Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Nov 12;20(1):1100.
doi: 10.1186/s12885-020-07575-w.

Preoperative AminoIndex Cancer Screening (AICS) abnormalities predict postoperative recurrence in patients undergoing curative resection for non-small cell lung cancer

Masahiko Higashiyama  1   2 , Ryohei Miyazaki  3 , Hiroshi Yamamoto  4 , Takashi Anayama  3 , Shinya Kikuchi  4 , Kentaro Hirohashi  3 , Jiro Okami  5 , Tomohiro Maniwa  5 , Toru Kimura  5 , Kazumasa Orihashi  3 , Fumio Imamura  6
Affiliations

Preoperative AminoIndex Cancer Screening (AICS) abnormalities predict postoperative recurrence in patients undergoing curative resection for non-small cell lung cancer

Masahiko Higashiyama et al. BMC Cancer. .

Abstract

Background: AminoIndex™ Cancer Screening (AICS (lung)) was developed as a screening test for lung cancer using a multivariate analysis of plasma-free amino acid (PFAA) profiles. According to the developed index composed of PFAA, the probability of lung cancer was categorized into AICS (lung) ranks A, B, and C in order of increasing risk. The aim of the present study was to investigate the relationship between the preoperative AICS (lung) rank and surgical outcomes in patients who underwent curative resection for non-small cell lung cancer (NSCLC).

Methods: Preoperative blood samples were collected from 297 patients who underwent curative resection for NSCLC between 2006 and 2015. PFAA concentrations were measured. The relationship between the preoperative AICS (lung) rank and clinicopathological factors was examined. The effects of the preoperative AICS (lung) rank on postoperative outcomes were also analyzed.

Results: The AICS (lung) rank was A in 93 patients (31.3%), B in 82 (27.6%), and C in 122 (41.1%). The AICS (lung) rank did not correlate with any clinicopathological factors, except for age. Based on follow-up data (median follow-up period of 6 years), postoperative recurrence was observed in 22 rank A patients (23.7%), 15 rank B (18.3%) and 49 rank C (40.2%). In the univariate analysis, preoperative AICS (lung) rank C was a worse factor of recurrence-free survival (p = 0.0002). The multivariate analysis identified preoperative AICS (lung) rank C (HR: 2.17, p = 0.0005) as a significant predictor of postoperative recurrence, particularly in patients with early-stage disease or adenocarcinoma.

Conclusion: Preoperative AICS (lung) rank C is a high-risk predictor of postoperative recurrence in patients undergoing curative resection for NSCLC.

Keywords: AICS; AICS (lung); Lung cancer; Non-small cell lung cancer; Prognosis; Recurrence; Surgery.

PubMed Disclaimer

Conflict of interest statement

HY and SK are employees of Ajinomoto Co., Inc. (Kanagawa, Japan). MH and TA have received research grants from Ajinomoto Co., Inc. (Kanagawa, Japan). This does not alter the authors’ adherence to journal’s policies. The remaining authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Kaplan-Meier curves for recurrence-free survival (RFS) of pre-operative AICS (lung) rank A + B and rank C
Fig. 2
Fig. 2
Kaplan-Meier curves for recurrence-free survival (RFS) of pre-operative AICS (lung) rank A + B and rank C according to the p-stage and histological type. a p-stage I, b p-stage II, c p-stage III, d Adenocarcinoma, e Squamous cell carcinoma
Fig. 3
Fig. 3
Kaplan-Meier curves for overall survival (OS) of pre-operative AICS (lung) rank A + B and rank C
See this image and copyright information in PMC

References

    1. Miyagi Y, Higashiyama M, Gochi A, Akaike M, Ishikawa T, Miura T, Saruki N, Bando E, Kimura H, Imamura F, et al. Plasma free amino acid profiling of five types of cancer patients and its application for early detection. PLoS One. 2011;6(9):e24143. - PMC - PubMed
    1. Okamoto N. Use of “AminoIndex Technology” for cancer screening. Ningen Dock. 2012;26(6):911–922.
    1. Hiller K, Metallo CM. Profiling metabolic networks to study cancer metabolism. Curr Opin Biotechnol. 2013;24(1):60–68. - PubMed
    1. Gu Y, Chen T, Fu S, Sun X, Wang L, Wang J, Lu Y, Ding S, Ruan G, Teng L, et al. Perioperative dynamics and significance of amino acid profiles in patients with cancer. J Transl Med. 2015;13:35. - PMC - PubMed
    1. Mazzone PJ, Wang XF, Beukemann M, Zhang Q, Seeley M, Mohney R, Holt T, Pappan KL. Metabolite Profiles of the Serum of Patients with Non-Small Cell Carcinoma. J Thorac Oncol. 2016;11(1):72–78. - PubMed

MeSH terms