Global mapping of cancers: The Cancer Genome Atlas and beyond

ASM variants in the spotlight: A structure-based atlas for unraveling pathogenic mechanisms in lysosomal acid sphingomyelinase

Lysosomal acid sphingomyelinase (ASM), a critical enzyme in lipid metabolism encoded by the SMPD1 gene, plays a crucial role in sphingomyelin hydrolysis in lysosomes. ASM deficiency leads to acid sphingomyelinase deficiency, a rare genetic disorder with diverse clinical manifestations, and the protein can be found mutated in other diseases. We employed a structure-based framework to comprehensively understand the functional implications of ASM variants, integrating pathogenicity predictions with molecular insights derived from a molecular dynamics simulation in a lysosomal membrane environment. Our analysis, encompassing over 400 variants, establishes a structural atlas of missense variants of lysosomal ASM, associating mechanistic indicators with pathogenic potential. Our study highlights variants that influence structural stability or exert local and long-range effects at functional sites. To validate our predictions, we compared them to available experimental data on residual catalytic activity in 135 ASM variants. Notably, our findings also suggest applications of the resulting data for identifying cases suited for enzyme replacement therapy. This comprehensive approach enhances the understanding of ASM variants and provides valuable insights for potential therapeutic interventions.

Research on the toxicological prognostic significance of age-related genes in endometrial cancer unveiling key factors in patient prognosis

This study investigates the influence of aging-related genes on endometrial cancer, a prominent gynecological malignancy with rising incidence and mortality. By analyzing gene expression differences between cancerous and normal endometrial tissues, 42 aging-related genes were identified as differentially expressed. Utilizing the TCGA-UCEC sample, consensus clustering divided the samples into two molecular subgroups, Aging low and Aging high, based on their expression profiles. These subgroups showed distinct prognoses and survival rates, with the Aging high group associated with DNA repair and cell cycle pathways, and the Aging low group showing suppressed metabolic pathways and increased immune cell infiltration, suggesting a potential for better immunotherapy outcomes. Mutation analysis did not find significant differences in mutation frequencies between the groups, but a high Tumor Mutation Burden (TMB) correlated with better prognosis. A risk score model was also developed, showcasing significant prognostic power. Further analysis of the SIX1 gene revealed its overexpression in cancer cells. Drug sensitivity tests indicated that the low-risk group might respond better to chemotherapy. This research underscores the significance of aging-related genes in endometrial cancer, offering insights into their prognostic value and therapeutic potential, which could lead to personalized treatment approaches and enhanced patient management.

Silencing lncRNA-DARS-AS1 suppresses nonsmall cell lung cancer progression by stimulating miR-302a-3p to inhibit ACAT1 expression

Long noncoding RNAs (LncRNAs) have been gaining attention as potential therapeutic targets for lung cancer. In this study, we investigated the expression and biological behavior of lncRNA DARS-AS1, its predicted interacting partner miR-302a-3p, and ACAT1 in nonsmall cell lung cancer (NSCLC). The transcript level of DARS-AS1, miR-302a-3p, and ACAT1 was analyzed using qRT-PCR. Endogenous expression of ACAT1 and the expression of-and changes in-AKT/ERK pathway-related proteins were determined using western blotting. MTS, Transwell, and apoptosis experiments were used to investigate the behavior of cells. The subcellular localization of DARS-AS1 was verified using FISH, and its binding site was verified using dual-luciferase reporter experiments. The binding of DARS-AS1 to miR-302a-3p was verified using RNA co-immunoprecipitation. In vivo experiments were performed using a xenograft model to determine the effect of DARS-AS1 knockout on ACAT1 and NSCLC. lncRNA DARS-AS1 was upregulated in NSCLC cell lines and tissues and the expression of lncRNA DARS-AS1 was negatively correlated with survival of patients with NSCLC. Knockdown of DARS-AS1 inhibited the malignant behaviors of NSCLC via upregulating miR-302a-3p. miR-302a-3p induced suppression of malignancy through regulating oncogene ACAT1. This study demonstrates that the DARS-AS1-miR-302a-3p-ACAT1 pathway plays a key role in NSCLC.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Genetically driven predisposition leads to an unusually genomic unstable renal cell carcinoma

Renal cell carcinoma originates from the lining of the proximal convoluted renal tubule and represents the most common type of kidney cancer. Risk factors and comorbidities might be associated to renal cell carcinoma, while a small fraction of 2-3% emerges from patients with predisposing cancer syndromes, typically associated to hereditary mutations in VHL, folliculin, fumarate hydratase or MET genes. Here, we report a case of renal cell carcinoma in patient with concurrent germline mutations in BRCA1 and RAD51 genes. This case displays an unusual high mutational burden and chromosomal aberrations compared to the typical profile of renal cell carcinoma. Mutational analysis on whole genome sequencing revealed an enrichment of the MMR2 mutational signature, which is indicative of impaired DNA repair capacity. Overall, the tumor displayed a profile of unusual high genomic instability which suggests a possible origin from germline predisposing mutations in the DNA repair genes BRCA1 and RAD51. While BRCA1 and RAD51 germline mutations are well-characterised in breast and ovarian cancer, their role in renal cell carcinoma is still largely unexplored. The genomic instability detected in this case of renal cell carcinoma, along with the presence of unusual mutations, might offer support to clinicians for the development of patient-tailored therapies.

Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.

Pan-cancer image segmentation based on feature pyramids and Mask R-CNN framework

BACKGROUND

Cancer, a disease with a high mortality rate, poses a great threat to patients' physical and mental health and can lead to huge medical costs and emotional damage. With the continuous development of artificial intelligence technologies, deep learning-based cancer image segmentation techniques are becoming increasingly important in cancer detection and accurate diagnosis. However, in segmentation tasks, there are differences in efficiency between large and small objects and limited segmentation effects on objects of individual sizes. The previous segmentation frameworks still have room for improvement in multi-scale collaboration when segmenting objects.

PURPOSE

This paper proposes a method to train a deep learning segmentation framework using a feature pyramid processing dataset to improve the average precision (AP) index, and realizes multi-scale cooperation in target segmentation.

OBJECTIVE

Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification (PanNuke) dataset was selected to include approximately 7500 pathology images with cells from 19 different types of tissues, including five classifications of cancer, non-cancer, inflammation, death, and connective tissue.

METHODS

First, the method uses whole-slide images in the pan-cancer histology dataset for nuclei instance segmentation and classification (PanNuke) dataset, combined with the mask region convolutional neural network (Mask R-CNN) segmentation framework and improved loss function to segment and detect each cellular tissue in cancerous sections. Second, to address the problem of non-synergistic object segmentation at different scales in cancerous tissue segmentation, a scheme using feature pyramids to process the dataset was adopted as part of the feature extraction module.

RESULTS

Extensive experimental results on this dataset show that the method in this paper yields 0.269 AP and a boost of about 4% compared to the original Mask R-CNN framework.

CONCLUSIONS

It is effective and feasible to use feature pyramid to process data set to improve the effect of medical image segmentation.

