Genome-wide analysis of somatic noncoding mutation patterns in cancer

Pan-cancer analysis shapes the understanding of cancer biology and medicine

Advances in multi-omics datasets and analytical methods have revolutionized cancer research, offering a comprehensive, pan-cancer perspective. Pan-cancer studies identify shared mechanisms and unique traits across different cancer types, which are reshaping diagnostic and treatment strategies. However, continued innovation is required to refine these approaches and deepen our understanding of cancer biology and medicine. This review summarized key findings from pan-cancer research and  explored their potential to drive future advancements in oncology.

The Erosion of Healthcare and Scientific Integrity: A Growing Concern

Background

Tremendous achievements in healthcare and science over the past 200 years have enhanced life expectancy in parallel with a shift from dogma to humanistic liberal education. Advancements in cancer have included vaccines treating causes of cancer (eg, hepatitis C- induced liver cancer and human papillomavirus-induced cervical cancer) along with improved cancer survival in children. In contrast, developments in cancer, frequently touted as "discoveries" or "breakthroughs" in media headlines, have been demonstrated to be ephemeral rather than game changers. In reality, cancer incidences are increasing, and relapse and mortality rates have not changed substantially. By this, we are experiencing today similar challenges to those before the so-called Humboldt reform. The trend towards managerialism with a focus on quantity in health care and science endangers their integrity.

Methods

Due to the complexity of integrity of healthcare and science, in-depth contemplation of this review contains foundations of actions in healthcare and science, information regarding cancer, as an example, quantity focus of healthcare, technology, publishing, marketing and media, predatory publishers, followed by psychologic and sociologic aspects which influence our perception.

Results

A complex paradoxical transformation has occurred, in which quality and humanism have been replaced by quantity, revenue, and marketing, together with "citation silence", (ignoring original findings), and increased corruption and misconduct. This shift explains why the integrity of healthcare and science is being eroded.

Conclusion

Countries and societies are only as strong as their healthcare and science, both of which are only as strong as their emphasis on quality and integrity. Awareness of this situation may represent a first step toward a renewed focus on accountability.

Sequencing of high-frequency mutated genes in breast cancer (BRCA) and associated-functions analysis

OBJECTIVE

Mutations or aberrant expression of genes in an organism tend not to be completely random and this cumulative effect predisposes to the development of malignant tumours. This study aims to reveal the possible aberrant expression of high frequency mutated genes, and then to investigate their role in development, prognosis, signalling pathway function and drug resistance in breast cancer.

METHODS

The mutated genes in breast cancer (BRCA) clinical samples were identified and detected by high-throughput sequencing. High-frequency mutant genes were counted. Gene expression profiles and the relationship with prognosis were analysed throughout TCGA database. qRT-PCR was used to analyse the mRNA levels of the six high-frequency mutant genes in BRCA tissues and cell lines. IHC was used to analyse the protein levels of the six high-frequency mutant genes in BRCA tissues. The linear interaction, single-cell layer clustering status and the influence in immune cell infiltration degree among these six high-frequency mutant genes were analysed by bioinformatics analysis. The STITCH and cMAP datasets were used for high-frequency mutant gene interaction networks, association signalling pathway enrichment and drug-transcriptome analyses. The effects of trastuzumab on the proliferative capacity of breast cancer cells, as well as on the expression of six high-frequency mutated genes were determined by CCK8 assay.

RESULTS

The genes that were statistically found to have high-frequency mutations in the samples recruited in the present study by high-throughput sequencing analysis included TP53, PIK3CA, NF1, TBX3, BRCA1 and BRCA2. The expression profiles of these genes and the correlation with prognosis were further demonstrated using the TCGA database: the trend in this study was similar to that of BRCA in TCGA. The mRNA and protein expression of these genes showed that the expression of TP53, NF1, TBX3, BRCA1 and BRCA2 was higher in tumor samples than that in normal samples, with an opposite trend for PIK3CA, a similar trend was observed in BRCA cell lines. The protein expressions of TP53, NF1, TBX3, BRCA1 and BRCA2 displayed the same trend by IHC. Other correlation results include 1) the single cell layer clustering of these six genes resulted in significant clustering with few overlapping regions; 2) these six genes showed different degrees of influence on BRCA immune cell infiltration; 3) these six genes showed a significant correlation between each other; 4) the network of each gene had partially overlapping molecules; and 5) the PI3K pathway was a key association pathway in BRCA. Finally, the cell proliferation ability results confirmed the optimal concentration of trastuzumab and its effect on mRNA expression of these six genes.

Microproteins in cancer: identification, biological functions, and clinical implications

Cancer continues to be a major global health challenge, accounting for 10 million deaths annually worldwide. Since the inception of genome-wide cancer sequencing studies 20 years ago, a core set of ~700 oncogenes and tumor suppressor genes has become the basis for cancer research. However, this research has been based largely on an understanding that the human genome encodes ~19 500 protein-coding genes. Complementing this genomic landscape, recent advances have described numerous microproteins which are now poised to redefine our understanding of oncogenic processes and open new avenues for therapeutic intervention. This review explores the emerging evidence for microprotein involvement in cancer mechanisms and discusses potential therapeutic applications, with an emphasis on highlighting recent advances in the field.

Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature

BACKGROUND

The present study aimed to develop a nomogram model for predicting overall survival (OS) in esophageal squamous cell carcinoma (ESCC) patients.

METHODS

A total of 205 patients with ESCC were enrolled and randomly divided into a training cohort (n = 153) and a test cohort (n = 52) at a ratio of 7:3. Multivariate Cox regression was used to construct the radiomics model based on CT data. The mutation signature was constructed based on whole genome sequencing data and found to be significantly associated with the prognosis of patients with ESCC. A nomogram model combining the Rad-score and mutation signature was constructed. An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors was constructed.

RESULTS

A total of 8 CT features were selected for multivariate Cox regression analysis to determine whether the Rad-score was significantly correlated with OS. The area under the curve (AUC) of the radiomics model was 0.834 (95% CI, 0.767-0.900) for the training cohort and 0.733 (95% CI, 0.574-0.892) for the test cohort. The Rad-score, S3, and S6 were used to construct an integrated RM nomogram. The predictive performance of the RM nomogram model was better than that of the radiomics model, with an AUC of 0. 830 (95% CI, 0.761-0.899) in the training cohort and 0.793 (95% CI, 0.653-0.934) in the test cohort. The Rad-score, TNM stage, lymph node metastasis status, S3, and S6 were used to construct an integrated RMC nomogram. The predictive performance of the RMC nomogram model was better than that of the radiomics model and RM nomogram model, with an AUC of 0. 862 (95% CI, 0.795-0.928) in the training cohort and 0. 837 (95% CI, 0.705-0.969) in the test cohort.

