Background Corona virus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in 2019 and rapidly evolved into a global public health crisis. Despite extensive research, the molecular mechanisms underlying inter-individual differences in COVID-19 susceptibility and disease severity remain incompletely understood, with both genetic and environmental factors believed to contribute. Methods In our study, we genotyped a subsample population of COVID positive patients from Pakistan. PCR based genotyping identified ACE DD allele (56.8%) to be most frequent in severe patients. Biomarker profiles for severely affected individuals were also evaluated which revealed no significant (p-value >0.05) differences among the three genotypes. Additionally, to discover the accumulative effects of our findings in a larger population, meta-analysis of similar studies was performed, in which the patients were divided into severe and non-severe categories. Results The results of meta-analysis demonstrated that all 5; dominant, recessive, allelic, homozygous and heterozygous genotype models show significant (p-value <0.05) association with severe form of COVID-19 disease. Conclusion The study provides a basis for multifactorial pathology of COVID-19 and elucidation of the exact molecular players in the disease will facilitate in better preparation management and tackling of similar outbreaks in future. Despite the integration of original data with a meta-analysis, the relatively small cohort size and between-study heterogeneity may have influenced the precision of the pooled estimates.
Background Angiogenic proteins (AGPs) play essential roles in vascular development, tissue repair, and disease progression. Accurate computational identification of AGPs from sequence data is critical for drug discovery and biomarker prioritization. However, existing approaches often face challenges regarding feature diversity and predictive generalization. Methods We propose FBGAN-AGP, a hybrid machine learning framework for sequence-based AGP prediction. The model integrates Filtered Position-Specific Scoring Matrix (FPSSM)-derived evolutionary profiles with ProtBert transformer-based contextual embeddings. These descriptors are concatenated into a unified 1,044-dimensional FusedFP feature space. A Feedback Generative Adversarial Network (FBGAN) is incorporated to iteratively refine representations and improve learning stability. Performance was evaluated using five-fold cross-validation and an independent test set. Results On the training dataset with FusedFP features, FBGAN-AGP achieved an accuracy of 99.92%, sensitivity of 99.94%, specificity of 99.89%, and MCC of 0.91. On the independent testing set, the model achieved an accuracy of 93.67%, sensitivity of 93.82%, specificity of 93.09%, and MCC of 0.83, outperforming existing predictors, including Deep-AGP (91.58%) and Ens-Deep-AGP (92.97%). Conclusions The results suggest that combining evolutionary profiles, protein language model embeddings, and adversarial learning can provide an effective computational tool for angiogenic protein identification and may support applications in target prioritization, therapeutic screening, and molecular design-oriented bioinformatics.
Drug resistance in gastrointestinal (GI) cancers, including gastric, colorectal, hepatocellular, and pancreatic carcinomas, represents a major obstacle to effective therapeutic intervention. A key contributor to this resistance is the dysregulation of RNA N6-methyladenosine (m6A) modification. Regulated by methyltransferases ("writers"), demethylases ("erasers"), and binding proteins ("readers"), m6A modifications play a crucial role in modulating RNA stability, translation, and degradation. Altered m6A levels and dysfunction of associated regulatory proteins perturb essential biological processes such as gene expression, metabolic reprogramming, DNA damage repair, cancer stem cell maintenance, and immune evasion. These disruptions collectively promote resistance to chemotherapy, targeted therapy, and immunotherapy across various GI malignancies. Therefore, targeting the m6A modification machinery—including writers, erasers, and readers—may offer a therapeutic entry point, although most strategies remain preclinical and require rigorous validation before clinical implementation. This review summarizes current findings on the mechanisms by which dysregulated m6A modifications drive therapeutic resistance in GI cancers and discusses emerging approaches aimed at targeting this pathway to overcome drug resistance.