Gas-Phase Fractionation Data-Independent Acquisition Analysis of 3D Cocultured Spheroid Tumor Model Reveals Altered Translational Processes and Signaling Using Proteomics

Colorectal cancer (CRC) contains considerable heterogeneity; therefore, models of the disease must also reflect the multifarious components. Compared to traditional 2D models, 3D cellular models, such as tumor spheroids, have the utility to determine the drug efficacy of potential therapeutics. Monoculture spheroids are well-known to recapitulate gene expression, cell signaling, and pathophysiological gradients of avascularized tumors. However, they fail to mimic the stromal cell influence present in CRC, which is known to perturb drug efficacy and is associated with metastatic, late-stage colorectal cancer. This study seeks to develop a cocultured spheroid model using carcinoma and noncancerous fibroblast cells. We characterized the proteomic profile of cocultured spheroids in comparison to monocultured spheroids using data-independent acquisition with gas-phase fractionation. Specifically, we determined that proteomic differences related to translation and mTOR signaling are significantly increased in cocultured spheroids compared to monocultured spheroids. Proteins related to fibroblast function, such as exocytosis of coated vesicles and secretion of growth factors, were significantly differentially expressed in the cocultured spheroids. Finally, we compared the proteomic profiles of both the monocultured and cocultured spheroids against a publicly available data set derived from solid CRC tumors. We found that the proteome of the cocultured spheroids more closely resembles that of the patient samples, indicating their potential as tumor mimics.

Decoding p53 tumor suppression: a crosstalk between genomic stability and epigenetic control?

Genomic instability, a hallmark of cancer, is a direct consequence of the inactivation of the tumor suppressor protein p53. Genetically modified mouse models and human tumor samples have revealed that p53 loss results in extensive chromosomal abnormalities, from copy number alterations to structural rearrangements. In this perspective article we explore the multifaceted relationship between p53, genomic stability, and epigenetic control, highlighting its significance in cancer biology. p53 emerges as a critical regulator of DNA repair mechanisms, influencing key components of repair pathways and directly participating in DNA repair processes. p53 role in genomic integrity however extends beyond its canonical functions. p53 influences also epigenetic landscape, where it modulates DNA methylation and histone modifications. This epigenetic control impacts the expression of genes involved in tumor suppression and oncogenesis. Notably, p53 ability to ensure cellular response to DNA demethylation contributes to the maintenance of genomic stability by preventing unscheduled transcription of repetitive non-coding genomic regions. This latter indicates a causative relationship between the control of epigenetic stability and the maintenance of genomic integrity in p53-mediated tumor suppression. Understanding these mechanisms offers promising avenues for innovative therapeutic strategies targeting epigenetic dysregulation in cancer and emphasizes the need for further research to unravel the complexities of this relationship. Ultimately, these insights hold the potential to transform cancer treatment and prevention strategies.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Aluminium bioaccumulation in colon cancer, impinging on epithelial-mesenchymal-transition and cell death

We investigated the presence of aluminium (Al) in human colon cancer samples and its potential association with biological processes involved in cancer progression, such as epithelial to mesenchymal transition (EMT) and cell death. 25 consecutive colon samples were collected from patients undergoing colonic resection. Both neoplastic and normal mucosa were collected from each patient and subjected to histological, ultrastructural and immunohistochemical analyses. Moreover, colon samples from two Al-positive patients underwent multi-omic analyses, including whole genome sequencing and RNA sequencing (RNAseq). Morin staining, used to identify in situ aluminium bioaccumulation, showed the presence of Al in tumor areas of 24 % of patients. Transmission electron microscopy and energy-dispersive X-ray microanalysis confirmed the presence of Al specifically in intracytoplasmic electrondense nanodeposits adjacent to mitochondria of colon cancer cells. Immunohistochemical analyses for vimentin and nuclear β-catenin were performed to highlight the occurrence of the EMT phenomenon in association to Al bioaccumulation. Al-positive samples showed a significant increase in both the number of vimentin-positive and nuclear β-catenin-positive cancer cells compared to Al-negative samples. Moreover, Al-positive samples exhibited a significant decrease in the number of apoptotic cells, as well as the expression of the anti-apoptotic molecule BCL-2. Multi-omic analyses revealed a higher tumor mutational burden (TMB) in Al-positive colon cancers (n = 2) compared to a control cohort (n = 100). Additionally, somatic mutations in genes associated with EMT (GATA3) and apoptosis (TP53) were observed in Al-positive colon cancers. In conclusion, this study provides the first evidence of Al bioaccumulation in colon cancer and its potential role in modulating molecular pathways involved in cancer progression, such as EMT and apoptosis. Understanding the molecular mechanisms underlying Al toxicity might contribute to improve strategies for prevention, early detection, and targeted therapies for the management of colon cancer patients.

The impact of toxic metal bioaccumulation on colorectal cancer: Unravelling the unexplored connection

Colorectal cancer is a major public health concern, with increasing incidence and mortality rates worldwide. Environmental factors, including exposure to toxic metals, such as lead, chromium, cadmium, aluminium, copper, arsenic and mercury, have been suggested to play a significant role in the development and progression of this neoplasia. In particular, the bioaccumulation of toxic metals can play a significant role in colorectal cancer by regulating biological phenomenon associated to both cancer occurrence and progression, such as cell death and proliferation. Also, frequently these metals can induce DNA mutations in well-known oncogenes. This review provides a critical analysis of the current evidence, highlighting the need for further research to fully grasp the complex interplay between toxic metal bioaccumulation and colorectal cancer. Understanding the contribution of toxic metals to colorectal cancer occurrence and progression is essential for the development of targeted preventive strategies and social interventions, with the ultimate goal of reducing the burden of this disease.

Identification of ACBD3 as a new molecular biomarker in pan-cancers through bioinformatic analysis: a preclinical study

BACKGROUND

Acyl-CoA-binding domain-containing 3 (ACBD3) is a multifunctional protein, that plays essential roles in cellular signaling and membrane domain organization. Although the precise roles of ACBD3 in various cancers remain unclear. Thus, we aimed to determine the diverse roles of ACBD3 in pan-cancers.

METHODS

Relevant clinical and RNA-sequencing data for normal tissues and 33 tumors from The Cancer Genome Atlas (TCGA) database, the Human Protein Atlas, and other databases were applied to investigate ACBD3 expression in various cancers. ACBD3-binding and ACBD3-related target genes were obtained from the STRING and GEPIA2 databases. The possible functions of ACBD3-binding genes were explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We also applied the diagnostic value and survival prognosis analysis of ACBD3 in pan-cancers using R language. The mutational features of ACBD3 in various TCGA cancers were obtained from the cBioPortal database.

RESULTS

When compared with normal tissues, ACBD3 expression was statistically upregulated in eleven cancers and downregulated in three cancers. ACBD3 expression was remarkably different among various pathological stages of tumors, immune and molecular subtypes of cancers, cancer phosphorylation levels, and immune cell infiltration. The survival of four tumors was correlated with the expression level of ACBD3, including pancreatic adenocarcinoma, adrenocortical carcinoma, sarcoma, and glioma. The high accuracy in diagnosing multiple tumors and its correlation with prognosis indicated that ACBD3 may be a potential biomarker of pan-cancers.

CONCLUSION

According to our pan-cancer analysis, ACBD3 may serve as a remarkable prognostic and diagnostic biomarker of pan-cancers as well as contribute to tumor development. ACBD3 may also provide new directions for cancer treatment targets in the future.

