Novel potential serological prostate cancer biomarkers using CT100+ cancer antigen microarray platform in a multi-cultural South African cohort

Epidemiologic study on prostate cancer in Sudanese men across African ethnic groups

This study sought to investigate the demographic and clinical characteristics of Sudanese men diagnosed with prostate cancer (PCa) to highlight differences in diagnosis among the three major ethnolinguistic groups. A total of 532 patients with confirmed PCa diagnosis through biopsy were enrolled from six medical centers in Sudan. The majority of patients, comprising 84.2% (448/532), were diagnosed with advanced-stage disease, with a Grade group above 3. There were no discernible differences in PCa aggressiveness among the ethnolinguistic groups. However, higher levels of prostate-specific antigen (PSA) were observed in the Niger-Congo group, where 55.2% had PSA values exceeding 50 ng/ml. Patients from this group were also diagnosed at a younger age. In contrast, 90.5% of Afro-Asiatic patients are over 60 years old. Further analysis conducted within an age-matched subgroup of patients (n = 273) revealed a higher incidence of perineural invasion in the Afro-Asiatic group. This research represents the first investigation of PCa across different African ethnic groups and associates a higher incidence of perineural invasion with a specific ethnic group. While recent efforts have been made to establish African-relevant risk models to mitigate PCa health disparities, there remains a need for further investigation into genetically distinct populations within the African continent.

A note of caution for using calmodulin antibodies

Calmodulin (CaM) is a ubiquitous intracellular calcium receptor that regulates a plethora of cellular functions through interactions with target proteins. In mammals, an identical Calmodulin protein is expressed by 3 independent genes (CALM1, CALM2, CALM3). Therefore, antibodies generated against either of the three products (CaM1, CaM2, CaM3) of these genes cannot be distinguished, and conclusions based on the supposedly specific CaM antibodies claiming functions of one of the 3 genes may be misleading. In this paper we present 44 articles, using such antibodies for Western blot, ELISA assay, immunohistochemistry or which are based on proteomics and the use of databases with incorrect annotations, all potentially reaching misleading conclusions. This should be taken as a note of caution for researchers working with Calmodulin antibodies and misleading databases.

Identification of autoantibodies as potential non-invasive biomarkers for intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) presents a challenging diagnosis due to its nonspecific early clinical manifestations, often resulting in late-stage detection and high mortality. Diagnosing iCCA is further complicated by its limited accuracy, often necessitating multiple invasive procedures for precise identification. Despite carbohydrate antigen 19-9 (CA19-9) having been investigated and employed for iCCA diagnosis, it demonstrates modest diagnostic performance. Consequently, the identification of novel biomarkers with improved sensitivity and specificity remains an imperative yet formidable task. Autoantibodies, as early indicators of the immune response against cancer, offer a promising avenue for enhancing diagnostic accuracy. Our study aimed to identify non-invasive blood-based autoantibody biomarkers capable of distinguishing iCCA patients from healthy individuals (CTRs). We profiled autoantibodies in 26 serum samples (16 iCCAs and 10 CTRs) using protein microarrays containing 1622 functional proteins. Leveraging machine learning techniques, we identified a signature composed of three autoantibody biomarkers (NDE1, PYCR1, and VIM) in conjunction with CA19-9 for iCCA detection. This combined signature demonstrated superior diagnostic performance with an AUC of 96.9%, outperforming CA19-9 alone (AUC: 83.8%). These results suggest the potential of autoantibody biomarkers to develop a complementary non-invasive diagnostic utility for routine iCCA screening.

Humoral immunoprofiling identifies novel biomarkers and an immune suppressive autoantibody phenotype at the site of disease in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous cancer, with minimal response to therapeutic intervention and with 85% of cases diagnosed at an advanced stage due to lack of early symptoms, highlighting the importance of understanding PDAC immunology in greater detail. Here, we applied an immunoproteomic approach to investigate autoantibody responses against cancer-testis and tumor-associated antigens in PDAC using a high-throughput multiplexed protein microarray platform, comparing humoral immune responses in serum and at the site of disease in order to shed new light on immune responses in the tumor microenvironment. We simultaneously quantified serum or tissue IgG and IgA antibody isotypes and subclasses in a cohort of PDAC, disease control and healthy patients, observing inter alia that subclass utilization in tumor tissue samples was predominantly immune suppressive IgG4 and inflammatory IgA2, contrasting with predominant IgG3 and IgA1 subclass utilization in matched sera and implying local autoantibody production at the site of disease in an immune-tolerant environment. By comparison, serum autoantibody subclass profiling for the disease controls identified IgG4, IgG1, and IgA1 as the abundant subclasses. Combinatorial analysis of serum autoantibody responses identified panels of candidate biomarkers. The top IgG panel included ACVR2B, GAGE1, LEMD1, MAGEB1 and PAGE1 (sensitivity, specificity and AUC values of 0.933, 0.767 and 0.906). Conversely, the top IgA panel included AURKA, GAGE1, MAGEA10, PLEKHA5 and XAGE3aV1 (sensitivity, specificity, and AUC values of 1.000, 0.800, and 0.954). Assessment of antigen-specific serum autoantibody glycoforms revealed abundant sialylation on IgA in PDAC, consistent with an immune suppressive IgA response to disease.

