Specific Predictive Biomarkers Research Articles

Insights into the Novel Biomarkers Expressed in Diabetic Nephropathy: Potential Clinical Applications.

Diabetic nephropathy (DN) is increasing worldwide in parallel with type 2 diabetes mellitus. Identifying diagnostic biomarkers for DN at an early stage is crucial due to the considerable societal and economic burden associated with diabetes mellitus (DM) and its risk factors. In the past, early indicators of microvascular problems, such as microalbuminuria (MA), have been used to predict the possibility of developing advanced chronic kidney disease (CKD). However, because of the incapacity of MA to appropriately estimate DN, particularly, non-albuminuric DN, additional markers have been suggested for recognizing the early renal abnormalities and structural lesions, even before MA. This study aims to assess the existing and future biomarkers used to diagnose or predict early DN. This review provides comprehensive insight into diagnostic approaches for early detection of CKD, addressing the following areas: (i) markers of glomerular damage, (ii) markers of tubular damage, (iii) oxidative stress biomarkers, (iv) inflammatory biomarkers and (v) futuristic biomarkers such as micro-ribonucleic acids (miRNAs), proteomics, metabolomics and genomics and gut microbiota. Early detection of DN may lead to improvement in clinical management and quality of life, emphasizing the importance of identifying a specific and reliable predictive biomarker. Emerging serum and urinary biomarkers offer promise for early DN diagnosis, potentially reducing prevalence and preventing progression to end-stage renal disease (ESRD). Further advancements in miRNAs, proteomics, metabolomics genomics and gut microbiota offer prospects for even earlier and more precise DN diagnosis.

Unraveling potential gene biomarkers for dengue infection through RNA sequencing.

Dengue virus hijacks host cell mechanisms and immune responses in order to replicate efficiently. The interaction between the host and the virus affects the host's gene expression, which remains largely unexplored. This pilot study aimed to profile the host transcriptome as a potential strategy for identifying specific biomarkers for dengue prediction and detection. High-throughput RNA sequencing (RNA-seq) was employed to generate host transcriptome profiles in 16 dengue patients and 10 healthy controls. Differentially expressed genes (DEGs) were identified in patients with severe dengue and those with dengue with warning signs compared to healthy individuals. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to elucidate the functions of upregulated and downregulated genes. Compared to healthy controls, 6466 genes were significantly differentially expressed (p < 0.05) in the dengue with warning signs group and 3082 genes in the severe dengue group, with over half being upregulated. The major KEGG pathways implicated included transport and catabolism (14.4%-16.3%), signal transduction (6.6%-7.3%), global and overview maps (6.7%-7.1%), viral diseases (4.6%-4.8%), and the immune system (4.4%-4.6%). Several genes exhibited consistent and significant upregulation across all dengue patients, regardless of severity: Interferon alpha inducible protein 27 (IFI27), Potassium Channel Tetramerization Domain Containing 14 (KCTD14), Syndecan 1 (SDC1), DCC netrin 1 receptor (DCC), Ubiquitin C-terminal hydrolase L1 (UCHL1), Marginal zone B and B1 cell-specific protein (MZB1), Nestin (NES), C-C motif chemokine ligand 2 (CCL2), TNF receptor superfamily member 17 (TNFSF17), and TNF receptor superfamily member 13B (TNFRSF13B). Further analysis revealed potential biomarkers for severe dengue prediction, including TNF superfamily member 15 (TNFSF15), Plasminogen Activator Inhibitor-2 (SERPINB2), motif chemokine ligand 7 (CCL7), aconitate decarboxylase 1 (ACOD1), Metallothionein 1G (MT1G), and Myosin Light Chain Kinase (MYLK2), which were expressed 3.5 times, 2.9 times, 2.3 times, 2.1 times, 1.7 times, and 1.4 times greater, respectively, than dengue patients exhibiting warning signs. The identification of these host biomarkers through RNA-sequencing holds promising implications and potential to augment existing dengue detection algorithms, contributing significantly to improved diagnostic and prognostic capabilities.

Identification of mitochondria-related genes as diagnostic biomarkers for diabetic nephropathy and their correlation with immune infiltration: New insights from bioinformatics analysis

