Genomic Signatures Research Articles

Connecting genomic and proteomic signatures of amyloid burden in the brain.

Alzheimer's disease (AD) has a high heritable component characteristic of complex diseases, yet many of the genetic risk factors remain unknown. We combined genome-wide association studies (GWAS) on amyloid endophenotypes measured in cerebrospinal fluid (CSF) and positron emission tomography (PET) as surrogates of amyloid pathology, which may be helpful to understand the underlying biology of the disease. We performed a meta-analysis of GWAS of CSF Aβ42 and PET measures combining six independent cohorts (n=2,076). Due to the opposite effect direction of Aβ phenotypes in CSF and PET measures, only genetic signals in the opposite direction were considered for analysis (n=376,599). Polygenic risk scores (PRS) were calculated and evaluated for AD status and amyloid endophenotypes. We then searched the CSF proteome signature of brain amyloidosis using SOMAscan proteomic data (Ace cohort, n=1,008) and connected it with GWAS results of loci modulating amyloidosis. Finally, we compared our results with a large meta-analysis using publicly available datasets in CSF (n=13,409) and PET (n=13,116). This combined approach enabled the identification of overlapping genes and proteins associated with amyloid burden and the assessment of their biological significance using enrichment analyses. After filtering the meta-GWAS, we observed genome-wide significance in the rs429358-APOE locus and nine suggestive hits were annotated. We replicated the APOE loci using the large CSF-PET meta-GWAS and identified multiple AD-associated genes as well as the novel GADL1 locus. Additionally, we found a significant association between the AD PRS and amyloid levels, whereas no significant association was found between any Aβ PRS with AD risk. CSF SOMAscan analysis identified 1,387 FDR-significant proteins associated with CSF Aβ42 levels. The overlap among GWAS loci and proteins associated with amyloid burden was very poor (n=35). The enrichment analysis of overlapping hits strongly suggested several signalling pathways connecting amyloidosis with the anchored component of the plasma membrane, synapse physiology and mental disorders that were replicated in the large CSF-PET meta-analysis. The strategy of combining CSF and PET amyloid endophenotypes GWAS with CSF proteome analyses might be effective for identifying signals associated with the AD pathological process and elucidate causative molecular mechanisms behind the amyloid mobilization in AD.

Comprehensive scRNA-seq analysis to identify new markers of M2 macrophages for predicting the prognosis of prostate cancer

Background Prostate cancer (PCa) has become the highest incidence of malignant tumor among men in the world. Tumor microenvironment (TME) is necessary for tumor growth. M2 macrophages play an important role in many solid tumors. This research aimed at the role of M2 macrophages’ prognosis value in PCa. Methods Single-cell RNA-seq (scRNA-seq) data and mRNA expression data were obtained from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). Quality control, normalization, reduction, clustering, and cell annotation of scRNA-seq data were preformed using the Seruat package. The sub-populations of the tumor-associated macrophages (TAMs) were analysis and the marker genes of M2 macrophage were selected. Differentially expressed genes (DEGs) in PCa were identified using limma and the immune infiltration was detected using CIBERSORTx. Then, a weighted correlation network analysis (WGCNA) was constructed to identify the M2 macrophage-related modules and genes. Integration of the marker genes of M2 macrophage from scRNA-seq data analysis and hub genes from WGCNA to select the prognostic gene signature based on Univariate and LASSO regression analysis. The risk score was calculated, and the DEGs, biological function, immune characteristics related to risk score were explored. And a predictive nomogram was constructed. CCK8, Transwell, and wound healing were used to verify cell phenotype changes after co-cultured. Results A total of 2431 marker genes of M2 macrophage and 650 hub M2 macrophage-related genes were selected based on scRNA-seq data and WGCNA. Then, 113 M2 macrophage-related genes were obtained by overlapping the scRNA-seq data and WGCNA results. Nine M2 macrophage-related genes (SMOC2, PLPP1, HES1, STMN1, GPR160, ABCG1, MAZ, MYC, and EPCAM) were screened as prognostic gene signatures. M2 risk score was calculated, the DEGs, Immune score, stromal score, ESTIMATE score, tumor purity, and immune cell infiltration, immune checkpoint expression, and responses of immunotherapy and chemotherapy were identified. And a predictive nomogram was constructed. CCK8, Transwell invasion, and wound healing further verified that M2 macrophages promoted the proliferation, invasion, and migration of PCa (p < 0.05). Conclusions We uncovered that M2 macrophages and relevant genes played key roles in promoting the occurrence, development, and metastases of PCa and played as convincing predictors in PCa.