Background Cancer remains one of the leading causes of mortality worldwide, with accurate and early diagnosis critical for effective treatment. Advances in machine learning algorithms have prompted the distinguishment ability between cancerous and normal tissues based on gene expression profiling. However, large-scale tumor classification models that can differentiate both among various cancer types and between cancerous and normal samples remain limited. Methods To address this challenge, this study proposes a novel deep learning framework, SpikeFormer, which integrates the attention mechanism of transformers with spiking neuron dynamics to model complex temporal dependencies in gene expression data effectively. SpikeFormer was evaluated on The Cancer Genome Atlas (TCGA) dataset, comprising 10,632 samples across 33 cancer types, demonstrating its robustness in both binary and multi-class classification tasks. Results In binary classification experiments, the model achieved an average accuracy of 99.44% across four TCGA sub-datasets, demonstrating its ability to distinguish between cancerous and normal samples. In multi-class classification, SpikeFormer achieved an accuracy of 96.90% and a Cohen’s Kappa score of 97.00%, outperforming the baseline Transformer model. Conclusions These results highlight the reliability and scalability of the proposed SpikeFormer model for cancer classification, suggesting its potential application in clinical diagnosis and treatment planning. Although SpikeFormer demonstrates promising performance on TCGA RNA-seq datasets, the current evaluation is limited to retrospective public data, and further validation using independent multi-center cohorts and prospective clinical datasets is required before potential clinical translation.This work was supported by the grant from National Natural Science Foundation of China [62561042], the Natural Science Foundation of Inner Mongolia Autonomous Region of China [2024ZD30], and the Fundamental Research Funds for the Central Universities [Y03023206300125113].The Cancer Genome Atlas (TCGA) data used in this study are available from dbGaP under accession number phs000178.
Background Ulcerative colitis is a chronic inflammatory disorder of the gastrointestinal tract. Numerous patients with ulcerative colitis experience symptoms; however, no definitive cure is currently available. Genome-wide association studies (GWASs) have identified numerous ulcerative colitis susceptibility loci, and the RNF186 gene has been shown to be significantly associated with ulcerative colitis. However, specific GWAS-identified markers of the RNF186 gene have not yet been fully elucidated. Methods A total of 150 buffy coat samples (74 controls and 76 patients with ulcerative colitis) were analyzed by PCR-based amplicon sequencing of the RNF186 3′ untranslated region (UTR). In silico analyses were performed using TargetScanHuman 8.0, miRDB, miRmap, RegulomeDB, and the GTEx database. Results Six single nucleotide polymorphisms (SNPs) were identified, including four within the 3′ UTR and two in the downstream intergenic region. No significant association was observed between RNF186 3′ UTR polymorphisms and ulcerative colitis. TargetScanHuman 8.0, miRDB, and miRmap predicted binding of hsa-miR-10a-5p and hsa-miR-10b-5p to the RNF186 3′ UTR, whereas the c.750A>G (rs574353014) G allele was predicted to disrupt these miRNA-binding sites. In addition, the identified SNPs were predicted to alter RNA-binding protein (RBP)-binding patterns. Although most 3′ UTR variants showed limited regulatory potential in RegulomeDB, the downstream variant c.1255T>C (rs3767216) demonstrated relatively strong regulatory potential and was identified as a significant eQTL in gastrointestinal tissues, including the esophagus, in the GTEx database. Conclusions RNF186 3′ UTR polymorphisms were not significantly associated with ulcerative colitis, although c.750A>G (rs574353014) and c.1255T>C (rs3767216) warrant further functional investigation. This study is limited by its Korean-only cohort and limited sample size.Following are results of a study on the "Glocal University Project Group in Gyeongkuk National University-Gyeongbuk Provincial College" Project, supported by the Ministry of Education and National Research Foundation of Korea. Sequencing data are available in GenBank under accession number PZ698340.
