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International audience; The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) illustrates that protein expression has now been credibly detected (neXtProt PE1 level) for 18,407 (93.2%) of the 19,750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from datasets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

While spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design and analysis of the results from the competition Human Protein Atlas – Single-Cell Classification hosted on the Kaggle platform. This represents a crowd-sourced competition to develop machine learning models trained on limited annotations to label single-cell protein patterns in fluorescent images. The particular challenges of this competition include class imbalance, weak labels and multi-label classification, prompting competitors to apply a wide range of approaches in their solutions. The winning models serve as the first subcellular omics tools that can annotate single-cell locations, extract single-cell features and capture cellular dynamics.

Background Patients with non-small cell lung cancer (NSCLC) are diagnosed in advanced stages and with a poor 5-year survival rate. There is a critical need to identify novel biomarkers to improve the therapy and overall prognosis of this disease. Methods Differentially expressed genes (DEGs) were identified from three profiles of GSE101586, GSE101684 and GSE112214 using Venn diagrams. hsa_circ_0043256 were validated using quantitative real-time polymerase chain reaction (RT-qPCR). The circular RNA-microRNA-messenger RNA (circRNA-miRNA-mRNA) regulatory network was constructed with Cytoscape 3.7.0. Hub genes were identified with protein interaction (PPI) and validated with the Gene Expression Profiling Interactive Analysis (GEPIA), Human Protein Atlas (HPA) databases, and immunohistochemistry. Survival analyses were also performed using a Kaplan-Meier (KM) plotter. The effects of hsa_circ_0043256 on cell proliferation and cell cycles were evaluated by EdU staining and flow cytometry, respectively. Results hsa_circ_0043256, hsa_circ_0029426 and hsa_circ_0049271 were obtained. Following RT-qPCR validation, hsa_circ_0043256 was selected for further analysis. In addition, functional experiment results indicated that hsa_circ_0043256 could inhibit cell proliferation and cell-cycle progression of NSCLC cells in vitro. Prediction by three online databases and combining with DEGs identified from The Cancer Genome Atlas (TCGA), a network containing one circRNAs, three miRNAs, and 209 mRNAs was developed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated DEGs might be associated with lung cancer onset and progression. A PPI network based on the 209 genes was established, and five hub genes (BIRC5, SHCBP1, CCNA2, SKA3, and GINS1) were determined. Following verification of five hub genes using GEPIA database, HPA database, and immunohistochemistry. High expression of all five hub genes led to poor overall survival. Conclusion Our study constructed a circRNA-miRNA-mRNA network of hsa_circ_0043256. hsa_circ_0043256 may be a potential therapeutic target for lung cancer.

The function of glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) in neurodegenerative diseases has been widely reported; however, its role in the occurrence of glioma remains less explored. We obtained clinical data and transcriptome data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Hub gene’s expression differential analysis and survival analysis were conducted by browsing the Gene Expression Profiling Interactive Analysis (GEPIA) database, Human Protein Atlas database, and LOGpc database. We conducted a variation analysis of datasets obtained from GEO and TCGA and performed a weighted gene coexpression network analysis (WGCNA) using the R programming language (3.6.3). Kaplan-Meier (KM) analysis was used to calculate the prognostic value of GRIN1. Finally, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Using STRING, we constructed a protein–protein interaction (PPI) network. Cytoscape software, a prerequisite of visualizing core genes, was installed, and CytoHubba detected the 10 most tumor-related core genes. We identified 185 differentially expressed genes (DEGs). GO and KEGG enrichment analyses illustrated that the identified DEGs are imperative in different biological functions and ascertained the potential pathways in which the DEGs may be enriched. The overall survival calculated by KM analysis showed that patients with lower expression of GRIN1 had worse prognoses than patients with higher expression of GRIN1 ( p = 0.004 ). The GEPIA and LOGpc databases were used to verify the expression difference of GRIN1 among GBM, LGG, and normal brain tissues. Ultimately, immunohistochemical assay results showed that GRIN1 was detected in normal tissue and not in the tumor specimens. Our results highlight a potential target for glioma treatment and will further our understanding of the molecular mechanisms underlying the treatment of glioma.

