An integrative analysis of the age-associated multi-omic landscape across cancers

Subtype-Specific Patterns of Tumor Purity and Mutation Load Suggest Treatment Implications: A Cross-Sectional Analysis of 7494 Soft Tissue and Bone Sarcomas (MSK Cohort)

OBJECTIVES

Sarcomas are complex mesenchymal malignancies whose molecular characteristics can significantly influence treatment strategies. This study aimed to investigate the relationship between tumor purity, mutation load, and clinical characteristics across sarcoma subtypes, focusing on potential implications for therapeutic stratification.

METHODS

This study analyzed the molecular characteristics of 7494 sarcoma cases from the Soft Tissue and Bone Sarcoma (MSK, Nat Commun 2022) data set using available case analysis. Correlations between tumor purity, mutation load, age, and sex were analyzed using nonparametric methods, with subtype-specific analyses conducted using Kruskal-Wallis tests and Bonferroni-corrected post hoc comparisons. A comprehensive analysis of mutation patterns was performed using microsatellite instability (MSI) status.

RESULTS

Significant correlations between mutation load and tumor purity (ρ=0.320, P <0.001) were identified, with marked heterogeneity across subtypes. Tumor purity ranged from 20.0% in brain sarcomas to 78.5% in dermatofibrosarcoma protuberans. Age-related molecular changes were observed in brain (ρ=0.711, P =0.006) and skin sarcomas (ρ=0.450, P =0.006), suggesting distinct evolutionary patterns. A subset of hypermutated, microsatellite stable cases (0.15%) with mutation loads exceeding 100 mutations/mb were identified, suggesting alternative mechanisms of genomic instability. MSI-high status was rare (0.24%) but associated with higher mutation loads (median: 25.84 vs. 2.42, P <0.001), particularly in uterine sarcomas (0.7% prevalence).

CONCLUSIONS

The identification of distinct molecular patterns across sarcoma subtypes challenge existing morphology-based classification systems and may hold implications for therapeutic stratification. These findings may help inform future immunotherapeutic and molecular-guided approaches to treatment in sarcoma patients, particularly for elderly patients with brain sarcomas or females with uterine sarcomas.

Age-dependent cytokine surge in blood precedes cancer diagnosis

Aging is associated with increased variability and dysregulation of the immune system. We performed a system-level analysis of serum cytokines in a longitudinal cohort of 133 healthy individuals over 9 y. We found that cancer incidence is a major contributor to increased cytokine abundance variability. Circulating cytokines increase up to 4 y before a cancer diagnosis in subjects with age over 80 y. We also analyzed cytokine expression in 10 types of early-stage cancers from The Cancer Genome Atlas. We found that a similar set of cytokines is upregulated in tumor tissues, specifically after the age of 80 y. Similarly, cellular senescence activity and CDKN1A/p21 expression increase with age in cancer tissues. Finally, we demonstrated that the cytokine levels in serum can be used to predict cancers among subjects age at 80+ y. Our results suggest that latent senescent cancers contribute to age-related chronic inflammation.

Comprehensive analysis of senescence-related genes identifies prognostic clusters with distinct characteristics in glioma

Cellular senescence, defined as a state of permanent arrest in cell growth, is regarded as a crucial tumor suppression mechanism. However, accumulating scientific evidence suggests that senescent cells play a detrimental role in the progression of cancer. Unfortunately, the current lack of reliable markers that specifically reflect the level of senescence in cancer greatly hinders our in-depth understanding of this important biological foundation. Therefore, the search for more specific and reliable markers to reveal the specific role of senescent cells in cancer progression is particularly urgent and important. To uncover the role of senescence in gliomas, we collected senescence-related genes for integrated analysis. Consensus clustering was used to subtype gliomas based on the senescence gene set, and we identified two robust prognostic clusters of gliomas with distinct survival outcomes, multi-omics landscapes, immune characteristics, and differential drug responses. Multiple external datasets were used to validate the stability of our subtypes. Various computational and experimental methods, including WGCNA (Weighted Gene Co-expression Network Analysis), ssGSEA (single-sample Gene Set Enrichment Analysis), and machine learning algorithms (lasso regression, support vector machines, random forests), were employed for analysis. We found that CEBPB and LMNA are associated with poor prognosis in gliomas and may mediate immunosuppression and tumor proliferation. Drug prediction indicated that dasatinib is a potential therapeutic agent. Our findings provide insights into the role of the senescence gene set in patient stratification and precision medicine.

Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas

Objective

Primary lung carcinomas (LCs) often metastasize to the brain, resulting in a grim prognosis for affected individuals. This population-based study aimed to investigate their survival period and immune status, while also establishing a predictive model.

Methods

The records of 86,763 primary LCs from the Surveillance, Epidemiology, and End Results (SEER) database were extracted, including 15,180 cases with brain metastasis (BM) and 71,583 without BM. Univariate and multivariate Cox regression were employed to construct a prediction model. Multiple machine learning methods were applied to validate the model. Flow cytometry and ELISA were used to explore the immune status in a real-world cohort.

Results

The research findings revealed a 17.49% prevalence of BM from LCs, with a median survival of 8 months, compared with 16 months for their counterparts (p <0.001). A nomogram was developed to predict survival at 1, 3, and 5 years on the basis of these variables, with the time-dependent area under the curve (AUC) of 0.857, 0.814, and 0.786, respectively. Moreover, several machine learning approaches have further verified the reliability of this model's performance. Flow cytometry and ELISA analysis suggested the prediction model was related the immune status.

Conclusions

BM from LCs have an inferior prognosis. Considering the substantial impact of these factors, the nomogram model is a valuable tool for guiding clinical decision-making in managing patients with this condition.

Integrative analysis of senescence-related genes identifies robust prognostic clusters with distinct features in hepatocellular carcinoma

INTRODUCTION

Senescence refers to a state of permanent cell growth arrest and is regarded as a tumor suppressive mechanism, whereas accumulative evidence demonstrate that senescent cells play an adverse role during cancer progression. The scarcity of specific and reliable markers reflecting senescence level in cancer impede our understanding of this biological basis.

OBJECTIVES

Senescence-related genes (SRGs) were collected for integrative analysis to reveal the role of senescence in hepatocellular carcinoma (HCC).

METHODS

Consensus clustering was used to subtype HCC based on SRGs. Several computational methods, including single sample gene set enrichment analysis (ssGSEA), fuzzy c-means algorithm, were performed. Data of drug sensitivities were utilized to screen potential therapeutic agents for different senescence patients. Additionally, we developed a method called signature-related gene analysis (SRGA) for identification of markers relevant to phenotype of interest. Experimental strategies consisting quantitative real-time PCR (qRT-PCR), β-galactosidase assay, western blot, and tumor-T cell co-culture system were used to validate the findings in vitro.

RESULTS

We identified three robust prognostic clusters of HCC patients with distinct survival outcome, mutational landscape, and immune features. We further extracted signature genes of senescence clusters to construct the senescence scoring system and profile senescence level in HCC at bulk and single-cell resolution. Senescence-induced stemness reprogramming was confirmed both in silico and in vitro. HCC patients with high senescence were immune suppressed and sensitive to Tozasertib and other drugs. We suggested that MAFG, PLIN3, and 4 other genes were pertinent to HCC senescence, and MAFG potentially mediated immune suppression, senescence, and stemness.

CONCLUSION

Our findings provide insights into the role of SRGs in patients stratification and precision medicine.

Downregulation of Aging-Associated Gene SUCLG1 Marks the Aggressiveness of Liver Disease

INTRODUCTION

The most common liver disease is nonalcoholic fatty liver disease, characterized by an intrahepatic accumulation of lipids that most often accompanies obesity. Fatty liver can evolve, in the presence of oxidative stress and inflammation, into disabling and deadly liver diseases such as cirrhosis, hepatocellular carcinoma (HCC), and cholangiocarcinoma (CC). Old age seems to favor HCC and CC, in agreement with the inflammaging theory, according to which aging accrues inflammation. Cancer, in general, is an age-related disease, as incidence and mortality for most types of cancer increase with age. However, how molecular drivers in tumors differ or are mutated more frequently among patients of different ages remains scarcely investigated. A recent integrative analysis of the age-associated multi-omic landscape across cancers and healthy tissues demonstrated that age-related gene expression changes are linked to numerous biological processes. HCC and CC have among the lowest five-year survival estimates due to their aggressive progression.