Combination of bulk RNA sequencing and scRNA sequencing uncover the molecular characteristics of MAPK signaling in kidney renal clear cell carcinoma

The MAPK signaling pathway significantly impacts cancer progression and resistance; however, its functions remain incompletely assessed across various cancers, particularly in kidney renal clear cell carcinoma (KIRC). Therefore, there is an urgent need for comprehensive pan-cancer investigations of MAPK signaling, particularly within the context of KIRC. In this research, we obtained TCGA pan-cancer multi-omics data and conducted a comprehensive analysis of the genomic and transcriptomic characteristics of the MAPK signaling pathway. For in-depth investigation in KIRC, status of MAPK pathway was quantitatively estimated by ssGSEA and Ward algorithm was utilized for cluster analysis. Molecular characteristics and clinical prognoses of KIRC patients with distinct MAPK activities were comprehensively explored using a series of bioinformatics algorithms. Subsequently, a combination of LASSO and COX regression analyses were utilized sequentially to construct a MAPK-related signature to help identify the risk level of each sample. Patients in the C1 subtype exhibited relatively higher levels of MAPK signaling activity, which were associated with abundant immune cell infiltration and favorable clinical outcomes. Single-cell RNA sequencing (scRNA-seq) analysis of KIRC samples identified seven distinct cell types, and endothelial cells in tumor tissues had obviously higher MAPK scores than normal tissues. The immunohistochemistry results indicated the reduced expression levels of PAPSS1, MAP3K11, and SPRED1 in KIRC samples. In conclusion, our study represents the first integration of bulk RNA sequencing and single-cell RNA sequencing to elucidate the molecular characteristics of MAPK signaling in KIRC, providing a solid foundation for precision oncology.

High-throughput autoantibody screening identifies differentially abundant autoantibodies in autism spectrum disorder

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by defects in two core domains, social/communication skills and restricted/repetitive behaviors or interests. There is no approved biomarker for ASD diagnosis, and the current diagnostic method is based on clinical manifestation, which tends to vary vastly between the affected individuals due to the heterogeneous nature of ASD. There is emerging evidence that supports the implication of the immune system in ASD, specifically autoimmunity; however, the role of autoantibodies in ASD children is not yet fully understood.

Materials and methods

In this study, we screened serum samples from 93 cases with ASD and 28 healthy controls utilizing high-throughput KoRectly Expressed (KREX) i-Ome protein-array technology. Our goal was to identify autoantibodies with differential expressions in ASD and to gain insights into the biological significance of these autoantibodies in the context of ASD pathogenesis.

Result

Our autoantibody expression analysis identified 29 differential autoantibodies in ASD, 4 of which were upregulated and 25 downregulated. Subsequently, gene ontology (GO) and network analysis showed that the proteins of these autoantibodies are expressed in the brain and involved in axonal guidance, chromatin binding, and multiple metabolic pathways. Correlation analysis revealed that these autoantibodies negatively correlate with the age of ASD subjects.

Conclusion

This study explored autoantibody reactivity against self-antigens in ASD individuals' serum using a high-throughput assay. The identified autoantibodies were reactive against proteins involved in axonal guidance, synaptic function, amino acid metabolism, fatty acid metabolism, and chromatin binding.

Detection of Cancer-Associated Gene Mutations in Urinary Cell-Free DNA among Prostate Cancer Patients in South Africa

Prostate cancer (PCa) is the most common cause of cancer death among African men. The presence of tumor-specific variations in cell-free DNA (cfDNA), such as mutations, microsatellite instability, and DNA methylation, has been explored as a source of biomarkers for cancer diagnosis. In this study, we investigated the diagnostic role of cfDNA among South African PCa patients. We performed whole exome sequencing (WES) of urinary cfDNA. We identified a novel panel of 31 significantly deregulated somatic mutated genes between PCa and benign prostatic hyperplasia (BPH). Additionally, we performed whole-genome sequencing (WGS) on matching PCa and normal prostate tissue in an independent PCa cohort from South Africa. Our results suggest that the mutations are of germline origin as they were also found in the normal prostate tissue. In conclusion, our study contributes to the knowledge of cfDNA as a biomarker for diagnosing PCa in the South African population.

