Single-cell analysis of somatic mutations in human bronchial epithelial cells in relation to aging and smoking

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.

Single-cell sequencing to multi-omics: technologies and applications

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

Association between smoking status and the prognosis of brain metastasis in patients with non-small cell lung cancer

Objective

This study aimed to explore the relationship between smoking status and the interval to brain metastasis in patients with non-small cell lung cancer (NSCLC) and its impact on survival time after brain metastasis.

Methods

Data were collected from patients with NSCLC with brain metastases who were treated at our centre between January 2005 and December 2017. Clinical indices such as clinicopathological features and smoking status were recorded, and patients were followed up until 1 September 2022. Based on our inclusion and exclusion criteria, 461 patients were analysed and matched using 1:1 propensity score matching. Three balanced groups were formed: non-smoking (n = 113), smoking cessation (n = 113), and smoking (n = 113). The interval to brain metastasis and overall survival were compared between the groups.

Results

There was a statistically significant difference in the interval to brain metastasis between the non-smoking and smoking cessation groups (p = 0.001), as well as between the non-smoking and smoking groups (p < 0.001). However, the difference between the smoking cessation and smoking groups was not statistically significant (p = 0.106). Multivariate and univariate analyses identified smoking status, clinical stage, lung cancer surgery, chemotherapy, and chest radiotherapy as independent predictors of the interval to brain metastasis. Additionally, the multivariate analysis showed that smoking status, driver gene mutations, and chest radiotherapy independently influenced survival after brain metastasis.

Conclusion

Smoking status in patients with NSCLC affects the interval to brain metastasis and survival after brain metastasis.

RB1 Mutations Induce Smoking-Related Bladder Cancer by Modulating the Cytochrome P450 Pathway

Cigarette smoking causes multiple cancers by directly influencing mutation burden of driver mutations. However, the mechanism between somatic mutation caused by cigarette smoking and bladder tumorigenesis remains elusive. Smoking-related mutation profile of bladder cancer was characterized by The Cancer Genome Atlas cohort. Integraticve OncoGenomics database was utilized to detect the smoking-related driver genes, and its biological mechanism predictions were interpreted based on bulk transcriptome and single-cell transcriptome, as well as cell experiments. Cigarette smoking was associated with an increased tumor mutational burden under 65 years old (p = 0.031), and generated specific mutational signatures in smokers. RB1 was identified as a differentially mutated driver gene between smokers and nonsmokers, and the mutation rate of RB1 increased twofold after smoking (p = 0.008). RB1 mutations and the 4-aminobiphenyl interference could significantly decrease the RB1 expression level and thus promote the proliferation, invasion, and migration ability of bladder cancer cells. Enrichment analysis and real-time quantitative PCR (RT-qPCR) data showed that RB1 mutations inhibited cytochrome P450 pathway by reducing expression levels of UGT1A6 and AKR1C2. In addition, we also observed that the component of immunological cells was regulated by RB1 mutations through the stronger cell-to-cell interactions between epithelial scissor+ cells and immune cells in smokers. This study highlighted that RB1 mutations could drive smoking-related bladder tumorigenesis through inhibiting cytochrome P450 pathway and regulating tumor immune microenvironment.

Genome-scale mutational signature analysis in fixed archived tissues

Mutation spectra and mutational signatures in cancerous and non-cancerous tissues can be identified by various established techniques of massively parallel sequencing (or next-generation sequencing) including whole-exome or whole-genome sequencing, and more recently by error-corrected/duplex sequencing. One rather underexplored area has been the genome-scale analysis of mutational signatures as markers of mutagenic exposures, and their impact on cancer driver events applied to formalin-fixed or alcohol-fixed paraffin embedded archived biospecimens. This review showcases successful applications of the next-generation sequencing methodologies in archived fixed tissues, including the delineation of the specific tissue fixation-related DNA damage manifesting as artifactual signatures, distinguishable from the true signatures that arise from biological mutagenic processes. Overall, we discuss and demonstrate how next-generation sequencing techniques applied to archived fixed biospecimens can enhance our understanding of cancer causes including mutagenic effects of extrinsic cancer risk agents, and the implications for prevention efforts aimed at reducing avoidable cancer-causing exposures.

LIG1 is a novel marker for bladder cancer prognosis: evidence based on experimental studies, machine learning and single-cell sequencing

Background

Bladder cancer, a highly fatal disease, poses a significant threat to patients. Positioned at 19q13.2-13.3, LIG1, one of the four DNA ligases in mammalian cells, is frequently deleted in tumour cells of diverse origins. Despite this, the precise involvement of LIG1 in BLCA remains elusive. This pioneering investigation delves into the uncharted territory of LIG1's impact on BLCA. Our primary objective is to elucidate the intricate interplay between LIG1 and BLCA, alongside exploring its correlation with various clinicopathological factors.

Methods

We retrieved gene expression data of para-carcinoma tissues and bladder cancer (BLCA) from the GEO repository. Single-cell sequencing data were processed using the "Seurat" package. Differential expression analysis was then performed with the "Limma" package. The construction of scale-free gene co-expression networks was achieved using the "WGCNA" package. Subsequently, a Venn diagram was utilized to extract genes from the positively correlated modules identified by WGCNA and intersect them with differentially expressed genes (DEGs), isolating the overlapping genes. The "STRINGdb" package was employed to establish the protein-protein interaction (PPI) network.Hub genes were identified through the PPI network using the Betweenness Centrality (BC) algorithm. We conducted KEGG and GO enrichment analyses to uncover the regulatory mechanisms and biological functions associated with the hub genes. A machine-learning diagnostic model was established using the R package "mlr3verse." Mutation profiles between the LIG1^high and LIG1^low groups were visualized using the BEST website. Survival analyses within the LIG1^high and LIG1^low groups were performed using the BEST website and the GENT2 website. Finally, a series of functional experiments were executed to validate the functional role of LIG1 in BLCA.

Results

Our investigation revealed an upregulation of LIG1 in BLCA specimens, with heightened LIG1 levels correlating with unfavorable overall survival outcomes. Functional enrichment analysis of hub genes, as evidenced by GO and KEGG enrichment analyses, highlighted LIG1's involvement in critical function such as the DNA replication, cellular senescence, cell cycle and the p53 signalling pathway. Notably, the mutational landscape of BLCA varied significantly between LIG1high and LIG1low groups.Immune infiltrating analyses suggested a pivotal role for LIG1 in immune cell recruitment and immune regulation within the BLCA microenvironment, thereby impacting prognosis. Subsequent experimental validations further underscored the significance of LIG1 in BLCA pathogenesis, consolidating its functional relevance in BLCA samples.

Conclusions

Our research demonstrates that LIG1 plays a crucial role in promoting bladder cancer malignant progression by heightening proliferation, invasion, EMT, and other key functions, thereby serving as a potential risk biomarker.

Distinctive field effects of smoking and lung cancer case-control status on bronchial basal cell growth and signaling

RATIONAL

Basal cells (BCs) are bronchial progenitor/stem cells that can regenerate injured airway that, in smokers, may undergo malignant transformation. As a model for early stages of lung carcinogenesis, we set out to characterize cytologically normal BC outgrowths from never-smokers and ever-smokers without cancers (controls), as well as from the normal epithelial "field" of ever-smokers with anatomically remote cancers, including lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) (cases).