MATERIALS AND METHODS

In this study, we extracted top gene candidates from the above-mentioned pan-analyses (i.e., B2M, C1qA, SUCLG1) and tested by qPCR their expression and their correlation with disease progression in 48 tissue samples covering liver disease stages (fatty liver, hepatitis, cirrhosis, HCC and CC) and normal tissues.

RESULTS

Here, we report a significant downregulation in the expression of the age-associated gene SUCLG1 during the progression of liver disease toward HCC and CC, which also associates with poor patient survival.

CONCLUSION

SUCGL1, a mitochondrial enzyme gene that catalyzes the conversion of succinyl CoA to succinate, might be therapeutically targeted for the development and progression of age-associated liver cancers with low survival rates.

Therapeutic implications and comprehensive insights into cellular senescence and aging in the tumor microenvironment of sarcoma

Sarcoma (SARC), a diverse group of stromal tumors arising from mesenchymal tissues, is often associated with a poor prognosis. Emerging evidence indicates that senescent cells within the tumor microenvironment (TME) significantly contribute to cancer progression and metastasis. Although the influence of senescence on SARC has been partially acknowledged, it has yet to be fully elucidated. In this study, we revealed that senescence level and age were associated with TME, immune treatment indicators, and prognosis in SARC. Utilizing the weighted gene co-expression network analysis and least absolute shrinkage and selection operator algorithm, we identified senescence-related genes and developed a senescence predictor. Three genes, RAD54, PIK3IP1, and TRIP13, were selected to construct a multiple linear regression model. Validation cohorts, immunohistochemistry, and quantitative reverse transcription polymerase chain reaction confirmed that the predictor derived from these three genes possessed prognostic and pathological relevance. Our senescence predictor provides comprehensive insights into the molecular mechanisms of SARC and identifies potential biomarkers for prognosis, paving the way for effective treatments. The results of this study hold promise for developing therapeutic strategies tailored to the unique characteristics of SARC.

Single-cell transcriptomics unveils multifaceted immune heterogeneity in early-onset versus late-onset cervical cancer

Early-onset (EOCC) and late-onset cervical cancers (LOCC) represent two clinically distinct subtypes, each defined by unique clinical manifestations and therapeutic responses. However, their immunological profiles remain poorly explored. Herein, we analyzed single-cell transcriptomic data from 4 EOCC and 4 LOCC samples to compare their immune architectures. Epithelial cells in EOCC exhibited a notable dual immunological phenotype, characterized by immune-suppressive properties driven by elevated CXCL production, alongside immune-stimulatory features linked to heightened HLA molecule expression. CD4 + and CD8 + T cells in LOCC demonstrated a heightened activation state, while NK cells exhibited diminished cytotoxicity. Macrophages in LOCC displayed enhanced polarization towards both M1 and M2 phenotypes, along with dendritic cells showing augmented antigen-presenting capacity. Regarding cancer-associated fibroblasts (CAFs), EOCC was enriched with inflammatory CAFs, whereas LOCC harbored a higher proportion of antigen-presenting CAFs. These findings reveal the multifaceted immune heterogeneity between EOCC and LOCC, underscoring the imperative for age-tailored immunotherapeutic strategies.

Application of a high-throughput swarm-based deep neural network Algorithm reveals SPAG5 downregulation as a potential therapeutic target in adult AML

Gene‒gene interactions play pivotal roles in disease pathogenesis and are fundamental in the development of targeted therapeutics, particularly through the elucidation of oncogenic gene drivers in cancer. The systematic analysis of pathways and gene interactions is critical in the drug discovery process for various cancer subtypes. SPAG5, known for its role in spindle formation during cell division, has been identified as an oncogene in several cancers, although its specific impact on AML remains underexplored. This study leverages a high-throughput swarm-based deep neural network (SDNN) and transcriptomic data-an approach that enhances predictive accuracy and robustness through collective intelligence-to augment, model, and enhance the understanding of the TP53 pathway in AML cohorts. Our integrative systems biology approach identified SPAG5 as a uniquely downregulated driver in adult AML, underscoring its potential as a novel therapeutic target. The interaction of SPAG5 with key hub genes such as MDM2 and CDK1 not only reinforces its role in tumour suppression through negative regulation but also highlights its potential in moderating the phenotypic and genomic alterations associated with AML progression. This study of the role and interaction dynamics of SPAG5 sets the stage for future research aimed at developing targeted and personalized treatment approaches for AML, utilizing the capabilities of genetic interventions.

Pan-cancer analysis reveals age-associated genetic alterations in protein domains

Cancer incidence and mortality differ among individuals of different ages, but the functional consequences of genetic alterations remain largely unknown. We systematically characterized genetic alterations within protein domains stratified by affected individual's age and showed that the mutational effects on domains varied with age. We further identified potential age-associated driver genes with hotspots across 33 cancers. The candidate drivers involved numerous cancer-related genes that participate in various oncogenic pathways and play central roles in human protein-protein interaction (PPI) networks. We found widespread age biases in protein domains and identified the associations between hotspots and age. Age-stratified PPI networks perturbed by hotspots were constructed to illustrate the function of mutations enriched in domains. We found that hotspots in young adults were associated with premature senescence. In summary, we provided a catalog of age-associated hotspots and their perturbed networks, which may facilitate precision diagnostics and treatments for cancer.

Linking LEDGF/p75 Overexpression With Microsatellite Instability and KRAS Mutations: A Small-Scale Study in Colorectal Cancer

INTRODUCTION

Colorectal cancer (CRC) ranks third in men and second in women, with 153,020 new cases and 52,550 deaths in 2023, and with a projected incidence of 2.2 million new cases by 2030 due to lifestyle changes and enhanced diagnostic capabilities. Identification and analysis of new biomarkers, like lens epithelium-derived growth factor splice variant of 75 kDa (LEDGF/p75), which is known to play a crucial role as stress-related oncogene, can make a significant contribution in facilitating early CRC detection.

METHODS

This study analyzed the expression of LEDGF/p75 and the ubiquitin E2 conjugating enzyme UBC13 in 15 CRC tissue samples and adjacent non-tumor tissues. All patient samples underwent NGS-based mutation analysis beforehand. The western blot technique was used for protein analysis, and the results were further validated using mRNA expression data from 521 patient samples from the TCGA database.

RESULTS

LEDGF/p75 expression was significantly elevated in nearly all tumor tissue samples compared to adjacent tissue (11/15, 73.3%). Additionally, the UBC13 enzyme, a key regulator in the degradation of signaling molecules, was also increased in most tumor tissue samples (9/15, 60.0%). Co-overexpression of LEDGF/p75 and UBC13 was evident in 6/6 patients. Patients with KRAS and MSH2 mutations showed a 75% and 100% correlation with LEDGF/p75 overexpression, respectively.

CONCLUSION

This study confirms the upregulation of LEDGF/p75 in CRC and shows its correlation with KRAS and MSH2 mutations. The interaction of LEDGF/p75 with DNA damage response proteins may contribute to drug resistance and increased tumor aggressiveness. LEDGF/p75's potential as a prognostic biomarker independent of lymph node involvement or CEA levels highlights its potential in personalized therapy, and warrants further research into its therapeutic targeting.

Multiomics of Aging and Aging-Related Diseases

Despite their astonishing biological diversity, surprisingly few shared traits connect all or nearly all living organisms. Aging, i.e., the progressive and irreversible decline in the function of multiple cells and tissues, is one of these fundamental features of all organisms, ranging from single-cell creatures to complex animals, alongside variability, adaptation, growth, healing, reproducibility, mobility, and, finally, death. Age is a key determinant for many pathologies, shaping the risks of incidence, severity, and treatment outcomes for cancer, neurodegeneration, heart failure, sarcopenia, atherosclerosis, osteoporosis, and many other diseases. In this review, we aim to systematically investigate the age-related features of the development of several diseases through the lens of multiomics: from genome instability and somatic mutations to pathway alterations and dysregulated metabolism.

Aging2Cancer: an integrated resource for linking aging to tumor multi-omics data

BACKGROUND

Aging and tumorigenesis share intricate regulatory processes, that alter the genome, epigenome, transcriptome and immune landscape of tissues. Discovering the link between aging and cancer in terms of multiomics characteristics remains a challenge for biomedical researchers.

