Molecular binary classification of NSCLC: miR-375 is a potential biomarker to differentiate SQCC from ADCC in Indian NSCLC patients

Sangeet Bhaumik,
Firoz Ahmad and
Bibhu Ranjan DasEmail author

Applied Cancer Research201737:28

https://doi.org/10.1186/s41241-017-0034-8

Received: 22 February 2017

Accepted: 20 June 2017

Published: 25 August 2017

Abstract

Background

Current developments in the targeted therapies of non-small-cell lung carcinoma demand accurate classification to dodge the adverse drug response and to yield maximum therapeutic outcome. Accurate classification depends on the classical hematoxylin and eosin staining and immunohistochemistry techniques. In selected critical cases, inter-observer variability, lack of standardization, tumor heterogeneity, and degree of differentiation makes it difficult to classify NSCLC. During the last decade, microRNAs (miRNAs) have been proven to be a promising biomarker in the field of oncology from diagnosis to therapy. The present study developed a binary classification method based on the expression of three well-known miRNAs: miR-205, miR-196b, miR-375, since it is the most demanding criteria to the clinicians trying to provide better therapy to the patients.

Methods

Quantitative real-time polymerase chain reaction was performed for 90 NSCLC samples. Receiver-Operator Characteristic Curve and Discriminant Function Analysis was done to classify the NSCLC. A discriminant formula was developed to calculate the Z-score of miR-375 (Z3 = −0.637 + (0.439 x NCt miR-375) + (−0.245 x NCt miR-21).

Results

The miR-375 classified NSCLC into SQCC and ADCC with higher accuracy. miR-375 appeared to differentiate SQCC from ADCC accurately in the test and validation set, signifying a sensitivity and specificity of 96.7% and 93.1%, respectively.

Discussion

miR-375 is over-expressed in ADCC and suppressed in SQCC. This evidence accentuated the oncogenic and tumor suppressor nature in ADCC and SQCC respectively.

Conclusion

miR-375 was proven to be the prominent biomarker of accurate NSCLC classification. The current study developed a molecular binary classification method in adjunctive of IHC which will help the clinicians in better classifying NSCLC and designing therapy.