Deciphering the molecular fingerprint of haemoglobin in lung cancer: A new strategy for early diagnosis using two-trace two-dimensional correlation near infrared spectroscopy (2T2D-NIRS) and machine learning techniques

Lung cancer remains one of the deadliest malignancies worldwide, highlighting the need for highly sensitive and minimally invasive early diagnostic methods. Near-infrared spectroscopy (NIRS) offers unique advantages in probing molecular vibrational information from blood, effectively capturing potential structural changes in haemoglobin (Hb) in lung cancer patients. In this study, we address the challenge of detecting subtle Hb features within the broader blood matrix and introduce an innovative two-stage spectral analysis framework. First, continuous wavelet transform (CWT) is employed to enhance spectral resolution and reinforce the key absorption bands of Hb. Subsequently, two-trace two-dimensional correlation spectroscopy (2T2D-COS) is applied to examine the fine vibrational differences-in both synchronous and asynchronous spectra-between lung cancer patients and healthy controls, revealing alterations in Hb secondary structures (e.g., alpha-helices and beta-sheets). Results show that critical Hb-related peaks at 4862 cm-1, 4615 cm-1, and 4432 cm-1 undergo significant changes in lung cancer samples. Furthermore, combining these refined spectral features with machine learning classifiers (e.g., support vector machines) achieves an overall accuracy of 97.50 % and a sensitivity of 100.00 %. This work not only confirms the value of NIRS in detecting protein-level molecular information in blood but also presents a promising, efficient spectroscopic strategy for early lung cancer diagnosis, offering broad biomedical applicability.