BackgroundDiabetic nephropathy (DN) is a common and severe microvascular complication of diabetes. Mitochondrial dysfunction and immune inflammation are important factors in the pathogenesis of DN. However, the specific mechanisms and their intricate interactions in DN remain unclear. Besides, there are no effective specific predictive or diagnostic biomarkers for DN so far. Therefore, this study aims to elucidate the role of mitochondrial-related genes and their possibility as predictive or diagnostic biomarkers, as well as their crosstalk with immune infiltration in the progression of DN. MethodsBased on the GEO database and limma R package, the differentially expressed genes (DEGs) of DN were identified. Mitochondrial-related DEGs (MitoDEGs) were then obtained by intersecting these DEGs with mitochondria-related genes from the MitoCarta 3.0 database. Subsequently, the candidate hub genes were further screened by gene co-expression network analysis (WGCNA), and verified mRNA levels of these genes by real-time quantitative PCR (qRT-PCR) in high-glucose-treated human proximal tubular (HK-2) cells. The verified hub genes were utilized to construct a combined diagnostic model for DN, with its diagnostic efficacy assessed across the GSE30122 and GSE96804 datasets. Additionally, the immune infiltration pattern in DN was assessed with the CIBERSORT algorithm, and the Nephroseq v5 database was used to analyze the correlation between hub genes and clinical features of DN. ResultsSeven mitochondria-related candidate hub genes were screened from 56 MitoDEGs. Subsequently, the expression levels of six of them, namely EFHD1, CASP3, AASS, MPC1, NT5DC2, and BCL2A1, exhibited significant inter-group differences in the HK-2 cell model. The diagnostic model based on the six genes demonstrated good diagnostic efficacy in both training and validation sets. Furthermore, correlation analysis indicated that EFHD1 and AASS, downregulated in DN, are positively correlated with eGFR and negatively with serum creatinine. Conversely, CASP3, NT5DC2, and BCL2A1, upregulated in DN, show opposite correlations. In addition, spearman analysis revealed that the six hub genes were significantly associated with the infiltration of immune cells, including M1 and M2 macrophages, mast cells, resting NK cells, gamma delta T cells, and follicular helper T cells. ConclusionThis study elucidated the characteristics of mitochondria-related genes and their correlation with immune cell infiltration in DN, providing new insights for exploring the pathogenesis of DN and facilitating the identification of new potential biomarkers and therapeutic targets.

PCDH11X mutation as a potential biomarker for immune checkpoint therapies in lung adenocarcinoma.

Immune checkpoint inhibitors (ICIs) have achieved impressive success in lung adenocarcinoma (LUAD). However, the response to ICIs varies among patients, and predictive biomarkers are urgently needed. PCDH11X is frequently mutated in LUAD, while its role in ICI treatment is unclear. In this study, we curated genomic and clinical data of 151 LUAD patients receiving ICIs from three independent cohorts. Relations between PCDH11X and treatment outcomes of ICIs were examined. A melanoma cohort collected from five published studies, a pan-cancer cohort, and non-ICI-treated TCGA-LUAD cohort were also examined to investigate whether PCDH11X mutation is a specific predictive biomarker for LUAD ICI treatment. Among the three ICI-treated LUAD cohorts, PCDH11X mutation (PCDH11X-MUT) was associated with better clinical response compared to wild-type PCDH11X (PCDH11X-WT). While in ICI-treated melanoma cohort, the pan-cancer cohort excluding LUAD, and the non-ICI-treated TCGA-LUAD cohort, no significant differences in overall survival (OS) were observed between the PCDH11X-MUT and PCDH11X-WT groups. PCDH11X mutation was associated with increased PD-L1 expression, tumor mutation burden (TMB), neoantigen load, DNA damage repair (DDR) mutations, and hot tumor microenvironment in TCGA-LUAD cohort. Our findings suggested that the PCDH11X mutation might serve as a specific biomarker to predict the efficacy of ICIs for LUAD patients. Considering the relatively small sample size of ICI-treated cohorts, future research with larger cohorts and prospective clinical trials will be essential for validating and further exploring the role of PCDH11X mutation in the context of immunotherapy outcomes in LUAD. KEY MESSAGES: PCDH11X mutation is associated with better clinical response compared to wild type PCDH11X in three ICIs-treated LUAD cohorts. In ICIs-treated melanoma cohort, the pan-cancer cohort excluding LUAD, and non-ICIs-treated TCGA-LUAD cohortsPCDH11X mutation is not associated with better clinical response, suggesting PCDH11X mutation might be a specific biomarker to predict the efficacy of ICIs treatment for LUAD patients. PCDH11X mutation is associated with increased PD-L1 expression, tumor mutation burden, and neoantigen load in TCGA-LUAD cohort. PCDH11X mutation is associated with hot tumor microenvironment in TCGA-LUAD cohort.

Predicting effect of anti-PD-1/PD-L1 inhibitors therapy for hepatocellular carcinoma by detecting plasma metabolite based on UHPLC-MS.