METHODS

Primary BCs were cultured and expanded from endobronchial brushings taken remote from the site of clinical or visible lesions/tumors. Donor subgroups were tested for growth, morphology, and underlying molecular features by qRT-PCR, RNAseq, flow cytometry, immunofluorescence, and immunoblot.

RESULTS

(a) the BC population includes epithelial cell adhesion molecule (EpCAM) positive and negative cell subsets; (b) smoking reduced overall BC proliferation corresponding with a 2.6-fold reduction in the EpCAMpos/ITGA6 pos/CD24pos stem cell fraction; (c) LUSC donor cells demonstrated up to 2.8-fold increase in dysmorphic BCs; and (d) cells procured from LUAD patients displayed increased proliferation and S-phase cell cycle fractions. These differences corresponded with: (i) disparate NOTCH1/NOTCH2 transcript expression and altered expression of potential downstream (ii) E-cadherin (CDH1), tumor protein-63 (TP63), secretoglobin family 1a member 1 (SCGB1A1), and Hairy/enhancer-of-split related with YRPW motif 1 (HEY1); and (iii) reduced EPCAM and increased NK2 homeobox-1 (NKX2-1) mRNA expression in LUAD donor BCs.

CONCLUSIONS

These and other findings demonstrate impacts of donor age, smoking, and lung cancer case-control status on BC phenotypic and molecular traits and may suggest Notch signaling pathway deregulation during early human lung cancer pathogenesis.

Causes and Consequences of Varying Transposable Element Activity: An Evolutionary Perspective

Transposable elements (TEs) are genomic parasites found in nearly all eukaryotes, including humans. This evolutionary success of TEs is due to their replicative activity, involving insertion into new genomic locations. TE activity varies at multiple levels, from between taxa to within individuals. The rapidly accumulating evidence of the influence of TE activity on human health, as well as the rapid growth of new tools to study it, motivated an evaluation of what we know about TE activity thus far. Here, we discuss why TE activity varies, and the consequences of this variation, from an evolutionary perspective. By studying TE activity in nonhuman organisms in the context of evolutionary theories, we can shed light on the factors that affect TE activity. While the consequences of TE activity are usually deleterious, some have lasting evolutionary impacts by conferring benefits on the host or affecting other evolutionary processes.

A new era of mutation rate analyses: Concepts and methods

The mutation rate is a pivotal biological characteristic, intricately governed by natural selection and historically garnering considerable attention. Recent advances in high-throughput sequencing and analytical methodologies have profoundly transformed our understanding in this domain, ushering in an unprecedented era of mutation rate research. This paper aims to provide a comprehensive overview of the key concepts and methodologies frequently employed in the study of mutation rates. It examines various types of mutations, explores the evolutionary dynamics and associated theories, and synthesizes both classical and contemporary hypotheses. Furthermore, this review comprehensively explores recent advances in understanding germline and somatic mutations in animals and offers an overview of experimental methodologies, mutational patterns, molecular mechanisms, and driving forces influencing variations in mutation rates across species and tissues. Finally, it proposes several potential research directions and pressing questions for future investigations.

Selective pressures of platinum compounds shape the evolution of therapy-related myeloid neoplasms

Therapy-related myeloid neoplasms (t-MN) arise as a complication of chemo- and/or radiotherapy. Although t-MN can occur both in adult and childhood cancer survivors, the mechanisms driving therapy-related leukemogenesis likely vary across different ages. Chemotherapy is thought to induce driver mutations in children, whereas in adults pre-existing mutant clones are selected by the exposure. However, selective pressures induced by chemotherapy early in life are less well studied. Here, we use single-cell whole genome sequencing and phylogenetic inference to show that the founding cell of t-MN in children starts expanding after cessation of platinum exposure. In patients with Li-Fraumeni syndrome, characterized by a germline TP53 mutation, we find that the t-MN already expands during treatment, suggesting that platinum-induced growth inhibition is TP53-dependent. Our results demonstrate that germline aberrations can interact with treatment exposures in inducing t-MN, which is important for the development of more targeted, patient-specific treatment regimens and follow-up.

AKR1C2 genetic variants mediate tobacco carcinogens metabolism involving bladder cancer susceptibility

Tobacco carcinogens metabolism-related genes (TCMGs) could generate reactive metabolites of tobacco carcinogens, which subsequently contributed to multiple diseases. However, the association between genetic variants in TCMGs and bladder cancer susceptibility remains unclear. In this study, we derived TCMGs from metabolic pathways of polycyclic aromatic hydrocarbons and tobacco-specific nitrosamines, and then explored genetic associations between TCMGs and bladder cancer risk in two populations: a Chinese population of 580 cases and 1101 controls, and a European population of 5930 cases and 5468 controls, along with interaction and joint analyses. Expression patterns of TCMGs were sourced from Nanjing Bladder Cancer (NJBC) study and publicly available datasets. Among 43 TCMGs, we observed that rs7087341 T > A in AKR1C2 was associated with a reduced risk of bladder cancer in the Chinese population [odds ratio (OR) = 0.84, 95% confidence interval (CI) = 0.72-0.97, P = 1.86 × 10-2]. Notably, AKR1C2 rs7087341 showed an interaction effect with cigarette smoking on bladder cancer risk (Pinteraction = 5.04 × 10-3), with smokers carrying the T allele increasing the risk up to an OR of 3.96 (Ptrend < 0.001). Genetically, rs7087341 showed an allele-specific transcriptional regulation as located at DNA-sensitive regions of AKR1C2 highlighted by histone markers. Mechanistically, rs7087341 A allele decreased AKR1C2 expression, which was highly expressed in bladder tumors that enhanced metabolism of tobacco carcinogens, and thereby increased DNA adducts and reactive oxygen species formation during bladder tumorigenesis. These findings provided new insights into the genetic mechanisms underlying bladder cancer.

Mutations causing premature termination codons discriminate and generate cellular and clinical variability in HHT

ABSTRACT

For monogenic diseases caused by pathogenic loss-of-function DNA variants, attention focuses on dysregulated gene-specific pathways, usually considering molecular subtypes together within causal genes. To better understand phenotypic variability in hereditary hemorrhagic telangiectasia (HHT), we subcategorized pathogenic DNA variants in ENG/endoglin, ACVRL1/ALK1, and SMAD4 if they generated premature termination codons (PTCs) subject to nonsense-mediated decay. In 3 patient cohorts, a PTC-based classification system explained some previously puzzling hemorrhage variability. In blood outgrowth endothelial cells (BOECs) derived from patients with ACVRL1+/PTC, ENG+/PTC, and SMAD4+/PTC genotypes, PTC-containing RNA transcripts persisted at low levels (8%-23% expected, varying between replicate cultures); genes differentially expressed to Bonferroni P < .05 in HHT+/PTC BOECs clustered significantly only to generic protein terms (isopeptide-bond/ubiquitin-like conjugation) and pulse-chase experiments detected subtle protein maturation differences but no evidence for PTC-truncated protein. BOECs displaying highest PTC persistence were discriminated in unsupervised hierarchical clustering of near-invariant housekeeper genes, with patterns compatible with higher cellular stress in BOECs with >11% PTC persistence. To test directionality, we used a HeLa reporter system to detect induction of activating transcription factor 4 (ATF4), which controls expression of stress-adaptive genes, and showed that ENG Q436X but not ENG R93X directly induced ATF4. AlphaFold accurately modeled relevant ENG domains, with AlphaMissense suggesting that readthrough substitutions would be benign for ENG R93X and other less rare ENG nonsense variants but more damaging for Q436X. We conclude that PTCs should be distinguished from other loss-of-function variants, PTC transcript levels increase in stressed cells, and readthrough proteins and mechanisms provide promising research avenues.

