Amongst the omics branches, genomics is the most fundamental. In principle, the field of genomics can offer comprehensive insights into the structure, function, and relationships between genes and their products in an organism, in addition to genome mapping and editing. However, its significance increases when the pathology of diseases is traced through genes and associated modifications. Thus, deciphering different facets of diseases can be accomplished with the use of technologies, including chromatin immunoprecipitation (ChIP) test, DNA microarray, gel electrophoresis, blotting, polymerase chain reaction (PCR), and DNA sequencing [49].

Cancer genomics, to define it specifically, is the study of genetic anomalies associated with the cancer process, which, remarkably, has a significant impact on personalized cancer treatment [50]. Identification of cancer-specific molecular signatures and mechanisms underlying cancer at the genomic level not only improves the field of genomics science and related technologies but may also aid in managing cancer from early detection to treatment [51]. This is because changes and mutations at the genome level are one of the inseparable facts of cancer. In this sense, DNA sequencing technologies have provided new molecular insights for research by revealing the mysteries of genetic coding in an organism [52].

Next-generation sequencing (NGS) is becoming widely used as a useful technique for discovering more about a tumor’s genomic composition. A thorough profile of the tumor can be obtained by concurrently sequencing millions of DNA fragments in a single sample in order to identify a variety of abnormalities. The use of NGS for clinical applications has increased significantly as a result of the thorough detection of aberrations as well as advances in cost, sequencing chemistry, pipeline study, dependability, and data analysis [53]. Mutation signature studies, or “mutational landscape studies,” are studies that identify patterns among millions of mutations detected by NGS. These studies focus on identifying recurring patterns in mutations caused by specific processes, such as UV light, smoking, or DNA repair deficiencies [54]. NGS also analyzes genome-wide association studies, variable frequency analysis, comparative genomic studies, oncogenomic studies, and population genomics to study evolution and demographic history [55].

Cancer panels are made especially to reliably identify somatic mutations that are clinically significant. For assessment of cancer risk, germline mutations in cancer-predisposing genes such as BRCA1/2, have been identified as well [56]. The Oncomine Dx Target Test, Praxis Extended RAS Panel, MSK-IMPACT, and Foundation One CDx are among the oncology-related NGS-based panels which are authorized by the Food and Drug Administration (FDA) in 2017 [57]. The FDA’s recent clearance of NTRK gene fusions for tumor-agnostic indications expands the clinical application of NGS (FDA approval for larotrectinib). Considering that liquid biopsy is non-invasive, it holds considerable potential. Liquid biopsy has been shown in numerous trials to be useful for drug-response monitoring, cancer diagnosis, and prognosis. Tumor DNA is frequently obtained via circulating tumor cells (CTCs), which are discharged from the tumor and enter the vascular system, exosomes, which are vesicles generated from cells, and cell-free DNA (cfDNA) released by dying tumor cells. Crucially, multiple research teams have demonstrated that NGS-sequencing protocols may be adjusted to reach sensitivity levels that are comparable to traditional sequencing processes [58]. However, clinical trials are currently needed to confirm this before the approach can be implemented in medical applications.

The Cancer Genome Atlas (TCGA) project demonstrates how NGS analysis can help with patient classification and the identification of new carcinogenic pathways. This knowledge has been utilized to clarify oncogenic pathways that are functionally significant for many types of tumors [59]. Machine learning techniques such as support vector machines (SVMs), random forest (RF), or gradient boosting algorithms could have been applied in a recent study [60] to classify data or predict outcomes, revealing the regulatory role of F-box/WD repeat-containing protein 7 (Fbw7) in cancer cell oxidative metabolism [61]. Molecular subgroups discovered through pan-cancer research also contribute to therapeutic personalization and enhance patient survival [62]. The flow chart of the genomics approach has been given in Fig. 2.