Alleviating hypoxia to improve cancer immunotherapy

Tumor hypoxia resulting from abnormal and dysfunctional tumor vascular network poses a substantial obstacle to immunotherapy. In fact, hypoxia creates an immunosuppressive tumor microenvironment (TME) through promoting angiogenesis, metabolic reprogramming, extracellular matrix remodeling, epithelial-mesenchymal transition (EMT), p53 inactivation, and immune evasion. Vascular normalization, a strategy aimed at restoring the structure and function of tumor blood vessels, has been shown to improve oxygen delivery and reverse hypoxia-induced signaling pathways, thus alleviates hypoxia and potentiates cancer immunotherapy. In this review, we discuss the mechanisms of tumor tissue hypoxia and its impacts on immune cells and cancer immunotherapy, as well as the approaches to induce tumor vascular normalization. We also summarize the evidence supporting the use of vascular normalization in combination with cancer immunotherapy, and highlight the challenges and future directions of this overlooked important field. By targeting the fundamental problem of tumor hypoxia, vascular normalization proposes a promising strategy to enhance the efficacy of cancer immunotherapy and improve clinical outcomes for cancer patients.

Involvement of transcribed lncRNA uc.291 in hyperproliferative skin disorders

The uc.291 transcript controls keratinocytes differentiation by physical interaction with ACTL6A and subsequent induction of transcription of the genes belonging to the epidermal differentiation complex (EDC). Uc.291 is also implicated in the dedifferentiation phenotype seen in poorly differentiated cutaneous squamous cell carcinomas. Here, we would like to investigate the contribution of uc.291 to the unbalanced differentiation state of keratinocytes observed in hyperproliferative skin disorders, e. g., psoriasis. Psoriasis is a multifactorial inflammatory disease, caused by alteration of keratinocytes homeostasis. The imbalanced differentiation state, triggered by the infiltration of immune cells, represents one of the events responsible for this pathology. In the present work, we explore the role of uc.291 and its interactor ACTL6A in psoriasis skin, using quantitative real-time PCR (RT-qPCR), immunohistochemistry and bioinformatic analysis of publicly available datasets. Our data suggest that the expression of the uc.291 and of EDC genes loricrin and filaggrin (LOR, FLG) is reduced in lesional skin compared to nonlesional skin of psoriatic patients; conversely, the mRNA and protein level of ACTL6A are up-regulated. Furthermore, we provide evidence that the expression of uc.291, FLG and LOR is reduced, while ACTL6A mRNA is up-regulated, in an in vitro psoriasis-like model obtained by treating differentiated keratinocytes with interleukin 22 (IL-22). Furthermore, analysis of a publicly available dataset of human epidermal keratinocytes treated with IL-22 (GSE7216) confirmed our in vitro results. Taken together, our data reveal a novel role of uc.291 and its functional axis with ACTL6A in psoriasis disorder and a proof of concept that biological inhibition of this molecular axis could have a potential pharmacological effect against psoriasis and, in general, in skin diseases with a suppressed differentiation programme.

Hyaluronic acid metabolism and chemotherapy resistance: recent advances and therapeutic potential

Hyaluronic acid (HA) is a major component of the extracellular matrix, providing essential mechanical scaffolding for cells and, at the same time, mediating essential biochemical signals required for tissue homeostasis. Many solid tumors are characterized by dysregulated HA metabolism, resulting in increased HA levels in cancer tissues. HA interacts with several cell surface receptors, such as cluster of differentiation 44 and receptor for hyaluronan-mediated motility, thus co-regulating important signaling pathways in cancer development and progression. In this review, we describe the enzymes controlling HA metabolism and its intracellular effectors emphasizing their impact on cancer chemotherapy resistance. We will also explore the current and future prospects of HA-based therapy, highlighting the opportunities and challenges in the field.

p63 orchestrates serine and one carbon metabolism enzymes expression in head and neck cancer

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is characterized by high proliferation and limited differentiation. The altered expression of the p53 family members, and specifically of p63, represents a pivotal event in the pathogenesis of HNSCC. Physiologically, p63 affects metabolism through the direct transactivation of the enzyme hexokinase 2, and subsequently controls the proliferation of epithelial cells; nonetheless, its role in cancer metabolism is still largely unclear. The high energetic demand of cancer and the consequent needs of a metabolic reshape, also involve the serine and glycine catabolic and anabolic pathways, including the one carbon metabolism (OCM), to produce energetic compounds (purines) and to maintain cellular homeostasis (glutathione and S-adenosylmethionine).

RESULTS

The involvement in serine/glycine starvation by other p53 family members has been reported, including HNSCC. Here, we show that in HNSCC p63 controls the expression of the enzymes regulating the serine biosynthesis and one carbon metabolism. p63 binds the promoter region of genes involved in the serine biosynthesis as well as in the one carbon metabolism. p63 silencing in a HNSCC cell line affects the mRNA and protein levels of these selected enzymes. Moreover, the higher expression of TP63 and its target enzymes, negatively impacts on the overall survival of HNSCC patients.

CONCLUSION

These data indicate a direct role of p63 in the metabolic regulation of HNSCC with significant clinical effects.

p63: a crucial player in epithelial stemness regulation

Epithelial tissue homeostasis is closely associated with the self-renewal and differentiation behaviors of epithelial stem cells (ESCs). p63, a well-known marker of ESCs, is an indispensable factor for their biological activities during epithelial development. The diversity of p63 isoforms expressed in distinct tissues allows this transcription factor to have a wide array of effects. p63 coordinates the transcription of genes involved in cell survival, stem cell self-renewal, migration, differentiation, and epithelial-to-mesenchymal transition. Through the regulation of these biological processes, p63 contributes to, not only normal epithelial development, but also epithelium-derived cancer pathogenesis. In this review, we provide an overview of the role of p63 in epithelial stemness regulation, including self-renewal, differentiation, proliferation, and senescence. We describe the differential expression of TAp63 and ΔNp63 isoforms and their distinct functional activities in normal epithelial tissues and in epithelium-derived tumors. Furthermore, we summarize the signaling cascades modulating the TAp63 and ΔNp63 isoforms as well as their downstream pathways in stemness regulation.

Pan-Cancer Analysis and Experimental Validation of SOX4 as a Potential Diagnosis, Prognosis, and Immunotherapy Biomarker

INTRODUCTION

SOX4 plays an important role in tumorigenesis and cancer progression. The role of SOX4 in pan-cancer and its underlying molecular mechanism in liver hepatocellular carcinoma (LIHC) are not fully understood. In this study, a comprehensive analysis and experimental validation were performed to explore the function of SOX4 across tumor types.

METHODS

Raw data in regard to SOX4 expression in malignant tumors were downloaded from the TCGA and GTEx databases. The expression levels, prognostic values, genetic mutation, and DNA promoter methylation of SOX4 across tumor types were explored via systematic bioinformatics analysis. The ceRNA regulatory network, immune characteristics, and prognostic models were analyzed in LIHC. Finally, we conducted in vitro experiments including Western blotting, cell proliferative assay, trypan blue staining, and fluorescence microscopy to further explore the function of SOX4 in LIHC.