Background The underlying circular RNAs (circRNAs)-related competitive endogenous RNA (ceRNA) mechanisms of pathogenesis and prognosis in non-small cell lung cancer (NSCLC) remain unclear. Methods Differentially expressed circRNAs (DECs) in two Gene Expression Omnibus datasets (GSE101684 and GSE112214) were identified by utilizing R package (Limma). Circinteractome and StarBase databases were used to predict circRNA associated-miRNAs and mRNAs, respectively. Then, protein–protein interaction (PPI) network of hub genes and ceRNA network were constructed by STRING and Cytoscape. Also, analyses of functional enrichment, genomic mutation and diagnostic ROC were performed. TIMER database was used to analyze the association between immune infiltration and target genes. Kaplan–Meier analysis, cox regression and the nomogram prediction model were used to evaluate the prognostic value of target genes. Finally, the expression of potential circRNAs and target genes was validated in cell lines and tissues by quantitative real-time PCR (qRT-PCR) and Human Protein Atlas (HPA) database. Results In this study, 15 DECs were identified between NSCLC tissues and adjacent-normal tissues in two GEO datasets. Following the qRT-PCR corroboration, 7 DECs (hsa_circ_0002017, hsa_circ_0069244, hsa_circ_026337, hsa_circ_0002346, hsa_circ_0007386, hsa_circ_0008234, hsa_circ_0006857) were dramatically downregulated in A549 and SK-MES-1 compared with HFL-1 cells. Then, 12 circRNA-sponged miRNAs were screened by Circinteractome and StarBase, especially, hsa-miR-767-3p and hsa-miR-767-5p were significantly up-regulated and relevant to the prognosis. Utilizing the miRDB and Cytoscape, 12 miRNA-target genes were found. Functional enrichment, genomic mutation and diagnostic analyses were also performed. Among them, FNBP1, AKT3, HERC1, COL4A1, TOLLIP, ARRB1, FZD4 and PIK3R1 were related to the immune infiltration via TIMER database. The expression of ARRB1, FNBP1, FZD4, and HERC1 was correlated with poor overall survival (OS) in NSCLC patients by cox regression and nomogram. Furthermore, the hub-mRNAs were validated in cell lines and tissues. Conclusion We constructed the circRNA-miRNA-mRNA network that might provide novel insights into the pathogenesis of NSCLC and reveal promising immune infiltration and prognostic biomarkers.

Objective The current research was aimed to identify candidate genes associated with development and progression of epithelial ovarian carcinoma using bioinformatics analysis. Materials and methods We screened and validated candidate genes associated with carcinogenesis and development of epithelial ovarian carcinoma via bioinformatic analysis in three microarray datasets (GSE14407, GSE29450, and GSE54388) downloaded from the Gene Expression Omnibus (GEO) database. Results Our bioinformatic analysis identified 514 differentially expressed genes (DEGs) and nine candidate hub genes (CCNB1, CDK1, BUB1, CDC20, CCNA2, BUB1B, AURKA, RRM2, and TTK). Survival analysis using the Kaplan-Meier plotter showed that high expression levels of seven candidate genes (CCNB1, RRM2, BUB1, CCNA2, AURKA, CDK1, and BUB1B) were associated with poor overall survival (OS). Gene Expression Profiling Interactive Analysis (GEPIA) revealed a higher expression level of these seven candidate genes in ovarian carcinoma samples than in normal ovarian samples. Immunostaining results from the Human Protein Atlas (HPA) database suggested that the protein expression levels of CCNB1, CCNA2, AURKA, and CDK1 were increased in ovarian cancer tissues. No difference was observed in RRM2 protein expression level between normal ovarian and ovarian cancer samples. Oncomine analysis revealed an association between the expression patterns of BUB1B, CCNA2, AURKA, CCNB1, CDK1, and BUB1 and patient clinicopathological information. Finally, six genes, namely CCNB1, CCNA2, AURKA, BUB1, BUB1B, and CDK1, were identified as hub genes and a transcription factor (TF)-gene regulatory network was constructed to identify TFs, including POLR2A, ZBTB11, KLF9, and ELF1, that were implicated in regulating these hub genes. Conclusion Six significant hub DEGs associated with a poor prognosis in epithelial ovarian cancer were identified. These could be potential biomarkers for ovarian cancer patients.