Research reveals that only 2% of human DNA is translated into protein-coding RNA (pcRNA), whereas 93% of DNA is transcribed into RNA [63]. Once these RNA molecules reach their mature state, they participate in translation and specify the amino acid sequence of the protein. The remaining portion of the human transcriptome is composed of non-coding RNA (ncRNA). Among those that have been identified so far are ribosomal RNA (rRNA), transfer RNA (tRNA), micro-RNA (miRNA), small interfering RNA (siRNA), PIWI-interacting RNA (piRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), extra-cellular RNA (exRNA), guide RNA (gRNA), small Cajal body-specific RNA (scaRNA), circular RNA (circRNA), long non-coding RNA (lncRNA), which includes X-inactive specific transcript (XIST) and HOX Transcript Antisense RNA (HOTAIR) [64]. They differ significantly from one another in terms of their cellular functions. Certain non-coding RNAs (ncRNAs) have catalytic activities, such as tRNA and rRNA synthesis, whereas other ncRNAs, such as miRNA, snRNA, and snoRNA, are engaged in mRNA regulation [65]. Each of the ncRNA types listed above could play a significant role in oncogenesis. Furthermore, new kinds of RNAs that were not known before are constantly being found—largely as a result of contemporary technologies. Transcriptome cognition could be improved by carefully examining the roles of RNAs and developing novel techniques.

Several approaches have been developed to identify each RNA’s function in the transcriptome, or collection of all RNAs, in human disorders. Northern blotting is a classical method used for the detection and quantification of specific mRNA transcripts, serving as one of the earliest approaches for examining gene expression patterns in the human transcriptome [66]. This laborious technique analyzes a maximum of several gene transcripts simultaneously using gene-specific DNA probes that are hybridized to the RNA [67]. Later, more advanced techniques for transcriptome analysis emerged based on complementary probe hybridization [68]. In the field of cancer transcriptomics, three primary approaches may currently be identified: the use of probe hybridization in a large-scale quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and microarrays, and the most recent advancement in NGS technology, RNA-Seq [69]. Deep Learning is used for gene expression prediction, transcript quantification, and disease state classification, while Machine Learning is used for feature selection, clustering, and dimensionality reduction [70]. Graph Neural Networks (GNNs) are used to analyze transcript annotations, identify non-coding RNAs, and interpret gene regulatory elements in gene transcripts [71]. The flow chart of the transcriptomics workflow is given in Fig. 3.

The study of all the proteins expressed in a cell, tissue, or individual is known as proteomics. Large-scale protein analysis has now gained widespread usage thanks to the development of mass spectrometry (MS) based protein analysis technologies [72]. The MS information gathered from such jobs can be utilized to diagnose and forecast illnesses, understand the mechanisms behind diseases, assist in the development of new drugs, and give the building blocks for biological discovery [73,74]. Significant advancements have been achieved in the identification of novel therapeutic targets and clinically relevant biomarkers with the development of proteomics technology and its application to a variety of diseases, including cancer [75].

Proteomics techniques such as protein-protein interactions and identification of single-nucleotide polymorphism (SNPs) enable the identification of biomarkers and protein expression patterns, aiding in tumor classification, disease prediction, and treatment efficacy [76,77]. Proteomics approaches have also been applied to better understand the basic biology of cancer, including how tumor cells alter their signaling pathways and how different pathways can be targeted for cancer therapy [78]. Consequently, enormous data sets have been gathered and combined with clinical and molecular biology data about cancer to develop cancer proteome databases.

Recently, emphasis has been placed on using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to analyze large cancer proteome datasets and to overcome data complexity and heterogeneity from diverse sources. Shen et al. [79] analyzed the TCGA, National Institute of Health, Medical Research, and AMC databases for mutant genes related to vascular invasion, using the Boruta algorithm. Mutations linked to vascular invasion were found in ten different genes. This work supports the idea that ML is useful, even if it has not yet been established whether this mutation can be used for clinical prediction. The working pipeline of proteomics is given in Fig. 4.