Ferroptosis-related gene signature predicts prognosis and immune microenvironment in prostate cancer.

Ferroptosis, an iron-dependent form of programmed cell death, significantly impacts cancer, yet its link to prostate cancer (PCa) prognosis remains underexplored. This study aims to develop and validate a ferroptosis-related gene signature to predict PCa prognosis and immune microenvironment differences, potentially identifying therapeutic targets. RNA-sequencing data of 478 PCa patients and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. We investigated the disease-free survival (DFS) rates of the high- and low-risk groups using the Kaplan-Meier method. Functional differences between the high- and low-risk groups were investigated by a gene set enrichment analysis (GSEA), and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The link between ferroptosis risk score and immune status was examined using CIBERSORT. The expression levels of core prognostic genes in benign prostatic hyperplasia (BPH) and PCa were verified using quantitative real-time polymerase chain reaction (qRT-PCR), Western blot, and immunohistochemistry (IHC). A novel ferroptosis-related prognostic gene signature was established and tested in the Gene Expression Omnibus (GEO) database based on univariate and multivariate Cox regression analyses. Patients with PCa were classified into high- and low-risk groups based on this ferroptosis signature. Patients in the high-risk group had worse outcomes than those in the low-risk group. The predictive accuracy of the model was demonstrated by a receiver operating characteristic (ROC) analysis. An additional enrichment analysis of TCGA cohort revealed the immune-related pathways were significantly upregulated in the high-risk group, with areas under the curve (AUCs) of 0.85 at 1 year, 0.82 at 3 years, and 0.76 at 5 years. In the GEO cohort, the AUCs reached 0.69 at 1 year, 0.74 at 3 years, and 0.75 at 5 years. An additional enrichment analysis indicated a significant upregulation of cytokine-related pathways, immune receptor activity, and other immune-related pathways in the high-risk group. Furthermore, the analysis revealed that the proportions of mast cells and plasma cells were significantly lower in the high-risk group compared to the low-risk group of PCa patients. Conversely, the proportion of regulatory T cells (Tregs) was significantly higher in the high-risk group than in the low-risk group. According to the qRT-PCR, Western blot, and IHC results, DRD4, SRC, AKR1C2, and AIFM2 expression was significantly higher in PCa than BPH. We also showed that the ferrostatin 1-treated LNCaP cells had higher expression levels of DRD4, SRC, and AKR1C2. A prognostic signature of eight ferroptosis-related genes (FRGs) that may accurately predict PCa patient outcomes was constructed and validated. FRGs may contribute to anti-tumor immunity and serve as therapeutic targets in PCa.

1805P PKD1L1 mutations in small cell lung cancer: A genomic signature for poor prognosis and drug susceptibility

1805P PKD1L1 mutations in small cell lung cancer: A genomic signature for poor prognosis and drug susceptibility

Identification of a prognostic disulfidptosis-related gene signature in hepatocellular cancer.

