Cancer progression is governed by a dynamic interplay between genetic alterations and epigenetic modifications, which shape tumor adaptation, immune evasion, and therapy resistance. Among these modifications, epigenetic regulation of autophagy, a cellular process critical for maintaining homeostasis, has emerged as a key player in cancer biology. While autophagy suppresses tumor initiation by eliminating damaged organelles and reducing oxidative stress, tumors can hijack this process to sustain survival under metabolic stress and promote resistance to chemotherapy, radiotherapy, and immunotherapy. This review explores the intricate crosstalk between DNA methylation, histone modifications, and non-coding RNAs in controlling autophagy-related genes and their dual role in cancer progression. We also highlight the therapeutic potential of targeting epigenetic modulators (HDAC and DNMT inhibitors) and autophagy regulators to overcome multidrug resistance. Understanding the epigenetic-autophagy axis provides novel insights into cancer treatment, emphasizing the need for personalized approaches that balance autophagic activity to enhance therapy efficacy and improve patient outcomes. This emerging field presents a promising frontier for epigenetic-driven precision medicine in oncology.

The epigenetic landscape of cancer reveals how microRNAs (miRNAs) regulate gene expression through epigenetic mechanisms, alongside advancements in miRNA-based biosensor technology for early detection. Epigenetics, which studies heritable gene function changes without altering DNA sequences, has transformed our understanding of cancer by highlighting the complex interaction between genetics and environment. Key mechanisms such as DNA methylation, histone modification, and chromatin remodelling shape gene expression and cellular identity, with dysregulation in these processes often marking cancer development. Within this framework, miRNAs act as crucial epigenetic regulators, influencing gene expression post-transcriptionally and functioning as either oncogenes (oncomiRs) or tumour suppressors. miRNAs interact with epigenetic modifiers like histone deacetylases (HDACs) and DNA methyltransferases (DNMTs), creating regulatory networks that affect cancer cell behaviours such as proliferation, differentiation, and apoptosis. To apply these molecular insights, the chapter reviews innovations in miRNA-targeting biosensors. Electrochemical and photoelectrochemical biosensors, enhanced with nanotechnology, are designed to detect miRNAs at ultra-low concentrations in biological fluids, addressing limitations of conventional cancer diagnostics. These biosensors offer high sensitivity, specificity, and point-of-care potential, with benefits such as rapid response, cost-efficiency, and miniaturization. Case studies showcase miRNA-based biosensors targeting cancer-related miRNAs, underscoring their promise in early cancer detection and monitoring. By bridging epigenetics, miRNA regulation, and biosensor technology, these emerging diagnostic tools offer new possibilities for improving cancer diagnosis and patient outcomes.

The dysregulation at the epigenetic level, such as DNA methylation, histone modifications, and changes in noncoding RNA, plays an important role in many serious human pathologies, including cancers. Epigenetics modulates the expression of tumor-related genes without any changes in the DNA sequences, thus understanding the epigenetic profile is a promising way to clarify the underlying mechanism of cancers as well as other diseases. Specific techniques like chromatin immunoprecipitation followed by sequencing (ChIP-seq), whole-genome bisulfite sequencing (WGBS), or mass spectrometry (MS) were developed to obtain epigenetic data. This leads to the need for robust tools to analyze and interpret these high-throughput data. With the development of bioinformatics tools, several complex interactions between DNA methylation and chromatin modification were clarified to help researchers control gene expression. Moreover, data science, and information technology, especially machine learning and deep learning, have revolutionized the study of epigenetics in cancer, providing powerful tools to integrate both genetic and epigenetic data, as well as clinical/para-clinical characteristics to enhance the accuracy of cancer diagnosis and treatment strategies. In this study, we will provide an overview of epigenetics' role in cancer, and more importantly, an update on the application of bioinformatics and data science in the epigenetic field for cancers. Integrating epigenetic data with other data like omics and clinical data with the help of bioinformatics and data science is an emerging direction for deciphering the epigenetic landscape of cancer and identifying potential therapeutic targets.

One of the three most common cancers in the world today is breast cancer. In recent years, molecular biology studies have highlighted the role of gene mutations. Epigenetic modifications have also been studied and have been shown to be one of the main contributors to the development and progression of breast cancer. Targeting these epigenetic regulatory mechanisms is considered a potential therapeutic strategy with the goal of inhibiting tumor progression and restoring normal gene activity. Epigenetic research has shed light on the important role of epigenetics in various aspects of breast cancer development, the underlying mechanisms of breast cancer, and its potential applications in improving diagnosis, prognosis, and treatment. These findings highlight the transformative potential of epigenetics in advancing the understanding and management of breast cancer. From there, it opens opportunities to detect breast cancer early even in a non-invasive way.

