Identification of sSIGLEC5 and sLAG3 as New Relapse Predictors in Lung Cancer

Biomedicines. 2022 Apr 30;10(5):1047. doi: 10.3390/biomedicines10051047.


Lung cancer (LC) continues to be the leading cause of cancer-related deaths in both men and women worldwide. After complete tumour resection, around half of the patients suffer from disease relapse, emphasising the critical need for robust relapse predictors in this disease. In search of such biomarkers, 83 patients with non-microcytic lung cancer and 67 healthy volunteers were studied. Pre-operative levels of sSIGLEC5 along with other soluble immune-checkpoints were measured and correlated with their clinical outcome. Soluble SIGLEC5 (sSIGLEC5) levels were higher in plasma from patients with LC compared with healthy volunteers. Looking into those patients who suffered relapse, sSIGLEC5 and sLAG3 were found to be strong relapse predictors. Following a binary logistic regression model, a sSIGLEC5 + sLAG3 score was established for disease relapse prediction (area under the curve 0.8803, 95% confidence intervals 0.7955−0.9652, cut-off > 2.782) in these patients. Based on score cut-off, a Kaplan−Meier analysis showed that patients with high sSIGLEC5 + sLAG3 score had significantly shorter relapse-free survival (p ≤ 0.0001) than those with low sSIGLEC5 + sLAG3 score.Our study suggests that pre-operative sSIGLEC5 + sLAG3 score is a robust relapse predictor in LC patients.

Keywords: lung cancer; predictive; relapse; sLAG3; sSiglec5.

Grants and funding

This work was supported by grants from the Foundation for the Hospital La Paz Institute for Health Research (FIBULP) (PI-3521) and the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowaska-Curie grant agreement to KMH (access number: 713673; “laCaixa”).