Disulfidptosis regulate various biological processes in cancer. However, there is limited research on the genes related to disulfidptosis in predicting the prognosis of hepatocellular carcinoma (HCC). We aimed to develop a reliable disulfidptosis-related gene signature, which will characterize different HCC subtypes and predict their prognosis. The Cancer Genome Atlas (TCGA)-HCC dataset, comprising RNA sequencing data and clinical information, was obtained from the TCGA database. The crucial disulfidptosis-related genes were selected for bioinformatic analysis in HCC. HCC tumor classification was established through a consistent cluster analysis. The prognosis and immune-cell infiltration were investigated in association with a disulfidptosis-related HCC model. In TCGA-HCC patients, a total of 3,621 prognostic genes and 30 key prognostic disulfidptosis-related genes were identified. Using key prognostic disulfidptosis-related genes, TCGA-HCC patients were categorized into low- and high-risk clusters. The upregulated differentially expressed genes (DEGs) in high-risk cluster 1 (C1) could significantly impact cell cycle, DNA replication, and the p53 signaling pathway, whereas the pathways associated with the downregulated DEGs in high-risk C1 could significantly impact metabolism of xenobiotics by cytochrome P450, the PPAR signaling pathway, and tyrosine metabolism. Furthermore, the immune activity of the high-risk C1 group was different to that of the low-risk cluster 2 (C2) group. The 13 disulfidptosis-related genes were finally screened using least absolute shrinkage and selection operator (LASSO) regression analysis, including ANP32E, BOP1, RPN1, SLC7A11, PPIH, PCBP2, ME1, PRDX1, FLNC, INF2, MYH11, LRPPRC, and HNRNPM. The genes related to disulfidptosis are closely associated with tumor classification and immunity in patients with HCC. This is the first gene signature related to disulfidptosis demonstrated a strong predictive performance for the prognosis of HCC, which provide new perspectives for the diagnosis and treatment of HCC.

Gelada genomes highlight events of gene flow, hybridisation and local adaptation that track past climatic changes.

Theropithecus gelada, the last surviving species of this genus, occupy a unique and highly specialised ecological niche in the Ethiopian highlands. A subdivision into three geographically defined populations (Northern, Central and Southern) has been tentatively proposed for this species on the basis of genetic analyses, but genomic data have been investigated only for two of these groups (Northern and Central). Here we combined newly generated whole genome sequences of individuals sampled from the population living south of the East Africa Great Rift Valley with available data from the other two gelada populations to reconstruct the evolutionary history of the species. Integrating genomic and paleoclimatic data we found that gene-flow across populations and with Papio species tracked past climate changes. The isolation and climatic conditions experienced by Southern geladas during the Holocene shaped local diversity and generated diet-related genomic signatures.

Dissecting the invasion history of Spotted-Wing Drosophila (Drosophila suzukii) in Portugal using genomic data

BackgroundThe invasive pest Spotted-Wing Drosophila, Drosophila suzukii (Matsumura), causes extensive damage and production losses of soft-skinned fruits. Native to Asia, the species has now spread worldwide, with first reports in Portugal in 2012. In this study, we focus on the genomic signatures of the recent Portuguese invasion, in the context of worldwide patterns established in previous works. We analyzed whole genome pool sequencing data from three Portuguese populations (N = 240) sampled in 2019 and 2021.ResultsThe correlation of allele frequencies suggested that Portuguese populations are related to South European ones, indicating a Mediterranean invasion route. While two populations exhibited levels of genetic variation comparable to others in the invasive range, a third showed low levels of genetic diversity, which may result from a recent colonization of the region. Genome-wide analyses of natural selection identified ten genes previously associated with D. suzukii’s invasive capacity, which may have contributed to the species’ success in Portugal. Additionally, we pinpointed six genes evolving under positive selection across Portuguese populations but not in European ones, which is indicative of local adaptation. One of these genes, nAChRalpha7, encodes a nicotinic acetylcholine receptor, which are known targets for insecticides widely used for D. suzukii control, such as neonicotinoids and spinosyns. Although spinosyn resistance has been associated with mutations in the nAChRalpha6 in other Drosophila species, the putative role of nAChRalpha7 in insecticide resistance and local adaptation in Portuguese D. suzukii populations encourages future investigation.ConclusionsOur results highlight the complex nature of rapid species invasions and the role of rapid local adaptation in determining the invasive capacity of these species.