The study of the complete set of epigenetic modifications (chemical alterations) on a cell’s genetic material is referred to as epigenomics. These modifications do not alter the DNA sequence but affect gene expression [80]. According to Castilho et al. [81], these modifications are key players in tumor initiation, progression, and response to the treatment of cancer patients. The advancement in epigenetics-based treatments triggers novel therapeutic targets and optimization of strategies for cancer patients [82]. The massive complexity of epigenomic data poses the main challenges in screening relevant biomarkers and therapeutic targets of cancer [83]. CNNs and RNNs are utilized to integrate epigenomic data [84]. In addition, “epigenetic regulatory networks (ERNs)” are more comprehensive to drug resistance and disease progression states. CNNs are widely accepted algorithms to predict chromatin accessibility from DNA methylation data that revealed gene expression regulation in various cancer types [85]. These targets are involved in epigenetic modifications, viz., “DNA methyltransferases and histone deacetylases”.

Vougas et al. [86] stated that ML is the specific framework that has been shown to accurately predict drug responses to therapies such as epigenetic inhibitors in clinical settings. Supervised learning approaches like RF and SVM are renowned algorithms utilized for the classification of cancer types through epigenetic markers such as DNA methylation and histone modification patterns analysis [87]. SVMs have a greater impact on the characterization of tumor subtypes and forecasting patient outcomes by unique epigenomic profiles of individuals [88]. This enables oncologists to tailor treatment plans, avoiding ineffective therapies and minimizing the drug’s adverse effects [89]. The working flowchart of epigenomics has been depicted in Fig. 5.

The metabolomics of blood, urine, tissue, plasma, and other body samples provide a promising avenue for early diagnosis and improved care of cancer-associated persons [90]. A mixed approach of metabolomics and AI accelerates the diagnosis and therapeutic process of cancer patients. In actuality, metabolomics is a novel approach that enables ML and statistical modeling to comprehensively understand the pathophysiological processes of cancer disease via accessing the network pathways analysis [91]. Metabolomics produces extensive datasets containing hundreds to thousands of metabolites with intricate relationships. AI, designed to replicate human intelligence, offers exceptional potential for analyzing large-scale metabolomic data [90]. ML algorithms such as SVM, decision trees (DT), RF, neural networks (NN), and deep learning (DL) also offer advanced proficiencies for the management of large, complex metabolite datasets [92]. The principal component analysis (PCA), mean ± standard deviation (SD), and some other tests like the Kruskal-Wallis and Mann-Whitney non-parametric test are used to identify differentially expressed metabolites (DEMs) in cancer patients compared to non-cancer patients (control) [93,94]. The clustering methods, such as hierarchical and k-means clustering, provide significant metabolic features of these DEMs [95]. Partial least squares discriminant analysis (PLS-DA) is also popular for metabolic features of differentially expressed metabolites [96]. The supervised, multivariate analysis dimensionality reduction maximizes covariance relationships between intensities of similar metabolic features of DEMs. To visualize and characterize the differences between two groups in the abundance of potential biomarkers, the hierarchical cluster analysis (HCA) algorithm is used to make a heat map [97]. These identified metabolites are utilized for disease classification, metabolic pathway analysis, and biomarker discovery [92]. Further, deep learning methods like CNNs and RNNs are increasingly used for classifying cancer diseases based on specified metabolomic features [98]. The flow chart of metabolomics study has been given in Fig. 6, and graphical illustration of AI-driven multi-omics approaches for cancer study has been illustrated in Fig. 7.

The curated gene interaction network for lung cancer was constructed using the STRING database and further analyzed its topological properties. The resulting network comprised 61 nodes and 603 edges, indicating a densely interconnected system [109]. The average number of neighbors was 19.770, reflecting high interactivity among the genes. The network diameter was calculated as 4, and the network radius was 2, suggesting a compact structure with relatively short paths between nodes [110]. The characteristic path length stood at 1.783, supporting efficient communication across the network. The clustering coefficient was notably high at 0.700, indicative of a strong modular organization [111]. Moreover, the network density was 0.330, while network heterogeneity and centralization were 0.665 and 0.504, respectively (Fig. 8), highlighting the presence of hub genes and centralized regulation. The entire network was fully connected with only one connected component, and the analysis was computationally efficient, taking just 0.127 s. These metrics underline the biological relevance and robustness of the lung cancer gene network in capturing key molecular interactions.