METHODS

We collected high-throughput datasets for 57 human tumors and 20 normal tissues, including 23,125 samples with age information. On the basis of these sufficient omics data, we introduced six useful modules including genomic (somatic mutation and copy number variation), gene expression, DNA methylation, hallmarks (aging and cancer), immune landscape (immune infiltration, immune pathways, immune signatures, and antitumor immune activities) and survival analysis. Correlation and differential analyses were performed for the multiomic signatures associated with aging at the gene level.

RESULTS

We developed Aging2Cancer ( http://210.37.77.200:8080/Aging2Cancer/index.jsp ), which is a comprehensive database and analysis platform for revealing the associations between aging and cancer. Users can search for and visualize the results of genes of interest to explore the relationships between aging and cancer at the gene level for different omics levels.

CONCLUSIONS

We believe that Aging2Cancer is a valuable resource for identifying novel biomarkers and will serve as a bridge for linking aging to cancer.

A powerful framework for differential co-expression analysis of general risk factors

Differential co-expression analysis (DCA) aims to identify genes in a pathway whose shared expression depends on a risk factor. While DCA provides insights into the biological activity of diseases, existing methods are limited to categorical risk factors and/or suffer from bias due to batch and variance-specific effects. We propose a new framework, Kernel-based Differential Co-expression Analysis (KDCA), that harnesses correlation patterns between genes in a pathway to detect differential co-expression arising from general (i.e., continuous, discrete, or categorical) risk factors. Using various simulated pathway architectures, we find that KDCA accounts for common sources of bias to control the type I error rate while substantially increasing the power compared to the standard eigengene approach. We then applied KDCA to The Cancer Genome Atlas thyroid data set and found several differentially co-expressed pathways by age of diagnosis and BRAF mutation status that were undetected by the eigengene method. Collectively, our results demonstrate that KDCA is a powerful testing framework that expands DCA applications in expression studies.

Systemic and local chronic inflammation and hormone disposition promote a tumor-permissive environment for breast cancer in older women

Estrogen receptor positive (ER+) breast cancer is the most common subtype of breast cancer and is an age-related disease. The peak incidence of diagnosis occurs around age 70, even though these post-menopausal patients have low circulating levels of estradiol (E2). Despite the hormone sensitivity of age-related tumors, we have a limited understanding of the interplay between systemic and local hormones, chronic inflammation, and immune changes that contribute to the growth and development of these tumors. Here, we show that aged F344 rats treated with the dimethylbenz(a)anthracene / medroxyprogestrone acetate (DMBA/MPA) carcinogen develop more tumors at faster rates than their younger counterparts, suggesting that the aged environment promotes tumor initiation and impacts growth. Single-nuclei RNA-seq (snRNA-seq) of the tumors showed broad local immune dysfunction that was associated with circulating chronic inflammation. Across a broad cohort of specimens from patients with ER+ breast cancer and age-matched donors of normal breast tissue, we observe that even with an estrone (E1)-predominant estrogen disposition in the systemic circulation, tumors in older patients increase HSD17B7 expression to convert E1 to E2 in the tumor microenvironment (TME) and have local E2 levels similar to pre-menopausal patients. Concurrently, trackable increases in several chemokines, defined most notably by CCL2, promote a chronically inflamed but immune dysfunctional TME. This unique milieu in the aged TME, characterized by high local E2 and chemokine-enriched chronic inflammation, promotes both accumulation of tumor-associated macrophages (TAMs), which serve as signaling hubs, as well as polarization of TAMs towards a CD206+/PD-L1+, immunosuppressive phenotype. Pharmacologic targeting of estrogen signaling (either by HSD17B7 inhibition or with fulvestrant) and chemokine inflammation both decrease local E2 and prevent macrophage polarization. Overall, these findings suggest that chronic inflammation and hormonal disposition are critical contributors to the age-related nature of ER+ breast cancer development and growth and offer potential therapeutic insight to treat these patients.

Translational Summary

We uncover the unique underpinnings establishing how the systemic host environment contributes to the aged breast tumor microenvironment, characterized by high local estradiol and chronic inflammation with immune dysregulation, and show that targeting avenues of estrogen conversion and chronic inflammation work to restore anti-tumor immunity.

Impact of Age on Pharmacogenomics and Treatment Outcomes of B-Cell Acute Lymphoblastic Leukemia

PURPOSE

Acute lymphoblastic leukemia (ALL) can occur across all age groups, with a strikingly higher cure rate in children compared with adults. However, the pharmacological basis of age-related differences in ALL treatment response remains unclear.

METHODS

Studying 767 children and 309 adults with newly diagnosed B-cell ALL enrolled on frontline trials at St Jude Children's Research Hospital, MD Anderson Cancer Center, the Alliance for Clinical Trials in Oncology, and the ECOG-ACRIN Cancer Research Group, we determined the ex vivo sensitivity of leukemia cells to 21 drugs. Twenty-three ALL molecular subtypes were identified using RNA sequencing. We systematically characterized the associations between drug response and ALL genomics in children, adolescents and young adults, and elderly adults. We evaluated the effect of age-related gene expression signature on ALL treatment outcomes.

RESULTS

Seven ALL drugs (asparaginase, prednisolone, mercaptopurine, dasatinib, nelarabine, daunorubicin, and inotuzumab ozogamicin) showed differential activity between children and adults, of which six were explained by age-related differences in leukemia molecular subtypes. Adolescents and young adults showed similar patterns of drug resistance as older adults, relative to young children. Mercaptopurine exhibited subtype-independent greater sensitivity in children. Transcriptomic profiling uncovered subclusters within CRLF2-, DUX4-, and KMT2A-rearranged ALL that were linked to age and cytotoxic drug resistance. In particular, a subset of children had adult-like ALL on the basis of leukemia gene expression patterns across subtypes, despite their chronological age. Resistant to cytotoxic drugs, children with adult-like ALL exhibited poor prognosis in pediatric ALL trials, even after adjusting for age and minimal residual diseases.

CONCLUSION

Our results provide pharmacogenomic insights into age-related disparities in ALL cure rates and identify leukemia prognostic features for treatment individualization across age groups.

Toward a Unified Theory of Why Young People Develop Cancer

Epidemiologic and genetic studies have now defined specific patterns of incidence and distinct molecular features of cancers in young versus aging people. Here, I review a general framework for the causes of cancer in children and young adults by relating somatic genetic mosaicism and developmental tissue mutagenesis. This framework suggests how aging-associated cancers such as carcinomas, glioblastomas, and myelodysplastic leukemias are causally distinct from cancers that predominantly affect children and young adults, including lymphoblastic and myeloid leukemias, sarcomas, neuroblastomas, medulloblastomas, and other developmental cancers. I discuss the oncogenic activities of known developmental mutators RAG1/2, AID, and PGBD5, and describe strategies needed to define missing developmental causes of young-onset cancers. Thus, a precise understanding of the mechanisms of tissue-specific somatic mosaicism, developmental mutators, and their control by human genetic variation and environmental exposures is needed for improved strategies for cancer screening, prevention, and treatment.

Targeted gene sequencing reveals disparate genomic mutations between young and older adults in renal cell carcinoma

BACKGROUND

Renal cell carcinoma (RCC) is a type of cancer that can develop at any point in adulthood, spanning the range of age-related changes that occur in the body. However, the specific molecular mechanisms underlying the connections between age and genetic mutations in RCC have not been extensively investigated.

METHODS

Clinical and genetic data from patients diagnosed with RCC were collected from two prominent medical centers in China as well as the TCGA dataset. The patients were categorized into two groups based on their prognosticated age: young adults (YAs) and older adults (OAs). Univariate and multivariate analysis were employed to evaluate the relationships between age and genetic mutations. Furthermore, a mediation analysis was conducted to assess the association between age and overall survival, with genetic disparities serving as a mediator.