Assessing Receptor Activation in 2D and 3D Cultured Hepatocytes: Responses to a Single Compound and a Complex Mixture.

Responding to global standards and legislative updates in Canada, including Bill S-5 (2023), toxicity testing is shifting towards more ethical, in vitro methods. Traditional two-dimensional (2D) monolayer cell cultures, limited in replicating the complex in vivo environment, have prompted the development of more relevant three-dimensional (3D) spheroidal hepatocyte cultures. This study introduces the first 3D spheroid model for McA-RH7777 cells, assessing xenobiotic receptor activation, cellular signaling, and toxicity against dexamethasone and naphthenic acid (NA)-fraction components; NAFCs. Our findings reveal that 3D McA-RH7777 spheroids demonstrate enhanced sensitivity and more uniform dose-response patterns in gene expression related to xenobiotic metabolism (AhR and PPAR) for both single compounds and complex mixtures. Specifically, 3D cultures showed significant gene expression changes upon dexamethasone exposure and exhibited varying degrees of sensitivity and resistance to the apoptotic effects induced by NAFCs, in comparison to 2D cultures. The optimization of 3D culture conditions enhances the model's physiological relevance and enables the identification of genomic signatures under varied exposures. This study highlights the potential of 3D spheroid cultures in providing a more accurate representation of the liver's microenvironment and advancing our understanding of cellular mechanisms in toxicity testing.

Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities.

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

Genomic signatures of cold adaptation in the family Colwelliaceae.

Psychrophily is a phenotype describing microbial growth at low temperatures; elucidating the biomolecular and genomic adaptations necessary for survival in the cold is important for understanding life in extreme environments on Earth and in outer space. We used comparative genomics and temperature growth experiments of bacteria from the family Colwelliaceae to identify genomic factors correlated with optimal growth temperature (OGT). A phylogenomic analysis of 67 public and 39 newly sequenced strains revealed three main clades of Colwelliaceae. Temperature growth experiments revealed significant differences in mean OGT by clade, wherein strains of Colwelliaceae had similar growth rates at -1°C but varied in their ability to tolerate 17°C. Using amino acid compositional indices, a multiple linear regression model was constructed to predict the OGT of these organisms (RMSE 5.2°C). Investigation of Colwelliaceae functional genes revealed a putative cold-adaptive gene cassette that was present in psychrophilic strains but absent in a closely related strain with a significantly higher OGT. This study also presents genomic evidence suggesting that the clade of Colwelliaceae containing Colwellia hornerae should be investigated as a new genus. These contributions offer key insights into the psychrophily phenotype and its underlying genomic foundation in the family Colwelliaceae.

Genomic Face-Off: An In Silico Comparison of the Probiotic Potential of Lactobacillus spp. and Akkermansia muciniphila

Introduction: The gut microbiota plays a crucial role in maintaining human health, and probiotics have gained significant attention for their potential benefits. Among the diverse array of gut bacteria, Akkermansia muciniphila, and Lactobacillus spp. have emerged as promising candidates for their putative probiotic properties. Method: In this study, we conducted a comprehensive comparative in silico analysis of the genomes of A. muciniphila and Lactobacillus to decipher their probiotic potential. Utilizing a range of bioinformatics tools, we evaluated various genomic attributes, including functional gene content, metabolic pathways, antimicrobial peptide production, adhesion factors, and stress response elements. These findings revealed distinctive genomic signatures between the two genera. A. muciniphila genomes exhibited a high prevalence of mucin-degrading enzymes, suggesting a specialized adaptation for mucin utilization in the gut environment. Results: Additionally, the presence of specific pathways for short-chain fatty acid production highlighted its potential impact on host health. Lactobacillus genomes, on the other hand, demonstrated a diverse repertoire of functional genes associated with probiotic attributes, including the production of antimicrobial peptides and adhesion factors, indicating potential for host-microbe interactions and immune modulation. Furthermore, this analysis unveiled the genetic basis of stress tolerance in both genera, revealing conserved mechanisms for surviving the dynamic conditions of the gut ecosystem. Conclusion: This study also shed light on the distribution of antibiotic-resistance genes, allowing us to assess safety concerns associated with their potential use as probiotics. Overall, this comparative in silico exploration provides valuable insights into the genomic foundation of A. muciniphila and Lactobacillus probiotic potential. These findings contribute to the understanding of their respective roles within the gut microbiota and offer a foundation for further experimental investigations. As probiotic applications continue to expand, this study advances our knowledge of the genetic underpinnings that govern their functionality and highlights promising avenues for future therapeutic interventions and personalized health strategies.

