Identification of a 7-gene signature that predicts relapse and survival for early stage patients with cervical carcinoma

UBL3 overexpression enhances EV-mediated Achilles protein secretion in conditioned media of MDA-MB-231 cells

Cancer cells communicate within the tumor microenvironment (TME) through extracellular vesicles (EVs), which act as crucial messengers in intercellular communication, transporting biomolecules to facilitate cancer progression. Ubiquitin-like 3 (UBL3) facilitates protein sorting into small EVs as a post-translational modifier. However, the effect of UBL3 overexpression in EV-mediated protein secretion has not been investigated yet. This study aimed to investigate the effect of UBL3 overexpression in enhancing EV-mediated Achilles protein secretion in MDA-MB-231 (MM) cells by a dual-reporter system integrating Akaluc and Achilles tagged with Ubiquitin where self-cleaving P2A linker connects Akaluc and Achilles. MM cells stably expressing Ubiquitin-Akaluc-P2A-Achilles (Ubi-Aka/Achi) were generated. In our study, both the bioluminescence of Ubiquitin-Akaluc (Ubi-Aka) and the fluorescence of Achilles secretion were observed. The intensity of Ubi-Aka was thirty times lower, while the Achilles was four times lower than the intensity of corresponding cells. The ratio of Ubi-Aka and Achilles in conditioned media (CM) was 7.5. They were also detected within EVs using an EV uptake luciferase assay and fluorescence imaging. To investigate the effect of the UBL3 overexpression in CM, Ubi-Aka/Achi was transiently transfected into MM-UBL3-KO, MM, and MM-Flag-UBL3 cells. We found that the relative fluorescence expression of Achilles in CM of MM-UBL3-KO, MM, and MM-Flag-UBL3 cells was 30 %, 28 %, and 45 %, respectively. These findings demonstrated that UBL3 overexpression enhances EV-mediated Achilles protein secretion in CM of MM cells. Targeting UBL3 could lead to novel therapies for cancer metastasis by reducing the secretion of pro-metastatic proteins, thereby inhibiting disease progression.

Increased Gene Expression of C1orf74 Is Associated with Poor Prognosis in Cervical Cancer

C1orf74, also known as URCL4, has been reported to have higher expression and be associated with poor prognosis in lung adenocarcinoma patients, and its role in regulation of the EGFR/AKT/mTORC1 pathway has been recently elucidated. In the current study, we used publicly available data and experimental validation of C1orf74 gene expression and its association with prognosis in cervical cancer patients. qRT-PCR was performed using RNA from cervical cancer cell lines and twenty-five cervical cancer patients. Data from TNMplot revealed that mRNA expression of the C1orf74 gene in primary tumor tissues, as well as metastatic tissues from cervical cancer patients, was significantly higher compared to normal cervical tissues. HPV-positive tumors had higher expression of this gene compared to HPV-negative tumors. qPCR analysis also demonstrated higher expression of C1orf74 in HPV-positive cervical cancer cell lines and most cervical cancer patients. The promoter methylation levels of the C1orf74 gene in cervical cancer tissues were lower compared to normal cervical tissues (p < 0.05). Collectively, our study indicates that higher expression of the C1orf74 gene caused by hypomethylation of its promoter is associated with poor overall survival in cervical cancer patients. Thus, C1orf74 is a novel prognostic marker in cervical cancer.

UBL3 Interacts with Alpha-Synuclein in Cells and the Interaction Is Downregulated by the EGFR Pathway Inhibitor Osimertinib

Ubiquitin-like 3 (UBL3) acts as a post-translational modification (PTM) factor and regulates protein sorting into small extracellular vesicles (sEVs). sEVs have been reported as vectors for the pathology propagation of neurodegenerative diseases, such as α-synucleinopathies. Alpha-synuclein (α-syn) has been widely studied for its involvement in α-synucleinopathies. However, it is still unknown whether UBL3 interacts with α-syn, and is influenced by drugs or compounds. In this study, we investigated the interaction between UBL3 and α-syn, and any ensuing possible functional and pathological implications. We found that UBL3 can interact with α-syn by the Gaussia princeps based split luciferase complementation assay in cells and immunoprecipitation, while cysteine residues at its C-terminal, which are considered important as PTM factors for UBL3, were not essential for the interaction. The interaction was upregulated by 1-methyl-4-phenylpyridinium exposure. In drug screen results, the interaction was significantly downregulated by the treatment of osimertinib. These results suggest that UBL3 interacts with α-syn in cells and is significantly downregulated by epidermal growth factor receptor (EGFR) pathway inhibitor osimertinib. Therefore, the UBL3 pathway may be a new therapeutic target for α-synucleinopathies in the future.

