression analysis. The above research results show a nonlinear correlation between D-dimer and platelet levels with the risk of PFS. Therefore, this study attempted to further interpret this relationship and found that the risk of disease progression initially increased with rising baseline D-dimer levels but then plateaued with further increases in D-dimer. For baseline platelet levels between 237.35×10⁹/L and 394.45×10⁹/L, the risk of disease progression increased with higher platelet levels. However, when baseline platelet levels exceeded 394.45×10⁹/L, the risk of disease progression decreased with further increases in platelet levels. In addition to analyzing the impact of baseline D-dimer and platelet levels on patient prognosis individually, we combined these two variables to provide a more accurate prognostic assessment for stage IV NSCLC patients. We found that the difference in PFS between the normal group and the abnormal group was statistically significant, while the PFS differences among the three subgroups in the abnormal group were not statistically significant. An increase in either D-dimer or platelet levels was associated with shorter PFS, therefore, their elevations might serve as prognostic factors. In the ORR analysis of the four subgroups based on the combination of D-dimer and platelets, pairwise comparisons of ORR among the four subgroups showed no significant differences. Consequently, this study did not conduct further logistic regression analysis. In the chemotherapy group, no correlation was observed between baseline D-dimer and platelet count with PFS and ORR in stage IV NSCLC patients. Consequently, the study did not conduct further Cox proportional hazards model or logistic regression statistical analyses. Nevertheless, in our research, we found that high baseline D-dimer and platelet levels are significantly associated with poor prognosis in stage IV NSCLC patients undergoing anti-PD-1 antibody. This finding may offer new insights into the potential application of anticoagulants and antiplatelet drugs in cancer therapy.

There are several limitations in this study. Firstly, it is a retrospective cohort study with a limited sample size, which may introduce patient selection bias. Secondly, PFS was used as the primary endpoint due to the retrospective nature of the study. OS, as the endpoint event, might relatively be inaccurate because it could be influenced by many factors, for example, changing the therapeutic regimen after the progress of first-line treatment may greatly affect OS. Thirdly, in this study, we attempted to complete the PD-L1 expression status for all patients but found that a significant number still had unknown PD-L1 expression. After obtaining consent from these patients and their families, we planned to conduct immunohistochemical experiments using funding from this project to obtain PD-L1 expression data. However, we found that most patient samples had expired and could not undergo the aforementioned tests. Fourthly, for the disease assessment of patients undergoing first-line chemotherapy, we used the RECIST (Version 1.1). There is still controversy over whether the Immune Response Evaluation Criteria in Solid Tumors (iRECIST) is more meaningful than the RECIST (Version 1.1) for patients receiving first-line anti-PD-1 antibody plus chemotherapy. To ensure consistency with the disease evaluation of the chemotherapy group, we ultimately decided to continue using RECIST (Version 1.1) for disease evaluation in the anti-PD-1 antibody plus chemotherapy group. Lastly, we only analyzed baseline D-dimer and platelet levels’ impact on prognosis and did not examine changes during treatment due to missing data, a common issue in retrospective studies. Larger, prospective studies are needed to confirm the relationship between baseline D-dimer, platelet levels, and patient prognosis. And further research might be needed in populations outside Asia to validate these findings as our data is entirely derived from the Asian population. Additionally, our study did not find predictive value for baseline D-dimer and platelet levels in ORR in stage IV NSCLC patients without driver gene mutations, possibly due to the small sample size.

Conclusions

Baseline plasma D-dimer and platelet levels are negatively correlated with PFS in stage IV NSCLC patients without driver gene mutations receiving anti-PD-1 antibody, making them important predictive biomarkers for prognosis. This provides a foundation for considering combined anticoagulant and antiplatelet therapy alongside antitumor treatment, aiding in the development of more precise and personalized anti-tumor therapies.

Acknowledgments

The present study was previously presented in the poster session of CSCO 2024.

Funding: This study was funded by Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-009A), the National Natural Science Foundation of China (No. 81702268) and the Natural Science Foundation of Tianjin (No. 18JCYBJC93400).

