Non-cancerous Samples Research Articles

PROTAC’ING CENTROSOME AMPLIfiCATION PROTEINS: TARGETED PROTEIN DEGRADATION AS A NOVEL THERAPEUTIC STRATEGY IN GLIOBLASTOMA

Abstract AIMS Glioblastomas (GBMs) are the most common and aggressive brain tumours, notoriously difficult to treat with universal treatment resistance. A key feature of cancer cells is centrosome amplification (CA), leading to chromosomal instability and worse patient outcomes, including metastasis development and treatment resistance. In this work, I identified CA proteins overexpressed in GBM and aimed to investigate how the targeted degradation of proteins associated with centrosome amplification can influence the growth and survival of GBM cells. METHOD Based on well-established literature data concerning CA-related genes, I evaluated the expression level of these targets in different types of samples from the Tumour Cancer Genome Atlas dataset of GBM patient samples. Following the identification of CA targets differentially overexpressed, to test the hypothesis that CA proteins could be involved in the survival of GBM cells and be potential therapeutic targets, I used a novel drug modality, named Proteolysis Targeting Chimeras (PROTACs) to degrade two CA proteins. PROTACs can quickly destroy specific proteins, potentially overcoming resistance to treatment. Target degradation was confirmed via immunoblotting and cell viability and cell cycle analysis were performed to evaluate the impact on GBM cells. RESULTS Both primary and recurrent GBM samples display high levels of CA gene signature when compared to noncancerous samples, and some CA genes are particularly upregulated in recurrent samples. The PROTACs were effective in degrading both CA targets in GBM cells, leading to a significant decrease in cell viability and G2 cell cycle arrest. CONCLUSION This study presents initial evidence that CA protein degradation could be a promising avenue for GBM research and therapy. Further research is needed to fully assess the translational potential of PROTACs in GBM treatment.

MXene-montmorillonite nanocomposites-based scaffold sensors for early pancreatic cancer diagnosis

Pancreatic cancer is increasingly prevalent and characterized by a high mortality rate. Due to the limitations of current diagnostic methods, early-stage detection remains elusive, contributing to persistently low survival rates among affected individuals. Nanomaterials have garnered significant attention in cancer research for their potential diagnostic applications. Among these, MXenes – a novel family of two-dimensional nanomaterials composed of transition metal carbides, nitrides, and carbonitrides – are of particular interest due to their unique properties. These include high electrical conductivity, hydrophilicity, thermal stability, large interlayer spacing, tunable structure, and high surface area. These characteristics make MXenes highly effective for detecting trace amounts of various analytes. In addition, their tunable structure enables precise manipulation of their properties, allowing for optimized sensing responses. Montmorillonite nanoclay (MMT), a member of the smectite group of natural clay minerals, is known for its ability to promote bone development and influence cell behavior. When combined with MXenes, MMT forms promising nanocomposites for early pancreatic cancer detection through sensing applications. The Ti3C2 MXene-MMT nanocomposites exhibit potential as scaffold sensors capable of distinguishing cancerous from non-cancerous samples by observing the distinctive patterns in resistance changes. In addition, MXenes possess excellent selectivity, allowing for the reliable identification of targeted analytes from a complex mixture of chemical and biological analytes. Due to the advanced sensing capabilities of MXene-MMT composite scaffold sensors, they hold great promise for early cancer diagnosis and tissue regeneration, providing a novel therapeutic approach to improving patient outcomes.

Prognostic modeling of hepatocellular carcinoma based on T-cell proliferation regulators: a bioinformatics approach.

