Advanced endometrial cancer (EC) poses significant therapeutic challenges due to molecular heterogeneity and immune evasion. Immune checkpoint inhibitors (ICIs) show promise, particularly in mismatch repair-deficient (MMRd) and POLE-mutated subtypes, but resistance remains a barrier. This review synthesizes recent advances in biomarker-driven immunotherapy for EC, focusing on predictive biomarkers (e.g., LRP2, FANCE, MSH2, miRNA signatures), combination strategies (ICIs with anti-angiogenics or PARP inhibitors), and challenges in clinical translation. We highlight the impact of tumor microenvironment components, emerging technologies like machine learning, and future directions for personalized immunotherapy. Standardizing biomarker testing and optimizing trial designs will be critical to overcome resistance and improve outcomes.

Metastasis is the most common cause of cancer patient deaths. It is a complex process strongly influenced by the extracellular matrix (ECM). A mass spectrometry analysis comparing ECM proteins from healthy mouse lungs versus metastatic lungs has previously been performed, and the basement membrane (BM) component nidogen-1 has been identified to be one of the most downregulated ECM proteins in metastatic lungs. Here, we investigated the role of stromal cell-derived nidogen-1 in metastasis. We found that nidogen-1 is expressed by fibroblasts but not cancer cells, and nidogen-1 is downregulated in breast tumors compared to healthy mammary gland. Using the HCmel12 melanoma model, we found that loss of stromal nidogen-1 causes increased lung metastasis. Using electron microscopy, we found that nidogen-1 knockout mice have defects in the lung alveoli, such as fragmented endothelium, poorly defined BM, and enlarged interstitium. This suggests that loss of nidogen-1 may cause BM defects, which compromise its barrier function, thereby increasing the ability of cancer cells to extravasate and colonize the lungs. Our findings provide novel insight into cancer-stromal interplay and the role of nidogen-1 at the metastatic niche.

Histone deacetylase 10 (HDAC10) is emerging as a critical modulator of tumor immunity, chemoresistance, and transcriptional plasticity in colorectal cancer. Its suppression has been linked to altered CD8+ T cell activity, increased p53 expression, and reduced programmed death ligand 1 levels, suggesting a potential role in immune evasion. However, mechanistic understanding of HDAC10's selective function, especially in shaping the tumor microenvironment, remains limited. We advocate for targeted investigations using isoform-selective inhibitors, functional in vivo studies, and immune subset profiling to clarify HDAC10's therapeutic relevance in colorectal cancer.

Lung squamous cell carcinoma (LUSC) is a common and aggressive malignancy. Necroptosis, a regulated mode of cell death, has been implicated in tumor immunity and oncogenic processes, yet the mechanistic involvement of necroptosis-related genes (NRGs) in LUSC pathogenesis remains unclear, necessitating systematic evaluation of their biological and clinical relevance.

Oral squamous cell carcinoma (OSCC) is one of the most frequent types of head and neck tumor. Keratinocytes play a crucial part in tumor cell growth but their role in OSCC remains unknown.

cBioPortal has established itself as a widely used platform for exploring and visualizing multidimensional cancer data. Additionally, users have the option to upload their own cancer study for a comprehensive experience. However, the uploading step can be challenging due to the numerous files required by the platform, as well as the meticulous review of genomic alterations that need to be included in the study. Therefore, there is an increasing need for efficient data management solutions to facilitate the creation of studies in cBioPortal and optimize user experience by streamlining research workflows. In this application note, we present Varan, an innovative data management tool developed to help users at the initial stage of cancer genomic studies' upload, enhancing the data preparation process. Varan addresses challenges related to data formatting, filtering variants based on annotation, metadata file creation, quality checks, and study versioning, thereby enabling researchers to shorten the preparation process time and have control over the type of data to be uploaded to cBioPortal. In conclusion, Varan significantly improves the efficiency and accuracy of preparing cancer genomic studies for cBioPortal, ultimately enhancing the user experience and advancing cancer research through streamlined data management.

Cholangiocarcinoma exhibits a complex tumor microenvironment, yet the cellular interactions governing its progression remain poorly understood. Here, through integrated analysis of two independent single-cell RNA sequencing datasets comprising both complete tissue and immune-focused profiling, we comprehensively mapped the cellular landscape and intercellular communication networks in human cholangiocarcinoma. Our analysis revealed significant remodeling of immune cell compositions and interaction patterns in the tumor microenvironment. Notably, we identified enhanced ICOS signaling between dendritic cells and T cells as a prominent feature of cholangiocarcinoma. Using CellChat analysis, we demonstrated that tumor-associated dendritic cells, particularly plasmacytoid DCs, exhibit stronger ICOS-mediated communication with T cells compared to their counterparts in normal tissues. Functional validation experiments confirmed that tumor-conditioned dendritic cells upregulate ICOSL expression and promote CD8+ T-cell activation through the ICOS-ICOSL axis, as evidenced by increased CD69 and CD25 expression. This activation was specifically abolished by ICOSL blockade, establishing the functional significance of this pathway. Our findings provide novel insights into tumor-immune interactions in cholangiocarcinoma and suggest the ICOS-ICOSL axis as a potential therapeutic target for immunotherapy.