Clonal evolution of hematopoietic stem cells after cancer chemotherapy

Normal hematopoietic stem and progenitor cells (HSPCs) inherently accumulate somatic mutations and lose clonal diversity with age, processes implicated in the development of myeloid malignancies 1 . The impact of exogenous stressors, such as cancer chemotherapies, on the genomic integrity and clonal dynamics of normal HSPCs is not well defined. We conducted whole-genome sequencing on 1,032 single-cell-derived HSPC colonies from 10 patients with multiple myeloma (MM), who had undergone various chemotherapy regimens. Our findings reveal that melphalan treatment distinctly increases mutational burden with a unique mutation signature, whereas other MM chemotherapies do not significantly affect the normal mutation rate of HSPCs. Among these therapy-induced mutations were several oncogenic drivers such as TET2 and PPM1D . Phylogenetic analysis showed a clonal architecture in post-treatment HSPCs characterized by extensive convergent evolution of mutations in genes such as TP53 and PPM1D . Consequently, the clonal diversity and structure of post-treatment HSPCs mirror those observed in normal elderly individuals, suggesting an accelerated clonal aging due to chemotherapy. Furthermore, analysis of matched therapy-related myeloid neoplasm (t-MN) samples, which occurred 1-8 years later, enabled us to trace the clonal origin of t-MNs to a single HSPC clone among a group of clones with competing malignant potential, indicating the critical role of secondary mutations in dictating clonal dominance and malignant transformation. Our findings suggest that cancer chemotherapy promotes an oligoclonal architecture with multiple HSPC clones possessing competing leukemic potentials, setting the stage for the selective emergence of a singular clone that evolves into t-MNs after acquiring secondary mutations. These results underscore the importance of further systematic research to elucidate the long-term hematological consequences of cancer chemotherapy.

Negative Selection Allows DNA Mismatch Repair-Deficient Mouse Fibroblasts In Vitro to Tolerate High Levels of Somatic Mutations

Substantial numbers of somatic mutations have been found to accumulate with age in different human tissues. Clonal cellular amplification of some of these mutations can cause cancer and other diseases. However, it is as yet unclear if and to what extent an increased burden of random mutations can affect cellular function without clonal amplification. We tested this in cell culture, which avoids the limitation that an increased mutation burden in vivo typically leads to cancer. We performed single-cell whole-genome sequencing of primary fibroblasts from DNA mismatch repair (MMR) deficient Msh2-/- mice and littermate control animals after long-term passaging. Apart from analyzing somatic mutation burden we analyzed clonality, mutational signatures, and hotspots in the genome, characterizing the complete landscape of somatic mutagenesis in normal and MMR-deficient mouse primary fibroblasts during passaging. While growth rate of Msh2-/- fibroblasts was not significantly different from the controls, the number of de novo single-nucleotide variants (SNVs) increased linearly up until at least 30,000 SNVs per cell, with the frequency of small insertions and deletions (INDELs) plateauing in the Msh2-/- fibroblasts to about 10,000 INDELS per cell. We provide evidence for negative selection and large-scale mutation-driven population changes, including significant clonal expansion of preexisting mutations and widespread cell-strain-specific hotspots. Overall, our results provide evidence that increased somatic mutation burden drives significant cell evolutionary changes in a dynamic cell culture system without significant effects on growth. Since similar selection processes against mutations preventing organ and tissue dysfunction during aging are difficult to envision, these results suggest that increased somatic mutation burden can play a causal role in aging and diseases other than cancer.

Association Between Smoking and Lipid Profile in Men Aged 35 to 70 Years: Dose-Response Analysis

Dyslipidemia is linked to various health complications, including cardiovascular disease and inflammation. This study aimed to assess the association between smoking and lipid profile in the Tabari cohort population. Data from the Tabari Cohort Study involving 4,149 men were analyzed. A standardized questionnaire collected smoking history, while blood samples measured lipid levels and anthropometric measurements were recorded. Statistical analysis utilized chi-square tests and logistic regression, adjusting for potential confounders. The prevalence of smoking was 893 (21.52%; urban: 20.6%, mountainous: 23.8%, significant level: .024). The adjusted odds ratio (OR) of low high-density lipoprotein (HDL) among smokers 1.48 (95% confidence interval [CI]: 1.25-1.77, p < .001) was the same as non-smokers. The adjusted OR of high low-density lipoprotein (LDL) in men with 1 to 10, 11 to 20, and more than 20 cigarettes per day was 0.95 (95% CI: 0.73-1.25), 1.30 (95% CI: 0.99-1.71), and 2.64 (95% CI: 1.32-5.27) and low HDL was equal to 1.34 (95% CI: 1.06-1.68), 1.61 (95% CI: 1.26-2.05), and 2.24 (95% CI: 1.13-4.42) compared with non-smokers, respectively. The study findings indicate that smoking is associated with lower HDL levels, even after adjusting for potential confounders. The odds of low HDL and high LDL increases with higher smoking intensity. The low HDL and high LDL levels in individuals smoking over 20 cigarettes/day, respectively, show a 2.24-fold and a 2.64-fold increased odds compared to non-smokers. These findings highlight the importance of smoking cessation in relation to lipid profiles and related health risks.

Molecular and immune characterization of Chinese early-stage non-squamous non-small cell lung cancer: a multi-omics cohort study

Background

Albeit considered with superior survival, around 30% of the early-stage non-squamous non-small cell lung cancer (Ns-NSCLC) patients relapse within 5 years, suggesting unique biology. However, the biological characteristics of early-stage Ns-NSCLC, especially in the Chinese population, are still unclear.

Methods

Multi-omics interrogation of early-stage Ns-NSCLC (stage I-III), paired blood samples and normal lung tissues (n=76) by whole-exome sequencing (WES), RNA sequencing, and T-cell receptor (TCR) sequencing were conducted.

Results

An average of 128 exonic mutations were identified, and the most frequently mutant gene was EGFR (55%), followed by TP53 (37%) and TTN (26%). Mutations in MUC17, ABCA2, PDE4DIP, and MYO18B predicted significantly unfavorable disease-free survival (DFS). Moreover, cytobands amplifications in 8q24.3, 14q13.1, 14q11.2, and deletion in 3p21.1 were highlighted in recurrent cases. Higher incidence of human leukocyte antigen loss of heterozygosity (HLA-LOH), higher tumor mutational burden (TMB) and tumor neoantigen burden (TNB) were identified in ever-smokers than never-smokers. HLA-LOH also correlated with higher TMB, TNB, intratumoral heterogeneity (ITH), and whole chromosomal instability (wCIN) scores. Interestingly, higher ITH was an independent predictor of better DFS in early-stage Ns-NSCLC. Up-regulation of immune-related genes, including CRABP2, ULBP2, IL31RA, and IL1A, independently portended a dismal prognosis. Enhanced TCR diversity of peripheral blood mononuclear cells (PBMCs) predicted better prognosis, indicative of a noninvasive method for relapse surveillance. Eventually, seven machine-learning (ML) algorithms were employed to evaluate the predictive accuracy of clinical, genomic, transcriptomic, and TCR repertoire data on DFS, showing that clinical and RNA features combination in the random forest (RF) algorithm, with area under the curve (AUC) of 97.5% and 83.3% in the training and testing cohort, respectively, significantly outperformed other methods.