Background Early diagnosis and precise non-invasive detection of colorectal cancer (CRC) is slated to greatly improve patient prognosis and promote the development of personalized treatment. Circulating tumor DNA (ctDNA), characterized by cancer-specific genetic and epigenetic features, has become an important biomarker with the development of liquid biopsy technologies. Methods In this study, extensive methylation and transcriptome sequencing data from The Cancer Genome Atlas (TCGA) were analyzed to identify five genes- ADHFE1, C9orf50, IKZF1, SDC2, and SEPT9- previously reported in colorectal cancer (CRC) studies. A range of statistical methods was employed to develop a novel CRC-specific diagnostic model. The model's performance was initially evaluated using tissue sample datasets from public sources and subsequently validated in plasma samples collected from 61 CRC patients and 26 normal controls. The association between model scores and tumor burden was further examined using plasma data. Public datasets were obtained from TCGA-COAD, TCGA-READ, and GEO accession numbers GSE75546, GSE77954, GSE77965, GSE42752, GSE48684, GSE68060, GSE77718, GSE101764, and GSE40279. Results The tissue-derived model achieved high diagnostic specificity and sensitivity in public tissue datasets and accurately differentiated CRC patients from normal controls in plasma samples (AUC=0.907). Model scores were significantly associated with tumor volume and treatment response, demonstrating the potential to monitor tumor burden non-invasively. Conclusion The five-gene ctDNA methylation model demonstrated high diagnostic accuracy and correlated with tumor burden in this exploratory study, highlighting its potential as a minimally invasive liquid-biopsy tool for CRC assessment. However, the findings are limited by the modest plasma cohort size, the lack of matched tissue-plasma methylation data, and the exploratory study design; therefore, larger prospective cohorts are required for further clinical validation.
Background Platinum-based chemotherapy is the standard first-line treatment for ovarian cancer; however, the development of platinum resistance frequently leads to tumor recurrence and poor prognosis. Therefore, identifying key genes involved in platinum resistance is essential for improving our understanding of platinum resistance and may provide potential biomarkers for ovarian cancer. Methods In this study, Aldehyde dehydrogenase 1 family member A1 (ALDH1A1) was identified as a differentially expressed gene in platinum-resistant ovarian cancer through analysis of public gene expression datasets and subsequently validated in clinical ovarian cancer samples. To investigate its functional role, gain- and loss-of-function assays were conducted in multiple platinum-sensitive and cisplatin-resistant ovarian cancer cell lines. These experiments evaluated the effects of ALDH1A1 on key cellular phenotypes, including cell proliferation and migration. Results Integrated analysis of three Gene Expression Omnibus (GEO) datasets consistently showed that ALDH1A1 was upregulated in platinum-resistant ovarian cancer cell lines. This finding was further validated in clinical samples, showing significantly higher ALDH1A1 expression in platinum-resistant recurrent tumors compared with platinum-sensitive cases. Functional experiments demonstrated that ALDH1A1 overexpression enhanced proliferation and migration of platinum-sensitive ovarian cancer cells, whereas its knockout in cisplatin-resistant cells attenuated these malignant phenotypes. Cell cycle and apoptosis analyses suggested that the biological impact of ALDH1A1 is partially cell line-dependent. The datasets supporting this study are available in the Gene Expression Omnibus (GEO) repository under persistent identifiers GSE28648, GSE33482, and GSE149146. Conclusion Our findings suggest that ALDH1A1 is associated with platinum-resistant phenotypes and promotes malignant cellular behaviors in ovarian cancer, highlighting its potential as a biomarker and candidate therapeutic target. However, the underlying molecular mechanisms remain unclear, and the current findings are limited to in vitro models and require further validation in vivo and in larger clinical cohorts.