RESULTS

SOX4 expression was significantly upregulated in 24 tumor types. SOX4 expression level was strongly associated with unfavorable prognoses, genetic mutations, and DNA methylation levels across different tumor types. Especially in LIHC, LINC00152/hsa-miR-139-3p/SOX4 was identified as a crucial ceRNA network. Moreover, this study also provides insight into the roles of SOX4 expression in immune cell infiltration, macrophage polarization, immune subtype, molecular subtype, and immunomodulators, as well as the tumor immune microenvironment (TIME)-related prognosis, in LIHC. The study established six favorable prognostic models to predict LIHC prognosis based on the SOX4-associated genes. Finally, lenvatinib treatment can increase the expression of SOX4 in hepatocellular carcinoma cells and lead to drug resistance. Silencing SOX4 can effectively eliminate the drug resistance caused by lenvatinib treatment and inhibit the proliferation of cancer cells.

CONCLUSIONS

This study highlights that SOX4 may serve as a promising therapeutic target for tumor treatment.

Macrophage polarization and metabolism in atherosclerosis

Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of fatty deposits in the inner walls of vessels. These plaques restrict blood flow and lead to complications such as heart attack or stroke. The development of atherosclerosis is influenced by a variety of factors, including age, genetics, lifestyle, and underlying health conditions such as high blood pressure or diabetes. Atherosclerotic plaques in stable form are characterized by slow growth, which leads to luminal stenosis, with low embolic potential or in unstable form, which contributes to high risk for thrombotic and embolic complications with rapid clinical onset. In this complex scenario of atherosclerosis, macrophages participate in the whole process, including the initiation, growth and eventually rupture and wound healing stages of artery plaque formation. Macrophages in plaques exhibit high heterogeneity and plasticity, which affect the evolving plaque microenvironment, e.g., leading to excessive lipid accumulation, cytokine hyperactivation, hypoxia, apoptosis and necroptosis. The metabolic and functional transitions of plaque macrophages in response to plaque microenvironmental factors not only influence ongoing and imminent inflammatory responses within the lesions but also directly dictate atherosclerotic progression or regression. In this review, we discuss the origin of macrophages within plaques, their phenotypic diversity, metabolic shifts, and fate and the roles they play in the dynamic progression of atherosclerosis. It also describes how macrophages interact with other plaque cells, particularly T cells. Ultimately, targeting pathways involved in macrophage polarization may lead to innovative and promising approaches for precision medicine. Further insights into the landscape and biological features of macrophages within atherosclerotic plaques may offer valuable information for optimizing future clinical treatment for atherosclerosis by targeting macrophages.

A BRCA2 germline mutation and high expression of immune checkpoints in a TNBC patient

Triple-negative breast cancer (TNBC) is the most aggressive subtype of mammary carcinoma. Here, we describe a case of an 81-year-old female diagnosed with ductal triple negative breast cancer with a germline pathogenic variant in BReast CAncer gene2 (BRCA2). Genetic testing also revealed the presence of four somatic mutations in the ephrin type-A receptor 3 (EphA3), TP53, BRCA1-associated protein (BAP1), and MYB genes. The BRCA2, TP53, and BAP1 gene mutations are highly predictive of a defective homologous recombination repair system and subsequent chromosomal instability in this patient. Coherently, the patient displayed a strong homologous recombination deficiency signature and high tumor mutational burden status, which are generally associated with increased probability of immune neoantigens formation and presentation, and with tumor immunogenicity. Analysis of immune checkpoint revealed high expression of programmed cell death ligand 1 (PD-L1), programmed cell death ligand 2 (PD-L2), programmed death 1 (PD1), and cytotoxic T-lymphocyte-associated protein 4 (CTLA 4), suggesting that the patient might likely benefit from immunotherapies. Altogether, these findings support an unveiled link between BRCA2 inactivation, HR deficiency and increased expression of immune checkpoints in TNBC. This clinical case highlights the importance of screening TNBC patients for genetic mutations and TMB biomarkers in order to predict the potential efficacy of immunotherapy.

A workflow to study mechanistic indicators for driver gene prediction with Moonlight

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.

Aged mesenchymal stem cells and inflammation: from pathology to potential therapeutic strategies

Natural ageing of organisms and corresponding age-related diseases result mainly from stem cell ageing and "inflammaging". Mesenchymal stem cells (MSCs) exhibit very high immune-regulating capacity and are promising candidates for immune-related disease treatment. However, the effect of MSC application is not satisfactory for some patients, especially in elderly individuals. With ageing, MSCs undergo many changes, including altered cell population reduction and differentiation ability, reduced migratory and homing capacity and, most important, defective immunosuppression. It is necessary to explore the relationship between the "inflammaging" and aged MSCs to prevent age-related diseases and increase the therapeutic effects of MSCs. In this review, we discuss changes in naturally ageing MSCs mainly from an inflammation perspective and propose some ideas for rejuvenating aged MSCs in future treatments.

Programmed Cell Death Pathways in Cholangiocarcinoma: Opportunities for Targeted Therapy

Cholangiocarcinoma is a highly aggressive cancer arising from the bile ducts. The limited effectiveness of conventional therapies has prompted the search for new approaches to target this disease. Recent evidence suggests that distinct programmed cell death mechanisms, namely, apoptosis, ferroptosis, pyroptosis and necroptosis, play a critical role in the development and progression of cholangiocarcinoma. This review aims to summarize the current knowledge on the role of programmed cell death in cholangiocarcinoma and its potential implications for the development of novel therapies. Several studies have shown that the dysregulation of apoptotic signaling pathways contributes to cholangiocarcinoma tumorigenesis and resistance to treatment. Similarly, ferroptosis, pyroptosis and necroptosis, which are pro-inflammatory forms of cell death, have been implicated in promoting immune cell recruitment and activation, thus enhancing the antitumor immune response. Moreover, recent studies have suggested that targeting cell death pathways could sensitize cholangiocarcinoma cells to chemotherapy and immunotherapy. In conclusion, programmed cell death represents a relevant molecular mechanism of pathogenesis in cholangiocarcinoma, and further research is needed to fully elucidate the underlying details and possibly identify therapeutic strategies.

Chromothripsis detection with multiple myeloma patients based on deep graph learning

MOTIVATION

Chromothripsis, associated with poor clinical outcomes, is prognostically vital in multiple myeloma. The catastrophic event is reported to be detectable prior to the progression of multiple myeloma. As a result, chromothripsis detection can contribute to risk estimation and early treatment guidelines for multiple myeloma patients. However, manual diagnosis remains the gold standard approach to detect chromothripsis events with the whole-genome sequencing technology to retrieve both copy number variation (CNV) and structural variation data. Meanwhile, CNV data are much easier to obtain than structural variation data. Hence, in order to reduce the reliance on human experts' efforts and structural variation data extraction, it is necessary to establish a reliable and accurate chromothripsis detection method based on CNV data.

RESULTS

To address those issues, we propose a method to detect chromothripsis solely based on CNV data. With the help of structure learning, the intrinsic relationship-directed acyclic graph of CNV features is inferred to derive a CNV embedding graph (i.e. CNV-DAG). Subsequently, a neural network based on Graph Transformer, local feature extraction, and non-linear feature interaction, is proposed with the embedding graph as the input to distinguish whether the chromothripsis event occurs. Ablation experiments, clustering, and feature importance analysis are also conducted to enable the proposed model to be explained by capturing mechanistic insights.