Anti-PD-1/PD-L1 inhibitors therapy has become a promising treatment for hepatocellular carcinoma (HCC), while the therapeutic efficacy varies significantly among effects for individual patients are significant difference. Unfortunately, specific predictive biomarkers indicating the degree of benefit for patients and thus guiding the selection of suitable candidates for immune therapy remain elusive.no specific predictive biomarkers are available indicating the degree of benefit for patients and thus screening the preferred population suitable for the immune therapy. Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) considered is an important method for analyzing biological samples, since it has the advantages of high rapid, high sensitivity, and high specificity. Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) has emerged as a pivotal method for analyzing biological samples due to its inherent advantages of rapidity, sensitivity, and specificity. In this study, potential metabolite biomarkers that can predict the therapeutic effect of HCC patients receiving immune therapy were identified by UHPLC-MS. A partial least-squares discriminant analysis (PLS-DA) model was established using 14 glycerophospholipid metabolites mentioned above, and good prediction parameters (R2 = 0.823, Q2 = 0.615, prediction accuracy = 0.880 and p < 0.001) were obtained. The relative abundance of glycerophospholipid metabolite ions is closely related to the survival benefit of HCC patients who received immune therapy. This study reveals that glycerophospholipid metabolites play a crucial role in predicting the efficacy of immune therapy for HCC.

Detection of autoantibodies to heat shock protein 70 in the saliva and urine of normal individuals.

Cells exposed to stressors of various origin activate protective mechanisms that include the expression of heat shock proteins (Hsps)/molecular chaperones belonging to several families. Well-characterized inducible Hsp70 is present in all human cell-types and biological fluids, including blood, urine, and saliva. The presence of anti-Hsp70 autoantibodies in the serum of healthy individuals has already been confirmed, and their elevated titers positively correlated with the severity of several pathological conditions, including coeliac disease and dermatitis herpetiformis - a cutaneous manifestation of coeliac disease. Here, using an indirect enzyme-linked immunosorbent assay, we demonstrate, for the first time, that anti-Hsp70 autoantibodies are present in the saliva and urine of healthy individuals. Although the occurrence of anti-Hsp70 autoantibodies in the biological fluids of healthy individuals is intriguing, their physiological role is currently unknown. It is believed that antibodies reacting with self-molecules present in the serum of healthy individuals are part of natural autoantibody pool with multiple regulatory functions. On the other hand, some autoantibodies (e.g., typical of autoimmune bullous skin diseases or systemic lupus erythematosus) may be present before the onset of the disease and serve as specific predictive biomarkers. Therefore, we would like to initiate a discussion or future research direction on the use of anti-Hsp70 autoantibodies as a potential "biomarker" in the diagnosis or prediction of autoimmune diseases. Our findings can be considered in biomedical research to develop noninvasive, inexpensive and easy-to-use tests. Nevertheless, large-scale comparative studies should be initiated, involving the collection and analysis of biological samples such as saliva or urine from patients suffering from autoimmune diseases or other inflammatory or neoplastic diseases, to determine whether the levels of anti-Hsp70 autoantibodies are indeed elevated and whether they correlate with the clinical picture of any disease or established biomarkers.

Cancer Biomarkers and Precision Oncology: A Review of Recent Trends and Innovations.

The discovery of cancer-specific biomarkers has resulted in major advancements in the field of cancer diagnostics and therapeutics, therefore significantly lowering cancer-related morbidity and mortality. Cancer biomarkers can be generally classified as prognostic biomarkers that predict specific disease outcomes and predictive biomarkers that predict disease response to targeted therapeutic interventions. As research in the area of predictive biomarkers continues to grow, precision medicine becomes far more integrated in cancer treatment. This article presents a general overview on the most recent advancements in the area of cancer biomarkers, immunotherapy, artificial intelligence, and pharmacogenomics of the Middle East.

Tribbles Genes in Gastric Cancer: A Tumor-Suppressive Role for TRIB2.

Tribbles pseudokinases (TRIB1-3) are important signaling modulators involved in several cancers. However, their function in gastric cancer (GC) remains undefined. GC is still a deadly disease since the lack of sensitive and specific biomarkers for early diagnosis and therapy response prediction negatively affects patients' outcome. The identification of novel molecular players may lead to more effective diagnostic and therapeutic avenues. Therefore, we investigated the role of TRIB genes in gastric tumorigenesis. Data mining of the TCGA dataset revealed that chromosomal instability (CIN) tumors have lower TRIB2 and higher TRIB3 expression versus microsatellite instability (MSI)-high tumors, while TRIB1 levels are similar in both tumor types. Moreover, in CIN tumors, low TRIB2 expression is significantly associated with aggressive stage IV disease. As no studies on TRIB2 in GC are available, we focused on this gene for further in vitro analyses. We checked the effect of TRIB2 overexpression (OE) on MKN45 and NCI-N87 CIN GC cell lines. In MKN45 cells, TRIB2 OE reduced proliferation and colony formation ability and induced G2/M arrest, while it decreased the proliferation and cell motility of NCI-N87 cells. These effects were not mediated by the MAPK pathway. Our results suggest a tumor-suppressive function of TRIB2 in GC with a CIN phenotype.