Background Lysine acetylation is an important post-translational modification involved in diverse cellular regulatory processes. Because experimental identification of acetylation sites is labor-intensive and costly, accurate computational prediction remains highly desirable, particularly for non-human species with limited experimentally verified annotations. Methods We developed PLMGraph-Ace, a species-specific dual-branch protein language model (PLM)–graph neural network (GNN) fusion framework for lysine acetylation-site prediction. It integrates residue-level contextual representations extracted from a frozen ESM-2 model with local dependency representations learned from a sequence-distance graph. Experiments were conducted on datasets from nine non-human species under a unified fixed-specificity setting (Sp = 0.900). Results Across the nine species, PLMGraph-Ace consistently outperformed PAIL, CapsNet, DeepDA-Ace, and MDDeep-Ace in sensitivity, accuracy, precision, and F1 score. Compared with the recent baseline MDDeep-Ace, PLMGraph-Ace achieved average absolute improvements of 6.4, 3.6, 2.2, and 6.1% points in sensitivity, accuracy, precision, and F1 score, respectively, under the fixed-specificity setting. Conclusion PLMGraph-Ace provides a practical framework for species-specific lysine acetylation-site prediction across the nine non-human species studied here. The main limitations are that evaluation was performed mainly on approximately balanced benchmark datasets, the sequence-distance graph does not explicitly incorporate three-dimensional structural information, and further validation under naturally imbalanced proteome-scale screening scenarios is still required. Funding was provided by grants from the National Natural Science Foundation of China (Nos. 62162032, 32260154, and 62562041), Jiangxi Provincial Department of Education research projects (GJJ2201004 and GJJ2400905), and Jingdezhen City Science and Technology Plan Project (No. 2025JCYJ003). Database registration number: Not applicable.
Background Melanoma is a highly aggressive skin malignancy with strong metastatic potential and limited long-term therapeutic benefit in some patients. Methyltransferase-like protein 9 (METTL9) has been implicated in tumor progression, but its role in melanoma remains unclear. This study aimed to investigate METTL9 expression in melanoma and determine its effects on melanoma cell proliferation, migration, invasion, and tumor growth. Methods METTL9 expression in melanoma and normal controls (including melanocytic nevi and normal tissues) was analyzed using public databases and immunohistochemistry. Its association with overall survival was explored through Kaplan–Meier analysis. METTL9 mRNA and protein levels were measured in melanoma cell lines and normal melanocytes by qRT-PCR and western blotting. METTL9 was stably silenced in A375 and SK-MEL-5 cells using lentiviral shRNA, and cell proliferation, colony formation, migration, and invasion were assessed using CCK-8, colony formation, wound healing, and Transwell assays. Tumor growth following METTL9 knockdown was evaluated in xenograft models in nude mice. Results METTL9 was highly expressed in melanoma tissues and cell lines, with raised levels significantly correlating with reduced patient overall survival (P < 0.05). Silencing of METTL9 notably suppressed melanoma cell invasion, migration, colony-forming ability, and proliferation (P < 0.05). Moreover, in vivo experiments demonstrated that METTL9 knockdown significantly hindered tumor growth, which was accompanied by a marked (P < 0.05) reduction in the levels of Ki-67 and METTL9 within xenograft tumor tissues. Conclusion METTL9 may promote melanoma progression and could represent a potential prognostic biomarker and therapeutic target. However, these findings are limited by the small clinical sample size and the lack of detailed downstream mechanistic validation, requiring further confirmation in larger cohorts and mechanistic studies.