The prognostic value and immune significance of T-cell proliferation regulators (TCRs) in hepatocellular carcinoma (HCC) have not been previously reported. This study aimed to develop a new prognostic model based on TCRs in patients with HCC. This study used The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and International Cancer Genome Consortium-Liver Cancer-Riken, Japan (ICGC-LIRI-JP) datasets along with TCRs. Differentially expressed TCRs (DE-TCRs) were identified by intersecting TCRs and differentially expressed genes between HCC and non-cancerous samples. Prognostic genes were determined using Cox regression analysis and were used to construct a risk model for HCC. Kaplan-Meier survival analysis was performed to assess the difference in survival between high-risk and low-risk groups. Receiver operating characteristic curve was used to assess the validity of risk model, as well as for testing in the ICGC-LIRI-JP dataset. Additionally, independent prognostic factors were identified using multivariate Cox regression analysis and proportional hazards assumption, and they were used to construct a nomogram model. TCGA-LIHC dataset was subjected to tumor microenvironment analysis, drug sensitivity analysis, gene set variation analysis, and immune correlation analysis. The prognostic genes were analyzed using consensus clustering analysis, mutation analysis, copy number variation analysis, gene set enrichment analysis, and molecular prediction analysis. Among the 18 DE-TCRs, six genes (DCLRE1B, RAN, HOMER1, ADA, CDK1, and IL1RN) could predict the prognosis of HCC. A risk model that can accurately predict HCC prognosis was established based on these genes. An efficient nomogram model was also developed using clinical traits and risk scores. Immune-related analyses revealed that 39 immune checkpoints exhibited differential expression between the high-risk and low-risk groups. The rate of immunotherapy response was low in patients belonging to the high-risk group. Patients with HCC were further divided into cluster 1 and cluster 2 based on prognostic genes. Mutation analysis revealed that HOMER1 and CDK1 harbored missense mutations. DCLRE1B exhibited an increased copy number, whereas RAN exhibited a decreased copy number. The prognostic genes were significantly enriched in tryptophan metabolism pathways. This bioinformatics analysis identified six TCR genes associated with HCC prognosis that can serve as diagnostic markers and therapeutic targets for HCC.

The functional DIAPH3-FOXM1 interaction modulates the aggressive transformation of anaplastic thyroid carcinoma cells and Wnt/β-catenin signalling.

Anaplastic thyroid carcinoma (ATC) is reckoned as an infrequent but extremely advanced neoplasm of the endocrine system. Diaphanous-related formin 3 (DIAPH3) has been extensively implicated in carcinogenic events, but it has not been introduced in ATC. Herein, the role of DAPIH3 and the interrelated functional mechanism are characterised in ATC. The Gene Expression Omnibus (GEO) database was checked for differential DIAPH3 expression in ATC samples and noncancerous samples. Western blotting examined DIAPH3 and forkhead box M1 (FOXM1) expression in ATC cells. In vitro cell counting kit 8 (CCK-8) method, 5-ethynyl-2'-deoxyuridine (EdU) incorporation, Scratch, Matrigel invasion, and terminal-deoxynucleotidyl transferase mediated nick end labelling (TUNEL) assays were used to assess the potential of cells to proliferate, migrate, and invade as well as the cellular apoptotic rate. Co-IP was applied to access DIAPH3-FOXM1 protein interaction. Western blotting also disclosed the expression of proteins associated with apoptosis and Wnt/β-catenin signalling. DIAPH3 was hyper-expressed in papillary cell carcinoma (PTC) tissues and cells. Depleting DIAPH3 strongly eliminated the proliferative, migratory, as well as invasive capabilities of PTC cells while intensifying the apoptotic ability. FOXM1 also harboured elevated expression in PTC cells. FOXM1 was the binding partner with DIAPH3, and the 2 were positively correlated. FOXM1 upregulation again exacerbated the potentials to proliferate, migrate, and invade but it repressed the apoptotic rate of DIAPH3-depleted cells. Furthermore, loss of DIAPH3 downregulated FOXM1 to block Wnt/b-catenin signalling in PTC cells. Combined with these findings, DIAPH3 might favour the aggressive advancement of ATC and motivate the Wnt/β-catenin signalling via binding with FOXM1.

Impacts of DROSHA (rs10719) and DICER (rs3742330) Variants on Breast Cancer Risk and Their Distribution in Blood and Tissue Samples of Egyptian Patients.