A New Potential Therapeutic Target for Cancer in Ubiquitin-Like Proteins-UBL3

Ubiquitin-like proteins (Ubls) are involved in a variety of biological processes through the modification of proteins. Dysregulation of Ubl modifications is associated with various diseases, especially cancer. Ubiquitin-like protein 3 (UBL3), a type of Ubl, was revealed to be a key factor in the process of small extracellular vesicle (sEV) protein sorting and major histocompatibility complex class II ubiquitination. A variety of sEV proteins that affects cancer properties has been found to interact with UBL3. An increasing number of studies has implied that UBL3 expression affects cancer cell growth and cancer prognosis. In this review, we provide an overview of the relationship between various Ubls and cancers. We mainly introduce UBL3 and its functions and summarize the current findings of UBL3 and examine its potential as a therapeutic target in cancers.

Pancancer Analysis of the Oncogenic and Prognostic Role of NOL7: A Potential Target for Carcinogenesis and Survival

Despite growing evidence suggesting the critical function of NOL7 in cancer initiation and development, a systematic pancancer analysis of NOL7 is lacking. Herein, we present a comprehensive study of NOL7 which aimed to explore its potential role and detailed mechanisms across 33 human tumors based on The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CATPAC) databases. As a result, both gene and protein levels of NOL7 were found to be increased in various tumor tissues, including breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), and head and neck squamous cell carcinoma (HNSC) as compared with corresponding normal tissues. Meanwhile, dysregulated NOL7 expression was found to be closely related to pathological stage and prognosis in several cancers, including LIHC, ovarian serous cystadenocarcinoma (OV), and bladder urothelial carcinoma (BLCA). The DNA methylation level of NOL7 was found to be decreased in most cancers and to be negatively associated with NOL7 expression. Furthermore, NOL7 expression was determined to be significantly associated with levels of infiltrating cells and immune checkpoint genes, including HMGB1. Analysis of NOL7-related genes revealed that RNA metabolism pathways, including “ribosome biogenesis”, “spliceosome”, and “RNA transport”, were mainly involved in the functional mechanism of NOL7 in human cancers. In summary, this pancancer study characterized the relationship between NOL7 expression and clinicopathologic features in multiple cancer types and further showed its potential regulatory network in human cancers. It represents a systemic analysis for further functional and therapeutic studies of NOL7 and highlights its predictive value with respect to the carcinogenesis and prognosis of various cancers, especially LIHC.

Ubiquitin-like protein 3 (UBL3) is required for MARCH ubiquitination of major histocompatibility complex class II and CD86

The MARCH E3 ubiquitin (Ub) ligase MARCH1 regulates trafficking of major histocompatibility complex class II (MHC II) and CD86, molecules of critical importance to immunity. Here we show, using a genome-wide CRISPR knockout screen, that ubiquitin-like protein 3 (UBL3) is a necessary component of ubiquitination-mediated trafficking of these molecules in mice and in humans. Ubl3-deficient mice have elevated MHC II and CD86 expression on the surface of professional and atypical antigen presenting cells. UBL3 also regulates MHC II and CD86 in human dendritic cells (DCs) and macrophages. UBL3 impacts ubiquitination of MARCH1 substrates, a mechanism that requires UBL3 plasma membrane anchoring via prenylation. Loss of UBL3 alters adaptive immunity with impaired development of thymic regulatory T cells, loss of conventional type 1 DCs, increased number of trogocytic marginal zone B cells, and defective in vivo MHC II and MHC I antigen presentation. In summary, we identify UBL3 as a conserved, critical factor in MARCH1-mediated ubiquitination with important roles in immune responses.

Identification of a potential tumor suppressor gene, UBL3, in non-small cell lung cancer

Objective: Oncogenes have been shown to be drivers of non-small cell lung cancer (NSCLC), yet the tumor suppressing genes involved in lung carcinogenesis remain to be systematically investigated. This study aimed to identify tumor suppressing ubiquitin pathway genes (UPGs) that were critical to lung tumorigenesis.