Next-generation sequencing (NGS) has revolutionized microbial genomics by enabling high-throughput, accurate, and cost-effective exploration of microbial genetic and functional diversity. In the post-genomic era, integrating NGS with multi-omics approaches—including metagenomics, metatranscriptomics, proteomics, and metabolomics—has significantly enhanced systems-level understanding of microbial physiology, ecological interactions, metabolic regulation, and host–microbe relationships. These advances have accelerated applications in antimicrobial resistance surveillance, clinical diagnostics, environmental bioremediation, industrial biotechnology, and sustainable agriculture. This review critically examines recent advances in major sequencing platforms, including Illumina, PacBio, and Oxford Nanopore, with an emphasis on their comparative strengths, limitations, error profiles, and suitability for diverse microbial applications. We also discuss modern bioinformatics workflows for sequence assembly, annotation, microbial community profiling, and integrative multi-omics analysis, highlighting emerging frameworks for integrating heterogeneous datasets. In addition, the review explores the growing role of artificial intelligence and machine learning in microbial systems biology, including predictive modeling, automated functional annotation, and microbiome-based diagnostics. However, this review has a few limitations. Given the rapidly changing landscape of bioinformatics, we focus primarily on mainstream software pipelines, so some niche or newly released tools may be overlooked. Additionally, the review leans heavily toward bacteria and viruses rather than complex eukaryotic microbes, and we do not delve into specific hardware requirements. Despite remarkable progress, substantial challenges remain in data integration, reproducibility, computational scalability, batch-effect correction, and experimental validation of in silico predictions. We conclude that the convergence of advanced sequencing technologies, reproducible bioinformatics pipelines, and AI-driven systems modeling will play a pivotal role in decoding microbial complexity and advancing precision microbiology, environmental sustainability, and next-generation biotechnological innovation.
Background In recent years, an increasing amount of evidence has shown that the ubiquitin-proteasome system (UPS) dysfunction is detected in Alzheimer's disease (AD). This study aimed to explore novel diagnostic markers for AD based on UPS features using multiple computational algorithms. Methods Two microarray datasets (GSE132903 and GSE122063) comprising 797 UPS genes were retrieved from Gene Expression Omnibus (GEO). Weighted gene co‑expression network analysis (WGCNA) was utilized to identify AD‑related co‑expression modules, followed by functional enrichment analysis. Genes identified as both key modular genes and UPS genes were used to construct a protein‑protein interaction (PPI) network and then clustered by MCODE algorithm. Key diagnostic genes were subsequently determined utilizing three machine learning algorithms (LASSO, SVM-RFE, and random forest). Finally, molecular docking and 100 ns molecular dynamics (MD) simulations were used to evaluate the binding stability of a candidate compound with its targets. Finally, the expressions of the key genes were verified by in vitro cell assays. Results WGCNA identified blue and green gene modules as significantly associated with AD, and functional analysis linked these modules to critical neural functions. Among 11 core genes, BIRC3, SPOP, PSMB2, and FBXO5 emerged as final candidates for the AD diagnostic model. This four-gene model demonstrated robust diagnostic performance, with BIRC3 and SPOP achieving area under the curve (AUC) values > 0.77. Molecular docking indicated that resveratrol stably bound to both BIRC3 and SPOP. The mRNA and protein expressions of BIRC3 and SPOP were significantly upregulated in LPS-treated HMC3 human microglia. However, multicenter clinical verification remains necessary to evaluate the clinical translation potential of these findings objectively. Conclusion BIRC3 and SPOP emerged as promising candidate biomarkers in AD, however, their clinical utility necessitates further validation in multi-center cohorts adjusted for potential confounders.
Background: Lactylation is a novel form of post-translational modification. The role of lactylation in breast cancer (BRCA), especially its interplay with immune response and metabolism, remains to be explored. This study utilized bioinformatics analysis to identify genes related to lactate production and preliminarily explored their roles in BRCA. Methods: Using data from The Cancer Genome Atlas database (TCGA) and dataset GSE20685, we analyzed the expression and mutation patterns of lactylation-related genes (LRGs). Unsupervised clustering was performed to screen lactylation-related clusters. We further investigated the presence, functional location, and association with BRCA of global lactylation. Following Cox regression and LASSO regression analyses, a LRG model was developed and validated. Subsequently, the associations of LRGs-based risk model with clinical features, immunotherapy responses, immune cell infiltration, mutation landscape, and biological functions were explored. Single-cell expression levels of core genes were also determined. Finally, the functional role of the core gene was validated through in vitro assays (MTT, colony formation, Transwell) and in vivo xenograft models. Results: An elevation of global lactylation was observed, particularly in malignant tumors such as BRCA. Thereafter, a 4-LRG risk model was developed for predicting the prognosis of BRCA. High-and low-risk BRCA groups exhibited significant differences in biological functions, checkpoint expressions, immune cell infiltrations, immunotherapy responses, drug sensitivities, and clinical features. Further analysis revealed that DDX21 was widely expressed in various cell types of BRCA, indicating a potential role in immunity regulation in BRCA. In addition, silencing DDX21 can inhibit the growth of BRCA cells in vitro and xenograft tumor growth in vivo. Conclusions: We developed a lactylation-related prognostic model and identified DDX21 as a key oncogenic driver and potential therapeutic target in BRCA. However, the lack of clinical research on DDX21 is the main limitation of this study.