Liang Chen,1,&ast; Yunhua Lin,2,&ast; Guoqing Liu,2 Rubin Xu,1 Yiming Hu,3 Jiaheng Xie,4 Hongzhu Yu1 1Department of General Surgery, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, Anhui, People’s Republic of China; 2The First Clinical Medical College, Guangxi Medical University, Nanning, Guangxi, People’s Republic of China; 3College of Pharmacy, Jiangsu Ocean University, Lianyungang, Jiangsu, People’s Republic of China; 4Department of Burn and Plastic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Hongzhu YuFuyang Hospital Affiliated to Anhui Medical University, Fuyang, Anhui, People’s Republic of ChinaTel +86 177 5686 0568Email hongzhu.620929@aliyun.comJiaheng XieDepartment of Burn and Plastic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of ChinaTel +86 177 5686 0568Email xiejiaheng@njmu.edu.cnObjective: Hepatocellular Carcinoma (HCC) has the characteristics of high incidence and poor prognosis. However, the underlying mechanism of HCC has not yet been fully elucidated. This study aims to investigate the potential mechanism and clinical significance of signal sequence receptor (SSR1) in HCC through bioinformatics methods.Methods: Four online (GEPIA, TIMER, TCGA, and GEO) databases were used to explore the expression level of SSR1 in HCC. The summary receiver operating characteristic (SROC) analysis and standardized mean difference (SMD) calculation were performed further to detect its diagnostic ability and expression level. The Human Protein Atlas (HPA) database was applied to verify the level of SSR1 protein expression. Chi-square test and Fisher’s exact test were carried out to determine the clinical relevance of SSR1 expression. KM survival analysis, univariate and multivariate COX regression analyses were employed to explore the prognostic impact of SSR1. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene set enrichment analysis (GSEA) were implemented to reveal the underlying mechanism of SSR1. Quantitative Real-Time Polymerase Chain Reaction (QRT-PCR) was used to verify the expression of SSR1 in HCC.Results: SSR1 was significantly overexpressed in HCC (SMD=1.25, P=0.03) and had the moderate diagnostic ability (AUC=0.84). SSR1 expression was significantly correlated with T stage, Gender, Pathologic stage (All P< 0.05). Patients with high SSR1 expression had shorter overall survival (OS). Univariate and multivariate Cox regression analyses showed that high SSR1 expression was an independent risk factor for poor prognosis. KEGG analysis showed that SSR1-related genes were enriched in the cell cycle, DNA replication, and TGF-beta signaling pathway. GSEA analysis also shows that the high expression of SSR1 is related to the activation of the above three signal pathways. qRT-PCR showed that the SSR1 expression in HCC was significantly higher than the Peri-carcinoma tissue (PHCC) and the corresponding normal liver tissue.Conclusion: SSR1 expression was significantly up-regulated, and it had the potential as a biomarker for the diagnosis and prognosis of HCC. It was very likely to participate in the occurrence and development of HCC by regulating the cell cycle. In summary, our study comprehensively analyzed the clinical value of SSR1 and also conducted a preliminary study on its potential mechanism, which will provide inspiration for the in-depth study of SSR1 in HCC.Keywords: hepatocellular carcinoma, signal sequence receptor, SSR1, prognosis, diagnostic