RESULTS

Our analysis revealed significant differences in clinical presentation between YAs and OAs with RCC, including histopathological types, histopathological tumor stage, and sarcomatoid differentiation. YAs were found to have lower mutation burden and significantly mutated genes (SMGs) of RCC. However, we did not observe any significant differences between the two groups in terms of 10 canonical oncogenic signaling pathways-related genes mutation, telomerase-related genes (TRGs) mutation, copy number changes, and genetic mutations associated with clinically actionable targeted drugs. Importantly, we demonstrate superior survival outcomes in YAs, and we confirmed the mediating effect of genetic disparities on these survival outcome differences between YAs and OAs.

CONCLUSION

Our findings reveal previously unrecognized associations between age and the molecular underpinnings of RCC. These associations may serve as valuable insights to guide precision diagnostics and treatments for RCC.

Development and validation of a prognostic nomogram to predict the recurrence of AFP-negative and DCP-positive hepatocellular carcinoma after curative resection

Purpose

Approximately one-third of hepatocellular carcinoma (HCC) cases are characterized by alpha-fetoprotein (AFP) negativity (AFP-NHCC. Among these patients, around 60% exhibit des-gamma-carboxyprothrombin (DCP) positivity, and DCP-positive patients have a poorer prognosis. As a curative treatment, recurrence after liver resection poses significant challenges to the prognosis of HCC patients. Therefore, it is necessary to determine the relevant risk factors of these patients and provide timely treatment options.

Methods

This study included 540 patients who underwent resection at Beijing You'an Hospital. 292 patients from 2014 to 2018 constituted the training cohort, while 248 patients from 2018 to 2020 constituted the validation cohort. All patients underwent routine follow-ups until December 2023. Variables were identified through Cox regression, and a nomogram was developed. The nomogram was evaluated using time-dependent receiver operating characteristic curves (ROC), calibration curves, Decision curve analysis (DCA), and Kaplan-Meier (KM) curve analysis.

Results

We found that age, tumor number, tumor size, γ-glutamyl transpeptidase (γ-GT), and prothrombin time (PT) are independent risk factors for HCC recurrence, and a nomogram was developed and validated based on this result to predict recurrence-free survival (RFS) at 1, 2, and 3 years. The performance of the nomogram was further confirmed by the ROC curve, calibration curve, and DCA, all of which showed favorable results. The KM curve analysis clearly distinguishes between two groups of people with different risks in terms of prognosis in both the training and validation sets.

Conclusion

In summary, we established and validated a novel nomogram by multivariate Cox regression analysis to predict recurrence in DCP-positive patients with AFP-NHCC after resection. The nomogram, including age, tumor number, tumor size, γ-GT, and PT, demonstrates better predictive ability for AFP-NHCC patients with DCP positive.

Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

Novel insight into nicotinamide adenine dinucleotide and related metabolites in cancer patients undergoing surgery

Nicotinamide adenine dinucleotide (NAD +) plays a pivotal role in numerous cellular functions. Reduced NAD + levels are postulated to be associated with cancer. As interest in understanding NAD + dynamics in cancer patients with therapeutic applications in mind grows, there remains a shortage of comprehensive data. This study delves into NAD + dynamics in patients undergoing surgery for different digestive system cancers. This prospective study enrolled 99 patients with eight different cancers. Fasting blood samples were obtained during the perioperative period. The concentrations of NAD + , nicotinamide mononucleotide (NMN), and nicotinamide riboside were analyzed using tandem mass spectrometry. After erythrocyte volume adjustment, NAD + remained relatively stable after surgery. Meanwhile, NMN decreased the day after surgery and displayed a recovery trend. Interestingly, liver and pancreatic cancer patients exhibited poor postoperative NMN recovery, suggesting a potential cancer type-specific influence on NAD + metabolism. This study illuminated the behavior of NAD + in surgically treated cancer patients. We identified which cancer types have particularly low levels and at what point depletion occurs during the perioperative period. These insights suggest the need for personalized NAD + supplementation strategies, calibrated to individual patient needs and treatment timelines. Clinical trial registration jRCT1020210066.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .

Prognostic impact and causality of age on oncological outcomes in women with endometrial cancer: a multimethod analysis of the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials

BACKGROUND

Numerous studies have shown that older women with endometrial cancer have a higher risk of recurrence and cancer-related death. However, it remains unclear whether older age is a causal prognostic factor, or whether other risk factors become increasingly common with age. We aimed to address this question with a unique multimethod study design using state-of-the-art statistical and causal inference techniques on datasets of three large, randomised trials.

METHODS

In this multimethod analysis, data from 1801 women participating in the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials were used for statistical analyses and causal inference. The cohort included 714 patients with intermediate-risk endometrial cancer, 427 patients with high-intermediate risk endometrial cancer, and 660 patients with high-risk endometrial cancer. Associations of age with clinicopathological and molecular features were analysed using non-parametric tests. Multivariable competing risk analyses were performed to determine the independent prognostic value of age. To analyse age as a causal prognostic variable, a deep learning causal inference model called AutoCI was used.

FINDINGS

Median follow-up as estimated using the reversed Kaplan-Meier method was 12·3 years (95% CI 11·9-12·6) for PORTEC-1, 10·5 years (10·2-10·7) for PORTEC-2, and 6·1 years (5·9-6·3) for PORTEC-3. Both overall recurrence and endometrial cancer-specific death significantly increased with age. Moreover, older women had a higher frequency of deep myometrial invasion, serous tumour histology, and p53-abnormal tumours. Age was an independent risk factor for both overall recurrence (hazard ratio [HR] 1·02 per year, 95% CI 1·01-1·04; p=0·0012) and endometrial cancer-specific death (HR 1·03 per year, 1·01-1·05; p=0·0012) and was identified as a significant causal variable.

INTERPRETATION

This study showed that advanced age was associated with more aggressive tumour features in women with endometrial cancer, and was independently and causally related to worse oncological outcomes. Therefore, our findings suggest that older women with endometrial cancer should not be excluded from diagnostic assessments, molecular testing, and adjuvant therapy based on their age alone.

FUNDING

None.

Age-dependent molecular variations in osteosarcoma: implications for precision oncology across pediatric, adolescent, and adult patients

Background

Osteosarcoma is a leading subtype of bone tumor affecting adolescents and adults. Comparative molecular characterization among different age groups, especially in pediatric, adolescents and adults, is scarce.

Methods

We collected samples from 194 osteosarcoma patients, encompassing pediatric, adolescent, and adult cohorts. Genomic analyses were conducted to reveal prevalent mutations and compare molecular features in pediatric, adolescent, and adult patients.

Results

Samples from 194 osteosarcoma patients across pediatric to adult ages were analyzed, revealing key mutations such as TP53, FLCN, NCOR1, and others. Children and adolescents showed more gene amplifications and HRD mutations, while adults had a greater Tumor Mutational Burden (TMB). Mutations in those over 15 were mainly in cell cycle and PI3K/mTOR pathways, while under 15s had more in cell cycle and angiogenesis with higher VEGFA, CCND3, TFEB mutations. CNV patterns varied with age: VEGFA and XPO5 amplifications more in under 25s, and CDKN2A/B deletions in over 25s. Genetic alterations in genes like MCL1 and MYC were associated with poor prognosis, with VEGFA mutations also indicating worse outcomes. 58% of patients had actionable mutations, suggesting opportunities for targeted therapies. Age-specific patterns were observed, with Multi-TKI mutations more common in younger patients and CDK4/6 inhibitor mutations in adults, highlighting the need for personalized treatment approaches in osteosarcoma. In a small group of patients with VEGFR amplification, postoperative treatment with multi-kinase inhibitors resulted in a PR in 3 of 13 cases, especially in patients under 15. A significant case involved a 13-year-old with a notable tumor size reduction achieving PR, even with other genetic alterations present in some patients with PD.

Conclusion

This study delineates the molecular differences among pediatric, adolescent, and adult osteosarcoma patients at the genomic level, emphasizing the necessity for precision diagnostics and treatment strategies, and may offer novel prognostic biomarkers for patients with osteosarcoma. These findings provide a significant scientific foundation for the development of individualized treatment approaches tailored to patients of different age groups.

Proteomic features of soft tissue tumours in adolescents and young adults

BACKGROUND

Adolescents and young adult (AYA) patients with soft tissue tumours including sarcomas are an underserved group with disparities in treatment outcomes.