Our manuscript synthesizes recent breakthroughs in single-cell genomics and their impact on cancer research. We highlight how high-resolution, single-cell technologies now enable systematic dissection of intratumoral heterogeneity, reconstruction of clonal evolutionary trajectories, and mechanistic interrogation of therapeutic resistance and metastasis. By resolving rare subclones and context-dependent tumor–microenvironment crosstalk, single-cell genomics has shifted the paradigm of cancer—from a static pathological entity to a dynamically adapting ecosystem. We further discuss emerging applications in precision medicine, including the detection of low-frequency resistance mutations, tracking of metastatic competent clones, and identification of actionable co-dependency axes between malignant and stromal cells. These insights are laying the groundwork for evolution-aware, interceptive therapeutic strategies. Finally, we examine persistent barriers to clinical integration: technical limitations in amplification uniformity and cell capture bias, lack of standardized analytical workflows, and high infrastructural demands. We outline priority directions, including higher-throughput, lower-cost platforms, clinical-grade standard operating procedures, and spatially resolved multi-omics that are essential to translate these powerful tools into routine oncology practice.
Ankylosing spondylitis (AS) is a chronic, systemic inflammatory autoimmune disease that primarily involves the axial skeleton, especially the sacroiliac joints and spine. The condition poses a substantial burden due to a high rate of disability. With increasing recognition of the links among immune dysregulation, abnormal bone remodeling, and gut-derived inflammation, AS research is entering a stage in which mechanistic understanding, model optimization, and therapeutic innovation must be more closely aligned. Existing reviews have typically examined AS pathogenesis, animal models, or therapeutic strategies in isolation and this gap has constrained translational progress. This review outlines key pathogenic mechanisms relevant to model selection, with a focus on HLA-B27-mediated immune activation, Th17-driven inflammation, dysregulated coupling of bone resorption and formation, and intestinal barrier dysfunction. It examines the advantages, limitations, and research applications of available three categories of AS animal models, such as genetically engineered models, immunization-induced models and spontaneous models. Additionally, it discusses current therapeutic strategies including physical therapy, conventional pharmacologic agents, biologic therapy, cell therapy, and natural product monomers. By integrating insights from pathogenesis, modeling, and therapy, the present work aims to offer a fresh perspective for both research and clinical management of AS in the current era of precision medicine.
Cancer genetics plays a revolutionary role in the field of oncology by providing novel insights into tumor evolution, heterogeneity, and treatment targets. Recent advances in sequencing technology, such as whole-genome and transcriptome sequencing, have enabled the precise molecular profiling of cancers, leading to the development of targeted and individualized treatment approaches. Liquid biopsy has emerged as a non-invasive approach for collecting samples, utilizing blood or other body fluids to identify tumor cells, molecular changes, and metabolites. Liquid biopsy helps to detect various biological markers, including ctDNA, CTCs, and exosomes. Immunogenomics has revolutionized cancer management through the use of immune checkpoint inhibitors and personalized neoantigen vaccines. Furthermore, the integration of machine learning facilitates the examination of extensive genomic datasets, enabling the detection of patterns, the prediction of treatment responses, and the identification of novel therapeutic targets. Innovative drug delivery technologies, such as nanoparticle-based and CRISPR-mediated genome editing, provide promise for more effective and less harmful treatments. This review discusses recent advancements in cancer genomics, including liquid biopsy, tumor heterogeneity, immunogenomics, and the function of machine learning in the analysis of genomic data. By understanding ethical and social issues, successful implementation can be possible. The combination of genomics, immunogenomics, and liquid biopsy holds promise for novel, personalized cancer treatments, indicating a new era in cancer drug development.