Conclusions

This study comprehensively profiled the genomic, transcriptomic, and TCR repertoire spectrums of Chinese early-stage Ns-NSCLC, shedding light on biological underpinnings and candidate biomarkers for prognosis development.

Predicting the risk of lung cancer using machine learning: A large study based on UK Biobank

In response to the high incidence and poor prognosis of lung cancer, this study tends to develop a generalizable lung-cancer prediction model by using machine learning to define high-risk groups and realize the early identification and prevention of lung cancer. We included 467,888 participants from UK Biobank, using lung cancer incidence as an outcome variable, including 49 previously known high-risk factors and less studied or unstudied predictors. We developed multivariate prediction models using multiple machine learning models, namely logistic regression, naïve Bayes, random forest, and extreme gradient boosting models. The performance of the models was evaluated by calculating the areas under their receiver operating characteristic curves, Brier loss, log loss, precision, recall, and F1 scores. The Shapley additive explanations interpreter was used to visualize the models. Three were ultimately 4299 cases of lung cancer that were diagnosed in our sample. The model containing all the predictors had good predictive power, and the extreme gradient boosting model had the best performance with an area under curve of 0.998. New important predictive factors for lung cancer were also identified, namely hip circumference, waist circumference, number of cigarettes previously smoked daily, neuroticism score, age, and forced expiratory volume in 1 second. The predictive model established by incorporating novel predictive factors can be of value in the early identification of lung cancer. It may be helpful in stratifying individuals and selecting those at higher risk for inclusion in screening programs.

APOBEC shapes tumor evolution and age at onset of lung cancer in smokers

APOBEC enzymes are part of the innate immunity and are responsible for restricting viruses and retroelements by deaminating cytosine residues1,2. Most solid tumors harbor different levels of somatic mutations attributed to the off-target activities of APOBEC3A (A3A) and/or APOBEC3B (A3B)3-6. However, how APOBEC3A/B enzymes shape the tumor evolution in the presence of exogenous mutagenic processes is largely unknown. Here, by combining deep whole-genome sequencing with multi-omics profiling of 309 lung cancers from smokers with detailed tobacco smoking information, we identify two subtypes defined by low (LAS) and high (HAS) APOBEC mutagenesis. LAS are enriched for A3B-like mutagenesis and KRAS mutations, whereas HAS for A3A-like mutagenesis and TP53 mutations. Unlike APOBEC3A, APOBEC3B expression is strongly associated with an upregulation of the base excision repair pathway. Hypermutation by unrepaired A3A and tobacco smoking mutagenesis combined with TP53-induced genomic instability can trigger senescence7, apoptosis8, and cell regeneration9, as indicated by high expression of pulmonary healing signaling pathway, stemness markers and distal cell-of-origin in HAS. The expected association of tobacco smoking variables (e.g., time to first cigarette) with genomic/epigenomic changes are not observed in HAS, a plausible consequence of frequent cell senescence or apoptosis. HAS have more neoantigens, slower clonal expansion, and older age at onset compared to LAS, particularly in heavy smokers, consistent with high proportions of newly generated, unmutated cells and frequent immuno-editing. These findings show how heterogeneity in mutational burden across co-occurring mutational processes and cell types contributes to tumor development, with important clinical implications.

Somatic mutations in human ageing: New insights from DNA sequencing and inherited mutations

The accumulation of somatic mutations is a driver of cancer and has long been associated with ageing. Due to limitations in quantifying mutation burden with age in non-cancerous tissues, the impact of somatic mutations in other ageing phenotypes is unclear. Recent advances in DNA sequencing technologies have allowed the large-scale quantification of somatic mutations in ageing tissues. These studies have revealed a gradual accumulation of mutations in normal tissues with age as well as a substantial clonal expansion driven mostly by cancer-related mutations. Nevertheless, it is difficult to envision how the burden and stochastic nature of age-related somatic mutations identified so far can explain most ageing phenotypes that develop gradually. Studies across species have also found that longer-lived species have lower somatic mutation rates, though these could be due to selective pressures acting on other phenotypes such as perhaps cancer. Recent studies in patients with higher somatic mutation burden and no signs of accelerated ageing further question the role of somatic mutations in ageing. Overall, with a few exceptions like cancer, recent DNA sequencing studies and inherited mutations do not support the idea that somatic mutations accumulating with age drive ageing phenotypes, and the phenotypic role, if any, of somatic mutations in ageing remains unclear.

Behavioural ecology meets oncology: quantifying the recovery of animal behaviour to a transient exposure to a cancer risk factor

Wildlife is increasingly exposed to sublethal transient cancer risk factors, including mutagenic substances, which activates their anti-cancer defences, promotes tumourigenesis, and may negatively impact populations. Little is known about how exposure to cancer risk factors impacts the behaviour of wildlife. Here, we investigated the effects of a sublethal, short-term exposure to a carcinogen at environmentally relevant concentrations on the activity patterns of wild Girardia tigrina planaria during a two-phase experiment, consisting of a 7-day exposure to cadmium period followed by a 7-day recovery period. To comprehensively explore the effects of the exposure on activity patterns, we employed the double hierarchical generalized linear model framework which explicitly models residual intraindividual variability in addition to the mean and variance of the population. We found that exposed planaria were less active compared to unexposed individuals and were able to recover to pre-exposure activity levels albeit with a reduced variance in activity at the start of the recovery phase. Planaria showing high activity levels were less predictable with larger daily activity variations and higher residual variance. Thus, the shift in behavioural variability induced by an exposure to a cancer risk factor can be quantified using advanced tools from the field of behavioural ecology. This is required to understand how tumourous processes affect the ecology of species.

Analyzing somatic mutations by single-cell whole-genome sequencing

Somatic mutations are the cause of cancer and have been implicated in other, noncancerous diseases and aging. While clonally expanded mutations can be studied by deep sequencing of bulk DNA, very few somatic mutations expand clonally, and most are unique to each cell. We describe a detailed protocol for single-cell whole-genome sequencing to discover and analyze somatic mutations in tissues and organs. The protocol comprises single-cell multiple displacement amplification (SCMDA), which ensures efficiency and high fidelity in amplification, and the SCcaller software tool to call single-nucleotide variations and small insertions and deletions from the sequencing data by filtering out amplification artifacts. With SCMDA and SCcaller at its core, this protocol describes a complete procedure for the comprehensive analysis of somatic mutations in a single cell, covering (1) single-cell or nucleus isolation, (2) single-cell or nucleus whole-genome amplification, (3) library preparation and sequencing, and (4) computational analyses, including alignment, variant calling, and mutation burden estimation. Methods are also provided for mutation annotation, hotspot discovery and signature analysis. The protocol takes 12-15 h from single-cell isolation to library preparation and 3-7 d of data processing. Compared with other single-cell amplification methods or single-molecular sequencing, it provides high genomic coverage, high accuracy in single-nucleotide variation and small insertions and deletion calling from the same single-cell genome, and fewer processing steps. SCMDA and SCcaller require basic experience in molecular biology and bioinformatics. The protocol can be utilized for studying mutagenesis and genome mosaicism in normal and diseased human and animal tissues under various conditions.