AVAILABILITY AND IMPLEMENTATION

The source code and data are freely available at https://github.com/luvyfdawnYu/CNV_chromothripsis.

Integrated Bioinformatics Investigation of Novel Biomarkers of Uterine Leiomyosarcoma Diagnosis and Outcome

Uterine leiomyosarcomas (uLMS) have a poor prognosis and a high percentage of recurrent disease. Bioinformatics has become an integral element in rare cancer studies by overcoming the inability to collect a large enough study population. This study aimed to investigate and highlight crucial genes, pathways, miRNAs, and transcriptional factors (TF) on uLMS samples from five Gene Expression Omnibus datasets and The Cancer Genome Atlas Sarcoma study. Forty-one common differentially expressed genes (DEGs) were enriched and annotated by the DAVID software. With protein-protein interaction (PPI) network analysis, we selected ten hub genes that were validated with the TNMplotter web tool. We used the USCS Xena browser for survival analysis. We also predicted TF-gene and miRNA-gene regulatory networks along with potential drug molecules. TYMS and TK1 correlated with overall survival in uLMS patients. Finally, our results propose further validation of hub genes (TYMS and TK1), miR-26b-5p, and Sp1 as biomarkers of pathogenesis, prognosis, and differentiation of uLMS. Regarding the aggressive behavior and poor prognosis of uLMS, with the lack of standard therapeutic regimens, in our opinion, the results of our study provide enough evidence for further investigation of the molecular basis of uLMS occurrence and its implication in the diagnosis and therapy of this rare gynecological malignancy.

Healthy lifestyle? or just the right genetic mutations

The development of genomic technologies over the past decades has enabled identification of genetic variants responsible of disease; occasionally however, protective rare variants emerged. Verweij et al have recently reported genetic variants in CIDEB gene that are protective from liver injury. Here, we briefly summarise the recent findings on the impact of CIDEB variants on liver disease, while emphasizing how phenotype-genotype studies tailored for the identification of "protective" mutations might direct development of prevention and therapeutic strategies for common diseases.

Recent advances in electrochemical nanomaterial-based aptasensors for the detection of cancer biomarkers

New technologies have provided suitable tools for rapid diagnosis of cancer which can reduce treatment costs and even increase patients' survival rates. Recently, the development of electrochemical aptamer-based nanobiosensors has raised great hopes for early, sensitive, selective, and low-cost cancer diagnosis. Here, we reviewed the flagged recent research (2021-2023) developed as a series of biosensors equipped with nanomaterials and aptamer sequences (nanoaptasensors) to diagnose/prognosis of various types of cancers. Equipping these aptasensors with nanomaterials and using advanced biomolecular technologies have provided specified biosensing interfaces for more optimal and reliable detection of cancer biomarkers. The primary intention of this review was to present and categorize the latest innovations used in the design of these diagnostic tools, including the hottest surface modifications and assembly of sensing bioplatforms considering diagnostic mechanisms. The main classification is based on applying various nanomaterials and sub-classifications considered based on the type of analyte and other vital features. This review may help design subsequent electrochemical aptasensors. Likewise, the up-to-date status, remaining limitations, and possible paths for translating aptasensors to clinical cancer assay tools can be clarified.

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features

Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein’s amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the “OncologyTT” datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins’ amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.

ZNF750: A Novel Prognostic Biomarker in Metastatic Prostate Cancer

Prostate cancer is the most frequently diagnosed cancer and the fifth leading cause of cancer death among men in 2020. The clinical decision making for prostate cancer patients is based on the stratification of the patients according to both clinical and pathological parameters such as Gleason score and prostate-specific antigen levels. However, these tools still do not adequately predict patient outcome. The aim of this study was to investigate whether ZNF750 could have a role in better stratifying patients, identifying those with a higher risk of metastasis and with the poorest prognosis. The data reported here revealed that ZNF750 protein levels are reduced in human prostate cancer samples, and this reduction is even higher in metastatic samples. Interestingly, nuclear positivity is significantly reduced in patients with metastatic prostate cancer, regardless of both Gleason score and grade group. More importantly, the bioinformatics analysis indicates that ZNF750 expression is positively correlated with better prognosis. Overall, our findings suggest that nuclear expression of ZNF750 may be a reliable prognostic biomarker for metastatic prostate cancer, which lays the foundation for the development of new biological therapies.

Gene expression in organoids: an expanding horizon

Recent development of human three-dimensional organoid cultures has opened new doors and opportunities ranging from modelling human development in vitro to personalised cancer therapies. These new in vitro systems are opening new horizons to the classic understanding of human development and disease. However, the complexity and heterogeneity of these models requires cutting-edge techniques to capture and trace global changes in gene expression to enable identification of key players and uncover the underlying molecular mechanisms. Rapid development of sequencing approaches made possible global transcriptome analyses and epigenetic profiling. Despite challenges in organoid culture and handling, these techniques are now being adapted to embrace organoids derived from a wide range of human tissues. Here, we review current state-of-the-art multi-omics technologies, such as single-cell transcriptomics and chromatin accessibility assays, employed to study organoids as a model for development and a platform for precision medicine.

A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for low grade glioma

Background

As a common primary intracranial tumor, the diagnosis and therapy of low-grade glioma (LGG) remains a pivotal barrier. Cuproptosis, a new way induces cell death, has attracted worldwide attention. However, the relationship between cuproptosis and LGG remains unknown. Our study is all about finding out if there are any genes related to coproptosis that can be used to predict the outcome of LGG.

Methods

RNA data and clinical information were selected from Cancer Genome Atlas (TCGA) datasets and the Genotype-Tissue Expression (GTEx), 5 lncRNAs (GAS5.AS1, MYLK.AS1, AC142472.1, AC011346.1, AL359643.3) were identified by Cox univariate and multivariate regression, as well as LASSO Cox regression. In the training and test sets, a dual validation of the predictive signature comprised of these 5 lncRNAs was undertaken. The findings demonstrate that the risk model is able to predict the survival regression of LGG patients and has a good performance in either the KM curve approach or the ROC curve. GO, GSEA and KEGG were carried out to explore the possible molecular processes that affecting the prognosis of LGG. The characteristics of immune microenvironment were investigated by using CIBERSORT, ESTIMATE and ssGSEA.

Results

We identified five lncRNAs related with cuproptosis that were closely associated with the prognosis of LGG and used these five lncRNAs to develop a risk model. Using this risk model, LGG patients were then divided into high-risk and low-risk groups. The two patient groups had significantly distinct survival characteristics. Analyses of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that the differential genes of the two patient groups were primarily concentrated in neural active ligand-receptor interaction and cytokine-cytokine receptor interaction. The ssGSEA score determined the information related to immune infiltration, and the two groups were differentially expressed in immune subpopulations such as T cells and B cells as well.

Conclusion

Our study discovered 5 cuproptosis-related lncRNAs which contribute to predicting patients' survival of LGG and provide ideas for the exploration of new targets for LGG in the future.