METHODS

To define the molecular features between AYA and older adult (OA) patients, we analysed the proteomic profiles of a large cohort of soft tissue tumours across 10 histological subtypes (AYA n = 66, OA n = 243), and also analysed publicly available functional genomic data from soft tissue tumour cell lines (AYA n = 5, OA n = 8).

RESULTS

Biological hallmarks analysis demonstrates that OA tumours are significantly enriched in MYC targets compared to AYA tumours. By comparing the patient-level proteomic data with functional genomic profiles from sarcoma cell lines, we show that the mRNA splicing pathway is an intrinsic vulnerability in cell lines from OA patients and that components of the spliceosome complex are independent prognostic factors for metastasis free survival in AYA patients.

CONCLUSIONS

Our study highlights the importance of performing age-specific molecular profiling studies to identify risk stratification tools and targeted agents tailored for the clinical management of AYA patients.

Nomogram built based on machine learning to predict recurrence in early-stage hepatocellular carcinoma patients treated with ablation

Introduction

To analyze the risk factors affecting recurrence in early-stage hepatocellular carcinoma (HCC) patients treated with ablation and then establish a nomogram to provide a clear and accessible representation of the patients' recurrence risk.

Methods

Collect demographic and clinical data of 898 early-stage HCC patients who underwent ablation treatment at Beijing You'an Hospital, affiliated with Capital Medical University from January 2014 to December 2022. Patients admitted from 2014 to 2018 were included in the training cohort, while 2019 to 2022 were in the validation cohort. Lasso and Cox regression was used to screen independent risk factors for HCC patients recurrence, and a nomogram was then constructed based on the screened factors.

Results

Age, gender, Barcelona Clinic Liver Cancer (BCLC) stage, tumor size, globulin (Glob) and γ-glutamyl transpeptidase (γ-GT) were finally incorporated in the nomogram for predicting the recurrence-free survival (RFS) of patients. We further confirmed that the nomogram has optimal discrimination, consistency and clinical utility by the C-index, Receiver Operating Characteristic Curve (ROC), calibration curve and Decision Curve Analysis (DCA). Moreover, we divided the patients into different risk groups and found that the nomogram can effectively identify the high recurrence risk patients by the Kaplan-Meier curves.

Conclusion

This study developed a nomogram using Lasso-Cox regression to predict RFS in early-stage HCC patients following ablation, aiding clinicians in identifying high-risk groups for personalized follow-up treatments.

Spatiotemporal distribution of mediastinal neoplasms: A comprehensive multi-center study

OBJECTIVES

Mediastinal neoplasms are typical but uncommon thoracic diseases with increasing incidence and unfavorable prognoses. A comprehensive understanding of their spatiotemporal distribution is essential for accurate diagnosis and timely treatment. However, previous studies are limited in scale and data coverage. Therefore, this study aims to elucidate the distribution of mediastinal lesions, offering valuable insights into this disease.

MATERIALS AND METHODS

This multi-center, hospital-based observational study included 20 nationwide institutions. A retrospective search of electronic medical records from January 1st, 2009, to December 31st, 2020, was conducted, collecting sociodemographic data, computed tomography images, and pathologic diagnoses. Analysis focused on age, sex, time, location, and geographical region. Comparative assessments were made with global data from a multi-center database.

RESULTS

Among 7,765 cases, thymomas (30.7%), benign mediastinal cysts (23.4%), and neurogenic tumors (10.0%) were predominant. Distribution varied across mediastinal compartments, with thymomas (39.6%), benign cysts (28.1%), and neurogenic tumors (51.9%) most prevalent in the prevascular, visceral, and paravertebral mediastinum, respectively. Age-specific variations were notable, with germ cell tumors prominent in patients under 18 and aged 18-29, while thymomas were more common in patients over 30. The composition of mediastinal lesions across different regions of China remained relatively consistent, but it differs from that of the global population.

CONCLUSION

This study revealed significant heterogeneity in the spatiotemporal distribution of mediastinal neoplasms. These findings provide useful demographic data when considering the differential diagnosis of mediastinal lesions, and would be beneficial for tailoring disease prevention and control strategies.

Multi-Omics Integration for Liver Cancer Using Regression Analysis

Genetic biomarkers have played a pivotal role in the classification, prognostication, and guidance of clinical cancer therapies. Large-scale and multi-dimensional analyses of entire cancer genomes, as exemplified by projects like The Cancer Genome Atlas (TCGA), have yielded an extensive repository of data that holds the potential to unveil the underlying biology of these malignancies. Mutations stand out as the principal catalysts of cellular transformation. Nonetheless, other global genomic processes, such as alterations in gene expression and chromosomal re-arrangements, also play crucial roles in conferring cellular immortality. The incorporation of multi-omics data specific to cancer has demonstrated the capacity to enhance our comprehension of the molecular mechanisms underpinning carcinogenesis. This report elucidates how the integration of comprehensive data on methylation, gene expression, and copy number variations can effectively facilitate the unsupervised clustering of cancer samples. We have identified regressors that can effectively classify tumor and normal samples with an optimal integration of RNA sequencing, DNA methylation, and copy number variation while also achieving significant p-values. Further, these regressors were trained using linear and logistic regression with k-means clustering. For comparison, we employed autoencoder- and stacking-based omics integration and computed silhouette scores to evaluate the clusters. The proof of concept is illustrated using liver cancer data. Our analysis serves to underscore the feasibility of unsupervised cancer classification by considering genetic markers beyond mutations, thereby emphasizing the clinical relevance of additional global cellular parameters that contribute to the transformative process in cells. This work is clinically relevant because changes in gene expression and genomic re-arrangements have been shown to be signatures of cellular transformation across cancers, as well as in liver cancers.

Deciphering age-specific molecular features in cervical cancer and constructing an angio-immune prognostic model

Cancer incidence is increasingly seen in younger individuals. Molecular distinctions between young and elderly patients at onset are understudied. This study used public databases to explore genomic, transcriptomic, and immune-related features across age groups in cervical cancer. Additionally, it aims to create a prognostic model applicable across diverse age cohorts, enabling precise patient stratification, and personalized therapies. Gene mutations, expression data, and clinicopathological information were obtained from 317 cervical cancer patients. These patients were divided into a young group and an old group based on the median age of onset. The characteristics of differential gene mutation, gene expression, and immune cells analysis were analyzed by R software. Finally, the prognostic model was constructed by univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox regression analyses of angiogenic and immune gene sets. Its validity was further confirmed using an additional 300 cervical squamous cell carcinoma and endocervical adenocarcinoma tissues. Cervical cancer patients at elderly onset age exhibit a significantly higher frequency of NOTCH1 and TP53 driver mutations compared to young patients, along with a notably higher tumor mutational burden. However, there were no significant differences between the 2 groups in terms of genomic instability and age-related mutational signatures. Differential gene expression analysis revealed that the young group significantly upregulated interferon-alpha and gamma responses and exhibited significantly higher activity in multiple metabolic pathways. Immune microenvironment analysis indicated enrichment of dendritic cells and natural killer cells in the young group, while transforming growth factor-β signature was enriched in the elderly group, indicating a higher degree of immune exclusion. A multigene prognostic model based on angiogenesis and T cell immune gene sets showed excellent prognostic performance independent of clinical factors such as age. High-risk groups identified by the model exhibit significant activation of tumor-promoting processes, such as metastasis and angiogenesis. Our study reveals distinct patterns in cancer-driving mechanisms, biological processes, and immune system status between young and elderly patients at onset with cervical cancer. These findings shed light on the age-specific underlying mechanisms of carcinogenesis. Furthermore, an independent molecular prognostic model is constructed to provide valuable references for patient stratification and the development of potential drug targets.

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.

Nomogram combining pre-operative clinical characteristics and spectral CT parameters for predicting the WHO/ISUP pathological grading in clear cell renal cell carcinoma

PURPOSE

To develop a novel clinical-spectral-computed tomography (CT) nomogram incorporating clinical characteristics and spectral CT parameters for the preoperative prediction of the WHO/ISUP pathological grade in clear cell renal cell carcinoma (ccRCC).

METHODS

Seventy-three ccRCC patients who underwent spectral CT were included in this retrospective analysis from December 2020 to June 2023. The subjects were pathologically divided into low- and high-grade groups (WHO/ISUP 1/2, n = 52 and WHO/ISUP 3/4, n = 21, respectively). Information on clinical characteristics, conventional CT imaging features, and spectral CT parameters was collected. Multivariate logistic regression analyses were conducted to create a nomogram combing clinical data and image data for preoperatively predicting the pathological grade of ccRCC, and the area under the curve (AUC) was utilized to assess the predictive performance of the model.

