Natural compounds obtained from plants are capable of garnering considerable attention from the scientific community, primarily due to their ability to check and prevent the onset and progress of cancer. These natural compounds are primarily used due to their nontoxic nature and the fewer side effects they cause compared to chemotherapeutic drugs. Furthermore, such natural products perform even better when given as an adjuvant along with traditional chemotherapeutic drugs, thereby enhancing the potential of chemotherapeutics and simultaneously reducing their undesired side effects. Curcumin, a naturally occurring polyphenol compound found in the plant Curcuma longa, is used as an Indian spice. It regulates not only the various pathways of the immune system, cell cycle checkpoints, apoptosis, and antioxidant response but also numerous intracellular targets, including pathways and protein molecules controlling tumor progression. Many recent studies conducted by major research groups around the globe suggest the use of curcumin as a chemopreventive adjuvant molecule to maximize and minimize the desired effects and side effects of chemotherapeutic drugs. However, low bioavailability of a curcumin molecule is the primary challenge encountered in adjuvant therapy. This review explores different therapeutic interactions of curcumin along with its targeted pathways and molecules that are involved in the regulation of onset and progression of different types of cancers, cancer treatment, and the strategies to overcome bioavailability issues and new targets of curcumin in the ever-growing field of cancer.

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With the advancement of microarray technology, gene expression profiling has shown great potential in outcome prediction for different types of cancers. Microarray cancer data, organized as samples versus genes fashion, are being exploited for the classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer type. Nevertheless, small sample size remains a bottleneck to design suitable classifiers. Traditional supervised classifiers can only work with labeled data. On the other hand, a large number of microarray data that do not have adequate follow-up information are disregarded. A novel approach to combine feature (gene) selection and transductive support vector machine (TSVM) is proposed. We demonstrated that 1) potential gene markers could be identified and 2) TSVMs improved prediction accuracy as compared to the standard inductive SVMs (ISVMs). A forward greedy search algorithm based on consistency and a statistic called signal-to-noise ratio were employed to obtain the potential gene markers. The selected genes of the microarray data were then exploited to design the TSVM. Experimental results confirm the effectiveness of the proposed technique compared to the ISVM and low-density separation method in the area of semisupervised cancer classification as well as gene-marker identification.