Genome-Wide Analysis of Genetic Diversity and Selection Signatures in Zaobei Beef Cattle.

This investigation provides a comprehensive analysis of genomic diversity and selection signatures in Zaobei beef cattle, an indigenous breed known for its adaptation to hot and humid climates and superior meat quality. Whole-genome resequencing was conducted on 23 Zaobei cattle, compared with 46 Simmental cattle to highlight genetic distinctions. Population structure analysis confirmed the genetic uniqueness of Zaobei cattle. Using methods such as DASDC v1.01, XPEHH, and θπ ratio, we identified 230, 232, and 221 genes through DASDC, including hard sweeps, soft sweeps, and linkage sweeps, respectively. Coincidentally, 109 genes were identified when using XPEHH and θπ ratio methods. Together, these analyses revealed eight positive selection genes (ARHGAP15, ZNF618, USH2A, PDZRN4, SPATA6, ROR2, KCNIP3, and VWA3B), which are linked to critical traits such as heat stress adaptation, fertility, and meat quality. Moreover, functional enrichment analyses showed pathways related to autophagy, immune response, energy metabolism, and muscle development. The comprehensive genomic insights gained from this study provide valuable knowledge for breeding programs aimed at enhancing the beneficial traits in Zaobei cattle.

Current and Novel Treatment Options in Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Metastatic Breast Cancer.

Metastatic breast cancer (mBC) remains an incurable disease, and most patients will experience disease progression during their treatment course. Although endocrine therapy remains the mainstay of treatment for hormone receptor-positive/human epidermal growth factor receptor 2-negative mBC, significant progress has been and continues to be made in the treatment of this BC subtype. The discovery of molecular markers, mutations in key cellular pathways, and genomic signatures have led to the development of novel and targeted agents, such as antibody-drug conjugates, oral selective estrogen receptor downregulators, and inhibitors of the PI3K/AKT/mTOR pathway. This has resulted in significant improvements in the survival and quality of life of patients. With the increasing number of treatment options for patients, appropriate drug sequencing remains a challenge. Treatment discussions should involve patient-physician shared decision making, with consideration of genomic data, previous lines of therapy, side effect profiles, and clinical trial enrollment.

Genomic correlates of the response to first-line PD-1 blockade plus chemotherapy in patients with advanced non-small-cell lung cancer.

Programmed death 1 (PD-1) blockade plus chemotherapy has become the new first-line standard of care for patients with advanced non-small-cell lung cancer (NSCLC). Yet not all NSCLC patients benefit from this regimen. This study aimed to investigate the predictors of PD-1 blockade plus chemotherapy in untreated advanced NSCLC. We integrated clinical, genomic, and survival data from 287 patients with untreated advanced NSCLC who were enrolled in one of five registered phase 3 trials and received PD-1 blockade plus chemotherapy or chemotherapy alone. We randomly assigned these patients into a discovery cohort (n=125), a validation cohort (n=82), and a control cohort (n=80). The candidate genes that could predict the response to PD-1 blockade plus chemotherapy were identified using data from the discovery cohort and their predictive values were then evaluated in the three cohorts. Immune deconvolution was conducted using transcriptome data of 1014 NSCLC patients from The Cancer Genome Atlas dataset. A genomic variation signature, in which one or more of the 15 candidate genes were altered, was correlated with significantly inferior response rates and survival outcomes in patients treated with first-line PD-1 blockade plus chemotherapy in both discovery and validation cohorts. Its predictive value held in multivariate analyses when adjusted for baseline parameters, programmed cell death ligand 1 (PD-L1) expression level, and tumor mutation burden. Moreover, applying both the 15-gene panel and PD-L1 expression level produced better performance than either alone in predicting benefit from this treatment combination. Immune landscape analyses revealed that tumors with one or more variation in the 15-gene panel were associated with few immune infiltrates, indicating an immune-desert tumor microenvironment. These findings indicate that a 15-gene panel can serve as a negative prediction biomarker for first-line PD-1 blockade plus chemotherapy in patients with advanced NSCLC.