To identify the most appropriate target within the lung cancer-associated protein-protein interaction (PPI) network, the Maximal Clique Centrality (MCC) algorithm was utilized via the CytoHubba module in Cytoscape [108]. The MCC method effectively detects highly interconnected nodes that are likely to play essential biological roles in disease progression. Among the top-ranked genes, ERBB2, KRAS, and TP53 were identified as the leading hub nodes, each attaining a peak MCC score of 2.45 × 10¹³. These genes are well-established oncogenic drivers in non-small cell lung cancer (NSCLC), with ERBB2 and KRAS mediating aberrant receptor tyrosine kinase and RAS/MAPK pathway signaling, respectively. The tumor suppressor TP53, frequently mutated in lung malignancies, underscores its central regulatory function in genomic surveillance and cell cycle control [112].

Other significant hub genes included PTEN, CDKN2A, NRAS, and PIK3CA, all scoring equivalently (2.45 × 10¹³) and representing key modulators of PI3K/AKT/mTOR signaling and cell proliferation checkpoints. Genes such as BRAF and NF1, scoring slightly lower (2.44–2.43 × 10¹³), further emphasize the RAS/RAF axis as a major oncogenic pathway in lung cancer biology [113]. EGFR, although ranked 10th with an MCC score of 2.42 × 10¹³, remains clinically relevant due to its role in tyrosine kinase inhibitor-targeted therapies. This prioritized gene set, derived through network topology analysis, provides a focused list of candidates for functional validation and therapeutic targeting, highlighting critical molecular drivers underpinning lung cancer development and progression. The pictorial representation of hub protein network and graph of rankings of lung cancer has been given in Figs. 9 and 10, respectively.

The constructed breast cancer gene interaction network comprises a total of 57 nodes and 507 edges, indicating a densely connected structure. The average number of neighbors per node is approximately 17.789, reflecting a high degree of interaction among the genes. The network displays a diameter of 5 and a radius of 3, suggesting relatively short paths between the most distant nodes and a compact overall structure [108]. The characteristic path length is 1.837, further supporting the idea of efficient communication within the network. A high clustering coefficient of 0.678 indicates strong modularity and the presence of tightly connected gene clusters [111]. The network density is 0.318, signifying a moderate level of overall connectivity. With a heterogeneity score of 0.637 and a centralization of 0.504, the network demonstrates a balanced distribution of node degrees and the presence of key hub genes (Fig. 11). The entire network forms a single connected component, and the analysis was completed in just 0.101 s, highlighting both the cohesiveness and computational efficiency of the analysis.

The present study elucidated the central molecular drivers in the PPI network of breast cancer. We employed the MCC algorithm—a highly sensitive topological method integrated within the CytoHubba plugin in Cytoscape. The analysis identified the top 10 hub genes based on their MCC scores, which represent their topological importance in the network architecture.

The gene TP53 emerged as the most prominent hub, exhibiting the highest MCC score of 4.24 × 10¹², indicating its critical regulatory role in tumor suppression and DNA damage response. Following TP53, ERBB2, ESR1, MYC, and BRCA1 showed equally high MCC scores (~4.23 × 10¹²), reflecting their pivotal involvement in hormonal signaling, cell proliferation, and genomic integrity—hallmarks of breast cancer pathophysiology. Genes such as PTEN and CDKN2A, both tumor suppressors, also scored highly, emphasizing their functional significance in cell cycle control and apoptosis [114]. CDH1, known for its role in epithelial-mesenchymal transition (EMT), and EGFR and EGF, key mediators of growth factor signaling, rounded out the list with slightly lower but still substantial MCC scores (~4.19–4.21 × 10¹²), suggesting their contributory role in breast cancer progression and metastasis. TP53, ERBB2, ESR1, MYC, and BRCA1 showed equally high MCC scores (~4.23 × 10¹²), reflecting their pivotal involvement in hormonal signaling, cell proliferation, and genomic integrity—hallmarks of breast cancer pathophysiology. This ranking provides a robust framework for prioritizing target genes for downstream functional validation and highlights potential biomarkers and therapeutic targets in breast cancer.

The pictorial representation of hub protein network and graph of rankings of lung cancer has been given in Figs. 12 and 13, respectively.