Single-cell lineage tracing with endogenous markers

Resolving lineage relationships between cells in an organism provides key insights into the fate of individual cells and drives a fundamental understanding of the process of development and disease. A recent rapid increase in experimental and computational advances for detecting naturally occurring somatic nuclear and mitochondrial mutation at single-cell resolution has expanded lineage tracing from model organisms to humans. This review discusses the advantages and challenges of experimental and computational techniques for cell lineage tracing using somatic mutation as endogenous DNA barcodes to decipher the relationships between cells during development and tumour evolution. We outlook the advantages of spatial clonal evolution analysis and single-cell lineage tracing using endogenous genetic markers.

Chronic disease and multimorbidity in the Chinese older adults' population and their impact on daily living ability: a cross-sectional study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS)

BACKGROUND

Owing to an increase in life expectancy, it is common for the older adults to suffer from chronic diseases that can result in disability and a low quality of life. This study aimed to explore the influence of chronic diseases and multimorbidities on activities of daily living (ADLs) and instrumental ADLs (IADLs) in an older Chinese population.

METHODS

Based on the Chinese Longitudinal Healthy Longevity Survey (2018), 9,155 older adults aged 65 years and above were included in the study. A self-administered questionnaire was used to collect information on demographic characteristics, chronic diseases, ADLs, and IADLs. The impact of factors affecting ADL and IADL impairment in older adults was analysed using binary logistic regression.

RESULTS

In total, 66.3% participants had chronic diseases. Hypertension, heart disease, arthritis, diabetes and cerebrovascular disease were among the top chronic diseases. Of these, 33.7% participants had multimorbidities. The most common combination of the two chronic diseases was hypertension and heart disease (11.2%), whereas the most common combination of the three chronic diseases was hypertension, heart disease, and diabetes (3.18%). After categorising the older adults into four age groups, dementia, visual impairment, and hearing impairment were found to be more prevalent with increasing age. The prevalence of hypertension, heart disease, cerebrovascular disease, gastrointestinal ulcers, arthritis and chronic nephritis gradually increased with age until the age of 75 years, peaked in the 75-84 years age group, and then showed a decreasing trend with age. Multimorbidity prevalence followed a similar pattern. Regression analysis indicated that the increase in age group and the number of chronic diseases independently correlated with impairments in ADL as well as IADL. Additionally, gender, physical activity, educational background, obesity, depressive symptoms, and falls also had an impact on ADLs or IADLs.

CONCLUSION

Chronic diseases and multimorbidities are common in older adults, and it is important to note that aging, multimorbidity, obesity, and unhealthy lifestyle choices may interfere with ADLs or IADLs in older adults. Therefore, it is imperative that primary healthcare providers pay special attention to older adults and improve screening for multimorbidity and follow-up needs.

Aging and atrial fibrillation: A vicious circle

Atrial fibrillation (AF) is the commonest sustained cardiac arrhythmia observed in clinical practice. Its prevalence increases dramatically with advancing age. This review article discusses the recent advances in studies investigating the relationship between aging and AF and the possible underlying mechanisms.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

The ticking of aging clocks

Computational models that measure biological age and aging rate regardless of chronological age are called aging clocks. The underlying counting mechanisms of the intrinsic timers of these clocks are still unclear. Molecular mediators and determinants of aging rate point to the key roles of DNA damage, epigenetic drift, and inflammation. Persistent DNA damage leads to cellular senescence and the senescence-associated secretory phenotype (SASP), which induces cytotoxic immune cell infiltration; this further induces DNA damage through reactive oxygen and nitrogen species (RONS). I discuss the possibility that DNA damage (or the response to it, including epigenetic changes) is the fundamental counting unit of cell cycles and cellular senescence, that ultimately accounts for cell composition changes and functional decline in tissues, as well as the key intervention points.

Clonal Somatic Mutations in Chronic Lung Diseases Are Associated with Reduced Lung Function

Rationale: Constantly exposed to the external environment and mutagens such as tobacco smoke, human lungs have one of the highest somatic mutation rates among all human organs. However, the relationship of these mutations to lung disease and function is not known. Objectives: To identify the prevalence and significance of clonal somatic mutations in chronic lung diseases. Methods: We analyzed the clonal somatic mutations from 1,251 samples of normal and diseased noncancerous lung tissue RNA sequencing with paired whole-genome sequencing from the Lung Tissue Research Consortium. We examined the associations of somatic mutations with lung function, disease status, and computationally deconvoluted cell types in two of the most common diseases represented in our dataset, chronic obstructive pulmonary disease (COPD; 29%) and idiopathic pulmonary fibrosis (IPF; 13%). Measurements and Main Results: Clonal somatic mutational burden was associated with reduced lung function in both COPD and IPF. We identified an increased prevalence of clonal somatic mutations in individuals with IPF compared with normal control subjects and individuals with COPD independent of age and smoking status. IPF clonal somatic mutations were enriched in disease-related and airway epithelial-expressed genes such as MUC5B in IPF. Patients who were MUC5B risk variant carriers had increased odds of developing somatic mutations of MUC5B that were explained by increased expression of MUC5B. Conclusions: Our identification of an increased prevalence of clonal somatic mutation in diseased lung that correlates with airway epithelial gene expression and disease severity highlights for the first time the role of somatic mutational processes in lung disease genetics.

Somatic mutation burden in relation to aging and functional life span: implications for cellular reprogramming and rejuvenation

The accrual of somatic mutations has been implicated as causal factors in aging since the 1950s. However, the quantitative analysis of somatic mutations has posed a major challenge due to the random nature of de novo mutations in normal tissues, which has limited analysis to tumors and other clonal lineages. Advances in single-cell and single-molecule next-generation sequencing now allow to obtain, for the first time, detailed insights into the landscape of somatic mutations in different human tissues and cell types as a function of age under various conditions. Here, we will briefly recapitulate progress in somatic mutation analysis and discuss the possible relationship between somatic mutation burden with functional life span, with a focus on differences between germ cells, stem cells, and differentiated cells.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

Single-cell transcriptomic and genomic changes in the aging human brain

Aging brings dysregulation of various processes across organs and tissues, often stemming from stochastic damage to individual cells over time. Here, we used a combination of single-nucleus RNA-sequencing and single-cell whole-genome sequencing to identify transcriptomic and genomic changes in the prefrontal cortex of the human brain across life span, from infancy to centenarian. We identified infant-specific cell clusters enriched for the expression of neurodevelopmental genes, and a common down-regulation of cell-essential homeostatic genes that function in ribosomes, transport, and metabolism during aging across cell types. Conversely, expression of neuron-specific genes generally remains stable throughout life. We observed a decrease in specific DNA repair genes in aging, including genes implicated in generating brain somatic mutations as indicated by mutation signature analysis. Furthermore, we detected gene-length-specific somatic mutation rates that shape the transcriptomic landscape of the aged human brain. These findings elucidate critical aspects of human brain aging, shedding light on transcriptomic and genomics dynamics.

Mechanisms of E-Cigarette Vape-Induced Epithelial Cell Damage

E-cigarette use has been reported to affect cell viability, induce DNA damage, and modulate an inflammatory response resulting in negative health consequences. Most studies focus on oral and lung disease associated with e-cigarette use. However, tissue damage can be found in the cardio-vascular system and even the bladder. While the levels of carcinogenic compounds found in e-cigarette aerosols are lower than those in conventional cigarette smoke, the toxicants generated by the heat of the vaping device may include probable human carcinogens. Furthermore, nicotine, although not a carcinogen, can be metabolized to nitrosamines. Nitrosamines are known carcinogens and have been shown to be present in the saliva of e-cig users, demonstrating the health risk of e-cigarette vaping. E-cig vape can induce DNA adducts, promoting oxidative stress and DNA damage and NF-kB-driven inflammation. Together, these processes increase the transcription of pro-inflammatory cytokines. This creates a microenvironment thought to play a key role in tumorigenesis, although it is too early to know the long-term effects of vaping. This review considers different aspects of e-cigarette-induced cellular changes, including the generation of reactive oxygen species, DNA damage, DNA repair, inflammation, and the possible tumorigenic effects.

