Drug resistance is one of the key factors affecting the effectiveness of cancer treatment methods, including chemotherapy, radiotherapy, and immunotherapy. Its occurrence is related to factors such as mRNA expression and methylation within cancer cells. If drug resistance in patients can be accurately identified early, doctors can devise more effective treatment plans, which is of great significance for improving patients' survival rates and quality of life. Cancer drug resistance prediction based on artificial intelligence (AI) technology has emerged as a current research hotspot, demonstrating promising application prospects in guiding clinical individualized and precise medication for cancer patients. This review aims to comprehensively summarize the research progress in utilizing AI algorithms to analyze multi-omics data including genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, and histopathology, for predicting cancer drug resistance. It provides a detailed exposition of the processes involved in data processing and model construction, examines the current challenges faced in this field and future development directions, with the aim of better advancing the progress of precision medicine.

As one of the key enzymes in cell metabolism, the activity of citrate synthase 3 (CS3) regulates the substance and energy metabolism of organisms. The protein members of CS3 family were identified from the whole genome of apple, and bioinformatics analysis was performed and expression patterns were analyzed to provide a theoretical basis for studying the potential function of CS3 gene in apple. BLASTp was used to identify members of the apple CS3 family based on the GDR database, and the basic information of CS3 protein sequence, subcellular localization, domain composition, phylogenetic relationship and chromosome localization were analyzed by Pfam, SMART, MEGA5.0, clustalx.exe, ExPASy Proteomics Server, MEGAX, SOPMA, MEME, WoLF PSORT and other software. The tissue expression and inducible expression characteristics of 6 CS3 genes in apple were determined by acid content and real-time fluorescence quantitative polymerase chain reaction (qRT-PCR). Apple CS3 gene family contains 6 members, and these CS3 proteins contain 473-608 amino acid residues, with isoelectric point distribution between 7.21 and 8.82. Subcellular localization results showed that CS3 protein was located in mitochondria and chloroplasts, respectively. Phylogenetic analysis divided them into 3 categories, and the number of genes in each subfamily was 2. Chromosome localization analysis showed that CS3 gene was distributed on different chromosomes of apple. The secondary structure of protein is mainly α-helix, followed by random curling, and the proportion of β-angle is the smallest. The 6 members were all expressed in different apple tissues. The overall expression trend from high to low was the highest relative expression content of MdCS3.4, followed by MdCS3.6, and the relative expression level of other members was in the order of MdCS3.3 > MdCS3.2 > MdCS3.1 > MdCS3.5. qRT-PCR results showed that MdCS3.1 and MdCS3.3 genes had the highest relative expression in the pulp of 'Chengji No. 1' with low acid content, and MdCS3.2 and MdCS3.3 genes in the pulp of 'Asda' with higher acid content had the highest relative expression. Therefore, in this study, the relative expression of CS3 gene in apple cultivars with different acid content in different apple varieties was detected, and its role in apple fruit acid synthesis was analyzed. The experimental results showed that the relative expression of CS3 gene in different apple varieties was different, which provided a reference for the subsequent study of the quality formation mechanism of apple.
Citric Acid ; Malus/genetics* ; Citrate (si)-Synthase ; Phylogeny ; Citrates