Multidrug resistance is a basic component that presents a significant therapeutic problem and is crucial to the management of disease outcomes. Finding new genes and biological mechanisms that cause medication resistance can help with this. These genes and pathways are usually good options for developing and finding new drugs. Additionally, a number of epigenetic changes may also contribute to drug resistance [128]. For example, it has been demonstrated that treatment resistance develops in ovarian and breast tumors that are positive for the estrogen receptor. It is anticipated that between 30 and 55 per cent of metastatic receptor-positive subtypes in breast cancer develop secondary resistance to therapy after receiving neoadjuvant aromatase inhibitor treatment. The existence of mutant ESR1 is thought to be the cause of the drug resistance. Mutations in the ligand-binding domain of ER, mutant ESR1 in circulating tumor DNA, and the activation of PI3K/mTOR pathways are attributable to ESR1 acquired secondary resistance, according to mutation analysis, RNA sequencing data collected from NGS, and bioinformatic analysis. Because of the tumor’s aggressive biology, the disease’s progression, recurrence, and metastasis, it has been observed that ESR1-related cancers typically have a poor prognosis [135]. ESR1 mutations are a crucial biomarker for managing ER+ breast tumors, since they can predict prognosis and change available therapy options. In a similar vein, mutations or altered gene expressions have been linked to the development of drug resistance to chemotherapy in more than 50% of relapsed patients with advanced ovarian cancer. Most patients with advanced ovarian cancer recur within two years due to the development of drug resistance, and the standard treatment strategy involves surgery followed by neoadjuvant chemotherapy. In individuals with advanced illness, neoadjuvant chemotherapy is associated with resistance to platinum-based treatment [136]. Apoptosis to avoid drug-induced cytotoxicity, tumor migration, drug metabolism, enhanced DNA repair, and activation of molecular pathways that promote tumor angiogenesis are some of the molecular mechanisms that lead to drug resistance [137]. P-glycoprotein, the p53 pathway, the mismatched DNA repair process, and multidrug resistance-associated protein are the proposed molecular pathways that cause drug resistance in ovarian cancer.

In a recent study, Meng et al. [138] identified the molecular pathways responsible for ovarian cancer drug resistance. The research demonstrated that dual oxidase maturation factor 1 (DUOXA1) is overexpressed in platinum-resistant ovarian cancer cells. The overexpression of this gene results in a higher generation of reactive oxygen species (ROS), which in turn maintains the activation of the ATR-Chk1 pathway and causes resistance to treatments based on platinum. The ATR-Chk1 pathway will be hampered by ROS inhibition, which will also reverse acquired drug resistance. This data was collected by RNA-sequencing and quantitative high-throughput combinational screening (qHTCS) based on NGS technology [138]. Aberrant RNA splicing signatures can also be found by RNA sequencing using NGS. Drug resistance can be counteracted and used as an advantage by focusing on the splice variants that increase drug resistance. Appropriate screening to find critical candidate biomarkers that can evade these molecular processes would advance our understanding of medication resistance and enhance the treatment plan for the best possible results. In research that revealed 46% of patients needed adjustments to their cancer management, Mody et al. [139] emphasized the significance of precision oncology in cancer therapy. Both DNA and RNA from a cohort of patients with solid tumors and relapsed and refractory hematological malignancies were sequenced using NGS technology. Leukemia and lymphoma were among the hematological malignancies in this group; on the other hand, brain, neuroblastoma, sarcoma, renal, liver, and ovarian cancers were among the solid tumor types. 10% of the patients in this cohort needed genetic counseling to assess future risk, and 15% of the patients needed modifications to their cancer therapy [139]. Their results made clear how important precision oncology is for better patient outcomes and clinical decision-making. In the management of cancer, metastatic tumors pose a significant challenge, especially when they are recurrent and have developed resistance to treatment. Robinson et al. [140] used integrated sequencing of DNA and RNA to show the mutational landscape of many metastatic tumors. Through the use of NGS, they were able to identify critical germline mutations, gene fusions, and complementary RNA transcriptional signatures of crucial molecular pathways that are very prevalent in a number of significant cancers, including ovarian, breast, colorectal, prostate, brain, and pancreatic cancer. Researchers also found predictive biomarkers for immune treatment for metastatic tumors using their precision oncology approach [140]. In light of the sequencing data, the patient’s treatment plans were modified, underscoring the significance of precision oncology in the treatment and management of cancer. NGS profiling of cancer genomes clarifies the molecular pathways linked to medication resistance and allows for the identification of genetic abnormalities. Finding these biomarkers will help with the development of new treatments that will enhance the prognosis for cancer treatment.