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-763/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-763/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-763/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-763/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Institutional Review Committee of Tianjin Medical University Cancer Hospital (No. bc2020198) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Memmott RM, Wolfe AR, Carbone DP, et al. Predictors of Response, Progression-Free Survival, and Overall Survival in Patients With Lung Cancer Treated With Immune Checkpoint Inhibitors. J Thorac Oncol 2021;16:1086-98. [Crossref] [PubMed]
2. Li CL, Song Y. Combination strategies of immunotherapy in non-small cell lung cancer: facts and challenges. Chin Med J (Engl) 2021;134:1908-19. [Crossref] [PubMed]
3. Reck M, Rodríguez-Abreu D, Robinson AG, et al. Five-Year Outcomes With Pembrolizumab Versus Chemotherapy for Metastatic Non-Small-Cell Lung Cancer With PD-L1 Tumor Proportion Score ≥ 50. J Clin Oncol 2021;39:2339-49. [Crossref] [PubMed]
4. Rodríguez-Abreu D, Powell SF, Hochmair MJ, et al. Pemetrexed plus platinum with or without pembrolizumab in patients with previously untreated metastatic nonsquamous NSCLC: protocol-specified final analysis from KEYNOTE-189. Ann Oncol 2021;32:881-95. [Crossref] [PubMed]
5. Zheng H, Zeltsman M, Zauderer MG, et al. Chemotherapy-induced immunomodulation in non-small-cell lung cancer: a rationale for combination chemoimmunotherapy. Immunotherapy 2017;9:913-27. [Crossref] [PubMed]
6. Conway JR, Kofman E, Mo SS, et al. Genomics of response to immune checkpoint therapies for cancer: implications for precision medicine. Genome Med 2018;10:93. [Crossref] [PubMed]
7. Ricciuti B, Wang X, Alessi JV, et al. Association of High Tumor Mutation Burden in Non-Small Cell Lung Cancers With Increased Immune Infiltration and Improved Clinical Outcomes of PD-L1 Blockade Across PD-L1 Expression Levels. JAMA Oncol 2022;8:1160-8. [Crossref] [PubMed]
8. Fan Y, Xie W, Huang H, et al. Association of Immune Related Adverse Events With Efficacy of Immune Checkpoint Inhibitors and Overall Survival in Cancers: A Systemic Review and Meta-analysis. Front Oncol 2021;11:633032. [Crossref] [PubMed]
9. Shibutani M, Kashiwagi S, Fukuoka T, et al. The Significance of the D-Dimer Level as a Prognostic Marker for Survival and Treatment Outcomes in Patients With Stage IV Colorectal Cancer. In Vivo 2023;37:440-4. [Crossref] [PubMed]
10. Chang F, Zhang H, Chen C, et al. Concomitant genetic alterations are associated with plasma D-dimer level in patients with non-small-cell lung cancer. Future Oncol 2022;18:679-90. [Crossref] [PubMed]
11. Noble S, Pasi J. Epidemiology and pathophysiology of cancer-associated thrombosis. Br J Cancer 2010;102:S2-9. [Crossref] [PubMed]
12. Desch A, Gebhardt C, Utikal J, et al. D-dimers in malignant melanoma: Association with prognosis and dynamic variation in disease progress. Int J Cancer 2017;140:914-21. [Crossref] [PubMed]
13. Liu Z, Guo H, Gao F, et al. Fibrinogen and D-dimer levels elevate in advanced hepatocellular carcinoma: High pretreatment fibrinogen levels predict poor outcomes. Hepatol Res 2017;47:1108-17. [Crossref] [PubMed]
14. Chen H, Xu B, Zhang Q, et al. Clinical value of measuring plasma D-dimer levels in patients with esophageal cancer. J Cardiothorac Surg 2024;19:352. [Crossref] [PubMed]
15. Izuegbuna OO, Agodirin OS, Olawumi HO, et al. Plasma D-Dimer and Fibrinogen Levels Correlates with Tumor Size and Disease Progression in Nigerian Breast Cancer Patients. Cancer Invest 2021;39:597-606. [Crossref] [PubMed]