Background Nonalcoholic fatty liver disease and its advanced stage, nonalcoholic steatohepatitis (NASH), are the major cause of hepatocellular carcinoma (HCC) and other end-stage liver disease. However, the potential mechanism and therapeutic strategies have not been clarified. This study aimed to identify potential roles of miRNA/mRNA axis in the pathogenesis and drug combinations in the treatment of NASH. Methods Microarray GSE59045 and GSE48452 were downloaded from the Gene Expression Omnibus and analyzed using R. Then we obtained differentially expressed genes (DE-genes). DAVID database was used for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis. Protein-protein interaction (PPI) networks were used for the identification of hub genes. We found upstream regulators of hub genes using miRTarBase. The expression and correlation of key miRNA and its targets were detected by qPCR. Drug Pair Seeker was employed to predict drug combinations against NASH. The expression of miRNA and hub genes in HCC was identified in the Cancer Genome Atlas database and Human Protein Atlas database. Results Ninety-four DE-genes were accessed. GO and KEGG analysis showed that these predicted genes were linked to lipid metabolism. Eleven genes were identified as hub genes in PPI networks, and they were highly expressed in cells with vigorous lipid metabolism. hsa-miR-335-5p was the upstream regulator of 9 genes in the 11 hub genes, and it was identified as a key miRNA. The hub genes were highly expressed in NASH models, while hsa-miR-335-5p was lowly expressed. The correlation of miRNA-mRNA was established by qPCR. Functional verification indicated that hsa-miR-335-5p had inhibitory effect on the development of NASH. Finally, drug combinations were predicted and the expression of miRNA and hub genes in HCC was identified. Conclusions In the study, potential miRNA-mRNA pathways related to NASH were identified. Targeting these pathways may be novel strategies against NASH.

Background Cancer-associated fibroblasts (CAFs) contribute notably to colorectal cancer (CRC) tumorigenesis, stiffness, angiogenesis, immunosuppression and metastasis, and could serve as a promising therapeutic target. Our purpose was to construct CAF-related prognostic signature for CRC. Methods We performed bioinformatics analysis on single-cell transcriptome data derived from Gene Expression Omnibus (GEO) and identified 208 differentially expressed cell markers from fibroblasts cluster. Bulk gene expression data of CRC was obtained from The Cancer Genome Atlas (TCGA) and GEO databases. Univariate Cox regression and least absolute shrinkage operator (LASSO) analyses were performed on TCGA training cohort (n = 308) for model construction, and was validated in TCGA validation (n = 133), TCGA total (n = 441), GSE39582 (n = 470) and GSE17536 (n = 177) datasets. Microenvironment Cell Populations-counter (MCP-counter) and Estimate the Proportion of Immune and Cancer cells (EPIC) methods were applied to evaluated CAFs infiltrations from bulk gene expression data. Real-time polymerase chain reaction (qPCR) was performed in tissue microarrays containing 80 colon cancer samples to further validate the prognostic value of the CAF model. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) databases and immunohistochemistry were used to evaluate the protein expressions. Results A nine-gene prognostic CAF-related signature was established in training cohort. Kaplan–Meier survival analyses revealed patients with higher CAF risk scores were correlated with adverse prognosis in each cohort. MCP-counter and EPIC results consistently revealed CAFs infiltrations were significantly higher in high CAF risk group. Patients with higher CAF risk scores were more prone to not respond to immunotherapy, but were more sensitive to several conventional chemotherapeutics, suggesting a potential strategy of combining chemotherapy with anti-CAF therapy to improve the efficacy of current T-cell based immunotherapies. Univariate and multivariate Cox regression analyses verified the CAF model was as an independent prognostic indicator in predicting overall survival, and a CAF-based nomogram was then built for clinical utility in predicting prognosis of CRC. Conclusion To conclude, the CAF-related signature could serve as a robust prognostic indicator in CRC, which provides novel genomics evidence for anti-CAF immunotherapeutic strategies.

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Biomarker discovery is critical to improve CRC diagnosis, however, machine learning offers a new platform to study the etiology of CRC for this purpose. Therefore, the current study aimed to perform an integrated bioinformatics and machine learning analyses to explore novel biomarkers for CRC prognosis. In this study, we acquired gene expression microarray data from Gene Expression Omnibus (GEO) database. The microarray expressions GSE103512 dataset was downloaded and integrated. Subsequently, differentially expressed genes (DEGs) were identified and functionally analyzed via Gene Ontology (GO) and Kyoto Enrichment of Genes and Genomes (KEGG). Furthermore, protein protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software to identify hub genes; however, the hub genes were subjected to Support Vector Machine (SVM), Receiver operating characteristic curve (ROC) and survival analyses to explore their diagnostic values. Meanwhile, TCGA transcriptomics data in Gene Expression Profiling Interactive Analysis (GEPIA) database and the pathology data presented by in the human protein atlas (HPA) database were used to verify our transcriptomic analyses. A total of 105 DEGs were identified in this study. Functional enrichment analysis showed that these genes were significantly enriched in biological processes related to cancer progression. Thereafter, PPI network explored a total of 10 significant hub genes. The ROC curve was used to predict the potential application of biomarkers in CRC diagnosis, with an area under ROC curve (AUC) of these genes exceeding 0.92 suggesting that this risk classifier can discriminate between CRC patients and normal controls. Moreover, the prognostic values of these hub genes were confirmed by survival analyses using different CRC patient cohorts. Our results demonstrated that these 10 differentially expressed hub genes could be used as potential biomarkers for CRC diagnosis.