Potential of miRNAs in Plasma Extracellular Vesicle for the Stratification of Prostate Cancer in a South African Population

Prostate cancer (PCa) is the most common cause of cancer death among African men. The analysis of microRNAs (miRNAs) in plasma extracellular vesicles (EVs) can be utilized as a non-invasive tool for the diagnosis of PCa. In this study, we used small RNA sequencing to profile miRNAs cargo in plasma EVs from South African PCa patients. We evaluated the differential expression of miRNAs between low and high Gleason scores in the plasma EVs of South African patients and in the prostatic tissue from data available in the Cancer Genome Atlas (TCGA) Data Portal. We identified 7 miRNAs differently expressed in both EVs and prostatic tissues. We evaluated their expression using qPCR in a larger cohort of 10 patients with benign prostatic hyperplasia (BPH) and 24 patients with PCa. Here, we reported that the ratio between two of these miRNAs (i.e., miR-194-5p/miR-16-5p) showed a higher concentration in PCa compared to BPH and in metastatic PCa compared to localized PCa. We explored for the first time the profiling of miRNAs cargo in plasma EVs as a tool for the identification of putative markers in the South African population. Our finding indicated the ratio miR-194-5p/miR-16-5p as a non-invasive marker for the evaluation of PCa aggressiveness in this population.

Auto-immunoproteomics analysis of COVID-19 ICU patients revealed increased levels of autoantibodies related to the male reproductive system

Background: Coronavirus disease (COVID-19) manifests many clinical symptoms, including an exacerbated immune response and cytokine storm. Autoantibodies in COVID-19 may have severe prodromal effects that are poorly understood. The interaction between these autoantibodies and self-antigens can result in systemic inflammation and organ dysfunction. However, the role of autoantibodies in COVID-19 complications has yet to be fully understood. Methods: The current investigation screened two independent cohorts of 97 COVID-19 patients [discovery (Disc) cohort from Qatar (case = 49 vs. control = 48) and replication (Rep) cohort from New York (case = 48 vs. control = 28)] utilizing high-throughput KoRectly Expressed (KREX) Immunome protein-array technology. Total IgG autoantibody responses were evaluated against 1,318 correctly folded and full-length human proteins. Samples were randomly applied on the precoated microarray slides for 2 h. Cy3-labeled secondary antibodies were used to detect IgG autoantibody response. Slides were scanned at a fixed gain setting using the Agilent fluorescence microarray scanner, generating a 16-bit TIFF file. Group comparisons were performed using a linear model and Fisher's exact test. Differentially expressed proteins were used for KEGG and WIKIpathway annotation to determine pathways in which the proteins of interest were significantly over-represented. Results and conclusion: Autoantibody responses to 57 proteins were significantly altered in the COVID-19 Disc cohort compared to healthy controls (p ≤ 0.05). The Rep cohort had altered autoantibody responses against 26 proteins compared to non-COVID-19 ICU patients who served as controls. Both cohorts showed substantial similarities (r 2 = 0.73) and exhibited higher autoantibody responses to numerous transcription factors, immunomodulatory proteins, and human disease markers. Analysis of the combined cohorts revealed elevated autoantibody responses against SPANXN4, STK25, ATF4, PRKD2, and CHMP3 proteins in COVID-19 patients. The sequences for SPANXN4 and STK25 were cross-validated using sequence alignment tools. ELISA and Western blot further verified the autoantigen-autoantibody response of SPANXN4. SPANXN4 is essential for spermiogenesis and male fertility, which may predict a potential role for this protein in COVID-19-associated male reproductive tract complications, and warrants further research.

Integrated analysis of single-cell and bulk RNA-sequencing reveals tumor heterogeneity and a signature based on NK cell marker genes for predicting prognosis in hepatocellular carcinoma

Background: Natural killer (NK) cells are a type of innate immune cell that recognize and eliminate tumor cells and infected cells, without prior sensitization or activation. Herein, we aimed to construct a predictive model based on NK cell-related genes for hepatocellular carcinoma (HCC) patients and assess the feasibility of utilizing this model for prognosis prediction. Methods: Single-cell RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database to identify marker genes of NK cells. Univariate Cox and lasso regression were performed to further establish a signature in the TCGA dataset. Subsequently, qPCR and immunohistochemistry (IHC) staining were employed to validate the expression levels of prognosis signature genes in HCC. The effectiveness of the model was further validated using two external cohorts from the GEO and ICGC datasets. Clinical characteristics, prognosis, tumor mutation burden, immune microenvironments, and biological function were compared for different genetic subtypes and risk groups. Finally, molecular docking was performed to evaluate the binding affinity between the hub gene and chemotherapeutic drugs. Results: A total of 161 HCC-related NK cell marker genes (NKMGs) were identified, 28 of which were significantly associated with overall survival in HCC patients. Based on differences in gene expression characteristics, HCC patients were classified into three subtypes. Ten prognosis genes (KLRB1, CD7, LDB2, FCER1G, PFN1, FYN, ACTG1, PABPC1, CALM1, and RPS8) were screened to develop a prognosis model. The model not only demonstrated excellent predictive performance on the training dataset, but also were successfully validated on two independent external datasets. The risk scores derived from the model were shown to be an independent prognosis factor for HCC and were correlated with pathological severity. Moreover, qPCR and IHC staining confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis. Finally, molecular docking revealed favorable binding energies between the hub gene ACTG1 and chemotherapeutic drugs. Conclusion: In this study, we developed a model for predicting the prognosis of HCC based on NK cells. The utilization of NKMGs as innovative biomarkers showed promise in the prognosis assessment of HCC.