16. Kim EY, Song KY. Prognostic value of D-dimer levels in patients with gastric cancer undergoing gastrectomy. Surg Oncol 2021;37:101570. [Crossref] [PubMed]
17. Li X, Lu D, Zhang Z, et al. Prognostic value of plasma D-dimer levels in advanced non-small cell lung cancer patients treated with immune checkpoint inhibitors: a retrospective study. J Thorac Dis 2022;14:4125-35. [Crossref] [PubMed]
18. Kawabata H, Fujimoto S, Sakai T, et al. Correction to: Patient's age and D-dimer levels predict the prognosis in patients with TAFRO syndrome. Int J Hematol 2021;114:301-2. [Crossref] [PubMed]
19. Chen C, Li J, Li J, et al. Application of an elevated plasma D-dimer cut-off value improves prognosis prediction of advanced non-small cell lung cancer. Ann Transl Med 2020;8:1153. [Crossref] [PubMed]
20. Kwaan HC, Lindholm PF. Fibrin and Fibrinolysis in Cancer. Semin Thromb Hemost 2019;45:413-22. [Crossref] [PubMed]
21. Draxler DF, Sashindranath M, Medcalf RL. Plasmin: A Modulator of Immune Function. Semin Thromb Hemost 2017;43:143-53. [Crossref] [PubMed]
22. Gotta J, Gruenewald LD, Eichler K, et al. Unveiling the diagnostic enigma of D-dimer testing in cancer patients: Current evidence and areas of application. Eur J Clin Invest 2023;53:e14060. [Crossref] [PubMed]
23. Li S, Lu Z, Wu S, et al. The dynamic role of platelets in cancer progression and their therapeutic implications. Nat Rev Cancer 2024;24:72-87. [Crossref] [PubMed]
24. Anderson R, Rapoport BL, Steel HC, et al. Pro-Tumorigenic and Thrombotic Activities of Platelets in Lung Cancer. Int J Mol Sci 2023;24:11927. [Crossref] [PubMed]
25. Jain S, Harris J, Ware J. Platelets: linking hemostasis and cancer. Arterioscler Thromb Vasc Biol 2010;30:2362-7. [Crossref] [PubMed]
26. Shafqat A, Omer MH, Ahmed EN, et al. Reprogramming the immunosuppressive tumor microenvironment: exploiting angiogenesis and thrombosis to enhance immunotherapy. Front Immunol 2023;14:1200941. [Crossref] [PubMed]
27. Obermann WMJ, Brockhaus K, Eble JA. Platelets, Constant and Cooperative Companions of Sessile and Disseminating Tumor Cells, Crucially Contribute to the Tumor Microenvironment. Front Cell Dev Biol 2021;9:674553. [Crossref] [PubMed]
28. Li X, Li M, Hu Z, et al. Tumor-infiltrating platelets promote the growth of lung adenocarcinoma. Transl Oncol 2024;39:101813. [Crossref] [PubMed]
29. Abdulrahman GO, Das N, Lutchman Singh K. The predictive role of thrombocytosis in benign, borderline and malignant ovarian tumors. Platelets 2020;31:795-800. [Crossref] [PubMed]
30. Hwang SG, Kim KM, Cheong JH, et al. Impact of pretreatment thrombocytosis on blood-borne metastasis and prognosis of gastric cancer. Eur J Surg Oncol 2012;38:562-7. [Crossref] [PubMed]
31. Wang Z, Fang M, Li J, et al. High Platelet Levels Attenuate the Efficacy of Platinum-Based Treatment in Non-Small Cell Lung Cancer. Cell Physiol Biochem 2018;48:2456-69. [Crossref] [PubMed]
32. Hyslop SR, Alexander M, Thai AA, et al. Targeting platelets for improved outcome in KRAS-driven lung adenocarcinoma. Oncogene 2020;39:5177-86. [Crossref] [PubMed]
33. Zhu Y, Wei Y, Zhang R, et al. Elevated Platelet Count Appears to Be Causally Associated with Increased Risk of Lung Cancer: A Mendelian Randomization Analysis. Cancer Epidemiol Biomarkers Prev 2019;28:935-42. [Crossref] [PubMed]
34. Yu LX, Yan L, Yang W, et al. Platelets promote tumour metastasis via interaction between TLR4 and tumour cell-released high-mobility group box1 protein. Nat Commun 2014;5:5256. [Crossref] [PubMed]
35. Rayes J, Watson SP, Nieswandt B. Functional significance of the platelet immune receptors GPVI and CLEC-2. J Clin Invest 2019;129:12-23. [Crossref] [PubMed]
