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Optimal biomarker selection using adaptive Social Ski-Driver optimization for liver cancer detection

Gunasekhar P., Vijayalakshmi S.

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 4

1611--1625

Most essential biomolecule found in the human body is a biomarker; with these biomarkers, the abnormal biological processes and disease states of each patient can be accurately determined. Nowadays, the biomarker applications are frequently applied during clinical trials to identify cancer patients. In this method, the major significance of miRNA biomarkers during liver cancer detection is analysed. For such analysis, a deep learning technique is introduced along with optimization algorithms. Six different filter-based approaches are considered for feature selection they are Chi-Squared (Chi2), Information Gain (IG), Gain Ratio (GR), Symmetrical Uncertainty (SU), RelieF (RF) and RF-W. Two high ranked features from these selected features are extracted by the Modified Social Ski-Driver optimization (MSSO) algorithm. With that high ranked features, the liver cancer tissues are accurately detected by Sunflower Optimization-based deep neural network (DSFNN) approach. The analysis part concludes that a miRNA biomarker having a higher rank provide better cancer detection results than other low-ranked biomarkers. In this work, 10 different, clinically verified miRNA biomarkers are selected for this detection process. The data required for liver cancer detection is selected from NCBI-GEO database. The performance of this entire cancer detection process is evaluated by accuracy, sensitivity, precision, specificity, and Area under curve (AUC) metrics. Furthermore, we also determined that the usage of 10, 5, and 3 clinically verified miRNAs provide better cancer detection results than other miRNAs. Among all clinically verified miRNAs, the selected three biomarkers (hsa-mir-10b, hsa-let-7c, hsa-mir- 145) has attained higher recognition result. The performance result attained by the proposed DSFNN is compared with five different algorithms for both training and validation datasets.

Hexahydroquinoline derivatives : synthesis and anti-hepatoma cancer activity

Wang B., Jin W.-X., Zhang Y.-L., Huang L.

Przemysł Chemiczny

2017

T. 96, nr 4

897--900

Arom. aldehydes were condensed in EtOH under boiling, pptd. by cooling to the room temp., sepd. by filtration, dried under vacuum and studied for anticancer activity against 3 human hepatoma tumor cell lines (SMMC-7721, BEL-740 and HCCLM3). Normal fibroblast cells WI 38 were also used. Fluorouracil was used as a ref. anticancer agent. One of the synthesized comps. showed a high inhibitory effect against 3 tumor cell lines (higher than the ref. agent). The compd. was non-cytotoxic to normal cells (IC50 values higher than 100 μg/mL).

Przedstawiono syntezę i charakterystykę 4 pochodnych heksahydrochinoliny. Związki te zsyntetyzowano w reakcji kondensacji różnych aldehydów aromatycznych i oceniono pod kątem właściwości antyrakowych na podstawie badania 3 ludzkich linii komórkowych raka wątrobowokomórkowego (SMMC-7721, BEL-740 i HCCLM3). W badaniu wykorzystano także komórki WI 38 jako normalne fibroblasty. Najlepsze wyniki uzyskano dla związku 4, który wykazał najskuteczniejsze działanie hamujące rozwój 3 linii komórek rakowych. Działanie to okazało się skuteczniejsze niż w przypadku referencyjnego fluorouracylu. Ponadto związek ten nie działał cytotoksycznie na normalne komórki (IC₅₀ > 100 μg/mL).