CONCLUSION

An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors can better predict the prognosis of patients with ESCC.

A nomogram for cancer-specific survival of lung adenocarcinoma patients: A SEER based analysis

Background

Non-small cell lung cancer (NSCLC) accounts for 85 % of lung cancer cases. Among NSCLC subtypes, lung adenocarcinoma (LUAD) stands as the most prevalent. Regrettably, LUAD continues to exhibit a notably unfavorable overall prognosis. This study's primary aim was to develop and validate prognostic tools capable of predicting the likelihood of cancer-specific survival (CSS) in patients with LUAD.

Methods

We retrospectively collected 21,099 patients diagnosed with LUAD between 2010 and 2015, and 8290 patients diagnosed between 2004 and 2009 from SEER database. The cohort of 21,099 patients served as the prognostic group for the exploration of LUAD-related prognostic risk factors. The cohort of 8290 patients was designated for external validation. We created a training set and an internal validation set in the prognostic group for the development and internal validation of CSS nomograms. CSS predictors were identified through the least absolute shrinkage and selection operator (Lasso) regression analysis. Prognostic model was constructed via Cox hazard regression analysis, presented in the form of both static and dynamic network-based nomograms.

Results

Several independent prognostic factors were incorporated into the construction of nomogram. The nomogram accurately predicted CSS at 1, 3, and 5 years, with respective AUC values of 0.769, 0.761, and 0.748 for the training group, and 0.741, 0.752, and 0.740 for the testing group. The study demonstrated a strong agreement between anticipated and actual CSS values, supported by decision curve analysis (DCA) and time-dependent calibrated curves. High-risk patients based on the nomogram exhibiting significantly lower survival rates compared to their low-risk counterparts according to Kaplan-Meier (K-M) curves. The nomogram demonstrates excellent predictive power in the external validation cohort.

Conclusions

A dependable and user-friendly nomogram has been developed, available in both static and online dynamic calculator formats, to facilitate healthcare professionals in accurately estimating the likelihood of CSS for patients diagnosed LUAD.

Disrupting CENP-N mediated SEPT9 methylation as a strategy to inhibit aerobic glycolysis and liver metastasis in colorectal cancer

Colorectal cancer (CRC) is a prevalent malignancy with a high mortality rate, primarily due to liver metastasis. This study explores the role of centromere protein N (CENP-N) in mediating the methylation of septin 9 (SEPT9) and its subsequent effects on aerobic glycolysis and liver metastasis in CRC. We employed in vitro and in vivo experiments, including single-cell RNA sequencing, methylation-specific PCR (MSP), ChIP assays, and various functional assays to assess the impact of CENP-N and SEPT9 on CRC cell proliferation, migration, invasion, and metabolic reprogramming. Our data reveal that CENP-N directly interacts with SEPT9, enhancing its methylation at specific lysine residues. This modification significantly upregulates key glycolytic enzymes, thereby promoting aerobic glycolysis, CRC cell proliferation, and migration. In vivo studies further demonstrate that the CENP-N/SEPT9 axis facilitates liver metastasis of CRC, as confirmed by fluorescence imaging and histological analysis. This study identifies a novel pathway where CENP-N-mediated methylation of SEPT9 drives metabolic reprogramming and metastasis in CRC. These findings suggest potential therapeutic targets for inhibiting CRC progression and liver metastasis, offering new insights into CRC pathogenesis.

Association of the COL4A2 Gene Polymorphisms with Primary Intracerebral Hemorrhage Risk and Outcome in Chinese Han Population

The relationship of single nucleotide polymorphisms (SNPs) in COL4A2 gene with risk and outcome of primary intracerebral hemorrhage (ICH) in the Chinese Han population remains unclear, which was investigated in this study. Primary ICH patients and non-stroke controls of Chinese Han ethnicity were enrolled. The genotypes of 8 tag-SNPs were determined using a custom-by-design 48-Plex SNPscan Kit. Poor 3-month outcome was defined as modified Rankin Scale score 4-6. Logistic regression was employed to examine association between COL4A2 variants and risk and poor outcome of primary ICH. 323 patients with primary ICH and 376 stroke-free controls were included. Compared to controls, the rs1049931 G and rs1049906 C alleles were associated with increased ICH risk (p = 0.027 and 0.033), and these two allele counts increased this risk after adjustments respectively (additive model: adjusted OR [aOR] 1.41, 95% CI 1.03-1.94, corrected p = 0.043; aOR 1.37, 95% CI 1.01-1.86, corrected p = 0.043). The rs1049931 AG/GG and rs1049906 CT/CC genotypes showed increased susceptibility to non-lobar hemorrhage (aOR 1.63, 95% CI 1.06-2.50, p = 0.025; aOR 1.63, 95% CI 1.07-2.47, p = 0.022). Haplotype analysis revealed an association between rs1049906-rs1049931 haplotype CG and ICH risk (OR 1.36, 95% CI 1.05-1.78, p = 0.021). Regarding clinical outcome, the rs3803230 C allele (dominant model: aOR 1.94, 95% CI 1.04-3.63, p = 0.037) and haplotype AC of rs7990214-rs3803230 (OR 1.98, 95% CI 1.13-3.46, p = 0.015) contributed to 3-month poor outcome. The COL4A2 polymorphisms are associated with an increased risk of primary ICH, mainly non-lobar hemorrhage, and with long-term poor outcome after ICH in Chinese Han population.

Towards functional maps of non-coding variants in cancer

Large scale cancer genomic studies in patients have unveiled millions of non-coding variants. While a handful have been shown to drive cancer development, the vast majority have unknown function. This review describes the challenges of functionally annotating non-coding cancer variants and understanding how they contribute to cancer. We summarize recently developed high-throughput technologies to address these challenges. Finally, we outline future prospects for non-coding cancer genetics to help catalyze personalized cancer therapy.