Pro-MAP: a robust pipeline for the pre-processing of single channel protein microarray data

BACKGROUND

The central role of proteins in diseases has made them increasingly attractive as therapeutic targets and indicators of cellular processes. Protein microarrays are emerging as an important means of characterising protein activity. Their accurate downstream analysis to produce biologically significant conclusions is largely dependent on proper pre-processing of extracted signal intensities. However, existing computational tools are not specifically tailored to the nature of these data and lack unanimity.

RESULTS

Here, we present the single-channel Protein Microarray Analysis Pipeline, a tailored computational tool for analysis of single-channel protein microarrays enabling biomarker identification, implemented in R, and as an interactive web application. We compared four existing background correction and normalization methods as well as three array filtering techniques, applied to four real datasets with two microarray designs, extracted using two software programs. The normexp, cyclic loess, and array weighting methods were most effective for background correction, normalization, and filtering respectively.

CONCLUSIONS

Thus, here we provided a versatile and effective pre-processing and differential analysis workflow for single-channel protein microarray data in form of an R script and web application ( https://metaomics.uct.ac.za/shinyapps/Pro-MAP/ .) for those not well versed in the R programming language.

Review of prostate cancer genomic studies in Africa

Prostate cancer (PCa) is the second most commonly diagnosed in men worldwide and one of the most frequent cancers in men in Africa. The heterogeneity of this cancer fosters the need to identify potential genetic risk factors/biomarkers. Omics variations may significantly contribute to early diagnosis and personalized treatment. However, there are few genomic studies of this disease in African populations. This review sheds light on the status of genomics research on PCa in Africa and outlines the common variants identified thus far. The allele frequencies of the most significant SNPs in Afro-native, Afro-descendants, and European populations were compared. We advocate how these few but promising data will aid in understanding, better diagnosing, and precisely treating this cancer and the need for further collaborative research on the genomics of PCa in the African continent.

Pathogenesis and prognosis of primary oral squamous cell carcinoma based on microRNAs target genes: a systems biology approach

Oral squamous cell carcinoma (OSCC) is the most prevalent head and neck malignancy, with frequent cervical lymph-node metastasis, leading to a poor prognosis in OSCC patients. The present study aimed to identify potential markers, including microRNAs (miRNAs) and genes, significantly involved in the etiology of early-stage OSCC. Additionally, the main OSCC's dysregulated Gene Ontology annotations and significant signaling pathways were identified. The dataset GSE45238 underwent multivariate statistical analysis in order to distinguish primary OSCC tissues from healthy oral epithelium. Differentially expressed miRNAs (DEMs) with the criteria of p-value < 0.001 and |Log2 fold change| > 1.585 were identified in the two groups, and subsequently, validated targets of DEMs were identified. A protein interaction map was constructed, hub genes were identified, significant modules within the network were illustrated, and significant pathways and biological processes associated with the clusters were demonstrated. Using the GEPI2 database, the hub genes' predictive function was assessed. Compared to the healthy controls, main OSCC had a total of 23 DEMs. In patients with head and neck squamous cell carcinoma (HNSCC), upregulation of CALM1, CYCS, THBS1, MYC, GATA6, and SPRED3 was strongly associated with a poor prognosis. In HNSCC patients, overexpression of PIK3R3, GIGYF1, and BCL2L11 was substantially correlated with a good prognosis. Besides, “proteoglycans in cancer” was the most significant pathway enriched in the primary OSCC. The present study results revealed more possible mechanisms mediating primary OSCC and may be useful in the prognosis of the patients with early-stage OSCC.

In-Silico Multi-Omics Analysis of the Functional Significance of Calmodulin 1 in Multiple Cancers

Aberrant activation of calmodulin 1 (CALM1) has been reported in human cancers. However, comprehensive understanding of the role of CALM1 in most cancer types has remained unclear. We systematically analyzed the expression landscape, DNA methylation, gene alteration, immune infiltration, clinical relevance, and molecular pathway of CALM1 in multiple cancers using various online tools, including The Cancer Genome Atlas, cBioPortal and the Human Protein Atlas databases. Kaplan–Meier and receiver operating characteristic (ROC) curves were plotted to explore the prognostic and diagnostic potential of CALM1 expression. Multivariate analyses were used to evaluate whether the CALM1 expression could be an independent risk factor. A nomogram predicting the overall survival (OS) of patients was developed, evaluated, and compared with the traditional Tumor-Node-Metastasis (TNM) model using decision curve analysis. R language was employed as the main tool for analysis and visualization. Results revealed CALM1 to be highly expressed in most cancers, its expression being regulated by DNA methylation in multiple cancers. CALM1 had a low mutation frequency (within 3%) and was associated with immune infiltration. We observed a substantial positive correlation between CALM1 expression and macrophage and neutrophil infiltration levels in multiple cancers. Different mutational forms of CALM1 hampered immune cell infiltration. Additionally, CALM1 expression had high diagnostic and prognostic potential. Multivariate analyses revealed CALM1 expression to be an independent risk factor for OS. Therefore, our newly developed nomogram had a higher clinical value than the TNM model. The concordance index, calibration curve, and time-dependent ROC curves of the nomogram exhibited excellent performance in terms of predicting the survival rate of patients. Moreover, elevated CALM1 expression contributes to the activation of cancer-related pathways, such as the WNT and MAPK pathways. Overall, our findings improved our understanding of the function of CALM1 in human cancers.