Ovarian cancer is a highly heterogeneous and lethal gynecological malignancy, with its progression tightly regulated by both genetic and epigenetic mechanisms. Among the epigenetic regulators, histone modifications-including acetylation, methylation, phosphorylation, ubiquitination, and SUMOylation-have emerged as pivotal modulators of chromatin architecture and gene transcription. These post-translational modifications, governed by specific histone-modifying enzymes, shape the transcriptional landscape of tumor cells and influence key oncogenic processes such as cellular proliferation, invasion, stemness, DNA repair, and chemotherapy resistance. Dysregulation of enzymes such as histone acetyltransferases (HATs), deacetylases (HDACs), methyltransferases (HMTs), demethylases (HDMs), ubiquitin ligases, and SUMO ligases has been implicated in the pathogenesis of ovarian cancer and is associated with adverse clinical outcomes. Importantly, these modifications are reversible, rendering them tractable therapeutic targets. This review provides a comprehensive synthesis of the major classes of histone modifications and their biological functions in ovarian cancer, highlights recent insights into the interplay between different modifications (crosstalk), and evaluates ongoing therapeutic strategies that aim to reverse epigenetic dysregulation. By elucidating the complex epigenetic landscape, this review supports the development of next-generation diagnostic, prognostic, and therapeutic approaches in ovarian cancer.

Malignant melanoma is the most aggressive and lethal type of skin cancer associated with increased mortality rates. Moreover, beyond the genetic background, the altered epigenetic landscape (e.g., abnormal patterns of DNA methylation, aberrant histone modifications and de-regulated expression levels of ncRNAs) further contributes to the pathophysiology of the disease. In addition, despite the improvement of current anti-melanoma strategies and the development of new therapeutic approaches, the 5-year survival among melanoma patients is still high, mainly due to acquired-drug resistance. On the other hand, phytochemicals have been associated with various health-promoting properties through pleiotropic mechanisms including acting as potent epigenetic regulators restoring back a normal phenotype in various experimental cancer models. In this review article, we discuss the general characteristics of malignant melanoma and current therapeutic approaches while we report the epigenetic basis of the disease along with the main compounds capable of restoring a normal epigenetic landscape. Finally, we describe the role of various phytochemicals in targeting the epigenome of malignant melanoma thereby potentially acting as an alternative therapeutic approach.

Spontaneous pneumothorax (SP) is defined by the presence of air in the pleural space arising from neither trauma nor iatrogenic causes. It can develop secondary to various etiologies. The familial clustering observed in some patients with SP supports the view that genetic factors play a role in the pathogenesis of SP. Several recognizable pneumothorax-associated genetic syndromes exist, including Birt-Hogg-Dubé syndrome (BHD), tuberous sclerosis complex-associated lymphangioleiomyomatosis (TSC-LAM), Marfan syndrome (MFS), cystic fibrosis (CF), alpha-1 antitrypsin deficiency (AATD), vascular Ehlers-Danlos syndrome (vEDS), Loeys-Dietz syndrome (LDS), and a few others. Recognition of these syndromes underlying SP facilitates optimal management and counseling regarding prognosis and potential comorbidities. In this review, we systematically summarize several genetic syndromes associated with pneumothorax, in which SP may manifest either as an initial presenting symptom or as a potential complication that adversely affects the prognosis.

Tertiary lymphoid structures (TLS) are organized lymphoid aggregates that form outside lymph nodes within the tumor microenvironment. They are linked to better prognosis and stronger antitumor immune responses. However, the key molecular factors that control TLS maturation in non-small cell lung cancer (NSCLC) are still not clear, and their prognostic value is not fully defined. We analyzed TLS-related genes to identify markers linked to prognosis. We used consensus clustering to define molecular subtypes with different survival outcomes. We then built a TLS risk score signature (TLSRS) using LASSO-Cox regression and tested it in independent meta-GEO cohorts. We also examined the association between DKK1 expression and TLS maturation by multiplex immunofluorescence in a clinical cohort. TLSRS separated patients into high- and low-risk groups in the training cohort (n = 503, P < 0.0001) and in the validation cohorts (n = 681, P < 0.0001). The time-dependent AUC values were 0.762, 0.710, and 0.708 at 1, 3, and 5 years. Multivariable analysis showed that TLSRS was an independent prognostic factor in all cohorts. Patients with low TLSRS showed higher immune cell infiltration and higher CCL19 expression, and they also showed stronger immune-related activity across cohorts. In an independent in-house cohort (n = 119), higher DKK1 expression was linked to worse overall survival. DKK1 expression was negatively correlated with the TLS maturity ratio, but it was not correlated with the number of TLS. We developed a prognostic TLSRS that captures key features of the immune microenvironment in NSCLC. We also found that DKK1 may act as a negative regulator of TLS maturation. These results help explain TLS biology and suggest that DKK1 could be a therapeutic target to improve antitumor immunity in NSCLC.