Quantifying human genome parameters in aging

Healthy human longevity is a global goal of the world health system. Determining the causes and processes influencing human longevity is the primary fundamental goal facing the scientific community. Currently, the main efforts of the scientific community are aimed at identifying the qualitative characteristics of the genome that determine the trait. At the same time, when evaluating qualitative characteristics, there are many challenges that make it difficult to establish associations. Quantitative traits are burdened with such problems to a lesser extent, but they are largely overlooked in current genomic studies of aging and longevity. Although there is a wide repertoire of quantitative trait analyses based on genomic data, most opportunities are ignored by authors, which, along with the inaccessibility of published data, leads to the loss of this important information. This review focuses on describing quantitative traits important for understanding aging and necessary for analysis in further genomic studies, and recommends the inclusion of the described traits in the analysis. The review considers the relationship between quantitative characteristics of the mitochondrial genome and aging, longevity, and age-related neurodegenerative diseases, such as the frequency of extensive mitochondrial DNA (mtDNA) deletions, mtDNA half-life, the frequency of A>G replacements in the mtDNA heavy chain, the number of mtDNA copies; special attention is paid to the mtDNA methylation sign. A separate section of this review is devoted to the correlation of telomere length parameters with age, as well as the association of telomere length with the amount of mitochondrial DNA. In addition, we consider such a quantitative feature as the rate of accumulation of somatic mutations with aging in relation to the lifespan of living organisms. In general, it may be noted that there are quite serious reasons to suppose that various quantitative characteristics of the genome may be directly or indirectly associated with certain aspects of aging and longevity. At the same time, the available data are clearly insufficient for definitive conclusions and the determination of causal relationships.

Associations between cadmium exposure and whole-body aging: mediation analysis in the NHANES

INTRODUCTION

Even though cadmium (Cd) exposure and cellular senescence (telomere length) have been linked in previous studies, composite molecular aging biomarkers are more significant and reliable factors to consider when examining the connection between metal exposure and health outcomes. The purpose of this research was to assess the association between urinary cadmium (U-Cd) and whole-body aging (phenotypic age).

METHODS

Phenotypic age was calculated from chronological age and 9 molecular biomarkers. Multivariate linear regression models, subgroup analysis, and smoothing curve fitting were used to explore the linear and nonlinear relationship between U-Cd and phenotypic age. Mediation analysis was performed to explore the mediating effect of U-Cd on the association between smoking and phenotypic age.

RESULTS

This study included 10,083 participants with a mean chronological age and a mean phenotypic age of 42.24 years and 42.34 years, respectively. In the fully adjusted model, there was a positive relationship between U-Cd and phenotypic age [2.13 years per 1 ng/g U-Cd, (1.67, 2.58)]. This association differed by sex, age, and smoking subgroups (P for interaction < 0.05). U-Cd mediated a positive association between serum cotinine and phenotypic age, mediating a proportion of 23.2%.

CONCLUSIONS

Our results suggest that high levels of Cd exposure are associated with whole-body aging.

Lung cancer risk score for ever and never smokers in China

BACKGROUND

Most lung cancer risk prediction models were developed in European and North-American cohorts of smokers aged ≥ 55 years, while less is known about risk profiles in Asia, especially for never smokers or individuals aged < 50 years. Hence, we aimed to develop and validate a lung cancer risk estimate tool for ever and never smokers across a wide age range.

METHODS

Based on the China Kadoorie Biobank cohort, we first systematically selected the predictors and explored the nonlinear association of predictors with lung cancer risk using restricted cubic splines. Then, we separately developed risk prediction models to construct a lung cancer risk score (LCRS) in 159,715 ever smokers and 336,526 never smokers. The LCRS was further validated in an independent cohort over a median follow-up of 13.6 years, consisting of 14,153 never smokers and 5,890 ever smokers.

RESULTS

A total of 13 and 9 routinely available predictors were identified for ever and never smokers, respectively. Of these predictors, cigarettes per day and quit years showed nonlinear associations with lung cancer risk (Pnon-linear < 0.001). The curve of lung cancer incidence increased rapidly above 20 cigarettes per day and then was relatively flat until approximately 30 cigarettes per day. We also observed that lung cancer risk declined sharply within the first 5 years of quitting, and then continued to decrease but at a slower rate in the subsequent years. The 6-year area under the receiver operating curve for the ever and never smokers' models were respectively 0.778 and 0.733 in the derivation cohort, and 0.774 and 0.759 in the validation cohort. In the validation cohort, the 10-year cumulative incidence of lung cancer was 0.39% and 2.57% for ever smokers with low (< 166.2) and intermediate-high LCRS (≥ 166.2), respectively. Never smokers with a high LCRS (≥ 21.2) had a higher 10-year cumulative incidence rate than those with a low LCRS (< 21.2; 1.05% vs. 0.22%). An online risk evaluation tool (LCKEY; http://ccra.njmu.edu.cn/lckey/web) was developed to facilitate the use of LCRS.

CONCLUSIONS

The LCRS can be an effective risk assessment tool designed for ever and never smokers aged 30 to 80 years.

Association of HLA diversity with the risk of 25 cancers in the UK Biobank

BACKGROUND

The human leukocyte antigen (HLA) is a highly polymorphic region, and HLA diversity may play a role in presenting tumour-associated peptides and inducing immune responses. However, the effect of HLA diversity on cancers has not been fully assessed. We aimed to explore the role of HLA diversity on cancer development.

METHODS

A pan-cancer analysis was performed to evaluate the effect of HLA diversity, measured by HLA heterozygosity and HLA evolutionary divergence (HED), on the susceptibility of 25 cancers in the UK Biobank.

FINDINGS

We observed that the diversity of HLA class II locus was associated with a lower risk of lung cancer (OR_hetero = 0.94, 95% CI = 0.90-0.97, P = 1.29 × 10^-4) and head and neck cancer (OR_hetero = 0.91, 95% CI = 0.86-0.96, P = 1.56 × 10^-3). Besides, a lower risk of non-Hodgkin lymphoma was associated with an increased diversity of HLA class I (OR_hetero = 0.92, 95% CI = 0.87-0.98, P = 8.38 × 10^-3) and class II locus (OR_hetero = 0.89, 95% CI = 0.86-0.92, P = 1.65 × 10^-10). A lower risk of Hodgkin lymphoma was associated with the HLA class I diversity (OR_hetero = 0.85, 95% CI = 0.75-0.96, P = 0.011). The protective effect of HLA diversity was mainly observed in pathological subtypes with higher tumour mutation burden, such as lung squamous cell carcinoma (P = 9.39 × 10^-3) and diffuse large B cell lymphoma (P_{class I} = 4.12 × 10^-4; P_{class Ⅱ} = 4.71 × 10^-5), as well as the smoking subgroups of lung cancer (P = 7.45 × 10^-5) and head and neck cancer (P = 4.55 × 10^-3).