Prostate Cancer Genomics Research Disparities in Africa: Advancing Knowledge in Resource Constrained Settings

Prostate cancer disproportionately affects men of African descent and it is estimated that Africa will bear the highest disease burden in the next decade. Underlying genomic factors may contribute to prostate cancer disparities; however, it is unclear whether Africa has prioritised genomics research toward addressing these disparities. A Pubmed review was performed of publications spanning a 15-year period, with specific focus on prostate cancer genomics research that included samples from Africa and investigators in Africa. Data are presented on research publications from Africa relative to similar publications from different geographical regions, and more specifically, the extent of disparities and the contributions to prostate cancer knowledge as a result of genomics research that included African samples and African institutions. Limited publication output may reflect the infrastructure and funding challenges in Africa. Widespread cooperation should be fostered by sharing capacity and leveraging existing expertise to address the growing cancer burden facing the continent.

On the Road to Accurate Protein Biomarkers in Prostate Cancer Diagnosis and Prognosis: Current Status and Future Advances

Prostate cancer (PC) is a leading cause of morbidity and mortality among men worldwide. Molecular biomarkers work in conjunction with existing clinicopathologic tools to help physicians decide who to biopsy, re-biopsy, treat, or re-treat. The past decade has witnessed the commercialization of multiple PC protein biomarkers with improved performance, remarkable progress in proteomic technologies for global discovery and targeted validation of novel protein biomarkers from clinical specimens, and the emergence of novel, promising PC protein biomarkers. In this review, we summarize these advances and discuss the challenges and potential solutions for identifying and validating clinically useful protein biomarkers in PC diagnosis and prognosis. The identification of multi-protein biomarkers with high sensitivity and specificity, as well as their integration with clinicopathologic parameters, imaging, and other molecular biomarkers, bodes well for optimal personalized management of PC patients.

Age, Disease Severity and Ethnicity Influence Humoral Responses in a Multi-Ethnic COVID-19 Cohort

The COVID-19 pandemic has affected all individuals across the globe in some way. Despite large numbers of reported seroprevalence studies, there remains a limited understanding of how the magnitude and epitope utilization of the humoral immune response to SARS-CoV-2 viral anti-gens varies within populations following natural infection. Here, we designed a quantitative, multi-epitope protein microarray comprising various nucleocapsid protein structural motifs, including two structural domains and three intrinsically disordered regions. Quantitative data from the microarray provided complete differentiation between cases and pre-pandemic controls (100% sensitivity and specificity) in a case-control cohort (n = 100). We then assessed the influence of disease severity, age, and ethnicity on the strength and breadth of the humoral response in a multi-ethnic cohort (n = 138). As expected, patients with severe disease showed significantly higher antibody titers and interestingly also had significantly broader epitope coverage. A significant increase in antibody titer and epitope coverage was observed with increasing age, in both mild and severe disease, which is promising for vaccine efficacy in older individuals. Additionally, we observed significant differences in the breadth and strength of the humoral immune response in relation to ethnicity, which may reflect differences in genetic and lifestyle factors. Furthermore, our data enabled localization of the immuno-dominant epitope to the C-terminal structural domain of the viral nucleocapsid protein in two independent cohorts. Overall, we have designed, validated, and tested an advanced serological assay that enables accurate quantitation of the humoral response post natural infection and that has revealed unexpected differences in the magnitude and epitope utilization within a population.

CALM1 promotes progression and dampens chemosensitivity to EGFR inhibitor in esophageal squamous cell carcinoma

Background

Calmodulin1 (CALM1) has been identified as one of the overexpression genes in a variety of cancers and EGFR inhibitor have been widely used in clinical treatment but it is unknown whether CALM1 and epidermal growth factor receptor (EGFR) have a synergistic effect in esophageal squamous cell carcinoma (ESCC). The aim of the present study was to explore the synergistic effects of knock-out CALM1 combined with EGFR inhibitor (Afatinib) and to elucidate the role of CALM1 in sensitizing the resistance to Afatinib in ESCC.

Method

Immunohistochemistry (IHC) and qRT-PCR were used to examine the expression of CALM1 and EGFR in ESCC tissues. Kaplan–Meier survival analysis was used to analyze the clinical and prognostic significance of CALM1 and EGFR expression in ESCC. Furthermore, to evaluate the biological function of CALM1 in ESCC, the latest gene editing technique CRISPR/Cas9(Clustered regularly interspaced short palindromic repeats)was applied to knockout CALM1 in ESCC cell lines KYSE150, Eca109 and TE-1. MTT, flow cytometry, Transwell migration, scratch wound-healing and colony formation assays were performed to assay the combined effect of knock-out CALM1 and EGFR inhibitor on ESCC cell proliferation and migration. In addition, nude mice xenograft model was used to observe the synergistic inhibition of knock-out CALM1 and Afatinib.