MicroRNAs (miRNAs) are small, noncoding RNAs that regulate gene expression and play critical roles in tumorigenesis. Genetic variants in miRNA processing genes, DROSHA and DICER, have been implicated in cancer susceptibility and progression in various populations. However, their role in Egyptian patients with breast cancer (BC) remains unexplored. This study aims to investigate the association of DROSHA rs10719 and DICER rs3742330 polymorphisms with BC risk and clinical outcomes. This case-control study included 209 BC patients and 106 healthy controls. Genotyping was performed using TaqMan assays in blood, tumor tissue, and adjacent non-cancerous tissue samples. Associations were analyzed using logistic regression and Fisher's exact test. The DROSHA rs10719 AA genotype was associated with a 3.2-fold increased risk (95%CI = 1.23-9.36, p < 0.001), and the DICER rs3742330 GG genotype was associated with a 3.51-fold increased risk (95%CI = 1.5-8.25, p = 0.001) of BC. Minor allele frequencies were 0.42 for rs10719 A and 0.37 for rs3742330 G alleles. The risk alleles were significantly more prevalent in tumor tissue than adjacent normal tissue (rs10719 A: 40.8% vs. 0%; rs3742330 G: 42.7% vs. 0%; p < 0.001). However, no significant associations were observed with clinicopathological features or survival outcomes over a median follow-up of 17 months. In conclusion, DROSHA rs10719 and DICER rs3742330 polymorphisms are associated with increased BC risk and more prevalent in tumor tissue among our cohort, suggesting a potential role in miRNA dysregulation during breast tumorigenesis. These findings highlight the importance of miRNA processing gene variants in BC susceptibility and warrant further validation in larger cohorts and different ethnic populations.

5-Hydroxymethylcytosines in circulating cell-free DNA as a diagnostic biomarker for nasopharyngeal carcinoma

ObjectiveTo evaluate the diagnostic value of 5-hydroxymethylcytosines (5hmC) in circulating cell-free DNA (cfDNA) for nasopharyngeal carcinoma (NPC) and to develop a diagnostic model. MethodsGenome-wide 5hmC profiles in cfDNA from 174 NPC patients and 146 non-cancer individuals were analyzed using the 5hmC-Seal technique. A cfDNA 5hmC-based diagnostic model to identify NPC patients was developed using least absolute shrinkage and selection operator (LASSO) logistic regression, and performance was evaluated with receiver operating characteristic (ROC) curves and confusion matrices. ResultsThe 5hmC-Seal data from patients with NPC showed a different genome-wide distribution than non-tumor samples. Our initial analysis revealed a 12-gene-based 5hmC marker panel to be an accurate diagnostic model effectively distinguishing between NPC samples and non-cancerous samples (training set: area under curve (AUC)= 0.97 [95 % CI: 0.94–0.99]; and test set: AUC= 0.93 [95 % CI: 0.88–0.98]) superior to EBV DNA testing. The diagnostic score performed well in differentiating the non-cancer subjects from early-stage NPC (training set: AUC=0.99 [95 % CI: 0.98–1]; test set: AUC=0.98 [95 % CI: 0.95–1]), and advanced-stage NPC (training set: AUC=0.96 [95 % CI: 0.93–0.99]; test set: AUC=0.93 [95 % CI: 0.88–0.98]). Notably, in EBV-negative patients, the diagnostic scores showed excellent capacity for distinguishing EBV-negative patients with NPC from non-cancer subjects in both the training set (AUC= 0.94 [95 % CI: 0.88–1]) and test set (AUC=0.91 [95 % CI: 0.81–1]). Conclusion5hmC modifications in cfDNA are promising noninvasive biomarkers for NPC, offering high sensitivity and specificity, particularly for early-stage and EBV-negative NPC.

