Colorectal cancer (CRC) is the third most prevalent cancer type and accounts for nearly one million deaths worldwide. The CRC mRNA gene expression datasets from TCGA and GEO (GSE144259, GSE50760, and GSE87096) were analyzed to find the significant differentially expressed genes (DEGs). These significant genes were further processed for feature selection through boruta and the confirmed features of importance (genes) were subsequently used for ML-based prognostic classification model development. These genes were analyzed for survival and correlation analysis between final genes and infiltrated immunocytes. A total of 770 CRC samples were included having 78 normal and 692 tumor tissue samples. 170 significant DEGs were identified after DESeq2 analysis along with the topconfects R package. The 33 confirmed features of importance-based RF prognostic classification model have given accuracy, precision, recall, and f1-score of 100% with 0% standard deviation. The overall survival analysis had finalized GLP2R and VSTM2A genes that were significantly downregulated in tumor samples and had a strong correlation with immunocyte infiltration. The involvement of these genes in CRC prognosis was further confirmed on the basis of their biological function and literature analysis. The current findings indicate that GLP2R and VSTM2A may play a significant role in CRC progression and immune response suppression.

The Y-box binding protein (YB-1) is known to be a multifunctional transcription and translation factor during expression of several proteins. It is a vital oncoprotein that regulates cancer cell progression and proliferation. YB-1 is over-expressed in various human cancers such as breast cancer, colon cancer, lung cancer, gastric cancer, oesophageal cancer and glioblastoma. Nuclear expression of YB-1 is found to be associated with multidrug resistance and cancer cell progression. YB-1 is reported to regulate many cellular signalling pathways in different types of cancer proliferation. Knowledge about nuclear localization and nuclear level expression of YB-1 in different cancers has been correlated with prospective prognosis of cancer. This review discusses the prospects of YB-1 as a potential biomarker as well as therapeutic target in lieu of their role during cancer progression and multidrug resistance.

Colorectal cancer (CRC) is a common cause of cancer-related deaths worldwide. The CRC mRNA gene expression dataset containing 644 CRC tumor and 51 normal samples from the cancer genome atlas (TCGA) was pre-processed to identify the significant differentially expressed genes (DEGs). Feature selection techniques Least absolute shrinkage and selection operator (LASSO) and Relief were used along with class balancing for obtaining features (genes) of high importance. The classification of the CRC dataset was done by ML algorithms namely, random forest (RF), K-nearest neighbour (KNN), and artificial neural networks (ANN). The significant DEGs were 2933, having 1832 upregulated and 1101 downregulated genes. The CRC gene expression dataset had 23,186 features. LASSO had performed better than Relief for classifying tumor and normal samples through ML algorithms namely RF, KNN, and ANN with an accuracy of 100%, while Relief had given 79.5%, 85.05%, and 100% respectively. Common features between LASSO and DEGs were 38, from them only 5 common genes namely, VSTM2A, NR5A2, TMEM236, GDLN, and ETFDH had shown statistically significant survival analysis. Functional review and analysis of the selected genes helped in downsizing the 5 genes to 2, which are VSTM2A and TMEM236. Differential expression of TMEM236 was statistically significant and was markedly reduced in the dataset which solicits appreciation for assessment as a novel biomarker for CRC diagnosis.

Alzheimer's is a chronic neurodegenerative disease where amyloid-beta (Aβ) plaques and neurofibrillary tangles are formed inside the brain. It is also characterized by progressive memory loss, depression, neuroinflammation, and derangement of other neurotransmitters. Due to its complex etiopathology, current drugs have failed to completely cure the disease. Natural compounds have been investigated as an alternative therapy for their ability to treat Alzheimer's disease (AD). Traditional herbs and formulations which are used in the Indian ayurvedic system are rich sources of antioxidant, anti-amyloidogenic, neuroprotective, and anti-inflammatory compounds. They promote quality of life by improving cognitive memory and rejuvenating brain functioning through neurogenesis. A rich knowledge base of traditional herbal plants (Turmeric, Gingko, Ashwagandha, Shankhpushpi, Giloy, Gotu kola, Garlic, Tulsi, Ginger, and Cinnamon) combined with modern science could suggest new functional leads for Alzheimer's drug discovery. In this article Ayurveda, the ancient Indian herbal medicine system based on multiple clinical and experimental, evidence have been reviewed for treating AD and improving brain functioning. This article presents a modern perspective on the herbs available in the ancient Indian medicine system as well as their possible mechanisms of action for AD treatment. The main objective of this research is to provide a systematic review of herbal drugs that are easily accessible and effective for the treatment of AD.