Methods: The 696 UPGs were silenced by an siRNA screening in NSCLC cells; the potential tumor suppressing UPGs were analyzed, and their clinical significance was investigated.

Results: We reported that silencing of 11 UPGs resulted in enhanced proliferation of NSCLC cells, and four UPGs (UBL3, TRIM22, UBE2G2, and MARCH1) were significantly downregulated in tumor samples compared to that in normal lung tissues and their expression levels were positively associated with overall survival (OS) of NSCLC patients. Among these genes, UBL3 was the most significant one. UBL3 expression was decreased in tumor samples compared to that in paired normal lung tissues in 59/86 (68.6%) NSCLCs, was correlated with TNM stage and sex of NSCLC patients, and was significantly higher in non-smoking patients than in smoking patients. Silencing UBL3 accelerated cell proliferation and ectopic expression of UBL3 suppressed NSCLC in vitro and in vivo.

Conclusions: These results showed that UBL3 represented a tumor suppressor in NSCLC and may have potential for use in therapeutics and for the prediction of clinical outcome of patients.

Integrative Systems Biology Approaches to Identify Potential Biomarkers and Pathways of Cervical Cancer

Nowadays, cervical cancer (CC) is treated as the leading cancer among women throughout the world. Despite effective vaccination and improved surgery and treatment, CC retains its fatality rate of about half of the infected population globally. The major screening biomarkers and therapeutic target identification have now become a global concern. In the present study, we have employed systems biology approaches to retrieve the potential biomarkers and pathways from transcriptomic profiling. Initially, we have identified 76 of each up-regulated and down-regulated gene from a total of 4643 differentially expressed genes. The up-regulatory genes mainly concentrate on immune-inflammatory responses, and the down-regulatory genes are on receptor binding and gamma-glutamyltransferase. The involved pathways associated with these genes were also assessed through pathway enrichment, and we mainly focused on different cancer pathways, immunoresponse, and cell cycle pathways. After the subsequent enrichment of these genes, we have identified 12 hub genes, which play a crucial role in CC and are verified by expression profile analysis. From our study, we have found that genes LILRB2 and CYBB play crucial roles in CC, as reported here for the first time. Furthermore, the survivability of the hub genes was also assessed, and among them, finally, CXCR4 has been identified as one of the most potential differentially expressed genes that might play a vital role in the survival of CC patients. Thus, CXCR4 could be used as a prognostic and/or diagnostic biomarker and a drug target for CC.

A bioinformatic analysis of the inhibin-betaglycan-endoglin/CD105 network reveals prognostic value in multiple solid tumors

Inhibins and activins are dimeric ligands belonging to the TGFβ superfamily with emergent roles in cancer. Inhibins contain an α-subunit (INHA) and a β-subunit (either INHBA or INHBB), while activins are mainly homodimers of either βA (INHBA) or βB (INHBB) subunits. Inhibins are biomarkers in a subset of cancers and utilize the coreceptors betaglycan (TGFBR3) and endoglin (ENG) for physiological or pathological outcomes. Given the array of prior reports on inhibin, activin and the coreceptors in cancer, this study aims to provide a comprehensive analysis, assessing their functional prognostic potential in cancer using a bioinformatics approach. We identify cancer cell lines and cancer types most dependent and impacted, which included p53 mutated breast and ovarian cancers and lung adenocarcinomas. Moreover, INHA itself was dependent on TGFBR3 and ENG/CD105 in multiple cancer types. INHA, INHBA, TGFBR3, and ENG also predicted patients' response to anthracycline and taxane therapy in luminal A breast cancers. We also obtained a gene signature model that could accurately classify 96.7% of the cases based on outcomes. Lastly, we cross-compared gene correlations revealing INHA dependency to TGFBR3 or ENG influencing different pathways themselves. These results suggest that inhibins are particularly important in a subset of cancers depending on the coreceptor TGFBR3 and ENG and are of substantial prognostic value, thereby warranting further investigation.

A Novel Four-Gene Prognostic Signature as a Risk Biomarker in Cervical Cancer

Background

Cervical cancer (CC) is a major malignancy affecting women worldwide, with limited treatment options for patients with advanced disease. The aim of this study was to identify novel prognostic biomarkers for CC.