Oncogenic pathway signatures predict the risk of progression and recurrence in well-differentiated pancreatic neuroendocrine tumors.

Pancreatic neuroendocrine tumors (pNETs) are genomically diverse tumors. The management of newly diagnosed well-differentiated pNETs is limited by a lack of sensitivity of existing biomarkers for prognostication. Our goal was to investigate the potential utility of genetic markers as a predictor of progression-free survival (PFS) and recurrence-free survival (RFS). Whole-exome sequencing of resected well-differentiated, low and intermediate-grade (G1 and G2) pNETs and normal adjacent tissue from patients who underwent resection from 2005 to 2015 was performed. Genetic alterations were classified using pan-genomic and oncogenic pathway classifications. Additional samples with genetic and clinicopathologic data available were obtained from the publicly available International Cancer Genome Consortium (ICGC) database and included in the analysis. The prognostic relevance of these genomic signatures on PFS and RFS was analyzed. Thirty-one patients who underwent resection for pNET were identified. Genomic analysis of mutational, copy number, cytogenetic, and complex phenomena revealed similar patterns to prior studies of pNETs with relatively few somatic gene mutations but numerous instances of copy number changes. Analysis of genomic and clinicopathologic outcomes using the combined data from our study as well as the ICGC pNET cohort (n = 124 patients) revealed that the recurrent pattern of whole chromosome loss (RPCL) and metastatic disease were independently associated with disease progression. When evaluating patients with local disease at the time of resection, RPCL and alterations in the TGFβ oncogenic pathway were independently associated with the risk of recurrence. Well-differentiated pNETs are genomically diverse tumors. Pathway signatures may be prognostic for predicting disease progression and recurrence.

Genetic diversity and signatures of selection in Icelandic horses and Exmoor ponies

BackgroundThe Icelandic horse and Exmoor pony are ancient, native breeds, adapted to harsh environmental conditions and they have both undergone severe historic bottlenecks. However, in modern days, the selection pressures on these breeds differ substantially. The aim of this study was to assess genetic diversity in both breeds through expected (HE) and observed heterozygosity (HO) and effective population size (Ne). Furthermore, we aimed to identify runs of homozygosity (ROH) to estimate and compare genomic inbreeding and signatures of selection in the breeds.ResultsHO was estimated at 0.34 and 0.33 in the Icelandic horse and Exmoor pony, respectively, aligning closely with HE of 0.34 for both breeds. Based on genomic data, the Ne for the last generation was calculated to be 125 individuals for Icelandic horses and 42 for Exmoor ponies. Genomic inbreeding coefficient (FROH) ranged from 0.08 to 0.20 for the Icelandic horse and 0.12 to 0.27 for the Exmoor pony, with the majority of inbreeding attributed to short ROHs in both breeds. Several ROH islands associated with performance were identified in the Icelandic horse, featuring target genes such as DMRT3, DOCK8, EDNRB, SLAIN1, and NEURL1. Shared ROH islands between both breeds were linked to metabolic processes (FOXO1), body size, and the immune system (CYRIB), while private ROH islands in Exmoor ponies were associated with coat colours (ASIP, TBX3, OCA2), immune system (LYG1, LYG2), and fertility (TEX14, SPO11, ADAM20).ConclusionsEvaluations of genetic diversity and inbreeding reveal insights into the evolutionary trajectories of both breeds, highlighting the consequences of population bottlenecks. While the genetic diversity in the Icelandic horse is acceptable, a critically low genetic diversity was estimated for the Exmoor pony, which requires further validation. Identified signatures of selection highlight the differences in the use of the two breeds as well as their adaptive trait similarities. The results provide insight into genomic regions under selection pressure in a gaited performance horse breed and various adaptive traits in small-sized native horse breeds. This understanding contributes to preserving genetic diversity and population health in these equine populations.