Prognostic significance of a 3-gene ferroptosis-related signature in lung cancer via LASSO analysis and cellular functions of UBE2Z

Ferroptosis is a newly identified form of non-apoptotic programmed cell death resulting from iron-dependent lipid peroxidation. It is controlled by integrated oxidation and antioxidant systems. Ferroptosis exerts a crucial effect on the carcinogenesis of several cancers, including pulmonary cancer. Herein, a ferroptosis-associated gene signature for lung cancer prognosis and diagnosis was identified using integrative bioinformatics analyses. From the FerrDB database, 256 ferroptotic regulators and markers were identified. Of these, 25 exhibited differential expression between lung cancer and non-cancerous samples, as evidenced by the GSE19804 and GSE7670 datasets from the GEO database. Utilizing LASSO Cox regression analysis on TCGA-LUAD data, a potent 3-gene risk signature comprising CAV1, RRM2, and EGFR was established. This signature adeptly differentiates various survival outcomes in lung cancer patients, including overall survival and disease-specific intervals. Based on the 3-gene risk signature, lung cancer patients were categorized into high-risk and low-risk groups. Comparative analysis revealed 69 differentially expressed genes between these groups, with UBE2Z significantly associated with overall survival in TCGA-LUAD. UBE2Z was found to be upregulated in LUAD tissues and cells compared to normal controls. Functionally, the knockdown of UBE2Z curtailed aggressive behaviors in LUAD cells, including viability, migration, and invasion. Moreover, this knockdown led to a decrease in the mesenchymal marker vimentin while elevating the epithelial marker E-cadherin within LUAD cell lines. In conclusion, the ferroptosis-associated 3-gene risk signature effectively differentiates prognosis and clinical features in patients with lung cancer. UBE2Z was identified through this model, and it is upregulated in LUAD samples. Its knockdown inhibits aggressive cellular behaviors, suggesting UBE2Z's potential as a therapeutic target for lung cancer treatment.

Medullary thyroid cancer: single-cell transcriptome and tumor evolution

BackgroundMedullary thyroid cancer (MTC) is a rare neuroendocrine tumor that originates from the parafollicular C cells of thyroid gland. Understanding the fundamental pathophysiology of MTC is essential for clinical management. Single-cell RNA sequencing (scRNA-seq) technology is a powerful tool for identifying distinct cell types, offering a new biological foundation for comprehending the MTC ecosystem and developing precise treatment.MethodsFormalin fixed and paraffin-embedded (FFPE) samples of primary and adjacent non-cancerous tissues of three MTC cases were collected, and single-cell transcriptome data of MTC were obtained by using scRNA-seq technology. Annotated cell subpopulations were categorized and functionally enriched by principal component analysis, differential gene expression, and cell clustering analysis, to explore the biological process of tumor evolution that may be involved in each cell subpopulation. The copy number variation (CNV) profile was used to distinguish the malignancy of parafollicular thyroid cells, and the evolutionary trajectories of normal cells and tumor cells were revealed by the proposed time series analysis. The highly expressed genes in each cell subpopulation were analyzed by the FindAllMarker function of Seurat software, and verified by immunohistochemistry and fluorescence in situ hybridization. The prognostic value of specific cell subtypes was validated using large-scale public datasets.ResultsA total of 32,544 cells were obtained from the MTC tissue samples and 11,751 cells from the adjacent non-cancerous samples, which were classified into 7 heterogenous subpopulations by using R package of Seurat module. Copy number variations (CNVs) were significantly higher in tumor tissues than in adjacent non-tumor samples, predominantly enriched in subtypes C2 and C4. In addition, the pseudo-time for trajectory analysis suggested that the evolution of MTC tumor cells might begin with the C2 subtype, then transition to the early cancer subgroup C3, and further differentiate into four major malignant cell subpopulations C0, C1, C5 and C6. Survival analysis of a thyroid cancer cohort using the TCGA dataset revealed that high expression of genes linked to the C0 subcluster was correlated with poorer overall survival compared to low expression. Immunohistochemical staining showed that MAP3K4 was highly expressed in MTC tissues compared to adjacent non-cancerous tissues. Fluorescence in situ hybridization also confirmed the amplification of these two genes in MTC samples.ConclusionsBy conducting scRNA-seq on FFPE samples, we mapped the single-cell transcriptome of MTC, uncovering the tumor heterogeneity and unique biological features of each cellular subpopulation. The biological roles of identified tumor cell subpopulations such as C0 and C3 subtypes of parafollicular cells suggested the potential to discover new therapeutic targets and biomarkers for MTC, providing valuable insights for future translational and clinical research.