A Genomics-Driven Artificial Intelligence-Based Model Classifies Breast Invasive Lobular Carcinoma and Discovers CDH1 Inactivating Mechanisms

Artificial intelligence (AI) systems can improve cancer diagnosis, yet their development often relies on subjective histologic features as ground truth for training. Herein, we developed an AI model applied to histologic whole-slide images using CDH1 biallelic mutations, pathognomonic for invasive lobular carcinoma (ILC) in breast neoplasms, as ground truth. The model accurately predicted CDH1 biallelic mutations (accuracy = 0.95) and diagnosed ILC (accuracy = 0.96). A total of 74% of samples classified by the AI model as having CDH1 biallelic mutations but lacking these alterations displayed alternative CDH1 inactivating mechanisms, including a deleterious CDH1 fusion gene and noncoding CDH1 genetic alterations. Analysis of internal and external validation cohorts demonstrated 0.95 and 0.89 accuracy for ILC diagnosis, respectively. The latent features of the AI model correlated with human-explainable histopathologic features. Taken together, this study reports the construction of an AI algorithm trained using a genetic rather than histologic ground truth that can robustly classify ILCs and uncover CDH1 inactivating mechanisms, providing the basis for orthogonal ground truth utilization for development of diagnostic AI models applied to whole-slide image. Significance: Genetic alterations linked to strong genotypic-phenotypic correlations can be utilized to develop AI systems applied to pathology that facilitate cancer diagnosis and biologic discoveries.

Somatic mutations in aging and disease

Time always leaves its mark, and our genome is no exception. Mutations in the genome of somatic cells were first hypothesized to be the cause of aging in the 1950s, shortly after the molecular structure of DNA had been described. Somatic mutation theories of aging are based on the fact that mutations in DNA as the ultimate template for all cellular functions are irreversible. However, it took until the 1990s to develop the methods to test if DNA mutations accumulate with age in different organs and tissues and estimate the severity of the problem. By now, numerous studies have documented the accumulation of somatic mutations with age in normal cells and tissues of mice, humans, and other animals, showing clock-like mutational signatures that provide information on the underlying causes of the mutations. In this review, we will first briefly discuss the recent advances in next-generation sequencing that now allow quantitative analysis of somatic mutations. Second, we will provide evidence that the mutation rate differs between cell types, with a focus on differences between germline and somatic mutation rate. Third, we will discuss somatic mutational signatures as measures of aging, environmental exposure, and activities of DNA repair processes. Fourth, we will explain the concept of clonally amplified somatic mutations, with a focus on clonal hematopoiesis. Fifth, we will briefly discuss somatic mutations in the transcriptome and in our other genome, i.e., the genome of mitochondria. We will end with a brief discussion of a possible causal contribution of somatic mutations to the aging process.

Large-scale multi-omic analysis identifies noncoding somatic driver mutations and nominates ZFP36L2 as a driver gene for pancreatic ductal adenocarcinoma

Identification of somatic driver mutations in the noncoding genome remains challenging. To comprehensively characterize noncoding driver mutations for pancreatic ductal adenocarcinoma (PDAC), we first created genome-scale maps of accessible chromatin regions (ACRs) and histone modification marks (HMMs) in pancreatic cell lines and purified pancreatic acinar and duct cells. Integration with whole-genome mutation calls from 506 PDACs revealed 314 ACRs/HMMs significantly enriched with 3,614 noncoding somatic mutations (NCSMs). Functional assessment using massively parallel reporter assays (MPRA) identified 178 NCSMs impacting reporter activity (19.45% of those tested). Focused luciferase validation confirmed negative effects on gene regulatory activity for NCSMs near CDKN2A and ZFP36L2. For the latter, CRISPR interference (CRISPRi) further identified ZFP36L2 as a target gene (16.0 - 24.0% reduced expression, P = 0.023-0.0047) with disrupted KLF9 binding likely mediating the effect. Our integrative approach provides a catalog of potentially functional noncoding driver mutations and nominates ZFP36L2 as a PDAC driver gene.

Integrative identification of non-coding regulatory regions driving metastatic prostate cancer

Large-scale sequencing efforts have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of variants occur within non-coding genomic regions. We designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Applying this framework to sequencing data from a large prostate cancer patient cohort revealed a large set of candidate drivers. We used (1) in silico analyses, (2) massively parallel reporter assays, and (3) in vivo CRISPR interference screens to systematically validate metastatic castration-resistant prostate cancer (mCRPC) drivers. One identified enhancer region, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of the U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. SF3A1 and CCDC157 promote tumor growth in vivo. We nominated a number of transcription factors, notably SOX6, to regulate expression of SF3A1 and CCDC157. Our integrative approach enables the systematic detection of non-coding regulatory regions that drive human cancers.

Expression patterns of housekeeping genes and tissue-specific genes in black goats across multiple tissues

Black goats are a significant meat breed in southern China. To investigate the expression patterns and biological functions of genes in various tissues of black goats, we analyzed housekeeping genes (HKGs), tissue-specific genes (TSGs), and hub genes (HUBGs) across 23 tissues. Additionally, we analyzed HKGs in 13 tissues under different feeding conditions. We identified 2968 HKGs, including six important ones. Interestingly, HKGs in grazing black goats demonstrated higher and more stable expression levels. We discovered a total of 9912 TSGs, including 134 newly identified ones. The number of TSGs for mRNA and lncRNA were nearly equal, with 127 mRNA TSGs expressed solely in one tissue. Additionally, the predicted functions of tissue-specific long non-coding RNAs (lncRNAs) targeting mRNAs corresponded with the physiological functions of the tissues.Weighted gene co-expression network analysis (WGCNA) constructed 30 modules, where the dark grey module consists almost entirely of HKGs, and TSGs are located in modules most correlated with their respective tissues. Additionally, we identified 289 HUBGs, which are involved in regulating the physiological functions of their respective tissues. Overall, these identified HKGs, TSGs, and HUBGs provide a foundation for studying the molecular mechanisms affecting the genetic and biological processes of complex traits in black goats.

Role of LMO7 in cancer (Review)

Cancer constitutes a multifaceted ailment characterized by the dysregulation of numerous genes and pathways. Among these, LIM domain only 7 (LMO7) has emerged as a significant player in various cancer types, garnering substantial attention for its involvement in tumorigenesis and cancer progression. This review endeavors to furnish a comprehensive discourse on the functional intricacies and mechanisms of LMO7 in cancer, with a particular emphasis on its potential as both a therapeutic target and prognostic indicator. It delves into the molecular attributes of LMO7, its implications in cancer etiology and the underlying mechanisms propelling its oncogenic properties. Furthermore, it underscores the extant challenges and forthcoming prospects in targeting LMO7 for combating cancer.