Structural analysis of genomic and proteomic signatures reveal dynamic expression of intrinsically disordered regions in breast cancer

Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here, we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the Catalog of Somatic Mutations In Cancer database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.

Prognostic genome and transcriptome signatures in colorectal cancers

Colorectal cancer is caused by a sequence of somatic genomic alterations affecting driver genes in core cancer pathways1. Here, to understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. From the 96 mutated driver genes, 9 were not previously implicated in colorectal cancer and 24 had not been linked to any cancer. Two distinct patterns of pathway co-mutations were observed, timing analyses identified nine early and three late driver gene mutations, and several signatures of colorectal-cancer-specific mutational processes were identified. Mutations in WNT, EGFR and TGFβ pathway genes, the mitochondrial CYB gene and 3 regulatory elements along with 21 copy-number variations and the COSMIC SBS44 signature correlated with survival. Gene expression classification yielded five prognostic subtypes with distinct molecular features, in part explained by underlying genomic alterations. Microsatellite-instable tumours divided into two classes with different levels of hypoxia and infiltration of immune and stromal cells. To our knowledge, this study constitutes the largest integrated genome and transcriptome analysis of colorectal cancer, and interlinks mutations, gene expression and patient outcomes. The identification of prognostic mutations and expression subtypes can guide future efforts to individualize colorectal cancer therapy.

Comprehensive analysis and experimental verification of the mechanism of anoikis related genes in pancreatic cancer

BackgroundPancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear. MethodsStudy data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature. ResultsA total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples. ConclusionOur study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.

Urachal and Nonurachal Adenocarcinomas of the Urinary Bladder: A Comprehensive Genomic Profiling Study.

Although both urachal (U) and nonurachal (NU) bladder adenocarcinomas (adenoCas) share several histologic similarities, they differ in location and sometimes in therapeutic options. We analyzed the differences in genomic alterations (GAs) between these tumor entities, with the aim of identifying potential therapeutic targets for clinical trials. Overall, 133 U and 328 NU adenoCas were analyzed. Hybrid capture-based comprehensive genomic profiling (CGP) was performed to evaluate all classes of GA. Germline status of GA was predicted using a validated somatic-germline computational method. CGP was performed using the FoundationOne and FoundationOne CDx assays (Foundation Medicine, Inc). The most frequent GA in both U and NU cohorts included TP53 (86.5% v 81.1%) and KRAS (34.6% v 27.7%). GAs characteristic of colorectal adenoCa, such as SMAD4 (P = .069) and GNAS (P = .071), were more common in U versus NU. Conversely, TERT (P < .01) and RB1 (P = .071) were more prevalent in NU adenoCa. Notably, both U and NU adenoCas exhibited possibly targetable GA in PIK3CA (7.5% v 7.9%) and ERBB2 (6.8% v 7.6%). Biomarkers associated with potential benefit from anti-PD-1/L1 were infrequent. Median tumor mutational burden was 2.6 and 3.5 mutations per megabase for U and NU, respectively, and PD-L1 expression >1% was rare. Genomic ancestry and genomic signature distribution were similar in both tumor types. GAs were most commonly of somatic nature. Limitations include lack of clinical data, tumor heterogeneity, and retrospective nature. U and NU adenoCAs revealed differences in GA, with PIK3CA and ERBB2 being identified as putative therapeutic targets. Biomarkers of response to anti-PD-(L)1 were uncommon. Results highlight the potential of CGP to personalize treatment options of bladder adenoCa and inform clinical trial designs.