A nomogram integrating M2 macrophages and extracellular matrix components outperforms TNM classification in pulmonary sarcomatoid carcinoma prognosis: A two-center retrospective study.

Objective: Tumor microenvironment composition significantly influences tumor progression. This study aimed to explore the distribution of M2 tumor-associated macrophages (TAMs), reticular fibers (RFs), and collagen fibers (CFs) within the tumor microenvironment of pulmonary sarcomatoid carcinoma (PSC) and assess their clinicopathological significance.

Methods: Formalin-fixed paraffin tissue sections of 127 PSC patients from two medical centers were collected and analyzed by immunohistochemistry and the Gomori method. HALO software was used to analyze the distributions of M₂TAMs, RFs, and CFs, and statistically analyzed for clinicopathological significance.

Results: Kaplan-Meier analysis showed that overall survival (OS) was longer in patients with low density of M₂TAMs (P = 0.038) and high density of CFs (P = 0.046) and RFs (P = 0.010). Patients classified within the low-risk group, based on the combined factors MR and MC, experienced significantly longer OS than those in the high-risk group. Multivariate analysis identified the densities of M₂TAMs, RFs, and CFs, along with MC and MR, as independent prognostic factors for patient OS. Nomogram models 1 and 2, with C-indices of 0.74 and 0.73, respectively, were highly effective in predicting OS. Decision curve analysis demonstrated that the Nomogram model outperformed pTNM staging in predicting medium- and long-term survival.

Conclusion: High densities of M₂TAMs and low densities of RFs and CFs are associated with poor prognosis in PSC patients and are independent prognostic factors. The Nomogram model proved was more effective than pTNM staging in predicting medium- and long-term survival, offering a valuable tool for the individualized clinical treatment of PSC patients.