Methods

RNA-Seq data from four Gene Expression Omnibus datasets (GSE5787, GSE6791, GSE26511, and GSE63514) were used to identify differentially expressed genes (DEGs) between CC and normal cervical tissues. Functional and enrichment analyses of the DEGs were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The Oncomine database, Cytoscape software, and Kaplan-Meier survival analyses were used for in-depth screening of hub DEGs. The Cox regression was then used to develop a prognostic signature, which was in turn used to create a nomogram.

Results

A total of 207 DEGs were identified in the tissue samples, eight of which were prognostically significant in terms of overall survival (OS). Thereafter, a novel four-gene signature consisting of DSG2, MMP1, SPP1, and MCM2 was developed and validated using stepwise Cox analysis. The area under the receiver operating characteristic (ROC) curve (AUC) values were 0.785, 0.609, and 0.686 in the training, verification, and combination groups, respectively. The protein expression levels of the four genes were well validated by the western blotting. Moreover, the nomogram analysis showed that a combination of this four-gene signature plus lymph node metastasis (LNM) status effectively predicted the 1- and 3-year OS probabilities of CC patients with accuracies of 69.01% and 83.93%, respectively.

Conclusions

We developed a four-gene signature that can accurately predict the prognosis in terms of OS, of CC patients, and could be a valuable tool for designing treatment strategies.

Elastic Net-Based Identification of a Multigene Combination Predicting the Survival of Patients with Cervical Cancer

Background

The objective of the present study was to identify prognostication biomarkers in patients with cervical cancer.

Material/Methods

Survival related genes were identified in The Cancer Genome Atlas (TCGA) cervical cancer study, and they were included into an elastic net regularized Cox proportional hazards regression model (CoxPH). The genes that their coefficients that were not zero were combined to build a prognostication combination. The prognostication performance of the multigene combination was evaluated and validated using Kaplan-Meier curve and univariate and multivariable CoxPH model. Meanwhile, a nomogram was built to translate the multigene combination into clinical application.

Results

There were 37 survival related genes identified, 9 of which were integrated to build a multigene combination. The area under the curve (AUC) of receiver operating characteristic (ROC) curve at 1-year, 3-year, 5-year, and 7-year in the training set were 0.757, 0.744, 0.799, and 0.854, respectively, and the multigene combination could stratify patients into significantly different prognostic groups (hazard ratio [HR]=0.2223, log-rank P<0.0001). Meanwhile, the corresponding AUCs in the test set was 0.767, 0.721, 0.735, and 0.703, respectively, and the multigene combination could classify patients into different risk groups (HR=0.3793, log-rank P=0.0021). The multigene combination could stratify patients with early stage and advanced stage into significantly different survival groups in the training set and test set. The prognostication performance of the multigene combination was better compared with 3 existing prognostic signatures. Finally, a multigene containing nomogram was developed.

Conclusions

We developed a multigene combination which could be treated as an independent prognostic factor in cervical cancer and be translated into clinical application.

Post-translational modification and protein sorting to small extracellular vesicles including exosomes by ubiquitin and UBLs

Exosomes, a type of small extracellular vesicles (sEVs), are secreted membrane vesicles that are derived from various cell types, including cancer cells, mesenchymal stem cells, and immune cells via multivesicular bodies (MVBs). These sEVs contain RNAs (mRNA, miRNA, lncRNA, and rRNA), lipids, DNA, proteins, and metabolites, all of which mediate cell-to-cell communication. This communication is known to be implicated in a diverse set of diseases such as cancers and their metastases and degenerative diseases. The molecular mechanisms, by which proteins are modified and sorted to sEVs, are not fully understood. Various cellular processes, including degradation, transcription, DNA repair, cell cycle, signal transduction, and autophagy, are known to be associated with ubiquitin and ubiquitin-like proteins (UBLs). Recent studies have revealed that ubiquitin and UBLs also regulate MVBs and protein sorting to sEVs. Ubiquitin-like 3 (UBL3)/membrane-anchored Ub-fold protein (MUB) acts as a post-translational modification (PTM) factor to regulate efficient protein sorting to sEVs. In this review, we focus on the mechanism of PTM by ubiquitin and UBLs and the pathway of protein sorting into sEVs and discuss the potential biological significance of these processes.