Immunogenic cell death-related classifications in breast cancer identify precise immunotherapy biomarkers and enable prognostic stratification

Background: Immunogenic cell death (ICD) remodels the tumor immune microenvironment, plays an inherent role in tumor cell apoptosis, and promotes durable protective antitumor immunity. Currently, appropriate biomarker-based ICD immunotherapy for breast cancer (BC) is under active exploration. Methods: To determine the potential link between ICD genes and the clinical risk of BC, TCGA-BC was used as the training set and GSE58812 was used as the validation set. Gene expression, consistent clustering, enrichment analysis, and mutation omics analyses were performed to analyze the potential biological pathways of ICD genes involved in BC. Furthermore, a risk and prognosis model of ICD was constructed to evaluate the correlation between risk grade and immune infiltration, clinical stage, and survival prognosis. Results: We identified two ICD-related subtypes by consistent clustering and found that the C2 subtype was associated with good survival prognosis, abundant immune cell infiltration, and high activity of immune biological processes. Based on this, we constructed and validated an ICD risk and prognosis model of BC, including ATG5, HSP90AA1, PIK3CA, EIF2AK3, MYD88, IL1R1, and CD8A. This model can effectively predict the survival rate of patients with BC and is negatively correlated with the immune microenvironment and clinical stage. Conclusion: This study provides new insights into the role of ICD in BC. The novel classification risk model based on ICD in BC established in this study can aid in estimating the potential prognosis of patients with BC and the clinical outcomes of immunotherapy and postulates targets that are more useful in comprehensive treatment strategies.

The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis

Objective. N7-methylguanosine modification-related lncRNAs (m7G-related lncRNAs) are involved in progression of many diseases. This study was aimed at revealing the risk correlation between N7-methylguanosine modification-related lncRNAs and survival prognosis of oral squamous cell carcinoma. Methods. In the present study, coexpression network analysis and univariate Cox analysis were used to obtained 31 m7G-related mRNAs and 399 m7G-related lncRNAs. And the prognostic risk score model of OSCC patients was evaluated and optimized through cross-validation. Results. Through the coexpression analysis and risk assessment analysis of m7G-related prognostic mRNAs and lncRNAs, it was found that six m7G-related prognostic lncRNAs (AC005332.6, AC010894.1, AC068831.5, AL035446.1, AL513550.1, and HHLA3) were high-risk lncRNAs. Three m7G-related prognostic lncRNAs (AC007114.1, HEIH, and LINC02541) were protective lncRNAs. Then, survival curves were drawn by comparing the survival differences between patients with high and low expression of each m7G-related prognostic lncRNA in the prognostic risk score model. Further, risk curves, scatter plots, and heat maps were drawn by comparing the survival differences between high-risk and low-risk OSCC patients in the prognostic model. Finally, forest maps and the ROC curve were generated to verify the predictive power of the prognostic risk score model. Our results will help to find early and accurate prognostic risk markers for OSCC, which could be used for early prediction and early clinical intervention of survival, prognosis, and disease risk of OSCC patients in the future.

Multiple roles of apolipoprotein B mRNA editing enzyme catalytic subunit 3B (APOBEC3B) in human tumors: a pan-cancer analysis

Although there have been some recent cell and animal experiments indicating that expression of the gene encoding apolipoprotein B mRNA editing enzyme catalytic subunit 3B (APOBEC3B) is closely related to cancer, it still lacks pan-cancer analysis. Here we analyzed the potential carcinogenic role of APOBEC3B in 33 tumors based on The Cancer Genome Atlas (TCGA). APOBEC3B was highly expressed in most tumors and weakly expressed in a few. Differences in expression level were significantly correlated with the pathological tumor stage and prognosis of affected patients. The high-frequency APOBEC3B changes were principally mutations and amplifications in some tumors, such as uterine corpus endometrial carcinomas or cutaneous melanomas. In testicular germ cell tumors and invasive breast carcinomas, APOBEC3B expression and CD8⁺ T lymphocyte counts were correlated. In other cancers, such as human papilloma virus (HPV)-related head and neck squamous cell carcinomas or esophageal adenocarcinomas, there was also cancer-associated fibroblast infiltration. The APOBEC3B enzyme acts in the mitochondrial respiratory electron transport chain and in oxidative phosphorylation. This first pan-cancer study provides a comprehensive understanding of the multiple roles of APOBEC3B in different tumor types.

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.

No Time to Die: How Kidney Cancer Evades Cell Death

The understanding of the pathogenesis of renal cell carcinoma led to the development of targeted therapies, which dramatically changed the overall survival rate. Nonetheless, despite innovative lines of therapy accessible to patients, the prognosis remains severe in most cases. Kidney cancer rarely shows mutations in the genes coding for proteins involved in programmed cell death, including p53. In this paper, we show that the molecular machinery responsible for different forms of cell death, such as apoptosis, ferroptosis, pyroptosis, and necroptosis, which are somehow impaired in kidney cancer to allow cancer cell growth and development, was reactivated by targeted pharmacological intervention. The aim of the present review was to summarize the modality of programmed cell death in the pathogenesis of renal cell carcinoma, showing in vitro and in vivo evidence of their potential role in controlling kidney cancer growth, and highlighting their possible therapeutic value.

Identification of the Putative Tumor Suppressor Characteristics of FAM107A via Pan-Cancer Analysis

Family with sequence similarity 107, member A(FAM107A) was supposed as a tumor suppressor for various types of tumors. However, no pan-cancer analysis of FAM107A is available. Therefore, we conducted a FAM107A-related pan-cancer analysis across thirty-three tumors based on TCGA database to explore the molecular characteristics of FAM107A. The FAM107A expression is reduced in most cancers, and its down-regulated expression was linked to poor overall survival and progression-free survival of tumor patients. Analysis of DNA methylation of the FAM107A gene showed a negative correlation between FAM107A expression and promoter methylation in numerous cancers. Furthermore, FAM107A expression was noted to be involved in myeloid-derived suppressor cell infiltration in multiple cancers. To explore the mechanism of FAM107A in cancers, KEGG, and GO enrichment analysis was performed and the result showed "cell adhesion" and "cAMP signaling pathway" terms as the potential impact of FAM107A on cancers. An experiment in vitro showed FAM107A knockdown promoted the proliferation, migration, and invasion of bladder cancer and renal cancer cells. Our study indicates that FAM107A may be a putative tumor suppressor in bladder cancer and other tumors.

Metastatic Transition of Pancreatic Ductal Cell Adenocarcinoma Is Accompanied by the Emergence of Pro-Invasive Cancer-Associated Fibroblasts