Tumor microenvironment (TME) is a significant factor regulating the malignant phenotype and drug resistance of kidney renal clear cell carcinoma (KIRC). The identification of biomarker signatures mediating immune infiltration in TME is of significance for prognostic assessment and personalized therapy of KIRC.

Oxford Nanopore Technology (ONT)-based methylation sequencing is emerging as a powerful approach for the rapid and accurate classification of brain tumors, an essential component of precision oncology. However, its broader clinical adoption has been limited by reliance on fresh-frozen (FF) tissue, whereas the vast majority of clinical specimens are formalin-fixed paraffin-embedded (FFPE). In this study, we address this limitation by evaluating the effects of FFPE processing on DNA methylation profiles and introducing a validated protocol for ONT-based classification using DNA extracted directly from pathology-marked regions on stained FFPE slides. This approach enables the targeted selection of tumor-rich areas following histological assessment, thereby improving DNA input quality and tumor content. We demonstrate that even small, low-input samples (≥25 ng) can be successfully classified using this method, with high concordance to final integrated neuropathological diagnoses. Our results show that, despite modest methylation loss associated with formalin fixation, classification performance remains robust. Notably, we identify a correlation between methylation degradation and fixation time, supporting a recommendation to limit formalin exposure to ≤3-4 days when possible. By enabling accurate methylation-based tumor classification from routinely processed, stained FFPE tissue, our protocol integrates seamlessly into existing clinical workflows. This expands the accessibility of ONT-based diagnostics and supports informed, timely treatment decisions-even in cases with minimal tissue availability or urgent clinical need.

Gene expression studies are fundamental in molecular biology, offering insights into development, disease progression, and therapeutic targets. To address the need for precise analysis of large datasets, we developed THRESHOLD, a novel tool that introduces the concept of gene saturation. Unlike traditional methods focused on absolute or binary expression levels, THRESHOLD quantifies the consistency of gene expression across patients, revealing co-regulation patterns critical for understanding disease mechanisms and stratifying patients by molecular signatures. The tool offers several features, including user-defined parameters, statistical comparisons, and interactive data visualization. THRESHOLD has uncovered compelling insights into disease progression using TCGA cancer datasets. For instance, bladder urothelial carcinoma demonstrated increasing upregulated gene saturation in progressive cancer stages (P < .00001). Moreover, THRESHOLD identified heightened gene saturation in patients with earlier onset of prostate adenocarcinoma (P < .0001) and revealed a critical fusion transcript, SLC45A2-AMACR, implicated in prostate adenocarcinoma progression, recurrence, and metastasis. Additionally, novel biomarkers and potential candidates for drug therapies were identified through protein-protein interaction networks and functional analyses of saturation data in colon adenocarcinoma and breast invasive carcinoma. THRESHOLD offers a new approach for studying gene expression dynamics and patient stratification. The tool is publicly available at Zenodo: https://zenodo.org/records/15287195.

Cancers are characterized by genomic instability events such as aneuploidy, chromosomal arm imbalances, and segmental copy number changes. These genomic features frequently define different subclones within a tumor. Single-cell DNA sequencing (scDNA-seq) identifies these large-scale genomic alterations that define subclonal features. However, scDNA-seq does not provide biological phenotypic information on individual subclones. Single-cell RNA sequencing (scRNA-seq) offers biological information but is less accurate in discovering genomic instability events. We developed a computational framework, scAlign, for integrating scRNA-seq and scDNA-seq from the same specimen and define subclonal cellular phenotypes at the resolution of individual cells. Subclones were defined by aneuploidy and chromosomal arm imbalance among primary and metastatic cancers. Using the cells in the G0/G1 phase, the extensive cellular sampling from both assays characterized the subclonal architecture of these cancers. The scDNA-seq provided a ground truth for copy number-based subclones. From the scRNA-seq data, the epithelial cells in G0/G1 were identified and assigned to specific subclones by the scAlign based on gene dosage. Afterward, we determined the differential gene expression and biological pathway activities of specific clones. Overall, integrative multi-omics analysis of single-cell datasets is more informative than any individual genomic modality, provides deep insights into intratumoral heterogeneity and reveals subclonal biology. scAlign is available at https://github.com/XQBai/scAlign.