Early detection of pancreatic cancer by comprehensive serum miRNA sequencing with automated machine learning.

Pancreatic cancer is often diagnosed at advanced stages, and early-stage diagnosis of pancreatic cancer is difficult because of nonspecific symptoms and lack of available biomarkers. We performed comprehensive serum miRNA sequencing of 212 pancreatic cancer patient samples from 14 hospitals and 213 non-cancerous healthy control samples. We randomly classified the pancreatic cancer and control samples into two cohorts: a training cohort (N = 185) and a validation cohort (N = 240). We created ensemble models that combined automated machine learning with 100 highly expressed miRNAs and their combination with CA19-9 and validated the performance of the models in the independent validation cohort. The diagnostic model with the combination of the 100 highly expressed miRNAs and CA19-9 could discriminate pancreatic cancer from non-cancer healthy control with high accuracy (area under the curve (AUC), 0.99; sensitivity, 90%; specificity, 98%). We validated high diagnostic accuracy in an independent asymptomatic early-stage (stage 0-I) pancreatic cancer cohort (AUC:0.97; sensitivity, 67%; specificity, 98%). We demonstrate that the 100 highly expressed miRNAs and their combination with CA19-9 could be biomarkers for the specific and early detection of pancreatic cancer.

Development of an Impedance and QCM-Based Electrochemical Biosensor for the Detection of Colon Cancer-Secreted Protein-2 (CCSP-2), a Potential Colorectal Cancer Biomarker

Research on colon cancer diagnosis is advancing due to the application of electrochemical biosensors. Early screening can significantly reduce the overall mortality rate from colorectal cancer (CRC). Colon cancer-secreted protein-2 (CCSP-2) is specifically overexpressed and secreted by cancerous cells and adenomas found in the colon. Utilizing a cost-effective and non-invasive electrochemical immunosensor for CCSP-2 detection can aid in the early diagnosis of disease recurrences in individuals with colon cancer. In this work, we designed a simplified electrochemical immunosensor consisting of a functionalized gold (Au) electrode using cysteine-modified recombinant protein G (RPGCys) to immobilize the CCSP-2 antibody. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) have been used to analyze the electrochemical response caused by the binding between the target CCSP-2 and anti-CCSP-2, and there are significant changes observed for charge transfer resistance (Rct) in EIS and current density in CV. The quantification of mass changes attributed to binding events within the sensor will be determined through the application of quartz crystal microbalance (QCM). Angle-resolved X-ray photoelectron spectroscopy (ARXPS) and atomic force microscopy (AFM) will be used for the characterization of the sensor surface. The sensor's sensitivity and specificity will be assessed through the determination of the limit of detection and comprehensive testing of the probe with varied proteins and cell lines sourced from both cancerous and non-cancerous patient samples. This electrochemical probe is expected to enable the rapid and direct detection of early-stage colorectal cancer.

Gene regulatory networks reveal sex difference in lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively.MethodsSample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data.ResultsWe found that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue and tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also discovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database.ConclusionsThese findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.Graphical

Predictive biomarkers for metachronous gastric cancer development after endoscopic resection of early gastric cancer.