Results

Both CALM1 and EGFR were found to be significantly over-expressed in ESCC compared with paired normal control. Over-expressed CALM1 and EGFR were significantly associated with clinical stage, T classification and poor overall prognosis, respectively. In vitro, the combined effect of knock-out CALM1 mediated by the lentivirus and EGFR inhibitor was shown to be capable of inhibiting the proliferation, inducing cell cycle arrest at G1/S stage and increasing apoptosis of KYSE-150 and Eca109 cells; invasion and migration were also suppressed. In vivo, the results of tumor weight and total fluorescence were markedly reduced compared with the sgCtrl-infected group and sgCAML1 group.

Conclusion

Our data demonstrated that knock-out of CALM1 could sensitize ESCC cells to EGFR inhibitor, and it may exert oncogenic role via promotion of EMT. Taken together, CALM1 may be a tempting target to overcome Afatinib resistance.

Developments and Applications of Functional Protein Microarrays

Protein microarrays are crucial tools in the study of proteins in an unbiased, high-throughput manner, as they allow for characterization of up to thousands of individually purified proteins in parallel. The adaptability of this technology has enabled its use in a wide variety of applications, including the study of proteome-wide molecular interactions, analysis of post-translational modifications, identification of novel drug targets, and examination of pathogen-host interactions. In addition, the technology has also been shown to be useful in profiling antibody specificity, as well as in the discovery of novel biomarkers, especially for autoimmune diseases and cancers. In this review, we will summarize the developments that have been made in protein microarray technology in both in basic and translational research over the past decade. We will also introduce a novel membrane protein array, the GPCR-VirD array, and discuss the future directions of functional protein microarrays.

Patterns of antibody responses to nonviral cancer antigens in head and neck squamous cell carcinoma patients differ by human papillomavirus status

There have been hints that nonviral cancer antigens are differentially expressed in human papillomavirus (HPV)-positive and HPV-negative head and neck squamous cell carcinoma (HNSCC). Antibody responses (AR) to cancer antigens may be used to indirectly determine cancer antigen expression in the tumor using a noninvasive and tissue-saving liquid biopsy. Here, we set out to characterize AR to a panel of nonviral cancer antigens in HPV-positive and HPV-negative HNSCC patients. A fluorescent microbead multiplex serology to 29 cancer antigens (16 cancer-testis antigens, 5 cancer-retina antigens and 8 oncogenes) and 29 HPV-antigens was performed in 382 HNSCC patients from five independent cohorts (153 HPV-positive and 209 HPV-negative). AR to any of the cancer antigens were found in 272/382 patients (72%). The ten most frequent AR were CT47, cTAGE5a, c-myc, LAGE-1, MAGE-A1, -A3, -A4, NY-ESO-1, SpanX-a1 and p53. AR to MAGE-A3, MAGE-A9 and p53 were found at significantly different prevalences by HPV status. An analysis of AR mean fluorescent intensity values uncovered remarkably different AR clusters by HPV status. To identify optimal antigen selections covering a maximum of patients with ≤10 AR, multiobjective optimization revealed distinct antigen selections by HPV status. We identified that AR to nonviral antigens differ by HPV status indicating differential antigen expression. Multiplex serology may be used to characterize antigen expression using serum or plasma as a tissue-sparing liquid biopsy. Cancer antigen panels should address the distinct antigen repertoire of HPV-positive and HPV-negative HNSCC.

miR-628 reduces prostate cancer proliferation and invasion via the FGFR2 signaling pathway

Recently, microRNA (miR)-628 was identified as a potential biomarker for several types of cancer, including prostate cancer (PCa). The aim of the present study was to investigate miR-628 expression and its underlying mechanism in PCa cell proliferation and invasion and the fibroblast growth factor receptor 2 (FGFR2) signaling pathway. The serum expression levels of miR-628, prostate-specific antigen, fibroblast growth factor 1, and FGFR2 were examined in patients with PCa. The relative expression levels of miR-628 and FGFR2 were determined by reverse transcription-quantitative polymerase chain reaction in PCa cells following transfection with miR-628-5p mimic or inhibitor. In addition, the protein expression level of FGFR2 was examined by western blot analysis following transfection with miR-628-5p mimic or inhibitor. Following bioinformatics analysis, dual-luciferase reporter assay was used to confirm the direct interaction between miR-628 and FGFR2. The current study demonstrated that the protein expression level of FGFR2 decreased following transfection with miR-628-5p mimic and increased following transfection with miR-628-5p inhibitor. Similarly, the proliferation and invasion of PCa cells were significantly enhanced following transfection with miR-628-5p inhibitor. By contrast, the proliferation and invasion of PCa cells were significantly inhibited following transfection with miR-628 mimic. Therefore, downregulating the expression level of miR-628 may increase the expression level of FGF in PCa, thereby promoting tumor proliferation and invasion. In conclusion, the FGF signaling pathway may be involved in promoting PCa cell proliferation and invasion. miR-628 may be a potential therapeutic target for patients with PCa.

LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer

Although modern methods of whole genome DNA methylation analysis have a wide range of applications, they are not suitable for clinical diagnostics due to their high cost and complexity and due to the large amount of sample DNA required for the analysis. Therefore, it is crucial to be able to identify a relatively small number of methylation sites that provide high precision and sensitivity for the diagnosis of pathological states. We propose an algorithm for constructing limited subsamples from high-dimensional data to form diagnostic panels. We have developed a tool that utilizes different methods of selection to find an optimal, minimum necessary combination of factors using cross-entropy loss metrics (LogLoss) to identify a subset of methylation sites. We show that the algorithm can work effectively with different genome methylation patterns using ensemble-based machine learning methods. Algorithm efficiency, precision and robustness were evaluated using five genome-wide DNA methylation datasets (totaling 626 samples), and each dataset was classified into tumor and non-tumor samples. The algorithm produced an AUC of 0.97 (95% CI: 0.94-0.99, 9 sites) for prostate adenocarcinoma and an AUC of 1.0 (from 2 to 6 sites) for urothelial bladder carcinoma, two types of kidney carcinoma and colorectal carcinoma. For prostate adenocarcinoma we showed that identified differential variability methylation patterns distinguish cluster of samples with higher recurrence rate (hazard ratio for recurrence = 0.48, 95% CI: 0.05-0.92; log-rank test, p-value < 0.03). We also identified several clusters of correlated interchangeable methylation sites that can be used for the elaboration of biological interpretation of the resulting models and for further selection of the sites most suitable for designing diagnostic panels. LogLoss-BERAF is implemented as a standalone python code and open-source code is freely available from https://github.com/bioinformatics-IBCH/logloss-beraf along with the models described in this article.

Emerging proteomics biomarkers and prostate cancer burden in Africa

Various biomarkers have emerged via high throughput omics-based approaches for use in diagnosis, treatment, and monitoring of prostate cancer. Many of these have yet to be demonstrated as having value in routine clinical practice. Moreover, there is a dearth of information on validation of these emerging prostate biomarkers within African cohorts, despite the huge burden and aggressiveness of prostate cancer in men of African descent. This review focusses of the global landmark achievements in prostate cancer proteomics biomarker discovery and the potential for clinical implementation of these biomarkers in Africa. Biomarker validation processes at the preclinical, translational and clinical research level are discussed here, as are the challenges and prospects for the evaluation and use of novel proteomic prostate cancer biomarkers.

Advances in the development of human protein microarrays

INTRODUCTION

High-content protein microarrays in principle enable the functional interrogation of the human proteome in a broad range of applications, including biomarker discovery, profiling of immune responses, identification of enzyme substrates, and quantifying protein-small molecule, protein-protein and protein-DNA/RNA interactions. As with other microarrays, the underlying proteomic platforms are under active technological development and a range of different protein microarrays are now commercially available. However, deciphering the differences between these platforms to identify the most suitable protein microarray for the specific research question is not always straightforward. Areas covered: This review provides an overview of the technological basis, applications and limitations of some of the most commonly used full-length, recombinant protein and protein fragment microarray platforms, including ProtoArray Human Protein Microarrays, HuProt Human Proteome Microarrays, Human Protein Atlas Protein Fragment Arrays, Nucleic Acid Programmable Arrays and Immunome Protein Arrays. Expert commentary: The choice of appropriate protein microarray platform depends on the specific biological application in hand, with both more focused, lower density and higher density arrays having distinct advantages. Full-length protein arrays offer advantages in biomarker discovery profiling applications, although care is required in ensuring that the protein production and array fabrication methodology is compatible with the required downstream functionality.

PMA: Protein Microarray Analyser, a user-friendly tool for data processing and normalization

Objective

Protein microarrays provide a high-throughput platform to measure protein interactions and associated functions, and can aid in the discovery of cancer biomarkers. The resulting protein microarray data can however be subject to systematic bias and noise, thus requiring a robust data processing, normalization and analysis pipeline to ensure high quality and robust results. To date, a comprehensive data processing pipeline is yet to be developed. Furthermore, a lack of analysis consistency is evident amongst different research groups, thereby impeding collaborative data consolidation and comparison. Thus, we sought to develop an accessible data processing tool using methods that are generalizable to the protein microarray field and which can be adapted to individual array layouts with minimal software engineering expertise.