RESULTS

Multivariate logistic regression analyses revealed that age, systemic immune-inflammation index (SII), and the slope of the spectrum curve in the cortex phase (CP-K) were independent predictors for predicting high-grade ccRCC. The clinical-spectral-CT model exhibited high evaluation efficacy, with an AUC of 0.933 (95% confidence interval [CI]: 0.878-0.998; sensitivity: 0.810; specificity: 0.923). The calibration curve revealed that the predicted probability of the clinical-spectral-CT nomogram could better fit the actual probability, with high calibration. The Hosmer-Lemeshow test showed that the model had a good fitness (χ2 = 5.574, p = 0.695).

CONCLUSION

The clinical-spectral-CT nomogram has the potential to predict WHO/ISUP grading of ccRCC preoperatively.

Aging-related biomarker discovery in the era of immune checkpoint inhibitors for cancer patients

Older patients with cancer, particularly those over 75 years of age, often experience poorer clinical outcomes compared to younger patients. This can be attributed to age-related comorbidities, weakened immune function, and reduced tolerance to treatment-related adverse effects. In the immune checkpoint inhibitors (ICI) era, age has emerged as an influential factor impacting the discovery of predictive biomarkers for ICI treatment. These age-linked changes in the immune system can influence the composition and functionality of tumor-infiltrating immune cells (TIICs) that play a crucial role in the cancer response. Older patients may have lower levels of TIICs infiltration due to age-related immune senescence particularly T cell function, which can limit the effectivity of cancer immunotherapies. Furthermore, age-related immune dysregulation increases the exhaustion of immune cells, characterized by the dysregulation of ICI-related biomarkers and a dampened response to ICI. Our review aims to provide a comprehensive understanding of the mechanisms that contribute to the impact of age on ICI-related biomarkers and ICI response. Understanding these mechanisms will facilitate the development of treatment approaches tailored to elderly individuals with cancer.

Comprehensive analysis of cellular senescence and immune microenvironment in papillary thyroid carcinoma

Senescence-induced therapy was previously considered as an effective treatment for tumors, and cellular senescence was initially regarded as an effective mechanism against cancer. However, whether cell senescence-related genes can be used to predict the prognosis of papillary thyroid carcinoma (PTC) and immunotherapy remains unclear. We developed and validated a cell senescence-related signature (CSRS) by analyzing the gene expression of 278 genes related to cellular senescence in 738 patients with PTC. Additionally, further analysis showed that CSRS was a reliable predictor of patient outcomes in combination with immune checkpoint expression and drug susceptibility, and patients with high risk scores may benefit from immunotherapy. The findings of this study demonstrate that CSRS serves as an immunotherapeutic response and prognosis biomarker affecting the tumor immune microenvironment of PTC.

A Competing Risk Nomogram for Prediction of Prognosis in Patients With Primary Squamous Cell Thyroid Carcinoma

Objective: Primary squamous cell thyroid carcinoma (PSCTC) is an extremely rare carcinoma, accounting for less than 1% of all thyroid carcinomas. However, the factors contributing to PSCTC outcomes remain unclear. This study aimed to identify the prognostic factors and develop a prognostic predictive model for patients with PSCTC. Methods: The analysis included patients diagnosed with thyroid carcinoma between 1975 and 2016 from the Surveillance, Epidemiology, and End Results database. Prognostic differences among the 5 pathological types of thyroid carcinomas were analyzed. To determine prognostic factors in PSCTC patients, the Cox regression model and Fine-Gray competing risk model were utilized. Based on the Fine-Gray competing risk model, a nomogram was established for predicting the prognosis of patients with PSCTC. Results: A total of 198,757 thyroid carcinoma patients, including 218 PSCTC patients, were identified. We found that PSCTC and anaplastic thyroid cancer had the worst prognosis among the 5 pathological types of thyroid carcinoma (P < .001). According to univariate and multivariate Cox regression analyses, age (71-95 years) was an independent risk factor for poorer overall survival and disease-specific survival in PSCTC patients. Using Fine-Gray regression analysis, the total number of in situ/malignant tumors for patient (Number 1) (≥2) was identified as an independent protective factor for prognosis of PSCTC. The area under the curve, the concordance index (C-index), calibration curves and decision curve analysis revealed that the nomogram was capable of predicting the prognosis of PSCTC patients accurately. Conclusion: The competing risk nomogram is highly accurate in predicting prognosis for patients with PSCTC, which may help clinicians to optimize individualized treatment decisions.

Risk factors of hepatocellular carcinoma associated with nonalcoholic fatty liver disease: Systematic review and meta-analysis

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is currently an important chronic liver disease threatening human life and health.

OBJECTIVE

To investigate the risk factors of hepatocellular carcinoma (HCC) associated with nonalcoholic fatty liver disease (NAFLD) by systematic review.

METHODS

We conducted a systematic review and meta-analysis. A systematic search of Chinese and English databases (PubMed, Web of Science, Cochrane Library, China national knowledge infrastructure (CNKI), Wanfang database, and VIP database) was performed until June 30, 2023. Studies were included to investigate the risk factors for HCC in patients with NAFLD. Quality evaluation was performed using the Newcastle-Ottawa Literature Quality Evaluation Scale, and then hazard ratios (HRs) for different influencing factors were combined.

RESULTS

We reviewed the results of 12 high-quality cohort studies involving 738,934 patients with NAFLD and 1,480 developed HCC. A meta-analysis based on a random-effects model showed that advanced age (HR = 1.81, 95% CI: 1.51-2.17), male gender (HR = 2.51, 95% CI: 1.67-3.78), hypertension (HR = 1.87, 95% CI: 1.05-3.33), and diabetes (HR = 2.27, 95% CI: 1.63-3.16) were risk factors for HCC in NAFLD, and the differences were statistically significant. However, there was no statistically significant effect of current smoking (HR = 1.45, 95% CI: 0.72-2.92) and dyslipidemia (HR = 1.03, 95% CI: 0.72-1.47) on HCC incidence in this study.

CONCLUSION

Age, sex, hypertension and diabetes are risk factors for HCC in NAFLD patients. Diabetic NAFLD patients have a 2.27-fold increased risk of HCC, and health education and intervention for elderly, male, NAFLD patients with diabetes and hypertension need to be strengthened to promote a reduction in the risk of HCC.

Risk Factors and Comorbidities Associated With Hepatocellular Carcinoma in Patients With Chronic Hepatitis B Virus Infection

INTRODUCTION/OBJECTIVES

Chronic hepatitis B virus infection (CHBVI) is a major public health problem affecting about 296 million people worldwide. HBV infects the liver, and when it becomes chronic, may cause cirrhosis and hepatocellular carcinoma (HCC). The aim of our study was to identify the risk factors and comorbid medical conditions that were associated with HCC in patients who had CHBVI.

METHODS

We performed a retrospective electronic medical record review of adult patients diagnosed with CHBVI, who presented to our primary care office between October 1, 2017 and October 21, 2022. Selected variables in patients with CHBVI with HCC (HCC group) were compared to those without HCC (NoHCC group).

RESULTS

Among 125 patients with CHBVI, 24% had HCC and 76% did not have HCC. There were higher frequencies of association of certain comorbidities in the HCC group compared to NoHCC group, such as anemia (63.3% vs 26.3%; P < .001), ascites (53.3% vs 1.1%; P < .001), portal hypertension (43.3% vs 0.0%; P < .001), chronic kidney disease (40.0% vs 13.7%; P = .002), and HCV coinfection (13.3% vs 7.4%; P < .001). The logistic regression model showed increased odds of HCC for each year of increase in age (OR = 1.06, 95% CI = 1.01-1.11; P = .014), and increased odds in men (OR = 5.96, 95% CI = 1.71-20.73; P = .005). Although Asians represented the racial majority in both the groups, there was no significant difference in the race distribution between the two groups.

CONCLUSION

In patients with CHBVI, increasing age and male sex are factors associated with increased odds of having HCC. Patients with CHBVI and HCC have higher frequencies of association of tobacco use, recreational drug use, anemia, ascites, portal hypertension, chronic kidney disease, and co-infection with HCV.