We aimed to identify predictive markers for metachronous gastric cancer (MGC) in early gastric cancer (EGC) patients curatively treated with endoscopic submucosal dissection (ESD). From EGC patients who underwent ESD, bulk RNA sequencing was performed on non-cancerous gastric mucosa samples at the time of initial EGC diagnosis. This included 23 patients who developed MGC, and 23 control patients without additional gastric neoplasms for over 3 years (1:1 matched by age, sex, and Helicobacter pylori infection state). Candidate differentially-expressed genes were identified, from which biomarkers were selected using real-time quantitative polymerase chain reaction and cell viability assays using gastric cell lines. An independent validation cohort of 55 MGC patients and 125 controls was used for marker validation. We also examined the severity of gastric intestinal metaplasia, a known premalignant condition, at initial diagnosis. From the discovery cohort, 86 candidate genes were identified of which KDF1 and CDK1 were selected as markers for MGC, which were confirmed in the validation cohort. CERB5 and AKT2 isoform were identified as markers related to intestinal metaplasia and were also highly expressed in MGC patients compared to controls (p<0.01). Combining these markers with clinical data (age, sex, H. pylori and severity of intestinal metaplasia) yielded an area under the curve (AUC) of 0.91 (95% CI, 0.85-0.97) for MGC prediction. Assessing biomarkers in non-cancerous gastric mucosa may be a useful method for predicting MGC in EGC patients and identifying patients with a higher risk of developing MGC, who can benefit from rigorous surveillance.

Significance and Possible Biological Mechanism for CLDN8 Downregulation in Kidney Renal Clear Cell Carcinoma Tissues.

The clinical role of claudin 8 (CLDN8) in kidney renal clear cell carcinoma (KIRC) remains unclarified. Herein, the expression level and potential molecular mechanisms of CLDN8 underlying KIRC were determined. High-throughput datasets of KIRC were collected from GEO, ArrayExpress, SRA, and TCGA databases to determine the mRNA expression level of the CLDN8. In-house tissue microarrays and immunochemistry were performed to examine CLDN8 protein expression. A summary receiver operating characteristic curve (SROC) and standardized mean difference (SMD) forest plot were generated using Stata v16.0. Single-cell analysis was conducted to further prove the expression level of CLDN8. A clustered regularly interspaced short palindromic repeats knockout screen analysis was executed to assess the growth impact of CLDN8. Functional enrichment analysis was conducted using the Metascape database. Additionally, single-sample gene set enrichment analysis was implied to explore immune cell infiltration in KIRC. A total of 17 mRNA datasets comprising 1,060 KIRC samples and 452 non-cancerous control samples were included in this study. Additionally, 105 KIRC and 16 non-KIRC tissues were analyzed using in-house immunohistochemistry. The combined SMD was -5.25 (95% confidence interval (CI): -6.13 to -4.37), and CLDN8 downregulation yielded an SROC area under the curve (AUC) close to 1.00 (95% CI: 0.99 - 1.00). CLDN8 downregulation was also confirmed at the single-cell level. Knocking out CLDN8 stimulated KIRC cell proliferation. Lower CLDN8 expression was correlated with worse overall survival of KIRC patients (hazard ratio of CLDN8 downregulation = 1.69, 95% CI: 1.2 - 2.4). Functional pathways associated with CLDN8 co-expressed genes were centered on carbon metabolism obstruction, with key hub genes ACADM, ACO2, NDUFS1, PDHB, SDHD, SUCLA2, SUCLG1, and SUCLG2. CLDN8 is downregulated in KIRC and is considered a potential tumor suppressor. CLDN8 deficiency may promote the initiation and progression of KIRC, potentially in conjunction with metabolic dysfunction.

The expression and biological role of complement C1s in esophageal squamous cell carcinoma.

The present work focused on investigating the role of the altered expression of complement C1s in proliferation and apoptosis of esophageal squamous cell carcinoma (ESCC) cells and explore its biological functions in ESCC, so as to lay a theoretical foundation and provide certain clinical reference for diagnosing and treating ESCC. Complement C1s expression within ESCC was assessed, and its clinical pathological characteristics in ESCC patients were analyzed. Subsequently, in vitro experiments were performed to further explore the mechanisms by which complement C1s affected ESCC. According to the results, complement C1s expression within ESCC markedly increased relative to adjacent non-cancerous samples. High C1s expression showed positive relation to race, residual lesion, and tumor location of ESCC patients. Complement C1s affected ESCC cell proliferation and apoptosis. Notably, C1s knockdown significantly inhibited ESCC cell proliferation and enhanced their apoptosis. C1s suppressed ESCC cell proliferation via Wnt1/β-catenin pathway and promoted their apoptosis through modulating the expression of Bcl2, Bax, and cleaved-caspase3.