Cancer-associated fibroblasts (CAFs) are now appreciated as key regulators of cancer metastasis, particularly in cancers with high stromal content, e.g., pancreatic ductal cell carcinoma (PDAC). However, it is not yet well understood if fibroblasts are always primed to be cooperative in PDAC transition to metastasis, if they undergo transformation which ensures their cooperativity, and if such transformations are cancer-driven or intrinsic to fibroblasts. We performed a fibroblast-centric analysis of PDAC cancer, as it transitioned from the primary site to trespass stromal compartment reaching the lymph node using published single-cell RNA sequencing data by Peng et al. We have characterized the change in fibroblast response to cancer from a normal wound healing response in the initial stages to the emergence of subclasses with myofibroblast and inflammatory fibroblasts such as signatures. We have previously posited "Evolved Levels of Invasibility (ELI)", a framework describing the evolution of stromal invasability as a selected phenotype, which explains the large and correlated reduction in stromal invasion by placental trophoblasts and cancer cells in certain mammals. Within PDAC samples, we found large changes in fibroblast subclasses at succeeding stages of PDAC progression, with the emergence of specific subclasses when cancer trespasses stroma to metastasize to proximal lymph nodes (stage IIA to IIB). Surprisingly, we found that the initial metastatic transition is accompanied by downregulation of ELI-predicted pro-resistive genes, and the emergence of a subclass of fibroblasts with ELI-predicted increased invasibility. Interestingly, this trend was also observed in stellate cells. Using a larger cohort of bulk RNAseq data from The Cancer Genome Atlas for PDAC cancers, we confirmed that genes describing this emergent fibroblast subclass are also correlated with lymph node metastasis of cancer cells. Experimental testing of selected genes characterizing pro-resistive and pro-invasive fibroblast clusters confirmed their contribution in regulating stromal invasability as a phenotype. Our data confirm that the complexity of stromal response to cancer is really a function of stage-wise emergence of distinct fibroblast clusters, characterized by distinct gene sets which confer initially a predominantly pro-resistive and then a pro-invasive property to the stroma. Stromal response therefore transitions from being tumor-limiting to a pro-metastatic state, facilitating stromal trespass and the onset of metastasis.

Substitution mutational signatures in whole-genome-sequenced cancers in the UK population

Whole-genome sequencing (WGS) permits comprehensive cancer genome analyses, revealing mutational signatures, imprints of DNA damage and repair processes that have arisen in each patient's cancer. We performed mutational signature analyses on 12,222 WGS tumor-normal matched pairs, from patients recruited via the UK National Health Service. We contrasted our results to two independent cancer WGS datasets, the International Cancer Genome Consortium (ICGC) and Hartwig Foundation, involving 18,640 WGS cancers in total. Our analyses add 40 single and 18 double substitution signatures to the current mutational signature tally. Critically, we show for each organ, that cancers have a limited number of 'common' signatures and a long tail of 'rare' signatures. We provide a practical solution for utilizing this concept of common versus rare signatures in future analyses.

Glycoconjugate Nanoparticle-Based Systems in Cancer Immunotherapy: Novel Designs and Recent Updates

For many years, cell-surface glycans (in particular, Tumor-Associated Carbohydrate Antigens, TACAs) have been the target of both passive and active anticancer immunotherapeutic design. Recent advances in immunotherapy as a treatment for a variety of malignancies has revolutionized anti-tumor treatment regimens. Checkpoint inhibitors, Chimeric Antigen Receptor T-cells, Oncolytic virus therapy, monoclonal antibodies and vaccines have been developed and many approvals have led to remarkable outcomes in a subset of patients. However, many of these therapies are very selective for specific patient populations and hence the search for improved therapeutics and refinement of techniques for delivery are ongoing and fervent research areas. Most of these agents are directed at protein/peptide epitopes, but glycans–based targets are gaining in popularity, and a handful of approved immunotherapies owe their activity to oligosaccharide targets. In addition, nanotechnology and nanoparticle-derived systems can help improve the delivery of these agents to specific organs and cell types based on tumor-selective approaches. This review will first outline some of the historical beginnings of this research area and subsequently concentrate on the last 5 years of work. Based on the progress in therapeutic design, predictions can be made as to what the future holds for increasing the percentage of positive patient outcomes for optimized systems.

Clinical Significance of TET2 in Female Cancers

Female cancers refer to malignant tumors of the female reproductive system and breasts, which severely affect the physical and mental health of women. Although emerging experiment-based studies have indicated a potential correlation between ten-eleven translocation methylcytosine dioxygenase (TET2) and female cancers, no comprehensive studies have been conducted. Therefore, this study aimed to summarize the clinical value and underlying oncogenic functions of TET2 in female cancers, such as breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), ovarian serous cystadenocarcinoma (OV), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS), based on the data obtained from The Cancer Genome Atlas. The expression of TET2 was decreased in most female cancers, and its high expression was distinctly associated with the favorable prognosis of most female cancers. Furthermore, CD8⁺ T-cell infiltration was not correlated with TET2 in OV, UCEC, and UCS, whereas tumor-associated fibroblast infiltration was significantly correlated with TET2 in BRCA, CESC, and OV. TET2 was co-expressed with the immune checkpoint molecules ADORA2A, CD160, CD200, CD200R1, CD44, CD80, NRP1 TNFSF4, and TNFSF15 in most female cancers. Enrichment analysis revealed that some signaling pathways involving TET2 and related genes were related to tumorigenesis. Immunohistochemical and immunofluorescence staining confirmed the results of cancer immune infiltration analysis in BRCA tissues. Therefore, this study provides evidence for the oncogenic functions and clinical value of TET2 in female cancers.

LOC102724163 promotes breast cancer cell proliferation and invasion by stimulating MUC19 expression

Breast cancer (BC) is a malignant disease and the most commonly diagnosed cancer in women. Numerous studies have previously verified the important role of long non-coding RNAs in a number of biological processes in BC. In the present study, analysis of The Cancer Genome Atlas database and reverse transcription-quantitative PCR demonstrated that LOC102724163 expression levels were significantly upregulated in BC tissues compared to matched adjacent normal tissues and were associated with an unfavorable prognosis in patients with BC. Gain or loss of function assays indicated that overexpression of LOC102724163 significantly increased tumorgenicity in vivo and cell migration, proliferation and invasion in vitro. In the mechanistical aspect, LOC102724163 sponged microRNA (miR)-508-5p to elevate MUC19 expression. Additionally, rescue assays ascertained the function of the LOC102724163/miR-508-5p/MUC19 axis in the proliferation and invasion of BC cells. To the best of our knowledge, this is the first study to have demonstrated that LOC102724163 may act as a competing endogenous RNA to control MUC19 expression levels by competitively sponging miR-508-5p to modulate BC progression. Therefore, the present study has provided new insights into BC diagnosis and treatment.

DDX39B contributes to the proliferation of colorectal cancer through direct binding to CDK6/CCND1

DDX39B (also called UAP56 or BAT1) which is a kind of DEAD-box family helicase plays pivotal roles in mRNA binding, splicing, and export. It has been found upregulated in many kinds of tumors as an oncogene. Nevertheless, the underlying molecular mechanisms of DDX39B in the proliferation of human colorectal cancer (CRC) remain fairly elusive. In our study, function experiments including the CCK8 and colony formation assay revealed that DDX39B facilitates CRC proliferation in vitro. DDX39B knockdown cells were administered for the orthotopic CRC tumor xenograft mouse model, after which tumor growth was monitored and immunohistochemistry (IHC) was performed to prove that DDX39B can also facilitates CRC proliferation in vivo. Flow cytometry demonstrated that DDX39B promotes the proliferation of CRC cells by driving the cell cycle from G0/G1 phase to the S phase. Mechanistically, RNA-binding protein immunoprecipitation-sequencing (RIP-seq) confirmed that DDX39B binds directly to the first exon of the CDK6/CCND1 pre-mRNA and upregulates their expression. Splicing experiments in vitro using a RT-PCR and gel electrophoresis assay confirmed that DDX39B promotes CDK6/CCND1 pre-mRNA splicing. Rescue experiments indicated that CDK6/CCND1 is a downstream effector of DDX39B-mediated CRC cell proliferation. Collectively, our results demonstrated that DDX39B and CDK6/CCND1 direct interactions serve as a CRC proliferation promoter, which can accelerate the G1/S phase transition to enhance CRC proliferation, and can offer novel and emerging treatment strategies targeting this cell proliferation-promoting gene.