Results

We developed an improved version of a previously developed pipeline of protein microarray data processing and implemented it as an open source software tool, with particular focus on widening its use and applicability. The Protein Microarray Analyser software presented here includes the following tools: (1) neighbourhood background correction, (2) net intensity correction, (3) user-defined noise threshold, (4) user-defined CV threshold amongst replicates and (5) assay controls, (6) composite ‘pin-to-pin’ normalization amongst sub-arrays, and (7) ‘array-to-array’ normalization amongst whole arrays.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3266-0) contains supplementary material, which is available to authorized users.

Genome-wide identification of cancer/testis genes and their association with prognosis in a pan-cancer analysis

Cancer/testis (CT) genes are excellent candidates for cancer immunotherapies because of their restrict expression in normal tissues and the capacity to elicit an immune response when expressed in tumor cells. In this study, we provide a genome-wide screen for CT genes with the identification of 745 putative CT genes. Comparison with a set of known CT genes shows that 201 new CT genes were identified. Integration of gene expression and clinical data led us to identify dozens of CT genes associated with either good or poor prognosis. For the CT genes related to good prognosis, we show that there is a direct relationship between CT gene expression and a signal for CD8+ cells infiltration for some tumor types, especially melanoma.

A competing endogenous RNA network identifies novel mRNA, miRNA and lncRNA markers for the prognosis of diabetic pancreatic cancer

Pancreatic cancer (PaC) is highly associated with diabetes mellitus (DM). However, the mechanisms are insufficient. The study aimed to uncover the underlying regulatory mechanism on diabetic PaC and find novel biomarkers for the disease prognosis. Two RNA-sequencing (RNA-seq) datasets, GSE74629 and GSE15932, as well as relevant data in TCGA were utilized. After pretreatment, differentially expressed genes (DEGs) or miRNAs (DEMs) or lncRNAs (DELs) between diabetic PaC and non-diabetic PaC patients were identified, and further examined for their correlations with clinical information. Prognostic RNAs were selected using KM curve. Optimal gene set for classification of different samples were recognized by support vector machine. Protein-protein interaction (PPI) network was constructed for DEGs based on protein databases. Interactions among three kinds of RNAs were revealed in the 'lncRNA-miRNA-mRNA' competing endogenous RNA (ceRNA) network. A group of 32 feature genes were identified that could classify diabetic PaC from non-diabetic PaC, such as CCDC33, CTLA4 and MAP4K1. This classifier had a high accuracy on the prediction. Seven lncRNAs were tied up with prognosis of diabetic PaC, especially UCA1. In addition, crucial DEMs were selected, such as hsa-miR-214 (predicted targets: MAP4K1 and CCDC33) and hsa-miR-429 (predicted targets: CTLA4). Notably, interactions of 'HOTAIR-hsa-miR-214-CCDC33' and 'CECR7-hsa-miR-429-CTLA4' were highlighted in the ceRNA network. Several biomarkers were identified for diagnosis of diabetic PaC, such as HOTAIR, CECR7, UCA1, hsa-miR-214, hsa-miR-429, CCDC33 and CTLA4. 'HOTAIR-hsa-miR-214-CCDC33' and 'CECR7-hsa-miR-429-CTLA4' regulations might be two important mechanisms for the disease progression.

Protein Array-based Approaches for Biomarker Discovery in Cancer

Biomarkers are deemed to be potential tools in early diagnosis, therapeutic monitoring, and prognosis evaluation for cancer, with simplicity as well as economic advantages compared with computed tomography and biopsy. However, most of the current cancer biomarkers present insufficient sensitivity as well as specificity. Therefore, there is urgent requirement for the discovery of biomarkers for cancer. As one of the most exciting emerging technologies, protein array provides a versatile and robust platform in cancer proteomics research because it shows tremendous advantages of miniaturized features, high throughput, and sensitive detections in last decades. Here, we will present a relatively complete picture on the characteristics and advance of different types of protein arrays in application for biomarker discovery in cancer, and give the future perspectives in this area of research.

Prediction of biomarkers of oral squamous cell carcinoma using microarray technology

Microarray data is used to screen the genes of oral squamous cell carcinoma (OSCC). Microarray data of OSCC and normal tissues were downloaded from GEO database and analyzed with Benjamini-Hochberg (BH) method. Differentially expressed genes (DEGs) were then uploaded on DAVID database to process enrichment analysis. Target genes were finally chosen for verification experiment in vitro and in vivo. 78 DEGs were selected from 54676 genes, including 46 up- and 32 down- regulation. GO term showed that these genes were related to epidermal growth (biological processes), extracellular region (cellular components) and cytokines activity (molecular function). Protein network interaction demonstrated that OSCC was closely allied to the five key genes including CXCL10, IFI6, IFI27, ADAMTS2 and COL5A1, which was consistent with the RT-PCR data. High-expressed gene CXCL10 was chosen for further cell experiment, and the results indicated that CXCL10 can promote the proliferation, migration and invasion of normal cells and inhibited the cancer cells after si-RNA transfection. Moreover, it has been proven that CXCL10 was possibly related to the occurrence and development of OSCC. Understanding the regulation of OSCC expression will shed light on the screening of cancer biomarker.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.