Breast cancer is a most common cancer that primarily affects women, in which cells become abnormal and multiply in an uncontrollable fashion. The etiology of these cancers is predominantly hereditary, with gene mutations and geographic indications being the predominant factors in most invasive breast cancer types. However, it is important to note that several other factors, including age, gender, ethnic background, and environmental influences, also contribute to the development of these diseases. Non-coding RNAs refer to a class of endogenous molecules that play a role in the development of various types of cancer. The objective of this research is to identify differentially expressed genes in cases of breast cancer. A series of analyses were conducted on the RNA-Seq data from the TCGA related to breast cancer. These analyses included both expression and survival studies. The objective of these analyses was to explore the gene expression of the samples and genes computationally through the use of R programming. The results obtained after each analysis were inferred both visually and logically. A total of 613 genes were identified as exhibiting differential expression among the samples, with 254 genes demonstrating increased expression and 359 genes exhibiting decreased expression. The differentially expressed genes obtained using the R package "TCGA Biolinks" were subsequently employed in the construction of the ceRNA network. A comprehensive analysis of the TCGA biolinks data set revealed the presence of aberrantly expressed long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) in breast cancer samples. The analysis identified a total of 352, 183, and 254 cases, respectively, demonstrating significant disparities in gene expression patterns among different breast cancer samples. A study of 352 long non-coding ribonucleic acids (lncRNAs) revealed that two of these molecules, LINC00461 and MALAT1, exhibited particularly high levels of expression. These findings suggest that these two molecules may serve as more effective therapeutic biomarkers. Furthermore, the study identified a significant enrichment of microRNA target genes within the samples examined, suggesting a potential regulatory relationship between these molecules and their target genes. Consequently, this investigation has constructed competing endogenous RNA networks and has further elucidated the underlying biomarkers for breast cancer cohorts.

This study explores the potential of quantum computing as an alternative information processing approach, utilizing quantum bits (qubits), superposition, and entanglement to significantly expand computational capabilities in the healthcare domain. It is evident that quantum mechanics has become a foundational component in the construction of our contemporary physical reality. This scientific field is distinguished by its rapid advancement and the potential to transform various aspects of our daily lives. In this era, quantum biology is of significant importance and has the potential to act as a transformative force, particularly in the field of medicine, specifically in addressing the challenges posed by cancer. Cancer is defined as a complex and abnormal alteration of cells, orchestrated through intricate signaling pathways. This transformation is characterized by the accumulation of deleterious mutations. The concept of phenocopy, representing genetic mutations influenced by the environment, challenges the linear process line of molecular biology involving DNA, RNA, and proteins. Notwithstanding the augmented focus on quantum biology in recent decades, a plethora of unresolved issues persist within the domain of cancer biology, thereby giving rise to unexplored avenues. Quantum theory has demonstrated its ability to explain models related to biological and biochemical processes, encompassing the effects of carcinogens on genes, the mechanism of interactions between chemotherapy drugs and DNA, and the understanding of DNA mutations and defective protein synthesis. Recent skepticism among quantum physicists regarding the fundamental role of quantum effects in biology has emerged, particularly with regard to open quantum systems and the impact of decoherence on the destruction of coherence necessary for significant quantum effects. The document under scrutiny herein undertakes an investigation of recent studies that are rooted in the principles of quantum physics, with a particular focus on the manner in which these principles apply to the domains of cancer biology and metabolism.

Motif interacting with ubiquitin-containing novel DUB family-1 (MINDY1) could enhance the stability of programmed death-ligand 1 (PD-L1). The study aimed to investigate whether MINDY1 regulates the immune escape of hepatocellular carcinoma (HCC) mediated by PD-L1.

Glioblastoma (GBM) remains the most aggressive primary brain tumour in adults, marked by pronounced cellular heterogeneity, diffuse infiltration, and resistance to conventional treatment. In recent years, transcriptomic profiling has provided valuable insights into the molecular mechanisms that govern the progression of glioblastoma. This systematic review aims to synthesise the current literature on dysregulated gene expression in GBM, focusing on gene signatures associated with stemness, immune modulation, extracellular matrix remodelling, metabolic adaptation, and therapeutic resistance.

KIT proto-oncogene, receptor tyrosine kinase (KIT, CD117) and platelet-derived growth factor-alpha (PDGFRA) are key drivers of gastrointestinal stromal tumors (GIST), but resistance to targeted therapy often arises from tumor protein p53 (p53) alterations and loss of cell cycle control. However, the role of p53 status in GIST therapeutic potential has rarely been studied, so this study aimed to employ both wild-type and mutant p53 GIST models to investigate how p53 dysfunction influences the efficacy of p53 pathway-targeted therapies.

The tumor microenvironment plays a pivotal role in prostate cancer progression and may differ across metastatic sites. This study aimed to evaluate and compare the primary and metastatic prostate adenocarcinoma tumor microenvironment.

Radiation sensitive 23 homolog B (RAD23B), a DNA repair-related protein, plays a contributory role in the development of multiple malignancies. This study aimed to explore the role of RAD23B in promoting colorectal cancer (CRC) metastasis and to elucidate the underlying molecular mechanisms.

Breast cancer is characterized by significant metabolic dysregulation, in which altered enzyme activity plays a central role. Malate dehydrogenase 2 (MDH2), a key enzyme in the tricarboxylic acid cycle, has been implicated in several malignancies, but its role in breast cancer tumorigenesis and progression remains unclear. We aimed to elucidate the oncogenic role of MDH2 in breast cancer and to evaluate its potential as a diagnostic, therapeutic, and prognostic biomarker.

Colorectal cancer (CRC) remains a major contributor to global cancer mortality, ranking second worldwide for cancer-related deaths in 2022, and is characterized by marked heterogeneity in prognosis and therapeutic response. We sought to construct a machine-learning prognostic model based on a complement-related risk signature (CRRS) and to situate this signature within the CRC immune microenvironment.