Gene expression of S100a8/a9 predicts Staphylococcus aureus-induced septic arthritis in mice.

Septic arthritis is the most aggressive joint disease associated with high morbidity and mortality. The interplay of the host immune system with the invading pathogens impacts the pathophysiology of septic arthritis. Early antibiotic treatment is crucial for a better prognosis to save the patients from severe bone damage and later joint dysfunction. To date, there are no specific predictive biomarkers for septic arthritis. Transcriptome sequencing analysis identified S100a8/a9 genes to be highly expressed in septic arthritis compared to non-septic arthritis at the early course of infection in an Staphylococcus aureus septic arthritis mouse model. Importantly, downregulation of S100a8/a9 mRNA expression at the early course of infection was noticed in mice infected with the S. aureus Sortase A/B mutant strain totally lacking arthritogenic capacity compared with the mice infected with parental S. aureus arthritogenic strain. The mice infected intra-articularly with the S. aureus arthritogenic strain significantly increased S100a8/a9 protein expression levels in joints over time. Intriguingly, the synthetic bacterial lipopeptide Pam2CSK4 was more potent than Pam3CSK4 in inducing S100a8/a9 release upon intra-articular injection of these lipopeptides into the mouse knee joints. Such an effect was dependent on the presence of monocytes/macrophages. In conclusion, S100a8/a9 gene expression may serve as a potential biomarker to predict septic arthritis, enabling the development of more effective treatment strategies.

Tissue assessment of therapeutic responses to neoadjuvant short course radiotherapy with and without anti-CD40 immunotherapy sotigalimab (sotiga) in rectal cancer.

191 Background: There has been tremendous progress in the management of rectal cancer in the neoadjuvant setting and in non-operative management (NOM). However, there is a critical need to personalize systemic therapy and radiotherapy to achieve responses conducive for NOM. This study was initiated to profile immune changes to therapy in serial tumor tissue, blood, and stool from the INNATE trial (NCT04130854). Methods: INNATE, is a multi-institutional Phase II randomized trial for patients with Stage III or high-risk Stage II rectal cancer. Patients were randomized 3:2 to receive daily short course radiotherapy (SCRT- 5 Gy x 5) and 6 cycles of FOLFOX +/- 6 infusions of sotiga, an anti-CD40 agonist, followed by surgery. The primary end point is the pathologic complete response rate and additional clinical endpoints are being assessed. Biopsies of tumor and adjacent tissue before and 2 weeks after SCRT +/- sotiga prior to chemotherapy were obtained from consenting patients. Fresh tissue was digested into single cell suspension for scRNAseq with proteomic and immune repertoire analysis. Fixed tissue was processed for histology and multiplexed immunofluorescence. Here, we report integrated and batch effect removed data from 9 patients pre- and post-therapy and analyzed patients as their own control. Results: A total of 25 of 30 patients were enrolled at UTSW, 21 of whom underwent pre- and post-SCRT biopsies. The study population was diverse: 11 patients (37%) were Hispanic and 15 (50%) uninsured receiving care at the Dallas County hospital, Parkland. The median age was 55 (27-75) years and 13 (43.3%) were diagnosed prior to age 50. From integrated scRNAseq, we observed a significant induction of genes associated with M1- and M2-like macrophages, and dendritic cell antigen presentation in the SCRT alone group. This was accompanied by a robust Th1 and B cell response, but not T cell mediated cytotoxicity. In the SCRT + sotiga group there was a greater induction of genes associated with M1-like macrophages and dendritic cell antigen presentation, with no change in M2 genes. In lymphocytes, sotiga treatment led to an induction of genes related to Th1 and B cell response. Unlike SCRT, the combination therapy group had evidence of induced T cell mediated cytotoxicity. To better understand individual responses to therapy, we analyzed pre- and post-treatment samples for each patient. This enabled identification of features associated with poor and robust response in each treatment group. Conclusions: On treatment biopsies during neoadjuvant therapy for rectal cancer enables deep assessment of therapeutic response, provides insight into action of investigational agents, and may identify therapy specific predictive biomarkers. Our data shows that SCRT can induce immune responses and adaptive immune responses can be further induced by sotiga.

Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images.

Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is complicated as PDAC remains asymptomatic until cancer advances to late stages when treatment is mostly ineffective. Stratifying the risk of developing PDAC can improve early detection as subsequent screening of high-risk individuals through specialized surveillance systems reduces the chance of misdiagnosis at the initial stage of cancer. Risk stratification is however challenging as PDAC lacks specific predictive biomarkers. Studies reported that the pancreas undergoes local morphological changes in response to underlying biological evolution associated with PDAC development. Accurate identification of these changes can help stratify the risk of PDAC. In this retrospective study, an extensive radiomic analysis of the precancerous pancreatic subregions was performed using abdominal Computed Tomography (CT) scans. The analysis was performed using 324 pancreatic subregions identified in 108 contrast-enhanced abdominal CT scans with equal proportion from healthy control, pre-diagnostic, and diagnostic groups. In a pairwise feature analysis, several textural features were found potentially predictive of PDAC. A machine learning classifier was then trained to perform risk prediction of PDAC by automatically classifying the CT scans into healthy control (low-risk) and pre-diagnostic (high-risk) classes and specifying the subregion(s) likely to develop a tumor. The proposed model was trained on CT scans from multiple phases. Whereas using 42 CT scans from the venous phase, model validation was performed which resulted in ~89.3% classification accuracy on average, with sensitivity and specificity reaching 86% and 93%, respectively, for predicting the development of PDAC (i.e., high-risk). To our knowledge, this is the first model that unveiled microlevel precancerous changes across pancreatic subregions and quantified the risk of developing PDAC. The model demonstrated improved prediction by 3.3% in comparison to the state-of-the-art method that considers the global (whole pancreas) features for PDAC prediction.

Target Therapy and Immunotherapy for Gastric Cancer - Recent Updates

Abstract: Gastric cancer, with more than 1 million cases all over the globe, is a leading contributor to cancer mortality. In the recent past, the evolution of target therapies and the refining of the already existing chemotherapy have widened the therapeutic choices for advanced gastric cancer to increase survival. In addition, more studies have been conducted all over the globe to extensively evaluate epigenomic and genomic heterogeneity of gastric cancer, leading to the recognition of new sensitive and specific prognostic and predictive biomarkers and the development of new molecular tumor classifications based on their molecular profiling. But, many clinical studies aimed at evaluating the efficiency of certain new molecular targets gave negative results. Hence, it is vital to further study the molecular features of subtypes of gastric carcinoma for the purpose of identifying new tools for selecting responsive tumor subtypes for future trials. This review provides an overview of recent developments of target therapy and immunotherapy in advanced gastric cancer.

Potential biomarkers of spinal dural arteriovenous fistula: C4BPA and C1QA

Background and purposeA major challenge in spinal dural arteriovenous fistula (SDAVF) is timely diagnosis, but no specific predictive biomarkers are known.MethodsIn the discovery cohort (case, n = 8 vs. control, n = 8), we used cerebrospinal fluid (CSF) and paired plasma samples to identify differentially expressed proteins by label-free quantitative proteomics. Further bioinformatics enrichment analyses were performed to screen target proteins. Finally, it was validated by ELISA in two of the new cohorts (case, n = 17 vs. control, n = 9), and univariate analysis, simple linear regression, and receiver operator characteristic (ROC) curve analysis were performed to evaluate the diagnostic potential.ResultsIn the discovery cohort, the most overexpressed proteins were APOB and C4BPA in CSF samples of patients. The GO/KEGG enrichment analysis indicated that the upregulated proteins were mainly involved in the acute inflammatory response and complement activation. Hub-gene analysis revealed that APP might be the key protein in the molecular interaction network. In the validation cohort, C4BPA and C1QA were significantly overexpressed in the CSF of patients, averaging 3046.9 ng/ml and 2167.2 ng/ml, respectively. Simple linear regression demonstrated that levels of C1QA and C4 were positively correlated with total protein in CSF (R2 = 0.8021, p = 0.0005; R2 = 0.7447, p = 0.0013). The areas under the ROC curves of C4BPA and C1QA were 0.86 and 1.00, respectively.ConclusionsThis study was the first to identify C4BPA and C1QA as potential biomarkers for the diagnosis of SDAVF and revealed that complement pathway activation might be one of the molecular mechanisms for venous hypertension myelopathy.

Association of presurgical circulating MicroRNAs with 1-year postsurgical pain reduction in spine facet osteoarthritis patients with lumbar spinal stenosis

PurposeUp to 30% of spine facet osteoarthritis patients with lumbar spinal stenosis (SF-OA ​+ ​LSS) have little to no improvement in their pain after surgery. Lack of meaningful improvement in pain following surgery provides a unique opportunity to identify specific predictive biomarker signatures that might be associated with the outcomes of surgical treatment. The objective of the present study was to determine whether a microRNA (miRNA) biomarker signature could be identified in presurgical blood plasma that corresponded with levels of SF-OA ​+ ​LSS patient post-surgical pain intensity one year later. MethodsRNA was extracted from baseline plasma of SF-OA ​+ ​LSS patients and prepared for miRNA sequencing. Statistical approaches were performed to identify differentially expressed miRNAs associated with reduced 1-year postsurgical pain (n ​= ​56). Using an integrated computational approach, we further created predicted gene and pathway networks for each identified miRNA. ResultsWe identified a panel of 4 circulating candidate miRNAs (hsa-miR-155-5p, hsa-let-7e-5p, hsa-miR-125a-5p, hsa-miR-99b-5p) with higher levels at presurgical baseline that were associated with greater changes in % NPRS20Δ, reflecting reduced pain intensity levels at one year. Genes encoding hsa-let-7e-5p, hsa-miR-125a-5p, and hsa-miR-99b-5p are part of an evolutionarily conserved miRNA cluster. Using integrated computational analyses, we showed that mammalian target of rapamycin, transforming growth factor-β1 receptor, Wnt signaling, epithelial–mesenchymal transition regulators, and cholecystokinin signaling were enriched pathways of predicted gene targets. ConclusionsTaken together, our findings suggest that 4 presurgical baseline circulating miRNAs correlate with 1-year postsurgical SF-OA ​+ ​LSS patient pain intensity and represent possible candidate biomarker signature of surgical pain response.