Diabetes Driven Oncogenesis and Anticancer Potential of Repurposed Antidiabetic Drug: A Systemic Review

Diabetes and cancer are two prevalent disorders, pose significant public health challenges and contribute substantially to global mortality rates, with solely 10 million reported cancer-related deaths in 2020. This review explores the pathological association between diabetes and diverse cancer progressions, examining molecular mechanisms and potential therapeutic intersections. From altered metabolic landscapes to dysregulated signaling pathways, the intricate links are delineated, offering a comprehensive understanding of diabetes as a modulator of tumorigenesis. Cancer cells develop drug resistance through mechanisms like enhanced drug efflux, genetic mutations, and altered drug metabolism, allowing them to survive despite chemotherapeutic agent. Glucose emerges as a pivotal player in diabetes progression, and serving as a crucial energy source for cancer cells, supporting their biosynthetic needs and adaptation to diverse microenvironments. Glycation, a non-enzymatic process that produces advanced glycation end products (AGEs), has been linked to the etiology of cancer and has been shown in a number of tumor forms, such as leiomyosarcomas, adenocarcinomas, and squamous cell carcinomas. Furthermore, in aggressive and metastatic breast cancer, the receptor for AGEs (RAGE) is increased, which may increase the malignancy of the tumor. Reprogramming glucose metabolism manifests as hallmark cancer features, including accelerated cell proliferation, angiogenesis, metastasis, and evasion of apoptosis. This manuscript encapsulates the dual narrative of diabetes as a driver of cancer progression and the potential of repurposed antidiabetic drugs as formidable countermeasures. The amalgamation of mechanistic understanding and clinical trial outcomes establishes a robust foundation for further translational research and therapeutic advancements in the dynamic intersection of diabetes and cancer.

Implications of noncoding regulatory functions in the development of insulinomas

Insulinomas are rare neuroendocrine tumors arising from pancreatic β cells, characterized by aberrant proliferation and altered insulin secretion, leading to glucose homeostasis failure. With the aim of uncovering the role of noncoding regulatory regions and their aberrations in the development of these tumors, we coupled epigenetic and transcriptome profiling with whole-genome sequencing. As a result, we unraveled somatic mutations associated with changes in regulatory functions. Critically, these regions impact insulin secretion, tumor development, and epigenetic modifying genes, including polycomb complex components. Chromatin remodeling is apparent in insulinoma-selective domains shared across patients, containing a specific set of regulatory sequences dominated by the SOX17 binding motif. Moreover, many of these regions are H3K27me3 repressed in β cells, suggesting that tumoral transition involves derepression of polycomb-targeted domains. Our work provides a compendium of aberrant cis-regulatory elements affecting the function and fate of β cells in their progression to insulinomas and a framework to identify coding and noncoding driver mutations.

Mechanism insights and therapeutic intervention of tumor metastasis: latest developments and perspectives

Metastasis remains a pivotal characteristic of cancer and is the primary contributor to cancer-associated mortality. Despite its significance, the mechanisms governing metastasis are not fully elucidated. Contemporary findings in the domain of cancer biology have shed light on the molecular aspects of this intricate process. Tumor cells undergoing invasion engage with other cellular entities and proteins en route to their destination. Insights into these engagements have enhanced our comprehension of the principles directing the movement and adaptability of metastatic cells. The tumor microenvironment plays a pivotal role in facilitating the invasion and proliferation of cancer cells by enabling tumor cells to navigate through stromal barriers. Such attributes are influenced by genetic and epigenetic changes occurring in the tumor cells and their surrounding milieu. A profound understanding of the metastatic process's biological mechanisms is indispensable for devising efficacious therapeutic strategies. This review delves into recent developments concerning metastasis-associated genes, important signaling pathways, tumor microenvironment, metabolic processes, peripheral immunity, and mechanical forces and cancer metastasis. In addition, we combine recent advances with a particular emphasis on the prospect of developing effective interventions including the most popular cancer immunotherapies and nanotechnology to combat metastasis. We have also identified the limitations of current research on tumor metastasis, encompassing drug resistance, restricted animal models, inadequate biomarkers and early detection methods, as well as heterogeneity among others. It is anticipated that this comprehensive review will significantly contribute to the advancement of cancer metastasis research.

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes

BACKGROUND

The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes.

RESULTS

We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin.

CONCLUSION

We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Passive Smoking-Induced Mutagenesis as a Promoter of Lung Carcinogenesis

INTRODUCTION

The International Agency for Research on Cancer has classified passive smoking (PS) or secondhand smoke exposure as a group 1 carcinogen linked to lung cancer. However, in contrast to active smoking, the mutagenic properties of PS remain unclear.

METHODS

A consecutive cohort of 564 lung adenocarcinoma samples from female never-smokers, who provided detailed information about their exposure to PS during adolescence and in their thirties through a questionnaire, was prepared. Of these, all 291 cases for whom frozen tumor tissues were available were subjected to whole exome sequencing to estimate tumor mutational burden, and the top 84 cases who were exposed daily, or not, to PS during adolescence, in their thirties or in both periods, were further subjected to whole genome sequencing.

RESULTS

A modest yet statistically significant increase in tumor mutational burden was observed in the group exposed to PS compared with the group not exposed to PS (median values = 1.44 versus 1.29 per megabase, respectively; p = 0.020). Instead of inducing driver oncogene mutations, PS-induced substantial subclonal mutations exhibiting APOBEC-type signatures, including SMAD4 and ADGRG6 hotspot mutations. A polymorphic APOBEC3A/3B allele-specific to the Asian population that leads to up-regulated expression of APOBEC3A accentuated the mutational load in individuals exposed daily to PS during adolescence.

CONCLUSIONS

This study reveals that PS-induced mutagenesis can promote lung carcinogenesis. The APOBEC3A/3B polymorphism may serve as a biomarker for identifying passive nonsmoking individuals at high risk of developing lung cancer.

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights

Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

Hidden secrets of the cancer genome: unlocking the impact of non-coding mutations in gene regulatory elements

Discoveries in the field of genomics have revealed that non-coding genomic regions are not merely "junk DNA", but rather comprise critical elements involved in gene expression. These gene regulatory elements (GREs) include enhancers, insulators, silencers, and gene promoters. Notably, new evidence shows how mutations within these regions substantially influence gene expression programs, especially in the context of cancer. Advances in high-throughput sequencing technologies have accelerated the identification of somatic and germline single nucleotide mutations in non-coding genomic regions. This review provides an overview of somatic and germline non-coding single nucleotide alterations affecting transcription factor binding sites in GREs, specifically involved in cancer biology. It also summarizes the technologies available for exploring GREs and the challenges associated with studying and characterizing non-coding single nucleotide mutations. Understanding the role of GRE alterations in cancer is essential for improving diagnostic and prognostic capabilities in the precision medicine era, leading to enhanced patient-centered clinical outcomes.