Causal genetic regulation of DNA replication on immune microenvironment in colorectal tumorigenesis: Evidenced by an integrated approach of trans-omics and GWAS

The interplay between DNA replication stress and immune microenvironment alterations is known to play a crucial role in colorectal tumorigenesis, but a comprehensive understanding of their association with and relevant biomarkers involved in colorectal tumorigenesis is lacking. To address this gap, we conducted a study aiming to investigate this association and identify relevant biomarkers. We analyzed transcriptomic and proteomic profiles of 904 colorectal tumor tissues and 342 normal tissues to examine pathway enrichment, biological activity, and the immune microenvironment. Additionally, we evaluated genetic effects of single variants and genes on colorectal cancer susceptibility using data from genome-wide association studies (GWASs) involving both East Asian (7062 cases and 195745 controls) and European (24476 cases and 23073 controls) populations. We employed mediation analysis to infer the causal pathway, and applied multiplex immunofluorescence to visualize colocalized biomarkers in colorectal tumors and immune cells. Our findings revealed that both DNA replication activity and the flap structure-specific endonuclease 1 ( FEN1) gene were significantly enriched in colorectal tumor tissues, compared with normal tissues. Moreover, a genetic variant rs4246215 G>T in FEN1 was associated with a decreased risk of colorectal cancer (odds ratio = 0.94, 95% confidence interval: 0.90-0.97, P meta = 4.70 × 10 -9). Importantly, we identified basophils and eosinophils that both exhibited a significantly decreased infiltration in colorectal tumors, and were regulated by rs4246215 through causal pathways involving both FEN1 and DNA replication. In conclusion, this trans-omics incorporating GWAS data provides insights into a plausible pathway connecting DNA replication and immunity, expanding biological knowledge of colorectal tumorigenesis and therapeutic targets.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data

Multi-omics sequencing is expected to become clinically routine within the next decade and transform clinical care. However, there is a paucity of viable and interpretable genome-wide modeling methods that can facilitate rational selection of patients for tailored intervention. Here we develop an integral genomic signature-based method called iGenSig-Rx as a white-box tool for modeling therapeutic response based on clinical trial datasets with improved cross-dataset applicability and tolerance to sequencing bias. This method leverages high-dimensional redundant genomic features to address the challenges of cross-dataset modeling, a concept similar to the use of redundant steel rods to reinforce the pillars of a building. Using genomic datasets for HER2 targeted therapies, the iGenSig-Rx model demonstrates stable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We expect that iGenSig-Rx as a class of biologically interpretable multi-omics modeling methods will have broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx. NOTE: the Github website will be released upon publication and the R package is available for review through google drive: https://drive.google.com/drive/folders/1KgecmUoon9-h2Dg1rPCyEGFPOp28Ols3?usp=sharing.

Multi-omics reveals aging-related pathway in natural aging mouse liver

Aging is associated with gradual changes in liver structure, altered metabolites and other physiological/pathological functions in hepatic cells. However, its characterized phenotypes based on altered metabolites and the underlying biological mechanism are unclear. Advancements in high-throughput omics technology provide new opportunities to understand the pathological process of aging. Here, in our present study, both metabolomics and phosphoproteomics were applied to identify the altered metabolites and phosphorylated proteins in liver of young (the WTY group) and naturally aged (the WTA group) mice, to find novel biomarkers and pathways, and uncover the biological mechanism. Analysis showed that the body weights, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) increased in the WTA group. The grips decreased with age, while the triglyceride (TG) and cholesterol (TC) did not change significantly. The increase of fibrosis, accumulation of inflammatory cells, hepatocytes degeneration, the deposition of lipid droplets and glycogen, the damaged mitochondria, and deduction of endoplasmic reticulum were observed in the aging liver under optical and electron microscopes. In addition, a network of metabolites and phosphorylated proteomes of the aging liver was established. Metabolomics detected 970 metabolites in the positive ion mode and 778 metabolites in the negative ion mode. A total of 150 pathways were pooled. Phosphoproteomics identified 2618 proteins which contained 16621 phosphosites. A total of 164 pathways were detected. 65 common pathways were detected in two omics. Phosphorylated protein heat shock protein HSP 90-alpha (HSP90A) and v-raf murine viral oncogene homolog B1(BRAF), related to cancer pathway, were significantly upregulated in aged mice liver. Western blot verified that protein expression of MEK and ERK, downstream of BRAF pathway were elevated in the liver of aging mice. However, the protein expression of BRAF was not a significant difference. Overall, these findings revealed a close link between aging and cancer and contributed to our understanding of the multi-omics changes in natural aging.

Considerations and Approaches for Cancer Immunotherapy in the Aging Host

Advances in cancer immunotherapy are improving treatment successes in many distinct cancer types. Nonetheless, most tumors fail to respond. Age is the biggest risk for most cancers, and the median population age is rising worldwide. Advancing age is associated with manifold alterations in immune cell types, abundance, and functions, rather than simple declines in these metrics, the consequences of which remain incompletely defined. Our understanding of the effects of host age on immunotherapy mechanisms, efficacy, and adverse events remains incomplete. A deeper understanding of age effects in all these areas is required. Most cancer immunotherapy preclinical studies examine young subjects and fail to assess age contributions, a remarkable deficit given the known importance of age effects on immune cells and factors mediating cancer immune surveillance and immunotherapy efficacy. Notably, some cancer immunotherapies are more effective in aged versus young hosts, while others fail despite efficacy in the young. Here, we review our current understanding of age effects on immunity and associated nonimmune cells, the tumor microenvironment, cancer immunotherapy, and related adverse effects. We highlight important knowledge gaps and suggest areas for deeper enquiries, including in cancer immune surveillance, treatment response, adverse event outcomes, and their mitigation.

Transcriptomic analysis reveals a tissue-specific loss of identity during ageing and cancer

INTRODUCTION

Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx.

RESULTS

We found significant downregulation of tissue-specific genes during ageing in 40% of the tissues analyzed, which suggests loss of tissue identity with age. For most cancer types, we have noted a consistent pattern of downregulation in genes that are specific to the tissue from which the tumor originated. Moreover, we observed in cancer an activation of genes not usually expressed in the tissue of origin as well as an upregulation of genes specific to other tissues. These patterns in cancer were associated with patient survival. The age of the patient, however, did not influence these patterns.

CONCLUSION

We identified loss of cellular identity in 40% of the tissues analysed during human ageing, and a clear pattern in cancer, where during tumorigenesis cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis.

Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups' separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.

Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer

Gene regulatory networks (GRNs) and gene co-expression networks (GCNs) allow genome-wide exploration of molecular regulation patterns in health and disease. The standard approach for obtaining GRNs and GCNs is to infer them from gene expression data, using computational network inference methods. However, since network inference methods are usually applied on aggregate data, distortion of the networks by demographic confounders might remain undetected, especially because gene expression patterns are known to vary between different demographic groups. In this paper, we present a computational framework to systematically evaluate the influence of demographic confounders on network inference from gene expression data. Our framework compares similarities between networks inferred for different demographic groups with similarity distributions obtained for random splits of the expression data. Moreover, it allows to quantify to which extent demographic groups are represented by networks inferred from the aggregate data in a confounder-agnostic way. We apply our framework to test four widely used GRN and GCN inference methods as to their robustness w. r. t. confounding by age, ethnicity and sex in cancer. Our findings based on more than $ {44000}$ inferred networks indicate that age and sex confounders play an important role in network inference for certain cancer types, emphasizing the importance of incorporating an assessment of the effect of demographic confounders into network inference workflows. Our framework is available as a Python package on GitHub: https://github.com/bionetslab/grn-confounders.

The metastasis patterns and their prognostic features in patients with de novo metastatic breast cancer of different ages

PURPOSE

The prognostic outcomes of metastasis patterns in patients with de novo metastatic breast cancer (dnMBC) of different ages are unknown. Our study used a large-scale data to investigate the metastasis patterns and prognostic features in dnMBC of different ages.

METHODS

Total 24,698 women with dnMBC in the Surveillance, Epidemiology and End Results database (2010-2018) were divided into three groups by age. Chi-squared test was used to compare metastasis patterns and logistic regression was performed to investigate the risk of age and specific organ metastases. Kaplan-Meier survival curves were used to compare the overall survival.