INTERPRETATION

We provided a systematic insight into the effect of HLA diversity on cancers, which might help to understand the etiological role of HLA on cancer development.

FUNDING

This study was supported by grants from the National Natural Science Foundation of China (82273705, 82003520); the Basic and Applied Basic Research Foundation of Guangdong Province, China (2021B1515420007); the Science and Technology Planning Project of Guangzhou, China (201804020094); Sino-Sweden Joint Research Programme (81861138006); the National Natural Science Foundation of China (81973131, 81903395, 81803319, 81802708).

Genomic Characterization Revealed PM2.5-Associated Mutational Signatures in Lung Cancer Including Activation of APOBEC3B

Fine particulate matter (PM_2.5) exposure causes DNA mutations and abnormal gene expression leading to lung cancer, but the detailed mechanisms remain unknown. Here, analysis of genomic and transcriptomic changes upon a PM_2.5 exposure-induced human bronchial epithelial cell-based malignant transformed cell model in vitro showed that PM_2.5 exposure led to APOBEC mutational signatures and transcriptional activation of APOBEC3B along with other potential oncogenes. Moreover, by analyzing mutational profiles of 1117 non-small cell lung cancers (NSCLCs) from patients across four different geographic regions, we observed a significantly higher prevalence of APOBEC mutational signatures in non-smoking NSCLCs than smoking in the Chinese cohorts, but this difference was not observed in TCGA or Singapore cohorts. We further validated this association by showing that the PM_2.5 exposure-induced transcriptional pattern was significantly enriched in Chinese NSCLC patients compared with other geographic regions. Finally, our results showed that PM_2.5 exposure activated the DNA damage repair pathway. Overall, here we report a previously uncharacterized association between PM_2.5 and APOBEC activation, revealing a potential molecular mechanism of PM_2.5 exposure and lung cancer.

Mitigating age-related somatic mutation burden

Genomes are inherently unstable and require constant DNA repair to maintain their genetic information. However, selective pressure has optimized repair mechanisms in somatic cells only to allow transmitting genetic information to the next generation, not to maximize sequence integrity long beyond the reproductive age. Recent studies have confirmed that somatic mutations, due to errors during genome repair and replication, accumulate in tissues and organs of humans and model organisms. Here, we describe recent advances in the quantitative analysis of somatic mutations in vivo. We also review evidence for or against a possible causal role of somatic mutations in aging. Finally, we discuss options to prevent, delay or eliminate de novo, random somatic mutations as a cause of aging.

Age or lifestyle-induced accumulation of genotoxicity is associated with a length-dependent decrease in gene expression

DNA damage has long been advocated as a molecular driver of aging. DNA damage occurs in a stochastic manner, and is therefore more likely to accumulate in longer genes. The length-dependent accumulation of transcription-blocking damage, unlike that of somatic mutations, should be reflected in gene expression datasets of aging. We analyzed gene expression as a function of gene length in several single-cell RNA sequencing datasets of mouse and human aging. We found a pervasive age-associated length-dependent underexpression of genes across species, tissues, and cell types. Furthermore, we observed length-dependent underexpression associated with UV-radiation and smoke exposure, and in progeroid diseases, Cockayne syndrome, and trichothiodystrophy. Finally, we studied published gene sets showing global age-related changes. Genes underexpressed with aging were significantly longer than overexpressed genes. These data highlight a previously undetected hallmark of aging and show that accumulation of genotoxicity in long genes could lead to reduced RNA polymerase II processivity.

Isolation of single cells from human hepatoblastoma tissues for whole-exome sequencing

By combining single-cell processing with whole-exome sequencing, we have developed single-cell whole-exome sequencing to investigate the mechanisms of hepatoblastoma development and to provide potential targets and therapeutic approaches for clinical treatment. In the following protocol, we outline the steps involved in single-cell sorting, whole-genome amplification, amplification uniformity estimation, and whole-exome library construction. In addition to the cells we use, this protocol is also suitable for other cell lines and cell types.

CHIPing away the progression potential of CHIP: A new reality in the making

Over the past few years, we have gained a deeper understanding of clonal hematopoiesis of indeterminate potential (CHIP), especially with regard to the epidemiology, clinical sequelae, and mechanical aspects. However, interventional strategies to prevent or delay the potential negative consequences of CHIP remain underdeveloped. In this review, we highlight the latest updates on clonal hematopoiesis research, including molecular mechanisms and clinical implications, with a particular focus on the evolving strategies for the interventions that are being evaluated in ongoing observational and interventional trials. There remains an urgent need to formulate standardized and evidence-based recommendations and guidelines for evaluating and managing individuals with clonal hematopoiesis. In addition, patient-centric endpoints must be defined for clinical trials, which will enable us to continue the robust development of effective preventive strategies and improve clinical outcomes.

Hallmarks of aging: An expanding universe

Aging is driven by hallmarks fulfilling the following three premises: (1) their age-associated manifestation, (2) the acceleration of aging by experimentally accentuating them, and (3) the opportunity to decelerate, stop, or reverse aging by therapeutic interventions on them. We propose the following twelve hallmarks of aging: genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient-sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. These hallmarks are interconnected among each other, as well as to the recently proposed hallmarks of health, which include organizational features of spatial compartmentalization, maintenance of homeostasis, and adequate responses to stress.

Meta-hallmarks of aging and cancer

Both aging and cancer are characterized by a series of partially overlapping "hallmarks" that we subject here to a meta-analysis. Several hallmarks of aging (i.e., genomic instability, epigenetic alterations, chronic inflammation, and dysbiosis) are very similar to specific cancer hallmarks and hence constitute common "meta-hallmarks," while other features of aging (i.e., telomere attrition and stem cell exhaustion) act likely to suppress oncogenesis and hence can be viewed as preponderantly "antagonistic hallmarks." Disabled macroautophagy and cellular senescence are two hallmarks of aging that exert context-dependent oncosuppressive and pro-tumorigenic effects. Similarly, the equivalence or antagonism between aging-associated deregulated nutrient-sensing and cancer-relevant alterations of cellular metabolism is complex. The agonistic and antagonistic relationship between the processes that drive aging and cancer has bearings for the age-related increase and oldest age-related decrease of cancer morbidity and mortality, as well as for the therapeutic management of malignant disease in the elderly.

Predicting mutational function using machine learning

Genetic variations are one of the major causes of phenotypic variations between human individuals. Although beneficial as being the substrate of evolution, germline mutations may cause diseases, including Mendelian diseases and complex diseases such as diabetes and heart diseases. Mutations occurring in somatic cells are a main cause of cancer and likely cause age-related phenotypes and other age-related diseases. Because of the high abundance of genetic variations in the human genome, i.e., millions of germline variations per human subject and thousands of additional somatic mutations per cell, it is technically challenging to experimentally verify the function of every possible mutation and their interactions. Significant progress has been made to solve this problem using computational approaches, especially machine learning (ML). Here, we review the progress and achievements made in recent years in this field of research. We classify the computational models in two ways: one according to their prediction goals including protein structural alterations, gene expression changes, and disease risks, and the other according to their methodologies, including non-machine learning methods, classical machine learning methods, and deep neural network methods. For models in each category, we discuss their architecture, prediction accuracy, and potential limitations. This review provides new insights into the applications and future directions of computational approaches in understanding the role of mutations in aging and disease.

lncRNA ZNRD1-AS1 promotes malignant lung cell proliferation, migration, and angiogenesis via the miR-942/TNS1 axis and is positively regulated by the m6A reader YTHDC2

RATIONALE

Lung cancer is the most prevalent form of cancer and has a high mortality rate, making it a global public health concern. The N⁶-methyladenosine (m⁶A) modification is a highly dynamic and reversible process that is involved in a variety of essential biological processes. Using in vitro, in vivo, and multi-omics bioinformatics, the present study aims to determine the function and regulatory mechanisms of the long non-coding (lnc)RNA zinc ribbon domain-containing 1-antisense 1 (ZNRD1-AS1).