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

BackgroundThe genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes.ResultsWe studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin.ConclusionWe show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

CD87-targeted BiTE and CAR-T cells potently inhibit invasive nonfunctional pituitary adenomas.

Recently, bispecific T-cell engagers (BiTEs) and chimeric antigen receptor-modified T cells (CAR-Ts) have been shown to have high therapeutic efficacy in hematological tumors. CD87 is highly expressed in solid tumors with an oncogenic function. To assess their cytotoxic effects on invasive nonfunctioning pituitary adenomas (iNFPAs), we first examined CD87 expression and its effects on the metabolism of iNFPA cells. We generated CD87-specific BiTE and CAR/IL-12 T cells, and their cytotoxic effects on iNFPAs cells and in mouse models were determined. CD87 had high expression in iNFPA tissue and cell samples but was undetected in noncancerous brain samples. CD87×CD3 BiTE and CD87 CAR/IL-12 T-cells showed antigenic specificity and exerted satisfactory cytotoxic effects, decreasing tumor cell proliferation in vitro and reducing existing tumors in experimental mice. Overall, the above findings suggest that CD87 is a promising target for the immunotherapeutic management of iNFPAs using anti-CD87 BiTE and CD87-specific CAR/IL-12 T cells.

The intratumoral microbiota biomarkers for predicting survival and efficacy of immunotherapy in patients with ovarian serous cystadenocarcinoma

BackgroundOvarian serous cystadenocarcinoma, accounting for about 90% of ovarian cancers, is frequently diagnosed at advanced stages, leading to suboptimal treatment outcomes. Given the malignant nature of the disease, effective biomarkers for accurate prediction and personalized treatment remain an urgent clinical need.MethodsIn this study, we analyzed the microbial contents of 453 ovarian serous cystadenocarcinoma and 68 adjacent non-cancerous samples. A univariate Cox regression model was used to identify microorganisms significantly associated with survival and a prognostic risk score model constructed using LASSO Cox regression analysis. Patients were subsequently categorized into high-risk and low-risk groups based on their risk scores.ResultsSurvival analysis revealed that patients in the low-risk group had a higher overall survival rate. A nomogram was constructed for easy visualization of the prognostic model. Analysis of immune cell infiltration and immune checkpoint gene expression in both groups showed that both parameters were positively correlated with the risk level, indicating an increased immune response in higher risk groups.ConclusionOur findings suggest that microbial profiles in ovarian serous cystadenocarcinoma may serve as viable clinical prognostic indicators. This study provides novel insights into the potential impact of intratumoral microbial communities on disease prognosis and opens avenues for future therapeutic interventions targeting these microorganisms.

Aging-associated Alterations in the Gene Regulatory Network Landscape Associate with Risk, Prognosis and Response to Therapy in Lung Adenocarcinoma.

Aging is the primary risk factor for many individual cancer types, including lung adenocarcinoma (LUAD). To understand how aging-related alterations in the regulation of key cellular processes might affect LUAD risk and survival outcomes, we built individual (person)-specific gene regulatory networks integrating gene expression, transcription factor protein-protein interaction, and sequence motif data, using PANDA/LIONESS algorithms, for both non-cancerous lung tissue samples from the Genotype Tissue Expression (GTEx) project and LUAD samples from The Cancer Genome Atlas (TCGA). In GTEx, we found that pathways involved in cell proliferation and immune response are increasingly targeted by regulatory transcription factors with age; these aging-associated alterations are accelerated by tobacco smoking and resemble oncogenic shifts in the regulatory landscape observed in LUAD and suggests that dysregulation of aging pathways might be associated with an increased risk of LUAD. Comparing normal adjacent samples from individuals with LUAD with healthy lung tissue samples from those without LUAD, we found that aging-associated genes show greater aging-biased targeting patterns in younger individuals with LUAD compared to their healthy counterparts of similar age, a pattern suggestive of age acceleration. This implies that an accelerated aging process may be responsible for tumor incidence in younger individuals. Using drug repurposing tool CLUEreg, we found small molecule drugs with potential geroprotective effects that may alter the accelerating aging profiles we found. We also observed that, in contrast to chronological age, a network-informed aging signature was associated with survival and response to chemotherapy in LUAD.