The IBM Watson for Oncology support system is another recent advancement in AI that uses algorithms to suggest treatments to help clinicians make decisions. In order to offer a dependable foundation for precision medicine and individualized patient care, IBM Watson for Oncology was created. The Watson for Oncology platform has proven effective in determining therapy regimens for breast [141], gastric [142], and non-small-cell lung cancer [143], despite some contradictory findings. The Watson for Oncology system’s dependability for therapy suggestions for gastric cancer was proven by [142]. The study demonstrated agreement between the medical team’s suggestions and those made by the Watson for Oncology system. Similar outcomes for metastatic non-small-cell lung tumors were reported by [143], who also found an 85.16% concordance between the medical team and the Watson for Oncology system. They underlined that the AI system helped the medical staff make decisions about therapy in a timely, accurate, and efficient manner [143]. According to their research, the AI system may be further enhanced with localized medical programs and is especially helpful in low-resource settings. The graphical illustration of SWOT analysis for cancer therapies is shown in Fig. 15.

The traditional process of discovering and developing new drugs is frequently an expensive and time-consuming process [144,145]. The increasing availability of big cancer datasets, both public and commercial, coupled with the affordability of various NGS and imaging technologies, has resulted in a surge of interest in utilizing AI to optimize this process. To address each element in the drug discovery spectrum, researchers create models that use a variety of information. Using a one-class support vector machine (AUC, 0.88) [146], for instance, integrated clinical data with gene expression patterns and protein–protein interaction networks to produce characteristics that potentially predict possible therapeutic targets in liver cancer. López-Cortés and colleagues [147] integrated multiple cancer databases, including PharmGKB, Cancer Genome Interpreter, and TCGA, among others, in a deep learning-based classification approach specific to breast cancer. They predicted proteins associated with breast cancer pathogenesis and reported several promising candidates to pursue as biomarkers or drug targets [148]. Researchers can now use hundreds of loss-of-function screen datasets from the DepMap Consortium to test a wide range of AI techniques [149]. For instance, using both gene-specific and cell line-specific data, the ECLIPSE ML approach uses the DepMap data to suggest cancer-specific therapeutic targets. Chen and associates have discovered that proteomics data, more especially reverse-phase protein array data, are highly predictive of cancer cell line dependencies after looking at a broad range of molecular characteristics from DepMap data [36]. This discovery highlights how versatile AI is in terms of its ability to identify the kinds of experimental data that are most relevant to a predictive model, in addition to predicting treatment targets.

Massively parallel sequencing of DNA and RNA simultaneously is made possible by high-throughput screening, which also produces massive datasets. This allows for the detection of both cancer-specific transcriptome alterations, such as alternative splicing, and pathogenic mutations. A complex biological process called alternative splicing is essential for controlling the expression of several genes that are involved in DNA repair, angiogenesis, adhesion, invasion, and cell division. Cancer cells are characterized by these functions.

In triple-negative, luminal, and human epidermal growth factor receptor 2 (HER2) breast cancer, novel subtype-specific splice variants in important genes such as LARP1, CDK4, ADD3, and PHLPP2 were found using RNA sequencing in a study by [150]. Targeting these cancer-specific isoforms with therapeutic techniques can lead to successful clinical outcomes. For example, there is a correlation between advanced gastric cancer and higher levels of CD44v6, while lower levels of CD44v6 are linked to prostate cancer. Similarly, different isoform expression levels can predict metastasis. The isoform CD44v10 is linked to pancreatic cancer [151]. RNA sequencing can be used to identify cancer-specific splice variants, which are becoming popular targets for the development of novel drugs.