Upstream open reading frames: new players in the landscape of cancer gene regulation

The translation of RNA by ribosomes represents a central biological process and one of the most dysregulated processes in cancer. While translation is traditionally thought to occur exclusively in the protein-coding regions of messenger RNAs (mRNAs), recent transcriptome-wide approaches have shown abundant ribosome activity across diverse stretches of RNA transcripts. The most common type of this kind of ribosome activity occurs in gene leader sequences, also known as 5' untranslated regions (UTRs) of the mRNA, that precede the main coding sequence. Translation of these upstream open reading frames (uORFs) is now known to occur in upwards of 25% of all protein-coding genes. With diverse functions from RNA regulation to microprotein generation, uORFs are rapidly igniting a new arena of cancer biology, where they are linked to cancer genetics, cancer signaling, and tumor-immune interactions. This review focuses on the contributions of uORFs and their associated 5'UTR sequences to cancer biology.

Emerging roles of long noncoding RNA H19 in human lung cancer

Lung cancer holds the position of being the primary cause of cancer-related fatalities on a global scale. Furthermore, it exhibits the highest mortality rate among all types of cancer. The survival rate within a span of 5 years is less than 20%, primarily due to the fact that the disease is often diagnosed at an advanced stage, resulting in less effective treatment options compared to earlier stages. There are two main types of primary lung cancer: nonsmall-cell lung cancer, which accounts for approximately 80%-85% of all cases, and small-cell lung cancer, which is categorized based on the specific type of cells in which the cancer originates. The understanding of the biology of this disease and the identification of oncogenic driver alterations have significantly transformed the landscape of therapeutic approaches. Long noncoding RNAs (lncRNAs) play a crucial role in regulating various physiological and pathological processes through diverse molecular mechanisms. Among these lncRNAs, lncRNA H19, initially identified as an oncofetal transcript, has garnered significant attention due to its elevated expression in numerous tumors. Extensive research has confirmed its involvement in tumorigenesis and malignant progression by promoting cell growth, invasion, migration, epithelial-mesenchymal transition, metastasis, and therapy resistance. This comprehensive review aims to provide an overview of the aberrant overexpression of lncRNA H19 and the molecular pathways through which it contributes to the advancement of lung cancer. The findings of this review highlight the potential for further investigation into the diagnosis and treatment of this disease, offering promising avenues for future research.

Multifaceted perspectives of detecting and targeting solid tumors

Solid tumors are the most prevalent form of cancer. Considerable technological and medical advancements had been achieved for the diagnosis of the disease. However, detection of the disease in an early stage is of utmost importance, still far from reality. On the contrary, the treatment and therapeutic area to combat solid tumors are still in its infancy. Conventional treatments like chemotherapy and radiation therapy pose challenges due to their indiscriminate impact on healthy and cancerous cells. Contextually, efficient drug targeting is a pivotal approach in solid tumor treatment. This involves the precise delivery of drugs to cancer cells while minimizing harm to healthy cells. Targeted drugs exhibit superior efficacy in eradicating cancer cells while impeding tumor growth and mitigate side effects by optimizing absorption which further diminishes the risk of resistance. Furthermore, tailoring targeted therapies to a patient's tumor-specific molecular profile augments treatment efficacy and reduces the likelihood of relapse. This chapter discuss about the distinctive characteristics of solid tumors, the possibility of early detection of the disease and potential therapeutic angle beyond the conventional approaches. Additionally, the chapter delves into a hitherto unknown attribute of magnetic field effect to target cancer cells which exploit the relatively less susceptibility of normal cells compared to cancer cells to magnetic fields, suggesting a future potential of magnetic nanoparticles for selective cancer cell destruction. Lastly, bioinformatics tools and other unconventional methodologies such as AI-assisted codon bias analysis have a crucial role in comprehending tumor biology, aiding in the identification of futuristic targeted therapies.

Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles

Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.

CMC: Cancer miRNA Census - a list of cancer-related miRNA genes

A growing body of evidence indicates an important role of miRNAs in cancer; however, there is no definitive, convenient-to-use list of cancer-related miRNAs or miRNA genes that may serve as a reference for analyses of miRNAs in cancer. To this end, we created a list of 165 cancer-related miRNA genes called the Cancer miRNA Census (CMC). The list is based on a score, built on various types of functional and genetic evidence for the role of particular miRNAs in cancer, e.g. miRNA-cancer associations reported in databases, associations of miRNAs with cancer hallmarks, or signals of positive selection of genetic alterations in cancer. The presence of well-recognized cancer-related miRNA genes, such as MIR21, MIR155, MIR15A, MIR17 or MIRLET7s, at the top of the CMC ranking directly confirms the accuracy and robustness of the list. Additionally, to verify and indicate the reliability of CMC, we performed a validation of criteria used to build CMC, comparison of CMC with various cancer data (publications and databases), and enrichment analyses of biological pathways and processes such as Gene Ontology or DisGeNET. All validation steps showed a strong association of CMC with cancer/cancer-related processes confirming its usefulness as a reference list of miRNA genes associated with cancer.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

Rethinking cancer initiation: The role of large-scale mutational events

Cancer initiation is revisited in light of recent discoveries in cancer pathogenesis. Of note is the detection of mutated cancer genes in benign conditions. More significantly, somatic clones, which harbor mutations in cancer genes, arise in normal tissues from early development through adulthood, but seldom do they transform into cancer. Further, clustered mutational events-kataegis, chromothripsis and chromoplexy-are widespread in cancer, generating point mutations and chromosomal rearrangements in a single cellular catastrophe. These observations are contrary to the prevailing somatic mutation theory, which states that a cancer is caused by the gradual accumulation of mutations over time. A different perspective is proposed within the framework of Waddington's epigenetic landscape wherein tumorigenesis is viewed primarily as a disruption of cell development. Cell types are defined by their specific gene-expression profiles, determined by the gene regulatory network, and can be regarded as attractor states of the network dynamics: they represent specific, self-stabilizing patterns of gene activities across the genome. However, large-scale mutational events reshape the landscape topology, creating abnormal "unphysiological" attractors. This is the crux of the process of initiation. Initiation primes the cell for conversion into a tumor phenotype by oncogenes and tumor suppressor genes, which drive cell proliferation and clonal diversification. This view of tumorigenesis calls for a different approach to therapy.