METHODS

The RNAs that were bound to the m⁶A 'reader' were identified using YTH domain-containing 2 (YTHDC2) RNA immunoprecipitation (RIP)-sequencing. Utilizing methylated RIP PCR/quantitative PCR, pull-down, and RNA stability assays, m⁶A modification and ZNRD1-AS1 regulation were analyzed. Using bioinformatics, the expression levels and clinical significance of ZNRD1-AS1 in lung cancer were evaluated. Using fluorescent in situ hybridization and quantitative PCR assays, the subcellular location of ZNRD1-AS1 was determined. Using cell migration, proliferation, and angiogenesis assays, the biological function of ZNRD1-AS1 in lung cancer was determined. In addition, the tumor suppressor effect of ZNRD1-AS1 in vivo was validated using a xenograft animal model. Through bioinformatics analysis and in vitro assays, the downstream microRNAs (miRs) and competing endogenous RNAs were also predicted and validated.

RESULTS

This study provided evidence that m⁶A modification mediates YTHDC2-mediated downregulation of ZNRD1-AS1 in lung cancer and cigarette smoke-exposed cells. Low levels of ZNRD1-AS1 expression were linked to adverse clinicopathological characteristics, immune infiltration, and prognosis. ZNRD1-AS1 overexpression was shown to suppress lung cancer cell proliferation, migration, and angiogenesis in vitro and in vivo, and to reduce tumor growth in nude mice. ZNRD1-AS1 expression was shown to be controlled by treatment of cells with either the methylation inhibitor 3-Deazaadenosine or the demethylation inhibitor Meclofenamic. Furthermore, the miR-942/tensin 1 (TNS1) axis was demonstrated to be the downstream regulatory signaling pathway of ZNRD1-AS1.

CONCLUSIONS

ZNRD1-AS1 serves an important function and has clinical relevance in lung cancer. In addition, the findings suggested that m⁶A modification could mediate the regulation of the ZNRD1-AS1/miR-942/TNS1 axis via the m⁶A reader YTHDC2.

Hesperidin inhibits tobacco smoke-induced pulmonary cell proliferation and EMT in mouse lung tissues via the p38 signaling pathway

Tobacco smoke (TS) is the major cause of lung cancer. The abnormal proliferation and epithelial-mesenchymal transition (EMT) of lung cells promote occurrence and development of lung cancer. The p38 pathway intervenes in this cancer development. Hesperidin also serves a role in human health and disease prevention. The roles of p38 in TS-mediated abnormal cell proliferation and EMT, and the hesperidin intervention thereof are not yet understood. In the present study, it was demonstrated that TS upregulated proliferating cell nuclear antigen, vimentin and N-cadherin expression, whereas it downregulated E-cadherin expression, as assessed using western blotting and reverse transcription-quantitative PCR. Furthermore, it was observed that inhibition of the p38 pathway inhibit TS-induced proliferation and EMT. Hesperidin treatment prevented the TS-induced activation of the p38 pathway, EMT and cell proliferation in mouse lungs. The findings of the present study may provide insights into the pathogenesis of TS-related lung cancer.

Immune senescence in non-small cell lung cancer management: therapeutic relevance, biomarkers, and mitigating approaches

INTRODUCTION

Lung cancer and mainly non-small cell lung cancer (NSCLC) still remain a prevalent malignancy worldwide despite sustained screening approaches. Furthermore, a significant proportion of the cases are diagnosed at advanced stages when conservative therapy is often unsuccessful. Cell senescence is an endogenous antitumor weapon but when it is upregulated exerts opposite activities favoring tumor metastasizing and poor response to therapy. However, little is known about this dangerous relationship between cell senescence and NSCLC outcome or on potential approaches to mitigate its unfavorable consequences.

AREAS COVERED

We discuss cell senescence focusing on immune senescence, its cell and humoral effectors (namely immune senescence associated secretory phenotype-iSASP), its impact on NSCLC outcome, and its biomarkers. Senotherapeutics as mitigating approaches are also considered based on the availability of experimental data pertinent to NSCLC.

EXPERT OPINION

Characterization of NSCLC subsets in which immune senescence is a risk factor for poor prognosis and poor therapeutic response might be very helpful in supporting the addition of senotherapeutics to conventional cancer therapy. This approach has the potential to improve disease outcome but more studies in this area are necessary.

Age-related somatic mutation burden in human tissues

The genome of multicellular organisms carries the hereditary information necessary for the development of all organs and tissues and to maintain function in adulthood. To ensure the genetic stability of the species, genomes are protected against changes in sequence information. However, genomes are not static. De novo mutations in germline cells are passed on to offspring and generate the variation needed in evolution. Moreover, postzygotic mutations occur in all somatic cells during development and aging. These somatic mutations remain limited to the individual, generating tissues that are genome mosaics. Insight into such mutations and their consequences has been limited due to their extremely low abundance, with most mutations unique for each cell. Recent advances in sequencing, including whole genome sequencing at the single-cell level, have now led to the first insights into somatic mutation burdens in human tissues. Here, we will first briefly describe the latest methodology for somatic mutation analysis, then review our current knowledge of somatic mutation burden in human tissues and, finally, briefly discuss the possible functional impact of somatic mutations on the aging process and age-related diseases, including cancer and diseases other than cancer.

Total mutational load and clinical features as predictors of the metastatic status in lung adenocarcinoma and squamous cell carcinoma patients

BACKGROUND

Recently, extensive cancer genomic studies have revealed mutational and clinical data of large cohorts of cancer patients. For example, the Pan-Lung Cancer 2016 dataset (part of The Cancer Genome Atlas project), summarises the mutational and clinical profiles of different subtypes of Lung Cancer (LC). Mutational and clinical signatures have been used independently for tumour typification and prediction of metastasis in LC patients. Is it then possible to achieve better typifications and predictions when combining both data streams?

METHODS

In a cohort of 1144 Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LSCC) patients, we studied the number of missense mutations (hereafter, the Total Mutational Load TML) and distribution of clinical variables, for different classes of patients. Using the TML and different sets of clinical variables (tumour stage, age, sex, smoking status, and packs of cigarettes smoked per year), we built Random Forest classification models that calculate the likelihood of developing metastasis.

RESULTS

We found that LC patients different in age, smoking status, and tumour type had significantly different mean TMLs. Although TML was an informative feature, its effect was secondary to the "tumour stage" feature. However, its contribution to the classification is not redundant with the latter; models trained using both TML and tumour stage performed better than models trained using only one of these variables. We found that models trained in the entire dataset (i.e., without using dimensionality reduction techniques) and without resampling achieved the highest performance, with an F1 score of 0.64 (95%CrI [0.62, 0.66]).

CONCLUSIONS

Clinical variables and TML should be considered together when assessing the likelihood of LC patients progressing to metastatic states, as the information these encode is not redundant. Altogether, we provide new evidence of the need for comprehensive diagnostic tools for metastasis.