Novel Genomic Biomarker Candidates for Cervical Cancer As Identified by Differential Co-Expression Network Analysis

Cervical cancer is the second most common malignancy and the third reason for mortality among women in developing countries. Although infection by the oncogenic human papilloma viruses is a major cause, genomic contributors are still largely unknown. Network analyses, compared with candidate gene studies, offer greater promise to map the interactions among genomic loci contributing to cervical cancer risk. We report here a differential co-expression network analysis in five gene expression datasets (GSE7803, GSE9750, GSE39001, GSE52903, and GSE63514, from the Gene Expression Omnibus) in patients with cervical cancer and healthy controls. Kaplan-Meier Survival and principle component analyses were employed to evaluate prognostic and diagnostic performances of biomarker candidates, respectively. As a result, seven distinct co-expressed gene modules were identified. Among these, five modules (with sizes of 9-45 genes) presented high prognostic and diagnostic capabilities with hazard ratios of 2.28-11.3, and diagnostic odds ratios of 85.2-548.8. Moreover, these modules were associated with several key biological processes such as cell cycle regulation, keratinization, neutrophil degranulation, and the phospholipase D signaling pathway. In addition, transcription factors ETS1 and GATA2 were noted as common regulatory elements. These genomic biomarker candidates identified by differential co-expression network analysis offer new prospects for translational cancer research, not to mention personalized medicine to forecast cervical cancer susceptibility and prognosis. Looking into the future, we also suggest that the search for a molecular basis of common complex diseases should be complemented by differential co-expression analyses to obtain a systems-level understanding of disease phenotype variability.

Prediction of Recurrence in Cervical Cancer Using a Nine-lncRNA Signature

Background and Objective

As a common cancer type in women, cervical cancer remains one of the leading causes of cancer-associated mortalities word wide. Recent evidence has demonstrated the regulatory role of a large number of long non-coding RNAs (lncRNAs) in cervical cancer. Here, we aimed to identify new biomarkers that related with the recurrence through comprehensive bioinformatics analysis.

Methods

Firstly, we collected online lncRNA expression data of cervical cancer patients which were divided into training, validation, and test set. Then we developed a nine-lncRNA signature from training set by conducting LASSO Cox regression model along with 10-fold cross validation. The prognostic value of this risk score was validated in all the three sets using Kaplan–Meier analysis, C-index, time-dependent ROC curves and dynamic AUC. Biological function of these lncRNAs in cervical cancer cells were evaluated by performing gene ontology biological process enrichment and Kyoto Encyclopedia of Genes and Genomes signaling pathways analysis.

Results

According to the results, a higher predict accuracy was observed in the nine-lncRNA signature than that of FIGO stage in all the three sets. Stratified analysis also demonstrated that the nine-lncRNA signature can predict the recurrence of cervical cancer within FIGO stage. The potential mechanisms underlying the nine-lncRNAs from the signature were also identified according to the gene enrichment analysis.

Conclusion

In the present article, we provided a reliable prognostic tool to facilitate the individual management of patients with cervical cancer after treatment.

A Comprehensive RNA Expression Signature for Cervical Squamous Cell Carcinoma Prognosis

Clinicopathological characteristics alone are not enough to predict the survival of patients with cervical squamous cell carcinoma (CESC) due to clinical heterogeneity. In recent years, many genes and non-coding RNAs have been shown to be oncogenes or tumor-suppressors in CESC cells. This study aimed to develop a comprehensive transcriptomic signature for CESC patient prognosis. Univariate, multivariate, and Least Absolute Shrinkage and Selection Operator penalized Cox regression were used to identify prognostic signatures for CESC patients from transcriptomic data of The Cancer Genome Atlas. A normalized prognostic index (NPI) was formulated as a synthetical index for CESC prognosis. Time-dependent receiver operating characteristic curve analysis was used to compare prognostic signatures. A prognostic transcriptomic signature was identified, including 1 microRNA, 1 long non-coding RNA, and 6 messenger RNAs. Decreased survival was associated with CESC patients being in the high-risk group stratified by NPI. The NPI was an independent predictor for CESC patient prognosis and it outperformed the known clinicopathological characteristics, microRNA-only signature, gene-only signature, and previously identified microRNA and gene signatures. Function and pathway enrichment analysis revealed that the identified prognostic RNAs were mainly involved in angiogenesis. In conclusion, we proposed a transcriptomic signature for CESC prognosis and it may be useful for effective clinical risk management of CESC patients. Moreover, RNAs in the transcriptomic signature provided clues for downstream experimental validation and mechanism exploration.