36. Deng HY, Ma XS, Zhou J, et al. High pretreatment D-dimer level is an independent unfavorable prognostic factor of small cell lung cancer: A systematic review and meta-analysis. Medicine (Baltimore) 2021;100:e25447. [Crossref] [PubMed]
37. Li J, Yan S, Zhang X, et al. Circulating D-Dimers Increase the Risk of Mortality and Venous Thromboembolism in Patients With Lung Cancer: A Systematic Analysis Combined With External Validation. Front Med (Lausanne) 2022;9:853941. [Crossref] [PubMed]
38. Ji Y, Sheng L, Du X, et al. Elevated platelet count is a strong predictor of poor prognosis in stage I non-small cell lung cancer patients. Platelets 2015;26:138-42. [Crossref] [PubMed]
39. Hou C, Jiang F, Ma H, et al. Prognostic role of preoperative platelet, fibrinogen, and D-dimer levels in patients with non-small cell lung cancer: A multicenter prospective study. Thorac Cancer 2019;10:304-11. [Crossref] [PubMed]
40. Li Y, Miao LY, Xiao YL, et al. Elevated platelets enhance cancer cell migration, promote hematogenous metastasis and associate with a poor prognosis in advanced non-small cell lung cancer cases. Asian Pac J Cancer Prev 2014;15:139-43. [Crossref] [PubMed]
41. Wu Y, Liu X, Li H, et al. D-dimer levels predict the treatment efficacy and prognosis of esophageal squamous cell carcinoma treated with PD-1/PD-L1 inhibitors. Int J Biol Markers 2024;39:209-16. [Crossref] [PubMed]
42. Zaborowska-Szmit M, Kowalski DM, Piórek A, et al. A decrease in D-dimer concentration and an occurrence of skin rash as iatrogenic events and complementary predictors of survival in lung cancer patients treated with EGFR tyrosine kinase inhibitors. Pharmacol Rep 2016;68:1140-8. [Crossref] [PubMed]
43. Wang J, Li H, Xu R, et al. The MLR, NLR, PLR and D-dimer are associated with clinical outcome in lung cancer patients treated with surgery. BMC Pulm Med 2022;22:104. [Crossref] [PubMed]
44. Graf C, Ruf W. Tissue factor as a mediator of coagulation and signaling in cancer and chronic inflammation. Thromb Res 2018;164:S143-7. [Crossref] [PubMed]
45. Ajona D, Ortiz-Espinosa S, Pio R. Complement anaphylatoxins C3a and C5a: Emerging roles in cancer progression and treatment. Semin Cell Dev Biol 2019;85:153-63. [Crossref] [PubMed]
46. Lin K, He S, He L, et al. Complement component 3 is a prognostic factor of non small cell lung cancer. Mol Med Rep 2014;10:811-7. [Crossref] [PubMed]
47. Singh AK, Malviya R. Coagulation and inflammation in cancer: Limitations and prospects for treatment. Biochim Biophys Acta Rev Cancer 2022;1877:188727. [Crossref] [PubMed]
48. Wang J, Hu B, Li T, et al. The EGFR-rearranged adenocarcinoma is associated with a high rate of venous thromboembolism. Ann Transl Med 2019;7:724. [Crossref] [PubMed]
49. Kuderer NM, Ortel TL, Francis CW. Impact of venous thromboembolism and anticoagulation on cancer and cancer survival. J Clin Oncol 2009;27:4902-11. [Crossref] [PubMed]
50. Nitori N, Ino Y, Nakanishi Y, et al. Prognostic significance of tissue factor in pancreatic ductal adenocarcinoma. Clin Cancer Res 2005;11:2531-9. [Crossref] [PubMed]
51. Zhu JF, Cai L, Zhang XW, et al. High plasma fibrinogen concentration and platelet count unfavorably impact survival in non-small cell lung cancer patients with brain metastases. Chin J Cancer 2014;33:96-104. [Crossref] [PubMed]
52. Soyano AE, Dholaria B, Marin-Acevedo JA, et al. Peripheral blood biomarkers correlate with outcomes in advanced non-small cell lung Cancer patients treated with anti-PD-1 antibodies. J Immunother Cancer 2018;6:129. [Crossref] [PubMed]
53. Wang Y, Wang Z. Predictive value of plasma D-dimer levels in patients with advanced non-small-cell lung cancer. Onco Targets Ther 2015;8:805-8. [Crossref] [PubMed]