Predictive value of DNA repair gene expression for response to neoadjuvant chemotherapy in breast cancer.

e12538 Background: Genome-wide analysis using microarrays has revolutionized the field of breast cancer (BC) research, classifying breast cancer by gene expression profiling. In patients with BC, a substantial body of evidence supports the clinical utility of gene expression profiling. For example, the 21-gene assay (Oncotype DX) and 70-gene assay (MammaPrint) predict BC recurrence and the magnitude of chemotherapy benefit. However, there is currently no gene-expression profiling able to predict chemosensitivity and chemoresistance to neoadjuvant chemotherapy (NACT) during BC treatment. Therefore, the development of specific predictive biomarkers for chemoresistance and chemosensitivity is desirable. In the present study, we explored the predictive value of DNA repair gene expression for response to NACT in BC by evaluating the mRNA expression of 11 selected genes which have a key role in DNA repair mechanisms. Methods: We selected 98 patients with BC treated with NACT. We assessed DNA repair expression in 98 formalin-fixed, paraffin-embedded core biopsy fragments used at diagnosis and in 32 formalin-fixed, paraffin-embedded post-NACT residual tumors using quantitative reverse transcription-polymerase chain reaction. The following genes were selected: BRCA1, PALB2, RAD51C, BRCA2, ATM, FANCA, MSH2, XPA, ERCC1, PARP1 and SNM1. We used the mean of 2 reference genes (GAPDH and ACTB) to minimize the risk of normalization bias that can result from variations in the expression of any single reference gene. Results: Of 98 patients, 33 (33.7%) achieved pathologic complete response (pCR). The DNA expression of 2 genes assessed in pre-NACT biopsies (PALB2 and ERCC1) was lower in pCR than in non-pCR patients (p=0.005 and p=0.009, respectively). After adjustment for tumor grade and molecular subtype, a linear regression model with rank transformation showed that the DNA expression of 2 genes (PALB2 and ERCC1) assessed in pre-NACT biopsies was lower in the pCR group than in the non-pCR group (p=0.014 and p=0.040 respectively). The genes BRCA2 (p=0.009), ATM (p=0.004), FANCA (p=0.001), and PARP1 (p=0.011) showed a lower expression in post-NACT residual tumor samples (n=32) than in pre-NACT biopsy samples (n=98). Conclusions: In the genomics era, treatment should be tailored to the individual patient. The present study showed that PALB2 and ERCC1 expressions, assessed in pre-NACT biopsies, were lower in pCR patients. These alterations in DNA repair could be considered suitable targets for cancer therapy. In addition, BRCA2, ATM, FANCA, and PARP1 expressions were lower in post-NACT residual tumor samples than in pre-NACT biopsies. The use of recently developed multiparameter gene-expression assays, based on the expression of genes involved in different DNA repair pathways, should be further explored in future studies, as they may facilitate the selection of patients most likely to benefit from NACT.

Berberine retarded the growth of gastric cancer xenograft tumors by targeting hepatocyte nuclear factor 4α.

