Introduction: Pulmonary cancer is a kind of deeply invasive tumour which is difficult to treat, and its mortality rate is high. Previous research has shown that activation of complement could contribute to the progression of non-small-cell lung cancer (SCLC). However, little research has been done on SCLC.
Methods: Complement factor H (CFH), complements C3 as well as C4 were measured in patients, and the prognostic impact of different parameters was assessed by log-rank function analysis and Cox multifactor models. Besides, we constructed a predictive model based on complement fractions and validated the accuracy of the model.
Results: Among these 242 patients, 200 (82.6%) died. The median survival time was 18.3 months. We found by multifactorial analysis that high levels of CFH decreased the risk of death (HR 0.23, 95% CI 0.10 to 0.57, p<0.001), while elevated complement C4 displayed poor prognosis (HR 2.28, 95% CI 1.66 to 3.13, p<0.001). We screened variables by Cox models and constructed CFH-based prediction models to plot a nomogram by internal validation. The nomogram showed excellent accuracy in assessing the probability of death, yielding an adjusted C-statistics of 0.905.
Conclusions: CFH can be recognised as a biomarker to predict the risk of death in SCLC. The prediction model established based on CFH, C3 and C4 levels has good accuracy in patients' prognostic assessment.
Keywords: complementary medicine; oncology.
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