Exploring molecular targets: herbal isolates in cervical cancer therapy.

OBJECTIVE: Cervical cancer (CxCa) stands as a significant global health challenge, ranking fourth in cancer-related mortality among the female population. While chemotherapy regimens have demonstrated incremental progress in extending overall survival, the outlook for recurrent CxCa patients remains disheartening. An imperative necessity arises to delve into innovative therapeutic avenues, with molecular targeted therapy emerging as a promising candidate. Previous investigations have shed light on the therapeutic effectiveness of five distinct herbal compounds, epicatechin, curcumin, myricetin, jatrorrhizine, and arborinine, within the context of CxCa. METHODS: A systems biology approach was employed to discern differentially expressed genes (DEGs) in CxCa tissues relative to healthy cervical epithelial tissues. A protein-protein interaction network (PPIN) was constructed, anchored in the genes related to CxCa. The central genes were discerned within the PPIN, and Kaplan-Meier survival curves explored their prognostic significance. An assessment of the binding affinity of the selected herbal compounds to the master regulator of prognostic markers in CxCa was conducted. RESULTS: A significant correlation between the overexpression of MYC, IL6, JUN, RRM2, and VEGFA and an adverse prognosis in CxCa was indicated. The regulation of these markers is notably influenced by the transcription factor CEBPD. Molecular docking analysis indicated that the binding affinity between myricetin and the CEBPD DNA binding site was robust. CONCLUSION: The findings presented herein have unveiled pivotal genes and pathways that play a central role in the malignant transformation of CxCa. CEBPD has emerged as a potential target for harnessing the therapeutic potential of myricetin in this context.

Retracing migration pattern in reproductive and non-reproductive female kutum Rutilus frisii, in south Caspian Sea, using otolith microchemistry.

Because trace elements of otoliths are considered a natural marker capable of recognizing the chemical composition of ambient water and fish migration history, these elements could be potentially used to analyse the movement of reproductive (R) and non-reproductive (NR) mature-sized fish. Supposedly, it is not essential for NR individuals to migrate to rivers for spawning because they do not have developed gonads. To investigate the potential differences in migration history between female R and NR kutum, Rutilus frisii, in the southwest waters of the Caspian Sea, the ratios of Sr, Ba, Mg, Na, K and P to Ca in otoliths (from the core to the edge) were examined using laser ablation-inductively coupled plasma-mass spectrometry. In NR fish, a significant increase in Sr:Ca ratio in the otoliths' growth rings, likely due to greater seawater residency, and an increase in Ba:Ca ratio in the last two rings were observed. Increased Ba:Ca ratio could be due to the movement of NR mature-sized fish to the coastal zones for foraging. Seasonal physiological factors such as gonad maturation and spawning activity are more likely to be involved in differences in the other elemental ratios (Mg, Na, K and P). These results suggest that microchemical analyses of growth rings of otolith can be used as a valuable tool for better understanding the movement pattern of different types of adult fish, which could be completed with data from other methods like tagging.

HybridRanker: Integrating network topology and biomedical knowledge to prioritize cancer candidate genes.

In the past few years, many researches have been conducted on identifying and prioritizing disease-related genes with the goal of achieving significant improvements in treatment and drug discovery. Both experimental and computational approaches have been exploited in recent studies to explore disease-susceptible genes. The experimental methods for identification of these genes are usually time-consuming and expensive. As a result, a substantial number of these studies have shown interest in utilizing computational techniques, commonly known as gene prioritization methods. From a conceptual point of view, these methods combine various sources of information about a particular disease of interest and then use it to discover and prioritize candidate disease genes. In this paper, we propose a gene prioritization method (HybridRanker), which exploits network topological features, as well as several biomedical data sources to identify candidate disease genes. In this approach, the genes are characterized using both local and global features of a protein-protein interaction (PPI) network. Furthermore, to obtain improved results for a particular disease of interest, HybridRanker incorporates data from diseases with similar symptoms and also from its comorbid diseases. We applied this new approach to identify and prioritize candidate disease genes of colorectal cancer (CRC) and the efficiency of HybridRanker was confirmed by leave-one-out cross-validation test. Moreover, in comparison with several well-known prioritization methods, HybridRanker shows higher performance in terms of different criteria.