Structural variants (SVs) are increasingly recognized as important contributors to oncogenesis through their effects on 3D genome folding. Recent advances in whole-genome sequencing have enabled large-scale profiling of SVs across diverse tumors, yet experimental characterization of their individual impact on genome folding remains infeasible. Here, we leveraged a convolutional neural network, Akita, to predict disruptions in genome folding caused by somatic SVs identified in 61 tumor types from the Children's Brain Tumor Network dataset. Our analysis reveals significant variability in SV-induced disruptions across tumor types, with the most disruptive SVs coming from lymphomas and sarcomas, metastatic tumors, and germline cell tumors. Dimensionality reduction of disruption scores identified five recurrently disrupted regions enriched for high-impact SVs across multiple tumors. Some of these regions are highly disrupted despite not being highly mutated, and harbor tumor-associated genes and transcriptional regulators. To further interpret the functional relevance of high-scoring SVs, we integrated epigenetic data and developed a modified Activity-by-Contact scoring approach to prioritize SVs with disrupted genome contacts at active enhancers. This method highlighted highly disruptive SVs near key oncogenes, as well as novel candidate loci potentially implicated in tumorigenesis. These findings highlight the utility of machine learning for identifying novel SVs, loci, and genetic mechanisms contributing to pediatric cancers. This framework provides a foundation for future studies linking SV-driven regulatory changes to cancer pathogenesis.

Lung cancer, being the top cause of global cancer-related mortality, calls for effective treatments. RN7SK (7SK) is a long non-coding RNA (lncRNA) that plays a significant role in the regulation of gene transcription and thereby controls essential cellular activities. Limited evidence supports the anticancer potential of 7SK, and its suppressive effects have not been tested against lung cancer. This study explored the anticancer effects of RN7SK (7SK), a long non-coding RNA known to regulate gene transcription, through exosome-mediated delivery in lung cancer cells. Treatment with exosome-loaded 7SK (Exo-7SK) significantly elevated 7SK levels in non-small-cell lung cancer cells and suppressed key cancer traits. Exo-7SK reduced cell viability and proliferation, promoted apoptosis, and inhibited migration and invasion by shifting gene expression away from epithelial-mesenchymal transition. It also impaired spheroid formation and reduced spheroid dispersion and viability in 3D microfluidic cultures. In conclusion, our findings highlight the cancer-suppressive potential of exosome-mediated 7SK delivery against lung cancer, demonstrating significant efficacy in both 2D and 3D culture models. These observations warrant further confirmation in future studies employing advanced designs and clinically relevant models.

Liquid biopsies offer a promising, noninvasive approach for monitoring and predicting responses to immunotherapy across multiple solid tumors. For the most part these are circulating tumor DNA (ctDNA) based assays. Here, we discuss the biological basis, clinical evidence, and potential applications of different types of ctDNA assays in tracking tumor dynamics, distinguishing pseudoprogression, and assessing minimal residual disease. We explore the current limitations, assay variability, and future directions, including integration with other biomarkers and real-world clinical trials aimed at validating ctDNA as a routine tool in precision immuno-oncology.

Colorectal cancer (CRC) remains a leading cause of cancer‑related morbidity and mortality worldwide. Although the adenoma-carcinoma sequence and its genetic drivers are well described, the earliest cellular and molecular events initiating tumorigenesis within histologically normal colonic epithelium remain poorly defined. This study aims to identify tumor‑initiating cells (TICs), distinguish them from normal stem‑like cells (nSTMs), and delineate early transcriptional and signaling programs using single‑cell RNA sequencing (scRNA‑seq) from paired normal‑appearing and transformed human colonic tissues.