BACKGROUNDGastric cancer is the third deadliest cancer in the world and ranks second in incidence and mortality of cancers in China. Despite advances in prevention, diagnosis, and therapy, the absolute number of cases is increasing every year due to aging and the growth of high-risk populations, and gastric cancer is still a leading cause of cancer-related death. Gastric cancer is a consequence of the complex interaction of microbial agents, with environmental and host factors, resulting in the dysregulation of multiple oncogenic and tumor-suppressing signaling pathways. Global efforts have been made to investigate in detail the genomic and epigenomic heterogeneity of this disease, resulting in the identification of new specific and sensitive predictive and prognostic biomarkers. Trastuzumab, a monoclonal antibody against the HER2 receptor, is approved in the first-line treatment of patients with HER2+ tumors, which accounts for 13%-23% of the gastric cancer population. Ramucirumab, a monoclonal antibody against VEGFR2, is currently recommended in patients progressing after first-line treatment. Several clinical trials have also tested novel agents for advanced gastric cancer but mostly with disappointing results, such as anti-EGFR and anti-MET monoclonal antibodies. Therefore, it is still of great significance to screen specific molecular targets for gastric cancer and drugs directed against the molecular targets.AIMTo investigate the effect and mechanism of berberine against tumor growth in gastric cancer xenograft models and to explore the role of hepatocyte nuclear factor 4α (HNF4α)-WNT5a/β-catenin pathways played in the antitumor effects of berberine.METHODSMGC803 and SGC7901 subcutaneous xenograft models were established. The control group was intragastrically administrated with normal saline, and the berberine group was administrated intragastrically with 100 mg/kg/d berberine. The body weight of nude mice during the experiment was measured to assess whether berberine has any adverse reaction. The volume of subcutaneous tumors during this experiment was recorded to evaluate the inhibitory effect of berberine on the growth of MGC803 and SGC7901 subcutaneous transplantation tumors. Polymerase chain reaction assays were conducted to evaluate the alteration of transcriptional expression of HNF4α, WNT5a and β-catenin in tumor tissues and liver tissues from the MGC803 and SGC7901 xenograft models. Western blotting and IHC were performed to assess the protein expression of HNF4α, WNT5a and β-catenin in tumor tissues and liver tissues from the MGC803 and SGC7901 xenograft models.RESULTSIn the both MGC803 and SGC7901 xenograft tumor models, berberine significantly reduced tumor volume and weight and thus retarded the growth rate of tumors. In the SGC7901 and MGC803 subcutaneously transplanted tumor models, berberine down-regulated the expression of HNF4α, WNT5a and β-catenin in tumor tissues from both transcription and protein levels. Besides, berberine also suppressed the protein expression of HNF4α, WNT5a and β-catenin in liver tissues.CONCLUSIONBerberine retarded the growth of MGC803 and SGC7901 xenograft model tumors, and the mechanism behind these anti-growth effects might be the downregulation of the expression of HNF4α-WNT5a/β-catenin signaling pathways both in tumor tissues and liver tissues of the xenograft models.

Blood Biomarkers and Serologic Immunological Profiles Related to Periodontitis in Abdominal Aortic Aneurysm Patients.

BackgroundPeriodontitis is a chronic inflammatory gum disease associated with systemic diseases such as cardiovascular diseases.AimTo investigate the association of systemic blood biomarkers, C-reactive protein (CRP), levels of lipopolysaccharide (LPS), and IgG levels against periodontal pathogens Aggregatibacter actinomycetemcomitans (Aa) and Porphyromonas gingivalis (Pg) with the stability, based on the aortic diameter, the growth rate and the eligibility for surgical intervention, of patients with abdominal aortic aneurysm (AAA).MethodsPatients with stable AAA (n = 30) and unstable AAA (n = 31) were recruited. The anti-A. actinomycetemcomitans and anti-P. gingivalis IgG levels were analyzed by ELISA, the LPS analysis was performed by using the limulus amebocyte lysate (LAL) test, and plasma levels of CRP were determined using an immune turbidimetric method. The association between these blood systemic biomarkers, AAA features, periodontal clinical parameters and oral microbial profiles were explored. Regression models were used to test the relationship between variables.ResultsThe presence of antibodies against Pg and Aa, LPS and high CRP concentrations were found in all AAA patients. The IgG levels were similar in patients with stable and unstable AAA (both for Aa and Pg). Among investigated blood biomarkers, only CRP was associated with AAA stability. The amount of LPS in saliva, supra, and subgingival plaque were significantly associated with the systemic LPS (p <0.05).ConclusionsThis post-hoc study emphasizes the presence of antibodies against Pg and Aa, LPS and high CRP concentrations in all AAA patients. The presence of Pg in saliva and subgingival plaque was significantly associated with the blood LPS levels. For further studies investigating periodontitis and systemic diseases, specific predictive blood biomarkers should be considered instead of the use of antibodies alone.

High Level of Aristolochic Acid Detected With a Unique Genomic Landscape Predicts Early UTUC Onset After Renal Transplantation in Taiwan.

