Systemic Inflammation Response Index (SIRI) Independently Predicts Survival in Advanced Lung Adenocarcinoma Patients Treated with First-Generation EGFR-TKIs.

Abstract

BACKGROUND:

Systemic inflammation response index (SIRI) has been reported to be an effective blood-based biomarker for predicting prognosis in various kinds of cancer patients. However, the prognostic role of SIRI in advanced lung adenocarcinoma patient remains unclear.

METHODS:

The aim of the present study is to evaluate the prognostic role of SIRI in EGFR-mutant advanced lung adenocarcinoma patients treated with first-generation EGFR-TKIs. A total of 245 patients who received gefitinib, erlotinib, or icotinib at the Second Xiangya Hospital were retrospectively evaluated. SIRI was defined as neutrophil count×monocyte/lymphocyte count. The optimal cut-off value was determined according to receiver operation characteristic curve analysis. Characteristics of patients were compared via chi-square test or Fisher's exact test. Survivals were estimated by the Kaplan-Meier method and compared by the Log rank test. Multivariate analysis was estimated using the Cox proportional hazards model.

RESULTS:

It is showed that high SIRI was associated with male patient, smoker, worse ECOG PS, 19-DEL mutation. Kaplan-Meier survival analysis showed that ECOG PS, brain metastasis, SIRI were significantly correlated with progression-free survival (PFS), and gender, ECOG PS, brain metastasis, NLR and SIRI were significantly correlated with overall survival (OS). Multivariate analysis showed that SIRI and ECOG PS independently predict PFS and OS.

CONCLUSION:

Our findings indicate that SIRI is an effective and convenient marker for predicting prognosis in advanced EGFR-mutant lung adenocarcinoma patients treated with first-generation TKI.

• Authors: Shun Jiang, Sisi Wang, Qianqian Wang, Chao Deng, Yuhua Feng, Fang Ma, Jin'an Ma, Xianling Liu, Chunhong Hu, Tao Hou
• Journal: Cancer management and research (Cancer Manag Res) Vol. 13 Pg. 1315-1322 ( 2021) ISSN: 1179-1322 [Print] New Zealand
• PMID: 33633464 (Publication Type: Journal Article)
• Copyright: © 2021 Jiang et al.