Millets are highly nutritious and traditional staple foods consumed by millions worldwide. However, their low palatability and limited aroma have restricted their broader acceptance in human diets. Aroma is a key trait influencing consumer preference, and in many crops, 2-acetyl-1-pyrroline (2AP) contributes significantly to fragrance and palatability. The betaine aldehyde dehydrogenase 2 (BADH2) gene is a critical regulator of 2AP biosynthesis in crops. Targeted manipulation of BADH2 in the metabolic pathway offers a promising strategy to enhance 2AP production in millets. Understanding the structure and function of BADH2 is essential for improving aroma traits, and genome editing (GE) approaches present a viable avenue for functional enhancement. In this review, we highlight the significance of 2AP and its metabolic pathways in crops and provide insights into the structural and functional features of BADH2. We mined putative BADH proteins from foxtail millet, finger millet, and sorghum using the rice OsBADH2 sequence as a reference, and analyzed their physicochemical and protein characteristics via in silico approaches. Furthermore, we discuss potential functional motif modifications of BADH2 in millets to enhance 2AP production through GE strategies. This review offers a comprehensive perspective on engineering BADH2 functional motifs to develop fragrant millet varieties. These insights could accelerate millet improvement and support the global promotion and adoption of millet-based foods.
Degenerative Disc Disease (DDD) and related musculoskeletal disorders depend heavily on the structural integrity of collagen in the Extracellular Matrix (ECM) of connective tissues. The roles of collagen types I, II, IX, and XI in Intervertebral Disc (IVD) dynamics have been previously examined. Genetic polymorphisms in collagen-encoding genes are associated with susceptibility to DDD. These polymorphisms influence the quality and arrangement of collagen fibrils, thereby affecting the overall health of the connective tissues. CRISPR/Cas9 offers a viable method for correcting pathogenic genetic variants associated with collagen-related pathologies. This review also emphasizes the need for a deeper understanding of gene-environment interactions, which is critical for the development of personalized medicine for the treatment and management of DDD and related conditions. Expanding genetic studies and integrating insights into biomaterials and tissue engineering are essential for this purpose. A comprehensive genetic approach promises advances in predictive, preventive, and personalized health care.
Cancer is a complex disease involving the abnormal growth and dissemination of cells. The traditional treatments of cancer suffer from challenges like drug resistance and non-specificity. However, immunotherapy has become a promising alternative, leveraging the body's immune system to attack cancer cells. However, the variability in patients' responses to immunotherapy underscores the significance of personalized treatment strategies. Genomics is key to understanding how genetic changes drive tumor development and response to immunotherapy. Genomic profiling techniques, such as NGS, enable the identification of molecular biomarkers, such as Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI), that predict response to immunotherapy. Furthermore, the Tumor Microenvironment (TME), consisting of different cells and molecules surrounding the tumor, plays an important role in cancer progression and treatment outcomes. In this review, we highlight how genomics-driven approaches are accelerating cancer immunotherapy. It presents how genomics can be used to personalize immunotherapy for cancer and discusses challenges such as tumor heterogeneity and resistance to therapy, which require innovative technologies such as single-cell RNA sequencing, AI, and machine learning in immunogenomics, and liquid biopsies to refine treatment strategies. In the future, the incorporation of CRISPR-based gene editing, personalized neoantigen vaccines, and combination therapies holds great promise for transforming cancer treatment.