BackgroundThe unusual high dialysis prevalence and upper urinary tract urothelial carcinoma (UTUC) incidence in Taiwan may attribute to aristolochic acid (AA), which is nephrotoxic and carcinogenic, exposure. AA can cause a unique mutagenic pattern showing A:T to T:A transversions (mutational Signature 22) analyzed by whole exome sequencing (WES). However, a fast and cost-effective tool is still lacking for clinical practice. To address this issue, we developed an efficient and quantitative platform for the quantitation of AA and tried to link AA detection with clinical outcomes and decipher the genomic landscape of UTUC in Taiwan.Patients and MethodsWe recruited 61 patients with de novo onset of UTUC after kidney transplantation who underwent radical nephroureterectomy. A liquid chromatography-tandem mass spectrometry (LC-MS/MS) platform was developed for the quantitation of AA. Pearson’s chi-square test, Kaplan–Meier method, and Cox proportional hazard model were utilized to assess the correlations among AA detection, clinicopathological characteristics, and clinical outcomes. Seven tumors and seven paired normal tissues were sequenced using WES (approximately 800x sequencing depth) and analyzed by bioinformatic tool.ResultsWe found that high level of 7-(deoxyadenosin-N6-yl)aristolactam I (dA-AL-I) detected in paired normal tissues was significantly correlated with fast UTUC initiation times after renal transplantation (p = 0.035) and with no use of sirolimus (p = 0.046). Using WES analysis, we further observed that all tumor samples were featured by Signature 22 mutations, apolipoprotein B mRNA-editing enzyme, catalytic polypeptide (APOBEC)-associated gene mutations, p53 mutations, no fibroblast growth factor receptor 3 (FGFR3) mutation, and high tumor mutation burden (TMB). Especially, mammalian target of rapamycin (mTOR) activation predominated in dA-AL-I-detected samples compared with those without dA-AL-I detection and might be associated with UTUC initiation through cell proliferation and suppression of UTUC progression via autophagy inhibition.ConclusionAccordingly, dA-AL-I detection can provide more direct evidence to AA exposure and serve as a more specific predictive and prognostic biomarker for patients with de novo onset of UTUC after kidney transplantation.

PATH-01. SMALL RNASEQ ANALYSIS OF MICRORNAS IN BRAIN METASTASIS

Abstract MicroRNAs (miRNAs) are a well-known subclass of short non-coding RNAs responsible for posttranscriptional gene silencing and have been described as dysregulated in many cancers. They have also been shown to be both specific diagnostic, prognostic, and predictive biomarkers as well as therapeutic targets. Therefore, specific miRNA expression patterns of BMs of various origins could serve as a promising diagnostic tool for determining both the original tumor and the prognosis in patients with BMs of unknown origin. For identifying significantly dysregulated miRNAs among BMs (n=90) with various origin and non-tumor brain tissues (n=12), small RNAseq analyses were used. cDNA libraries were prepared using QIAseq miRNA Library Kit and purified by Qiaseq beads. The final sequencing analyses were performed by Next 500/550 High Output v2 Kit-75 cycles using the NextSeq 500 instrument. For miRNA mapping and analysis, Miraligner and MirBase were used. Bioinformatic analysis of obtained sequencing data identified 472 significantly dysregulated miRNAs (logFc &amp;gt;2, adj.p-value&amp;lt; 0.05) between BM and non-tumor samples. The comparison of BMs origin from lung BMs (n = 26) with other BMs revealed 132 significantly dysregulated miRNAs, mainly miR-4662a-5p, miR-1179, miR-211-5p, miR-146a-5p, and miR-194-5p. The most significantly dysregulated miRNAs in breast BMs were miR-4728-3p, miR-211-5p, miR-184, miR-365b-5p, and miR-2115-3p. In BMs originating from melanoma, miR-200c-3p, miR-141-5p, miR-200b-5p, miR-514a-3p, and miR-200b-3p showed the most aberrant expression. We have demonstrated that miRNA profiling could be a potent tool for the partition of brain metastases based on their origin. We found that miRNA signatures corresponding to particular origins are rather distinct from the profiles of the rest of BMs. Our results suggest that after validation, miRNA profiling can be used to identify the origin of brain metastases and potentially for the refinement of the diagnosis. Supported by the Ministry of Health of the Czech Republic, grant nr. NV18-03-00398.

Heat Shock Proteins in Urine as Cancer Biomarkers.

Heat shock proteins (HSPs) are a large family of molecular chaperones, which have shown to be implicated in various hallmarks of cancer such as resistance to apoptosis, invasion, angiogenesis, induction of immune tolerance, and metastasis. Several studies reported aberrant expression of HSPs in liquid biopsies of cancer patients and this has opened new perspectives on the use of HSPs as biomarkers of cancer. However, no specific diagnostic, predictive, or prognostic HSP chaperone-based urine biomarker has been yet discovered. On the other hand, divergent expression of HSPs has also been observed in other pathologies, including neurodegenerative and cardiovascular diseases, suggesting that new approaches should be employed for the discovery of cancer-specific HSP biomarkers. In this study, we propose a new strategy in identifying cancer-specific HSP-based biomarkers, where HSP networks in urine can be used to predict cancer. By analyzing HSPs present in urine, we could predict cancer with approximately 90% precision by machine learning approach. We aim to show that coupling the machine learning approach and the understanding of how HSPs operate, including their functional cycles, collaboration with and within networks, is effective in defining patients with cancer, which may provide the basis for future discoveries of novel HSP-based biomarkers of cancer.