ioSearch: An approach for identifying interacting multiomics biomarkers using a novel algorithm with application on breast cancer data sets

Identification of biomarkers by integrating multiple omics together is important because complex diseases occur due to an intricate interplay of various genetic materials. Traditional single-omics association tests neither explore this crucial interomics dependence nor identify moderately weak signals due to the multiple-testing burden. Conversely, multiomics data integration imparts complementary information but suffers from an increased multiple-testing burden, data diversity inherent with different omics features, high-dimensionality, and so forth. Most of the available methods address subtype classification using dimension-reduction techniques to circumvent the sample size issue but interacting multiomics biomarker identification methods are unavailable. We propose a two-step model that first investigates phenotype-omics association using logistic regression. Then, selects disease-associated omics using sparse principal components which explores the interrelationship of multiple variables from two omics in a multivariate multiple regression framework. On the basis of this model, we developed a multiomics biomarker identification algorithm, interacting omics search (ioSearch), that jointly tests the effect of multiple omics with disease and between-omics associations by using pathway information that subsequently reduces the multiple-testing burden. Further, inference in terms of p values potentially makes it an easily interpretable biomarker identification tool. Extensive simulation demonstrates ioSearch as statistically powerful with a controlled Type-I error rate. Its application to publicly available breast cancer data sets identified relevant omics features in important pathways.

Super-enhancer driven SOX2 promotes tumor formation by chromatin re-organization in nasopharyngeal carcinoma

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a malignant head and neck cancer with a high incidence in Southern China and Southeast Asia. Patients with remote metastasis and recurrent NPC have poor prognosis. Thus, a better understanding of NPC pathogenesis may identify novel therapies to address the unmet clinical needs.

METHODS

H3K27ac ChIP-seq and HiChIP was applied to understand the enhancer landscapes and the chromosome interactions. Whole genome sequencing was conducted to analyze the relationship between genomic variations and epigenetic dysregulation. CRISPRi and JQ1 treatment were used to evaluate the transcriptional regulation of SOX2 SEs. Colony formation assay, survival analysis and in vivo subcutaneous patient-derived xenograft assays were applied to explore the function and clinical relevance of SOX2 in NPC.

FINDINGS

We globally mapped the enhancer landscapes and generated NPC enhancer connectomes, linking NPC specific enhancers and SEs. We found five overlapped genes, including SOX2, among super-enhancer regulated genes, survival related genes and NPC essential genes. The mRNA expression of SOX2 was repressed when applying CRISPRi targeting different SOX2 SEs or JQ1 treatment. Next, we identified a genetic variation (Chr3:181422197, G > A) in SOX2 SE which is correlated with higher expression of SOX2 and poor survival. In addition, SOX2 was highly expressed in NPC and is correlated with short survival in patients with NPC. Knock-down of SOX2 suppressed tumor growth in vitro and in vivo.

INTERPRETATION

Our study demonstrated the super-enhancer landscape with chromosome interactions and identified super-enhancer driven SOX2 promotes tumorigenesis, suggesting that SOX2 is a potential therapeutic target for patients with NPC.

FUNDING

A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Identification of a RAD51B enhancer variant for susceptibility and progression to glioma

BACKGROUND

RAD51B plays a significant role in homologous recombination-mediated repair of DNA double-strand breaks. Many enhancer variants are involved in cancer development and progression. However, the significance of enhancer variants of RAD51B in glioma susceptibility and progression remains unclear.

METHODS

A case-control study consisting of 1056 individuals was conducted to evaluate the associations of enhancer variants of RAD51B with glioma susceptibility and progression. Sequenom MassARRAY technology was used for genotyping. The function of enhancer variants was explored by biochemical assays.

RESULTS

A significantly decreased risk of glioma was associated with rs6573816 GC genotype compared with rs6573816 GG genotype (OR = 0.66, 95% CI 0.45-0.97; P = 0.034). Multivariable Cox regression revealed that rs6573816 was significantly associated with glioma progression in a sex-dependent manner. Worse PFS was found in the male patients with high grade glioma carrying rs6573816 GC or CC genotype (HR = 2.28, 95% CI 1.14-4.57; P = 0.020). The rs6573816 C allele repressed enhancer activity by affecting transcription factor POU2F1 binding, which resulted in lower expression of RAD51B. Remarkably attenuated expression of RAD51B was observed following POU2F1 knockdown. Consistently, positive correlation between the expression of POU2F1 and RAD51B was found in lymphoblastic cells and glioma tissues.

CONCLUSIONS

These results indicate that an enhancer variant of RAD51B rs6573816 influences enhancer activity by changing a POU2F1 binding site and confers susceptibility and progression to glioma.

Genome-guided discovery of cancer therapeutic targets

The success of precision oncology-which aims to match the right therapies to the right patients based on molecular status-is predicated on a robust pipeline of molecular targets against which therapies can be developed. Recent advances in genomics and functional genetics have enabled the unbiased discovery of novel molecular targets at scale. We summarize the promise and challenges in integrating genomic and functional genetic landscapes of cancer to establish the next generation of cancer targets.

Methylation across the central dogma in health and diseases: new therapeutic strategies

The proper transfer of genetic information from DNA to RNA to protein is essential for cell-fate control, development, and health. Methylation of DNA, RNAs, histones, and non-histone proteins is a reversible post-synthesis modification that finetunes gene expression and function in diverse physiological processes. Aberrant methylation caused by genetic mutations or environmental stimuli promotes various diseases and accelerates aging, necessitating the development of therapies to correct the disease-driver methylation imbalance. In this Review, we summarize the operating system of methylation across the central dogma, which includes writers, erasers, readers, and reader-independent outputs. We then discuss how dysregulation of the system contributes to neurological disorders, cancer, and aging. Current small-molecule compounds that target the modifiers show modest success in certain cancers. The methylome-wide action and lack of specificity lead to undesirable biological effects and cytotoxicity, limiting their therapeutic application, especially for diseases with a monogenic cause or different directions of methylation changes. Emerging tools capable of site-specific methylation manipulation hold great promise to solve this dilemma. With the refinement of delivery vehicles, these new tools are well positioned to advance the basic research and clinical translation of the methylation field.