Immunological Signatures for Early Detection of Human Head and Neck Squamous Cell Carcinoma through RNA Transcriptome Analysis of Blood Platelets.

Although there has been a reduction in head and neck squamous cell carcinoma occurrence, it continues to be a serious global health concern. The lack of precise early diagnostic biomarkers and postponed diagnosis in the later stages are notable constraints that contribute to poor survival rates and emphasize the need for innovative diagnostic methods. In this study, we employed machine learning alongside weighted gene co-expression network analysis (WGCNA) and network biology to investigate the gene expression patterns of blood platelets, identifying transcriptomic markers for HNSCC diagnosis. Our comprehensive examination of publicly available gene expression datasets revealed nine genes with significantly elevated expression in samples from individuals diagnosed with HNSCC. These potential diagnostic markers were further assessed using TCGA and GTEx datasets, demonstrating high accuracy in distinguishing between HNSCC and non-cancerous samples. The findings indicate that these gene signatures could revolutionize early HNSCC identification. Additionally, the study highlights the significance of tumor-educated platelets (TEPs), which carry RNA signatures indicative of tumor-derived material, offering a non-invasive source for early-detection biomarkers. Despite using platelet and tumor samples from different individuals, our results suggest that TEPs reflect the transcriptomic and epigenetic landscape of tumors. Future research should aim to directly correlate tumor and platelet samples from the same patients to further elucidate this relationship. This study underscores the potential of these biomarkers in transforming early diagnosis and personalized treatment strategies for HNSCC, advocating for further research to validate their predictive and therapeutic potential.

Genome-Scale Multimodal Analysis of Cell-Free DNA Whole-Methylome Sequencing for Noninvasive Esophageal Cancer Detection.

Simultaneous profiling of cell-free DNA (cfDNA) methylation and fragmentation features to improve the performance of cfDNA-based cancer detection is technically challenging. We developed a method to comprehensively analyze multimodal cfDNA genomic features for more sensitive esophageal squamous cell carcinoma (ESCC) detection. Enzymatic conversion-mediated whole-methylome sequencing was applied to plasma cfDNA samples extracted from 168 patients with ESCC and 251 noncancer controls. ESCC characteristic cfDNA methylation, fragmentation, and copy number signatures were analyzed both across the genome and at accessible cis-regulatory DNA elements. To distinguish ESCC from noncancer samples, a first-layer classifier was developed for each feature type, the prediction results of which were incorporated to construct the second-layer ensemble model. ESCC plasma genome displayed global hypomethylation, altered fragmentation size, and chromosomal copy number alteration. Methylation and fragmentation changes at cancer tissue-specific accessible cis-regulatory DNA elements were also observed in ESCC plasma. By integrating multimodal genomic features for ESCC detection, the ensemble model showed improved performance over individual modalities. In the training cohort with a specificity of 99.2%, the detection sensitivity was 81.0% for all stages and 70.0% for stage 0-II. Consistent performance was observed in the test cohort with a specificity of 98.4%, an all-stage sensitivity of 79.8%, and a stage 0-II sensitivity of 69.0%. The performance of the classifier was associated with the disease stage, irrespective of clinical covariates. This study comprehensively profiles the epigenomic landscape of ESCC plasma and provides a novel noninvasive and sensitive ESCC detection approach with genome-scale multimodal analysis.