In biomedical studies, testing for differences in covariance may offer scientific insights, especially when differences are driven by complex joint behavior between features. However, when differences in joint behavior are weakly dispersed across many dimensions and arise from differences in low-rank structures within the data, as is often the case in genomics and neuroimaging, existing two-sample covariance testing methods may suffer from power loss. The Ky-Fan(k) norm, defined by the sum of the top k singular values, is a simple and intuitive matrix norm able to capture signals caused by differences in low-rank structures between matrices, but its statistical properties in hypothesis testing have not been studied well. In this paper, we investigate the behavior of the Ky-Fan(k) norm in two-sample covariance testing. Ultimately, we propose a novel methodology, rank-adaptive covariance testing (RACT), which is able to leverage differences in low-rank structures found in the covariance matrices of two groups in order to maximize power. RACT uses permutation for statistical inference, ensuring an exact Type I error control. We validate RACT in simulation studies and evaluate its performance when testing for differences in gene expression networks between two types of lung cancer, as well as testing for covariance heterogeneity in diffusion tensor imaging data taken on two different scanner types.

Quercetin, a plant-derived dietary flavonoid, has multifunctional biological activities, including anticancer action; however, its applications may be restricted due to limited bioavailability. Thus, novel synthetic quercetin derivatives (QDs) with improved properties and/or drug combinations should be designed and tested. In the present study, anticancer activity of fourteen newly synthesized QDs was investigated using four cellular models of melanoma, namely A375, MM370, G-361, and SH-4 cells. Thioquercetins (thioQ, thioQ(OAc)4, and thioQ(OAc)5), when used at low micromolar range, induced apoptotic cell death in melanoma cells compared to normal cells. Thioquercetins also reduced the population of spheroid-forming cells and suppressed the growth of A375 cells in 3D spheroid models. Thioquercetin-mediated antimelanoma action was potentiated upon heat shock protein 90 (HSP90) inhibition. Co-treatment with the HSP90 inhibitor 17-DMAG and thioquercetins augmented oxidative stress (increased superoxide production, decreased levels of antioxidant proteins SOD1, and PRDX1-2), and impaired the aryl hydrocarbon receptor (AhR)/cytochrome P450 1A1 (CYP1A1) signaling pathway-based detoxification of thioquercetins by the inhibition of AhR translocation to the nucleus and AhR-mediated stimulation of CYP1A1 expression leading to enhanced cytotoxic effects against melanoma cells. The senolytic activity of thioQ(OAc)4 with four acetylated hydroxy groups against cisplatin-induced senescent melanoma cells was also revealed in selected experimental settings. We suggest that the use of novel thioquercetin-based derivatives along with HSP90 inhibitors should be further validated in vivo and considered for the design of more effective antimelanoma strategies in the future.

Patients with a DPYD genetic deficiency who receive capecitabine are at increased risk of severe, potentially fatal toxicities due to impaired drug metabolism. Genetic testing for this deficiency allows for proactive dose adjustments to mitigate these risks. We evaluated the cost-effectiveness of DPYD genotyping prior to capecitabine administration, followed by dose modification for patients with metastatic breast cancer.

HCC is a highly vascularized solid tumor that develops rapidly and has a poor prognosis. Previous studies have shown that fibroblasts and angiogenesis in the tumor microenvironment play significant roles in the progression of HCC, and the combined effect of both on HCC is worth exploring. Therefore, we developed the CAF-VEGF prognostic scoring model to assess the prognosis of HCC patients. Single-cell sequencing was done on cancer tissues and nearby normal tissues in the study using data that we downloaded from the GEO database. We used the CellChat and Monocle3 packages to analyze the angiogenesis pathways and differentiation trajectories of fibroblasts. Subsequently, we conducted functional enrichment on fibroblasts. We constructed the CAF-VEGF prognostic model using the COX and LASSO algorithms and evaluated its prognostic value through survival and ROC curves. Based on the prognostic model, we identified key genes through differential expression screening, WGCNA, and PPI network analysis. The conclusions were ultimately validated by expression experiments and functional assays. We found that fibroblasts had a higher infiltration rate in HCC tissues and successfully constructed a CAF-VEGF prognostic model in HCC, proving its effectiveness. Using the CAF-VEGF score, we identified the key molecular markers ESCO2 and WDHD1, both significantly upregulated in HCC cells. Their overexpression may lead to poor prognosis in HCC patients. Additionally, through experiments, we found that both can promote angiogenesis and enhance the proliferation and invasion-migration abilities of HCC cells. This study successfully constructed the CAF-VEGF prognostic model for HCC, and may help improve the prognosis of HCC patients. We also found that the genes WDHD1 and ESCO2 can promote HCC infiltration by regulating angiogenesis, providing insights for future HCC treatment.