Using evolutionary constraint to define novel candidate driver genes in medulloblastoma

Current knowledge of cancer genomics remains biased against noncoding mutations. To systematically search for regulatory noncoding mutations, we assessed mutations in conserved positions in the genome under the assumption that these are more likely to be functional than mutations in positions with low conservation. To this end, we use whole-genome sequencing data from the International Cancer Genome Consortium and combined it with evolutionary constraint inferred from 240 mammals, to identify genes enriched in noncoding constraint mutations (NCCMs), mutations likely to be regulatory in nature. We compare medulloblastoma (MB), which is malignant, to pilocytic astrocytoma (PA), a primarily benign tumor, and find highly different NCCM frequencies between the two, in agreement with the fact that malignant cancers tend to have more mutations. In PA, a high NCCM frequency only affects the BRAF locus, which is the most commonly mutated gene in PA. In contrast, in MB, >500 genes have high levels of NCCMs. Intriguingly, several loci with NCCMs in MB are associated with different ages of onset, such as the HOXB cluster in young MB patients. In adult patients, NCCMs occurred in, e.g., the WASF-2/AHDC1/FGR locus. One of these NCCMs led to increased expression of the SRC kinase FGR and augmented responsiveness of MB cells to dasatinib, a SRC kinase inhibitor. Our analysis thus points to different molecular pathways in different patient groups. These newly identified putative candidate driver mutations may aid in patient stratification in MB and could be valuable for future selection of personalized treatment options.

Regulation of T cell differentiation and function by long noncoding RNAs in homeostasis and cancer

Long noncoding RNAs (lncRNAs) increase in genomes of complex organisms and represent the largest group of RNA genes transcribed in mammalian cells. Previously considered only transcriptional noise, lncRNAs comprise a heterogeneous class of transcripts that are emerging as critical regulators of T cell-mediated immunity. Here we summarize the lncRNA expression landscape of different T cell subsets and highlight recent advances in the role of lncRNAs in regulating T cell differentiation, function and exhaustion during homeostasis and cancer. We discuss the different molecular mechanisms of lncRNAs and highlight lncRNAs that can serve as novel targets to modulate T cell function or to improve the response to cancer immunotherapies by modulating the immunosuppressive tumor microenvironment.

Gain-of-function mutations in the catalytic domain of DOT1L promote lung cancer malignant phenotypes via the MAPK/ERK signaling pathway

Lung cancer is a lethal malignancy lacking effective therapies. Emerging evidence suggests that epigenetic enzyme mutations are closely related to the malignant phenotype of lung cancer. Here, we identified a series of gain-of-function mutations in the histone methyltransferase DOT1L. The strongest of them is R231Q, located in the catalytic DOT domain. R231Q can enhance the substrate binding ability of DOT1L. Moreover, R231Q promotes cell growth and drug resistance of lung cancer cells in vitro and in vivo. Mechanistic studies also revealed that the R231Q mutant specifically activates the MAPK/ERK signaling pathway by enriching H3K79me2 on the RAF1 promoter and epigenetically regulating the expression of downstream targets. The combination of a DOT1L inhibitor (SGC0946) and a MAPK/ERK axis inhibitor (binimetinib) can effectively reverse the R231Q-induced phenomena. Our results reveal gain-of-function mutations in an epigenetic enzyme and provide promising insights for the precise treatment of lung cancer patients.

Integrative identification of non-coding regulatory regions driving metastatic prostate cancer

Large-scale sequencing efforts of thousands of tumor samples have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of germline and somatic variants occur within non-coding portions of the genome. These genomic regions do not directly encode for specific proteins, but can play key roles in cancer progression, for example by driving aberrant gene expression control. Here, we designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Application of this approach to whole-genome sequencing (WGS) data from a large cohort of metastatic castration-resistant prostate cancer (mCRPC) revealed a large set of recurrently mutated regions. We used (i) in silico prioritization of functional non-coding mutations, (ii) massively parallel reporter assays, and (iii) in vivo CRISPR-interference (CRISPRi) screens in xenografted mice to systematically identify and validate driver regulatory regions that drive mCRPC. We discovered that one of these enhancer regions, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. We found that both SF3A1 and CCDC157 are promoters of tumor growth in xenograft models of prostate cancer. We nominated a number of transcription factors, including SOX6, to be responsible for higher expression of SF3A1 and CCDC157. Collectively, we have established and confirmed an integrative computational and experimental approach that enables the systematic detection of non-coding regulatory regions that drive the progression of human cancers.

Applications of tissue-specific and cancer-selective gene promoters for cancer diagnosis and therapy

Current treatment of solid tumors with standard of care chemotherapies, radiation therapy and/or immunotherapies are often limited by severe adverse toxic effects, resulting in a narrow therapeutic index. Cancer gene therapy represents a targeted approach that in principle could significantly reduce undesirable side effects in normal tissues while significantly inhibiting tumor growth and progression. To be effective, this strategy requires a clear understanding of the molecular biology of cancer development and evolution and developing biological vectors that can serve as vehicles to target cancer cells. The advent and fine tuning of omics technologies that permit the collective and spatial recognition of genes (genomics), mRNAs (transcriptomics), proteins (proteomics), metabolites (metabolomics), epiomics (epigenomics, epitranscriptomics, and epiproteomics), and their interactomics in defined complex biological samples provide a roadmap for identifying crucial targets of relevance to the cancer paradigm. Combining these strategies with identified genetic elements that control target gene expression uncovers significant opportunities for developing guided gene-based therapeutics for cancer. The purpose of this review is to overview the current state and potential limitations in developing gene promoter-directed targeted expression of key genes and highlights their potential applications in cancer gene therapy.

Clinical implications of lncRNA LINC-PINT in cancer

Long noncoding RNAs (lncRNAs) possess the potential for therapeutic targeting to treat many disorders, including cancers. Several RNA-based therapeutics (ASOs and small interfering RNAs) have gained FDA approval over the past decade. And with their potent effects, lncRNA-based therapeutics are of emerging significance. One important lncRNA target is LINC-PINT, with its universalized functions and relationship with the famous tumor suppressor gene TP53. Establishing clinical relevance, much like p53, the tumor suppressor activity of LINC-PINT is implicated in cancer progression. Moreover, several molecular targets of LINC-PINT are directly or indirectly used in routine clinical practice. We further associate LINC-PINT with immune responses in colon adenocarcinoma, proposing the potential utility of LINC-PINT as a novel biomarker of immune checkpoint inhibitors. Collectively, current evidence suggests LINC-PINT can be considered for use as a diagnostic/prognostic marker for cancer and several other diseases.

Widespread perturbation of ETS factor binding sites in cancer

Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted.