Adrenocortical carcinoma (ACC) is a rare but aggressive disease that lacks definitive treatment. We aim to evaluate role of ASXL1 in ACC and exploit its therapeutic merits therein. We performed in silico reproduction of datasets of the Cancer Genome Atlas (TCGA), GDSC (Genomics of Drug Sensitivity in Cancer) and Human Protein Atlas using platforms of cBioPortal, UALCAN, NET-GE, GSEA and GEPIA. Validation in ACC was performed in tissue, in vitro and in vivo using the NCI-H295R and SW-13 cells. ASXL1 was gained in over 50% of ACC cases with its mRNA overexpressed in DNA gained cases. ASXL1 overexpression was associated with recurrence and worsened prognosis in ACC. ASXL1 gain was associated with resistance to etoposide, doxorubicin and cisplatin (EDP). ASXL1 expression was positively correlated with FSCN1 expression. Targeting ASXL1 significantly impaired fitness of ACC cells, which could be in part rescued by FSCN1 overexpression. Targeting FSCN1 however could not rescue resistance to EDP induced by ASXL1 overexpression. Targeting ASXL1 sensitized ACC cells to EDP regimen but constitutive ASXL3 overexpression in SW-13 cells could induce resistance upon prolonged treatment. Functional gain of ASXL1 was common in ACC and exerted pro-tumorigenic and chemoresistance role. Targeting ASXL1 hold promise to ACC treatment.

Background Autophagy-related genes (ARGs) have been significantly implicated in tumorigenesis and served as promising prognostic biomarkers for human cancer. Hence, this study was aimed to develop an ARGs-based prognostic signature for Head and neck squamous cell carcinoma (HNSCC). Methods Prognostic ARG candidates were identified by univariate and multivariate Cox regression analysis in the training dataset (TCGA-HNSC) and incorporated into a 3-ARGs (EGFR, FADD, and PARK2) prognostic signature which was further verified in two independent validation cohorts (GSE41613 and GSE42743). Kaplan-Meier plots, Cox regression analyses, and receiver operating characteristics curves (ROC) were employed to evaluate the prognostic prediction of 3-ARGs signature. Differential expression of these 3 ARG between cancer and normal counterparts as well as their associations with autophagy markers were assessed in 60 pairs of freshly collected HNSCC and adjacent non-tumor samples and datasets from Human Protein Atlas, respectively. Results Patients with high-risk score had significantly inferior overall survival. Multivariate regression analyses revealed that 3-ARGs signature could be an independent prognostic factor after adjusting various clinicopathological parameters. ROC analyses revealed high predictive accuracy and sensitivity of the 3-ARGs signature. Increased mRNA and protein expression of EGFR, FADD, and PARK2 were found in HNSCC samples, and their expression significantly correlated with the abundances of ATG5, Beclin1, and LC3. Conclusion Our results reveal that 3-ARGs signature is a powerful prognostic biomarker for HNSCC, which could be integrated into the current prognostic regime to realize individualized outcome prediction. EGFR, FADD, and PARK2 likely contributed to autophagy during HNSCC tumorigenesis.

