The increase in cancer incidence in recent years necessitates urgent exploration of novel and alternative sources of natural bioactives for targeted cancer therapy. Approximately 75% of the Earth’s surface is covered by oceans, which are thought to harbor untapped physiologically active compounds with potential efficacy against cancer. Recently, a growing focus has been on isolating and investigating novel bioactive compounds derived from marine sources. Bioactive metabolites with diverse chemical structures, isolated from various marine species such as algae, mollusks, and actinomycetes, demonstrate potential efficacy against a wide range of cancers. To our knowledge, this is one of the articles that has reviewed recent papers on the application of marine-derived bioactives in targeted cancer therapy. This study aims to showcase some of the most current developments in targeted cancer therapy with various bioactives that have been identified from marine sources.

The study was conducted to compare the quality and shelf life of traditionally dried (collected from the local markets) Bombay duck (Harpodon nehereus) with improved dried products (produced using a newly developed fish dryer) to assess its suitability. The quality of these products was evaluated through organoleptic, water reconstitution, nutritional, chemical, and microbiological characteristics. The organoleptic quality of improved dried fish was excellent while those produced traditionally were with grayish and dark brown color, rancid odor, and soft and fragile texture with insect infestation. The water reconstitution properties of the improved dried sample were 75.71% and 89.39% at room temperature and 80 °C, respectively, which were comparatively higher than the traditional dried products. The protein, ash, and contents were significantly higher in improved dried fish products while the lipid and total volatile base nitrogen (TVB-N) content were much lower than those of market-dried samples. The total viable counts (TVC) of bacteria were significantly higher in the traditional products which indicated poor quality. To find out the best storage method, dried fish was kept at three different conditions: in the open air at room temperature, in a sealed pack at room temperature, and a sealed pack at refrigeration temperature (4 °C). The shelf life of the products in different storage conditions was evaluated by estimating their moisture, protein, lipid, ash, TVB-N, and TVC values. The products kept at 4 °C temperature was found almost unaltered in terms of their nutritional properties after 4-months storage period. Results indicated that the newly developed fish dryer produced high-quality dried fish products with longer shelf life can be expected if the dried fish is stored at 4 °C refrigeration temperature. Our findings will be a valuable tool for the fish processors to ease the fish drying process and its storage that will enable them to commercially supply good quality dried Harpodon nehereus in the market chain at a low-cost.

Microbial communities within human tissues are increasingly recognized as promising biomarkers for cancer detection. However, leveraging microbiome data for multiclass cancer classification remains challenging due to its compositional structure, high dimensionality, and lack of model interpretability. In this study, we address these challenges by introducing MicroAIbiome, a machine learning-based artificial intelligence (AI) pipeline designed to classify five cancer types such as esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ), using genus-level microbial relative abundances. Our pipeline incorporates zero-replacement, centered log-ratio (CLR) transformation, correlation filtering, and recursive feature elimination (RFE) to enable robust learning from compositional data. Among five evaluated classifiers, XGBoost achieved the highest accuracy of 78.23%, outperforming prior work. We further enhance interpretability using SHapley Additive exPlanations (SHAP)-based feature attribution to uncover class-specific microbial signatures, such as Corynebacterium in ESCA and Bacteroides in COAD. Our results highlight the importance of compositional preprocessing and explainable AI in advancing microbiome-based cancer diagnostics.