Protein expression of the amino acid transporter SLC7A5 in tumor tissue is prognostic in early-stage colorectal cancer

肿瘤组织中氨基酸转运蛋白 SLC7A5 的蛋白质表达对早期结直肠癌具有预后意义

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作者:Makiko Ogawa, Atsushi Tanaka, Masaki Maekawa, Kei Namba, Yusuke Otani, Jinru Shia, Julia Y Wang, Michael H Roehrl

Abstract

Proteins overexpressed in early-stage cancers may serve as early diagnosis and prognosis markers as well as targets for cancer therapies. In this study, we examined the expression of an essential amino acid carrier SLC7A5 (LAT1, CD98, or 4F2 light chain) in cancer tissue from two well-annotated cohorts of 575 cases of early-stage and 106 cases of late-stage colorectal cancer patients. Immunohistochemistry showed SLC7A5 overexpression in 72.0% of early-stage and 56.6% of late-stage cases. SLC7A5 expression was not influenced by patient gender, age, location, or mismatch repair status, although it appeared to be slightly less prevalent in tumors of mucinous differentiation or with lymphovascular invasion. Statistical analyses revealed a positive correlation between SLC7A5 overexpression and both overall survival and disease-free survival in early-stage but not late-stage cancers. Co-expression analyses of the TCGA and CPTAC colorectal cancer cohorts identified a network of gene transcripts positively related to SLC7A5, with its heterodimer partner SLC3A2 having the highest co-expression score. Network analysis uncovered the SLC7A network to be significantly associated with ncRNA such as tRNA processing and the mitotic cell cycle. Since SLC7A5 is also a marker of activated lymphocytes such as NK, T, and B lymphocytes, SLC7A5 overexpression in early colorectal cancers might trigger a strong anti-tumor immune response which could results in better clinical outcome. Overall, our study provides clear evidence of differential SLC7A5 expression and its prognostic value for early-stage colorectal cancer, although the understanding of its functions in colorectal tumorigenesis and cancer immunity is currently rather limited and awaits further characterization.

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