Background: Breast cancer is a common malignancy in women. DCTPP1 is a potential target for the development of antitumor drugs, and plays an important role in the process of DNA replication. Aims: To investigate the biological role of DCTPP1 gene, as well as its expression in breast cancer and its relation to patient prognosis. Materials and Methods: Breast cancer data was derived from the TCGA database. Using the UALCAN database, the expression level of DCTPP1 mRNA in breast cancer tissues was investigated. The expression of DCTPP1 in various pathological types of breast cancer was studied using the Human Protein Atlas. UALCAN was also used to investigate the relationship between DCTPP1 gene expression and breast cancer patient prognosis. Bioinformatics studied the proteins related to DCTPP1 expression and their roles in the GeneMANIA and WebGestalt databases. Results: DCTPP1 mRNA was significantly expressed in breast cancer compared to normal breast tissue (P

Abnormal ATPase H+ Transporting Accessory Protein 1 (ATP6AP1) expression may promote carcinogenesis. We investigated the association of ATP6AP1 with breast cancer (BC) and COVID-19. The Oncomine, Gene Expression Profiling Interactive Analysis, Human Protein Atlas and Kaplan-Meier plotter databases were used to evaluate the expression and prognostic value of ATP6AP1 in BC. ATP6AP1 was upregulated in BC tissues, and higher ATP6AP1 expression was associated with poorer outcomes. Data from the Tumor Immune Estimation Resource, Tumor-Immune System Interaction Database and Kaplan-Meier plotter indicated that ATP6AP1 expression correlated with immune infiltration, and that its prognostic effects in BC depended on tumor-infiltrating immune cell subtype levels. Multiple databases were used to evaluate the association of ATP6AP1 with clinicopathological factors, assess the mutation and methylation of ATP6AP1, and analyze gene co-expression and enrichment. The ATP6AP1 promoter was hypomethylated in BC tissues and differentially methylated between different disease stages and subtypes. Data from the Gene Expression Omnibus indicated that ATP6AP1 levels in certain cell types were reduced after SARS-CoV-2 infections. Ultimately, higher ATP6AP1 expression was associated with a poorer prognosis and with higher or lower infiltration of particular immune cells in BC. BC patients may be particularly susceptible to SARS-CoV-2 infections, which may alter their prognoses.

Background Ovarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes and signaling pathways associated with ovarian cancer by bioinformatics analysis. Methods The data from three mRNA expression profiling microarrays (GSE14407, GSE29450, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes between ovarian cancer tissues and normal tissues were identified using R software. The overlapping genes from the three GEO datasets were identified, and profound analysis was performed. The overlapping genes were used for pathway and Gene Ontology (GO) functional enrichment analysis using the Metascape online tool. Protein–protein interactions were analyzed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subnetwork models were selected using the plugin molecular complex detection (MCODE) application in Cytoscape. Kaplan–Meier curves were used to analyze the univariate survival outcomes of the hub genes. The Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to validate hub genes. Results In total, 708 overlapping genes were identified through analyses of the three microarray datasets (GSE14407, GSE29450, and GSE54388). These genes mainly participated in mitotic sister chromatid segregation, regulation of chromosome segregation and regulation of the cell cycle process. High CCNA2 expression was associated with poor overall survival (OS) and tumor stage. The expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in ovarian cancer tissues compared with normal tissues according to the Oncomine database. Higher expression levels of these seven candidate genes in ovarian cancer tissues compared with normal tissues were observed by GEPIA. The protein expression levels of CCNA2, CCNB1, CDC20, CDCA8, CDK1, KIF11 and TOP2A were high in ovarian cancer tissues, which was further confirmed via the HPA database. Conclusion Taken together, our study provided evidence concerning the altered expression of genes in ovarian cancer tissues compared with normal tissues. In vivo and in vitro experiments are required to verify the results of the present study.