Hereditable variants of classical protein tyrosine phosphatase genes: Will they prove innocent or guilty?

Protein tyrosine phosphatases, together with protein tyrosine kinases, control many molecular signaling steps that control life at cellular and organismal levels. Impairing alterations in the genes encoding the involved proteins is expected to profoundly affect the quality of life-if compatible with life at all. Here, we review the current knowledge on the effects of germline variants that have been reported for genes encoding a subset of the protein tyrosine phosphatase superfamily; that of the thirty seven classical members. The conclusion must be that the newest genome research tools produced an avalanche of data that suggest 'guilt by association' for individual genes to specific disorders. Future research should face the challenge to investigate these accusations thoroughly and convincingly, to reach a mature genotype-phenotype map for this intriguing protein family.

Zn-Co metal organic frameworks coated with chitosand and Au nanoparticles for chemo-photothermal-targeted combination therapy of liver cancer

The toxic effects of chemotherapy drugs on normal tissues are still a major limiting factor in cancer treatment. In this paper, we report a metal-organic framework (Zn-Co ZIF) with chitosan-coated outer layer as a carrier for the drug adriamycin hydrochloride (DOX), a treatment for liver cancer, as a novel anti-cancer nanodrug-enhanced carrier. Gold nanoparticles, a good photothermal conversion agent, were combined with the target SH-RGD during surface functionalisation to prepare Zn-Co ZIF@DOX-CS-Au-RGD (ZD-CAR), a nanoplatform with good photothermal conversion properties and targeting for combined liver cancer therapy. ZD-CAR was developed after RGD accurately targeted the tumour and entered the tumour microenvironment (TME), it cleaves and releases the liver cancer therapeutic agent (DOX) in a weak acidic environment to effectively kill tumour cells. The metal skeleton cleavage releases Co²⁺, which catalyzes the production of oxygen from H₂O₂ to alleviate the tumour hypoxic environment. The dissolved oxygen could reach 14 mg/L after adding 80 mg/mL of ZD-CAR. Meanwhile, gold nanoparticles could convert light energy into heat energy under 808 NIR irradiation to induce local superheating and kill tumour cells. In summary, this study developed a nanoplatform that combines chemo-photothermal-targeted therapy. It has shown good therapeutic effeciency in cellular experiments and performance tests and has promising applications in anti-cancer therapy.

Small molecule inhibitors targeting the cancers

Compared with traditional therapies, targeted therapy has merits in selectivity, efficacy, and tolerability. Small molecule inhibitors are one of the primary targeted therapies for cancer. Due to their advantages in a wide range of targets, convenient medication, and the ability to penetrate into the central nervous system, many efforts have been devoted to developing more small molecule inhibitors. To date, 88 small molecule inhibitors have been approved by the United States Food and Drug Administration to treat cancers. Despite remarkable progress, small molecule inhibitors in cancer treatment still face many obstacles, such as low response rate, short duration of response, toxicity, biomarkers, and resistance. To better promote the development of small molecule inhibitors targeting cancers, we comprehensively reviewed small molecule inhibitors involved in all the approved agents and pivotal drug candidates in clinical trials arranged by the signaling pathways and the classification of small molecule inhibitors. We discussed lessons learned from the development of these agents, the proper strategies to overcome resistance arising from different mechanisms, and combination therapies concerned with small molecule inhibitors. Through our review, we hoped to provide insights and perspectives for the research and development of small molecule inhibitors in cancer treatment.

Efficient Selection of Enhancers and Promoters from MIA PaCa-2 Pancreatic Cancer Cells by ChIP-lentiMPRA

A library of active genome regulatory elements (putative promoters and enhancers) from MIA PaCa-2 pancreatic adenocarcinoma cells was constructed using a specially designed lentiviral vector and a massive parallel reporter assay (ChIP-lentiMPRA). Chromatin immunoprecipitation of the cell genomic DNA by H3K27ac antibodies was used for primary enrichment of the library for regulatory elements. Totally, 11,264 unique genome regions, many of which are capable of enhancing the expression of the CopGFP reporter gene from the minimal CMV promoter, were identified. The regions tend to be located near promoters. Based on the proximity assay, we found an enrichment of highly expressed genes among those associated with three or more mapped distal regions (2 kb distant from the 5'-ends of genes). It was shown significant enrichment of genes related to carcinogenesis or Mia PaCa-2 cell identity genes in this group. In contrast, genes associated with 1-2 distal regions or only with proximal regions (within 2 kbp of the 5'-ends of genes) are more often related to housekeeping functions. Thus, ChIP-lentiMPRA is a useful strategy for creating libraries of regulatory elements for the study of tumor-specific gene transcription.

Multiplexed functional genomic assays to decipher the noncoding genome

Linkage disequilibrium and the incomplete regulatory annotation of the noncoding genome complicates the identification of functional noncoding genetic variants and their causal association with disease. Current computational methods for variant prioritization have limited predictive value, necessitating the application of highly parallelized experimental assays to efficiently identify functional noncoding variation. Here, we summarize two distinct approaches, massively parallel reporter assays and CRISPR-based pooled screens and describe their flexible implementation to characterize human noncoding genetic variation at unprecedented scale. Each approach provides unique advantages and limitations, highlighting the importance of multimodal methodological integration. These multiplexed assays of variant effects are undoubtedly poised to play a key role in the experimental characterization of noncoding genetic risk, informing our understanding of the underlying mechanisms of disease-associated loci and the development of more robust predictive classification algorithms.

Use of Publication Dynamics to Distinguish Cancer Genes and Bystander Genes

de Magalhães has shown recently that most human genes have several papers in PubMed mentioning cancer, leading the author to suggest that every gene is associated with cancer, a conclusion that contradicts the widely held view that cancer is driven by a limited number of cancer genes, whereas the majority of genes are just bystanders in carcinogenesis. We have analyzed PubMed to decide whether publication metrics supports the distinction of bystander genes and cancer genes. The dynamics of publications on known cancer genes followed a similar pattern: seminal discoveries triggered a burst of cancer-related publications that validated and expanded the discovery, resulting in a rise both in the number and proportion of cancer-related publications on that gene. The dynamics of publications on bystander genes was markedly different. Although there is a slow but continuous time-dependent rise in the proportion of papers mentioning cancer, this phenomenon just reflects the increasing publication bias that favors cancer research. Despite this bias, the proportion of cancer papers on bystander genes remains low. Here, we show that the distinctive publication dynamics of cancer genes and bystander genes may be used for the identification of cancer genes.