RESULTS

In three groups, young group had the largest proportion of liver metastases (35.2% vs. 28.2% vs. 21.1%, p < 0.001), and elderly group had the largest proportion of lung metastases (22.6% vs. 30.0% vs. 35.0%, p < 0.001) and the lowest proportion of bone metastases (65.7% vs. 67.6% vs. 64.4%, p < 0.001). In young group, patients with liver metastases had better prognosis than patients with lung metastases (MST: 34 months vs. 29 months, p = 0.041), but in middle-aged and elderly groups, the prognosis of lung metastases was better than that of liver metastases (MST in middle-aged group: 24 months vs. 20 months, p = 0.002; MST in elderly group: 12 months vs. 6 months, p < 0.001).

CONCLUSION

DnMBC patients at different age have distinct metastasis patterns and prognostic features. The findings lend support to consideration of tailored management and surveillance strategies for different age patients.

m6A-TSHub: Unveiling the Context-specific m6A Methylation and m6A-affecting Mutations in 23 Human Tissues

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs (lncRNAs), N6-methyladenosine (m6A) RNA methylation has been shown to participate in essential biological processes. Recent studies have revealed the distinct patterns of m6A methylome across human tissues, and a major challenge remains in elucidating the tissue-specific presence and circuitry of m6A methylation. We present here a comprehensive online platform, m6A-TSHub, for unveiling the context-specific m6A methylation and genetic mutations that potentially regulate m6A epigenetic mark. m6A-TSHub consists of four core components, including (1) m6A-TSDB, a comprehensive database of 184,554 functionally annotated m6A sites derived from 23 human tissues and 499,369 m6A sites from 25 tumor conditions, respectively; (2) m6A-TSFinder, a web server for high-accuracy prediction of m6A methylation sites within a specific tissue from RNA sequences, which was constructed using multi-instance deep neural networks with gated attention; (3) m6A-TSVar, a web server for assessing the impact of genetic variants on tissue-specific m6A RNA modifications; and (4) m6A-CAVar, a database of 587,983 The Cancer Genome Atlas (TCGA) cancer mutations (derived from 27 cancer types) that were predicted to affect m6A modifications in the primary tissue of cancers. The database should make a useful resource for studying the m6A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue (or cancer type). m6A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m6ats.

Integration of single-cell regulon atlas and multi-omics data for prognostic stratification and personalized treatment prediction in human lung adenocarcinoma

Transcriptional programs are often dysregulated in cancers. A comprehensive investigation of potential regulons is critical to the understanding of tumorigeneses. We first constructed the regulatory networks from single-cell RNA sequencing data in human lung adenocarcinoma (LUAD). We next introduce LPRI (Lung Cancer Prognostic Regulon Index), a precision oncology framework to identify new biomarkers associated with prognosis by leveraging the single cell regulon atlas and bulk RNA sequencing or microarray datasets. We confirmed that LPRI could be a robust biomarker to guide prognosis stratification across lung adenocarcinoma cohorts. Finally, a multi-omics data analysis to characterize molecular alterations associated with LPRI was performed from The Cancer Genome Atlas (TCGA) dataset. Our study provides a comprehensive chart of regulons in LUAD. Additionally, LPRI will be used to help prognostic prediction and developing personalized treatment for future studies.

The Clinical Significance of Genetic Variation in Ovarian Cancer

Genetic variation is a well-known contributor to the onset and progression of cancer. The goal of this study is to provide a comprehensive examination of the nucleotide and chromosomal variation associated with the onset and progression of serous ovarian cancer. Using a variety of computational and statistical methods, we examine the exome sequence profiles of genetic variants present in the primary tumors of 432 ovarian cancer patient samples to compute: (1) the tumor mutational burden for all genes and (2) the chromosomal copy number alterations associated with the onset/progression of ovarian cancer. Tumor mutational burden is reduced in the late vs. early stages, with the highest levels being associated with loss-of-function mutations in DNA-repair genes. Nucleotide variation and copy number alterations associated with known cancer driver genes are selectively favored over ovarian cancer development. The results indicate that genetic variation is a significant contributor to the onset and progression of ovarian cancer. The measurement of the relative levels of genetic variation associated with individual ovarian cancer patient tumors may be a clinically valuable predictor of potential tumor aggressiveness and resistance to chemotherapy. Tumors found to be associated with high levels of genetic variation may help in the clinical identification of high-risk ovarian cancer patients who could benefit from more frequent monitoring.

Decreased SPTBN2 expression regulated by the ceRNA network is associated with poor prognosis and immune infiltration in low‑grade glioma

The majority of low-grade gliomas (LGGs) in adults invariably progress to glioblastoma over time. Spectrin β non-erythrocytic 2 (SPTBN2) is detected in numerous tumors and is involved in tumor occurrence and metastasis. However, the specific roles and detailed mechanisms of SPTBN2 in LGG are largely unknown. The present study performed pan-cancer analysis for the expression and prognosis of SPTBN2 in LGG using The Cancer Genome Atlas and The Genotype-Tissue Expression. Western blotting was used to detect the amount of SPTBN2 between glioma tissues and normal brain tissues. Subsequently, based on expression, prognosis, correlation and immune infiltration, non-coding RNAs (ncRNAs) were identified that regulated SPTBN2 expression. Finally, tumor immune infiltrates associated with SPTBN2 and prognosis were performed. Lower expression of SPTBN2 was correlated with an unfavorable outcome in LGG. A significant correlation between the low SPTBN2 mRNA expression and poor clinicopathological features was observed, including wild-type isocitrate dehydrogenase status (P<0.001), 1p/19q non-codeletion (P<0.001) and elders (P=0.019). The western blotting results revealed that, compared with normal brain tissues, the amount of SPTBN2 was significantly lower in LGG tissues (P=0.0266). Higher expression of five microRNAs (miRs/miRNAs), including hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-miR-16-5p, hsa-miR-34c-5p and hsa-miR-424-5p, correlated with poor prognosis by targeting SPTBN2 in LGG. Subsequently, four long ncRNAs (lncRNAs) [ARMCX5-GPRASP2, BASP1-antisense RNA 1 (AS1), EPB41L4A-AS1 and LINC00641] were observed in the regulation of SPTBN2 via five miRNAs. Moreover, the expression of SPTBN2 was significantly correlated with tumor immune infiltration, immune checkpoint expression and biomarkers of immune cells. In conclusion, SPTBN2 was lowly expressed and correlated with an unfavorable prognosis in LGG. A total of six miRNAs and four lncRNAs were identified as being able to modulate SPTBN2 in a lncRNA-miRNA-mRNA network of LGG. Furthermore, the current findings also indicated that SPTBN2 possessed anti-tumor roles by regulating tumor immune infiltration and immune checkpoint expression.

A Pan-Cancer Analysis of Prognostic and Immunological Roles for Cell Death Genes

The dysregulation of cell death is closely associated with the development, progression, tumor microenvironment (TME), and prognosis of cancer. However, there is no study that comprehensively explores the prognostic and immunological role of cell death in human pan-cancer. We used published human pan-cancer RNA-sequencing and clinical data to explore the prognostic and immunological roles of programmed cell death, which included apoptosis, autophagy, ferroptosis, necroptosis, and pyroptosis. A total of 9925 patients were included for bioinformatic analysis, with 6949 and 2976 patients in the training cohort and validation cohort, respectively. Five-hundred and ninety-nine genes were defined as programmed-cell-death-related genes. In the training cohort, 75 genes were identified to define PAGscore by survival analysis. According to the median value of PAGscore, patients were divided into high- and low-risk groups, and subsequent analyses demonstrated that the high-risk group had a higher level of genomic mutation frequency, hypoxia score, immuneScore, expression of immune genes, activity of malignant signaling pathways, and cancer immunity cycle. Most anti-tumor and pro-tumor components of the TME showed greater activity in high-risk patients. Scores of malignant cell properties were also higher in high-risk patients. These findings were confirmed in the validation cohort and external cohort. Our study constructed a reliable gene signature to distinguish prognosis-favorable and prognosis-unfavorable patients and demonstrated that cell death was significantly associated with cancer prognosis and the TME.