Background The Chromobox (CBX) domain protein family, a core component of polycomb repressive complexes 1, is involved in transcriptional repression, cell differentiation, and program development by binding to methylated histone tails. Each CBX family member plays a distinct role in various biological processes through their own specific chromatin domains, due to differences in conserved sequences of the CBX proteins. It has been demonstrated that colorectal cancer (CRC) is a multiple-step biological evolutionary process, whereas the roles of the CBX family in CRC remain largely unclear. Methods In the present study, the expression and prognostic significance of the CBX family in CRC were systematically analyzed through a series of online databases, including Cancer Cell Line Encyclopedia (CCLE), Oncomine, Human Protein Atlas (HPA), and Gene Expression Profiling Interactive Analysis (GEPIA). For in vitro verification, we performed cell cloning, flow cytometry and transwell experiments to verify the proliferation and invasion ability of CRC cells after knocking down CBX2. Results Most CBX proteins were found to be highly expressed in CRC, but only the elevated expression of CBX2 could be associated with poor prognosis in patients with CRC. Further examination of the role of CBX2 in CRC was performed through several in vitro experiments. CBX2 was overexpressed in CRC cell lines via the CCLE database and the results were verified by RT-qPCR. Moreover, the knockdown of CBX2 significantly suppressed CRC cell proliferation and invasion. Furthermore, the downregulation of CBX2 was found to promote CRC cell apoptosis. Conclusions Based on these findings, CBX2 may function as an oncogene and potential prognostic biomarker. Thus, the association between the abnormal expression of CBX2 and the initiation of CRC deserves further exploration.

Cancer/Testis (CT) genes are induced in germ cells, repressed in somatic cells, and derepressed in somatic tumors, where these genes can contribute to cancer progression. CT gene identification requires data obtained using standardized protocols and technologies. This is a challenge because data for germ cells, gonads, normal somatic tissues, and a wide range of cancer samples stem from multiple sources and were generated over substantial periods of time. We carried out a GeneChip‐based RNA profiling analysis using our own data for testis and enriched germ cells, data for somatic cancers from the Expression Project for Oncology, and data for normal somatic tissues from the Gene Omnibus Repository. We identified 478 candidate loci that include known CT genes, numerous genes associated with oncogenic processes, and novel candidates that are not referenced in the Cancer/Testis Database (www.cta.lncc.br). We complemented RNA expression data at the protein level for SPESP1, GALNTL5, PDCL2, and C11orf42 using cancer tissue microarrays covering malignant tumors of breast, uterus, thyroid, and kidney, as well as published RNA profiling and immunohistochemical data provided by the Human Protein Atlas (www.proteinatlas.org). We report that combined RNA/tissue profiling identifies novel CT genes that may be of clinical interest as therapeutical targets or biomarkers. Our findings also highlight the challenges of detecting truly germ cell‐specific mRNAs and the proteins they encode in highly heterogenous testicular, somatic, and tumor tissues., Cancer/Testis (CT) genes are induced in germ cells, repressed in somatic cells, and derepressed in somatic tumors where they can contribute to cancer progression. We report that combined RNA/tissue profiling identifies novel CT genes with potential oncogenic functions and highlight the challenges of detecting germ cell‐specific mRNAs and the proteins they encode in highly heterogenous testicular, somatic, and tumor tissues.

Background: Cervical cancer (CC) is one of the most common female cancers in many developing and underdeveloped countries. High incidence, late presentation, and mortality suggested the need for molecular markers. Mitochondrial defects due to abnormal expression of nuclear-encoded mitochondrial genes (NEMG) have been reported during cancer progression. Nevertheless, the application of NEMG for the prognosis of CC is still elusive. Herein, we aimed to investigate the associations between NEMG and CC prognosis. Materials and Methods: The differentially expressed genes (DEG) in the TCGA-CESC dataset and NEMGs were retrieved from TACCO and Mitocarta2.0 databases, respectively. The impact of methylation on NEMG expression were predicted using DNMIVD and UALCAN tools. HCMDB tool was used to predict genes having metastatic potential. The prognostic models were constructed using DNMIVD, TACCO, GEPIA2, and SurvExpress. The functional enrichment analysis (FEA) was performed using clusterProfiler. The protein-protein interaction network (PPIN) was constructed to identify the hub genes (HG) using String and CytoHubba tools. Independent validation of the HG was performed using Oncomine and Human Protein Atlas databases. The druggable genes were predicted using DGIdb. Results: Among the 52 differentially expressed NEMG, 15 were regulated by DNA methylation. The expression level of 16, 10, and 7 has the potential for CC staging, prediction of metastasis, and prognosis. Moreover, 1 driver gene and 16 druggable genes were also identified. The FEA identified the enrichment of cancer-related pathways, including AMPK and carbon metabolism in cancer. The combined expression of 10 HG has been shown to affect patient survival. Conclusion: Our findings suggest that the abnormal expression of NEMGs may play a critical role in CC development and progression. The genes identified in our study may serve as a prognostic indicator and therapeutic target in CC.

