Literature DB >> 24819254

Complement component 3 is a prognostic factor of non‑small cell lung cancer.

Kailong Lin1, Siyi He2, Luhang He1, Junyin Chen1, Xiaoming Cheng3, Guoqiang Zhang4, Bo Zhu1.   

Abstract

Lung cancer is the leading cause of cancer‑related mortality worldwide. The complement component 3 (C3) is a central protein of the complement system, expressed in numerous cancer tissues and considered crucial for tumor progression. This study aimed to investigate the prognostic value of C3 in non‑small cell lung cancer (NSCLC) and the underlying mechanisms. We used immunohistochemistry to observe the expression of C3 in malignant pulmonary lesion specimens from biopsy of 80 patients with NSCLC at stages I‑III, who underwent lobectomy. We further assessed the correlation between C3 expression and a number of clinical features, as well as its prognostic value. To investigate the mechanism by which C3 exerts its effects, the correlation of C3 expression to T lymphocyte infiltration was also assessed. There was no significant correlation between the C3 level and clinical features such as gender, smoking status, degree of differentiation, histological type and malignant tumor stage based on the TNM classification system, while a significant correlation was found to age. However, analysis of overall survival (OS) and disease‑free survival (DFS) rates showed that low C3 expression was related to poor prognosis. Univariate survival analysis revealed that C3 level and TNM stage are independent prognostic factors. Multivariate analysis demonstrated that the low level of C3 and TNM stage are also associated with poor prognosis. Furthermore, in tissues from biopsies, the C3 level positively correlated to the number of infiltrated CD4+ and CD8+ T lymphocytes (P<0.01). These findings indicate that C3 is a valuable marker for prognostic evaluation of NSCLC and may be considered as a therapeutic target for the treatment of lung cancer.

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Year:  2014        PMID: 24819254     DOI: 10.3892/mmr.2014.2230

Source DB:  PubMed          Journal:  Mol Med Rep        ISSN: 1791-2997            Impact factor:   2.952


  13 in total

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10.  Bioinformatics analysis of microarray data to identify the candidate biomarkers of lung adenocarcinoma.

Authors:  Tingting Guo; Hongtao Ma; Yubai Zhou
Journal:  PeerJ       Date:  2019-07-10       Impact factor: 2.984

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