The Future of Precision Prevention for Advanced Melanoma

Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular total body photography with sequential digital dermoscopic imaging. This approach aims to address both underdiagnosis (a missed or delayed melanoma diagnosis) and overdiagnosis (the diagnosis and treatment of indolent lesions that would not have caused a problem). Holistic risk stratification considers several types of melanoma risk factors: clinical phenotype, comprehensive imaging-based phenotype, familial and polygenic risks. Artificial intelligence computer-aided diagnostics combines these risk factors to produce a personalized risk score, and can also assist in assessing the digital and molecular markers of individual lesions. However, to ensure uptake and efficient use of AI systems, researchers will need to carefully consider how best to incorporate privacy and standardization requirements, and above all address consumer trust concerns.

Molecular Mechanisms of lncRNAs in the Dependent Regulation of Cancer and Their Potential Therapeutic Use

Deep whole genome and transcriptome sequencing have highlighted the importance of an emerging class of non-coding RNA longer than 200 nucleotides (i.e., long non-coding RNAs (lncRNAs)) that are involved in multiple cellular processes such as cell differentiation, embryonic development, and tissue homeostasis. Cancer is a prime example derived from a loss of homeostasis, primarily caused by genetic alterations both in the genomic and epigenetic landscape, which results in deregulation of the gene networks. Deregulation of the expression of many lncRNAs in samples, tissues or patients has been pointed out as a molecular regulator in carcinogenesis, with them acting as oncogenes or tumor suppressor genes. Herein, we summarize the distinct molecular regulatory mechanisms described in literature in which lncRNAs modulate carcinogenesis, emphasizing epigenetic and genetic alterations in particular. Furthermore, we also reviewed the current strategies used to block lncRNA oncogenic functions and their usefulness as potential therapeutic targets in several carcinomas.

Arginine Depletion in Human Cancers

Simple Summary

Thousands of cancer genomes are now publicly available which has led to new insights into the underlying features of cancers. These include the identification of mutational signatures at both nucleotide and amino acid levels. Here, we discuss C > T transitions as a key nucleotide-level mutational signature that leads to a dramatic overrepresentation of arginine substitutions in cancers. We propose that this underlying C > T mutational signature canalizes possible arginine substitution outcomes, favoring histidine, cysteine, glutamine, and tryptophan. This initial asymmetry is then acted on at the amino acid level by purifying selection. Thus, a model of “sequential selection” could explain the documented bias towards arginine substitutions in multiple cancers.

Abstract

Arginine is encoded by six different codons. Base pair changes in any of these codons can have a broad spectrum of effects including substitutions to twelve different amino acids, eighteen synonymous changes, and two stop codons. Four amino acids (histidine, cysteine, glutamine, and tryptophan) account for over 75% of amino acid substitutions of arginine. This suggests that a mutational bias, or “purifying selection”, mechanism is at work. This bias appears to be driven by C > T and G > A transitions in four of the six arginine codons, a signature that is universal and independent of cancer tissue of origin or histology. Here, we provide a review of the available literature and reanalyze publicly available data from the Catalogue of Somatic Mutations in Cancer (COSMIC). Our analysis identifies several genes with an arginine substitution bias. These include known factors such as IDH1, as well as previously unreported genes, including four cancer driver genes (FGFR3, PPP6C, MAX, GNAQ). We propose that base pair substitution bias and amino acid physiology both play a role in purifying selection. This model may explain the documented arginine substitution bias in cancers.

Dual Role of p73 in Cancer Microenvironment and DNA Damage Response

Understanding the mechanisms that regulate cancer progression is pivotal for the development of new therapies. Although p53 is mutated in half of human cancers, its family member p73 is not. At the same time, isoforms of p73 are often overexpressed in cancers and p73 can overtake many p53 functions to kill abnormal cells. According to the latest studies, while p73 represses epithelial–mesenchymal transition and metastasis, it can also promote tumour growth by modulating crosstalk between cancer and immune cells in the tumor microenvironment, M2 macrophage polarisation, Th2 T-cell differentiation, and angiogenesis. Thus, p73 likely plays a dual role as a tumor suppressor by regulating apoptosis in response to genotoxic stress or as an oncoprotein by promoting the immunosuppressive environment and immune cell differentiation.

The p53 family member p73 in the regulation of cell stress response

During oncogenesis, cells become unrestrictedly proliferative thereby altering the tissue homeostasis and resulting in subsequent hyperplasia. This process is paralleled by resumption of cell cycle, aberrant DNA repair and blunting the apoptotic program in response to DNA damage. In most human cancers these processes are associated with malfunctioning of tumor suppressor p53. Intriguingly, in some cases two other members of the p53 family of proteins, transcription factors p63 and p73, can compensate for loss of p53. Although both p63 and p73 can bind the same DNA sequences as p53 and their transcriptionally active isoforms are able to regulate the expression of p53-dependent genes, the strongest overlap with p53 functions was detected for p73. Surprisingly, unlike p53, the p73 is rarely lost or mutated in cancers. On the contrary, its inactive isoforms are often overexpressed in cancer. In this review, we discuss several lines of evidence that cancer cells develop various mechanisms to repress p73-mediated cell death. Moreover, p73 isoforms may promote cancer growth by enhancing an anti-oxidative response, the Warburg effect and by repressing senescence. Thus, we speculate that the role of p73 in tumorigenesis can be ambivalent and hence, requires new therapeutic strategies that would specifically repress the oncogenic functions of p73, while keeping its tumor suppressive properties intact.

miR‑497/MIR497HG inhibits glioma cell proliferation by targeting CCNE1 and the miR‑588/TUSC1 axis

Emerging evidence has shown that microRNA (miR)-497 serves pivotal roles in tumorigenesis, cancer progression, metastasis and chemotherapy resistance in several types of cancer. In the present study, the expression and biological functions of miR-497 host gene (MIR497HG) were investigated in glioma tissue. The expression levels of miR-497 and MIR497HG were measured in glioma, adjacent non-cancerous and normal brain tissue and their association with the prognosis of patients with glioma were analyzed. The biological roles of miR-497 and MIR497HG were investigated in glioma cell lines. In addition, bioinformatics analysis, luciferase reporter assay and functional experiments were performed to identify and validate the downstream targets of miR-497 or MIR497HG. The expression levels of miR-497 and MIR497HG were downregulated in glioma tissue and cell lines compared with those in adjacent non-cancerous and normal brain tissue and normal human cortical neuron cell line. Patients with low miR-497 or MIR497HG expression levels exhibited a poor prognostic outcome. In addition, forced overexpression of miR-497 or MIR497HG significantly inhibited the proliferation and cell cycle progression of glioma cell lines. Furthermore, the results indicated that miR-497 and MIR497HG exerted their biological functions by direct targeting of cyclin E1 and miR-588/tumor suppressor candidate 1. In summary, the data indicated that miR-497 and MIR497HG served as tumor suppressors and may be used as potential therapeutic targets and prognostic biomarkers in glioma.