Background Muscle invasive bladder cancer (MIBC) remains amongst the deadliest genitourinary malignancies due to treatment failure and extensive molecular heterogeneity, delaying effective targeted therapeutics. Hypoxia and nutrient deprivation, oversialylation and O-glycans shortening are salient features of aggressive tumours, creating cell surface glycoproteome fingerprints with theranostics potential. Methods A glycomics guided glycoproteomics workflow was employed to identify potentially targetable biomarkers using invasive bladder cancer cell models. The 5637 and T24 cells O-glycome was characterized by mass spectrometry (MS), and the obtained information was used to guide glycoproteomics experiments, combining sialidase, lectin affinity and bottom-up protein identification by nanoLC-ESI-MS/MS. Data was curated by a bioinformatics approach developed in-house, sorting clinically relevant molecular signatures based on Human Protein Atlas insights. Top-ranked targets and glycoforms were validated in cell models, bladder tumours and metastases by MS and immunoassays. Cells grown under hypoxia and glucose deprivation disclosed the contribution of tumour microenvironment to the expression of relevant biomarkers. Cancer-specificity was validated in healthy tissues by immunohistochemistry and MS in 20 types of tissues/cells of different individuals. Results Sialylated T (ST) antigens were found to be the most abundant glycans in cell lines and over 900 glycoproteins were identified potentially carrying these glycans. HOMER3, typically a cytosolic protein, emerged as a top-ranked targetable glycoprotein at the cell surface carrying short-chain O-glycans. Plasma membrane HOMER3 was observed in more aggressive primary tumours and distant metastases, being an independent predictor of worst prognosis. This phenotype was triggered by nutrient deprivation and concomitant to increased cellular invasion. T24 HOMER3 knockdown significantly decreased proliferation and, to some extent, invasion in normoxia and hypoxia; whereas HOMER3 knock-in increased its membrane expression, which was more pronounced under glucose deprivation. HOMER3 overexpression was associated with increased cell proliferation in normoxia and potentiated invasion under hypoxia. Finally, the mapping of HOMER3-glycosites by EThcD-MS/MS in bladder tumours revealed potentially targetable domains not detected in healthy tissues. Conclusion HOMER3-glycoforms allow the identification of patients’ subsets facing worst prognosis, holding potential to address more aggressive hypoxic cells with limited off-target effects. The molecular rationale for identifying novel bladder cancer molecular targets has been established. Graphical abstract

Objectives The aim of this investigation was the detailed analysis of the human pulp proteome using the new picosecond infrared laser (PIRL)-based sampling technique, which is based on a completely different mechanism compared to mechanical sampling. Proteome analysis of healthy pulp can provide data to define changes in the proteome associated with dental disease. Material and methods Immediately after extraction of the entire, undamaged tooth, 15 wisdom teeth were deep frozen in liquid nitrogen and preserved at −80°C. Teeth were crushed, and the excised frozen pulps were conditioned for further analysis. The pulps were sampled using PIRL, and the aspirates digested with trypsin and analyzed with mass spectrometry. Pulp proteins were categorized according to their gene ontology terminus. Proteins identified exclusively in this study were searched in the Human Protein Atlas (HPA) for gaining information about the main known localization and function. Results A total of 1348 proteins were identified in this study. The comparison with prior studies showed a match of 72%. Twenty-eight percent of the proteins were identified exclusively in this study. Considering HPA, almost half of these proteins were assigned to tissues that could be pulp specific. Conclusion PIRL is releasing proteins from the dental pulp which are not dissolved by conventional sampling techniques. Clinical Relevance The presented data extend current knowledge on dental pulp proteomics in healthy teeth and can serve as a reference for studies on pulp proteomics in dental disease.

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein‐protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan‐Meier plot demonstrated that low‐immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. ‐, P