Autophagy has a critical involvement in the initiation and progression of various cancers, including colorectal cancer (CRC). The feasibility of using autophagy-related genes (ATGs) as prognostic tools for CRC patients is yet to be determined.

The fusion of data features from different modes, such as pathology images and sequence data, has the potential to predict the overall survival (OS) of patients with cervical cancer. This study aims to develop a novel prediction model for overall survival (OS) that incorporates pathology images, clinical data and molecular data. The model underwent training using comprehensive cervical cancer data from The Cancer Genome Atlas (TCGA), which include 119 patients. To independently validate the model, we used a manually collected dataset from Peking Union Medical College Hospital (PUMCH), comprising 53 patients with cervical cancer. LASSO Cox regression analysis was applied to identify relevant features associated with overall survival (OS), resulting in the identification of 484 genes, including RGR, DBN1 and CALCR, as well as numerous image features. Building upon these findings, a multimodal deep learning model was developed to effectively classify the overall survival (OS) of patients with cervical cancer into two categories: short term (ST: ≤ 3 years) and long term (LT: > 3 years) based on the integration of pathology images and clinical features. The developed model achieved reasonably good prediction accuracy in the independent testing dataset from PUMCH, with an area under the curve (AUC) value of 0.783. In conclusion, the combination of pathology images with clinical and molecular data enables the creation of accurate and reliable prediction models for cervical cancer.

Recent studies have increasingly demonstrated that chemoresistance in ovarian cancer primarily stems from resistance to oxidative stress and ferroptosis. Ferroptosis, a non-apoptotic form of cell death dependent on intracellular iron and marked by the buildup of lipid reactive oxygen species (ROS), has shown enhanced effectiveness in triggering cell death in ovarian cancer cells. Thus, this study aimed to explore the potential of gene knockdown associated with ferroptosis as an innovative therapeutic strategy against ovarian cancer. Up-regulated genes were identified using a gene bank, and their expression levels were validated through Western blotting (WB) and quantitative PCR (qPCR). Levels of MDA, Fe2+, GSH, ROS, SQSTM1, LC3-I and LC3-II in ovarian cancer cells treated with sorafenib and subjected to gene knockout were assessed using specific kits. Expression levels of proteins related to ferroptosis were analyzed by WB. Tumor size, volume, ferroptosis and autophagy in ovarian cancer tumor tissues were also examined. IGF2BP3 was elevated in human ovarian cancer and decreased during ferroptosis induced by sorafenib in human ovarian cancer cells. IGF2BP3 knockdown inhibited ovarian cancer cell function and promoted ferroptosis, in addition to autophagy-mediated EMC2 degradation. IGF2BP3 knockdown increased ovarian cancer sensitivity to sorafenib. This study confirmed that IGF2BP3 knockdown inhibited ovarian cancer cell malignancy, promoted ferroptosis and inhibited autophagy-mediated EMC2 degradation, and verified that IGF2BP3 knockdown increased the sensitivity to sorafenib in ovarian cancer mice.

Hepatocellular carcinoma (HCC) is a prevalent form of primary liver cancer, commonly related to chronic hepatitis B virus (HBV) infection. As an essential enzyme in glycolysis, Enolase-1 (ENO1) is implicated in the progression of multiple types of cancer. The aim of this study was to explore the ENO1's function in HBV-associated HCC. The expression of ENO1 in HBV-HCC was determined with RT-qPCR, immunohistochemistry, and Western blot. ENO1 and HBx overexpression or knockdown was performed through transfection. EdU staining, TUNEL staining, wound healing assay, and Transwell assay were utilized to evaluate the malignant biological behavior of HBV-HCC cells. HBV replication in HBV-HCC cells was assessed by measuring HBV DNA, HBsAg, and HBV cccDNA levels. The interaction of HBx with ENO1 was analyzed by co-IP and Western blot analysis. This study showed the elevated ENO1 expression in HBV-HCC. ENO1 overexpression in HBV-HCC cells markedly enhanced proliferation, migration, invasion, and HBV replication, while significantly inhibiting apoptosis. Conversely, ENO1 silencing produced the opposite effects. HBx was found to upregulate ENO1 expression. By rescue assays, HBx silencing suppressed the malignant behavior of HBV-HCC cells, but was reversed by ENO1 overexpression. Additionally, the enhancement of HBx overexpression on the malignant behavior of HBV-HCC cells was counteracted by ENO1 silencing. HBx facilitates the malignant advancement of HBV-associated HCC by elevating ENO1 expression.