GeneExpressScore Signature: a robust prognostic and predictive classifier in gastric cancer

Although several prognostic signatures have been developed for gastric cancer (GC), the utility of these tools is limited in clinical practice due to lack of validation with large and multiple independent cohorts, or lack of a statistical test to determine the robustness of the predictive models. Here, a prognostic signature was constructed using a least absolute shrinkage and selection operator (LASSO) Cox regression model and a training dataset with 300 GC patients. The signature was verified in three independent datasets with a total of 658 tumors across multiplatforms. A nomogram based on the signature was built to predict disease-free survival (DFS). Based on the LASSO model, we created a GeneExpressScore signature (GES_GC ) classifier comprised of eight mRNA. With this classifier patients could be divided into two subgroups with distinctive prognoses [hazard ratio (HR) = 4.00, 95% confidence interval (CI) = 2.41-6.66, P < 0.0001]. The prognostic value was consistently validated in three independent datasets. Interestingly, the high-GES_GC group was associated with invasion, microsatellite stable/epithelial-mesenchymal transition (MSS/EMT), and genomically stable (GS) subtypes. The predictive accuracy of GES_GC also outperformed five previously published signatures. Finally, a well-performed nomogram integrating the GES_GC and four clinicopathological factors was generated to predict 3- and 5-year DFS. In summary, we describe an eight-mRNA-based signature, GES_GC , as a predictive model for disease progression in GC. The robustness of this signature was validated across patient series, populations, and multiplatform datasets.

Potential new biomarkers for squamous carcinoma of the uterine cervix

Aim

An in silico pathway analysis was performed in an attempt to identify new biomarkers for cervical carcinoma.

Methods

Three publicly available Affymetrix gene expression data sets (GSE5787, GSE7803, GSE9750) were retrieved, vouching for a total 9 cervical cancer cell lines, 39 normal cervical samples, 7 CIN3 samples and 111 cervical cancer samples. An Agilent data set (GSE7410; 5 normal cervical samples, 35 samples from invasive cervical cancer) was selected as a validation set. Predication analysis of microarrays was performed in the Affymetrix sets to identify cervical cancer biomarkers. We compared the lists of differentially expressed genes between normal and CIN3 samples on the one hand (n=1923) and between CIN3 and invasive cancer samples on the other hand (n=628).

Results

Seven probe sets were identified that were significantly overexpressed (at least 2 fold increase expression level, and false discovery rate <5%) in both CIN3 samples respective to normal samples and in cancer samples respective to CIN3 samples. From these, five probes sets could be validated in the Agilent data set (P<0.001) comparing the normal with the invasive cancer samples, corresponding to the genes DTL, HMGB3, KIF2C, NEK2 and RFC4. These genes were additionally overexpressed in cervical cancer cell lines respective to the cancer samples. The literature on these markers was reviewed.

Conclusion

Novel biomarkers in combination with primary human papilloma virus (HPV) testing may allow complete cervical screening by objective, non-morphological molecular methods, which may be particularly important in developing countries.

Systematic assessment of cervical cancer initiation and progression uncovers genetic panels for deep learning-based early diagnosis and proposes novel diagnostic and prognostic biomarkers

Although many outstanding achievements in the management of cervical cancer (CxCa) have obtained, it still imposes a major burden which has prompted scientists to discover and validate new CxCa biomarkers to improve the diagnostic and prognostic assessment of CxCa. In this study, eight different gene expression data sets containing 202 cancer, 115 cervical intraepithelial neoplasia (CIN), and 105 normal samples were utilized for an integrative systems biology assessment in a multi-stage carcinogenesis manner. Deep learning-based diagnostic models were established based on the genetic panels of intrinsic genes of cervical carcinogenesis as well as on the unbiased variable selection approach. Survival analysis was also conducted to explore the potential biomarker candidates for prognostic assessment. Our results showed that cell cycle, RNA transport, mRNA surveillance, and one carbon pool by folate were the key regulatory mechanisms involved in the initiation, progression, and metastasis of CxCa. Various genetic panels combined with machine learning algorithms successfully differentiated CxCa from CIN and normalcy in cross-study normalized data sets. In particular, the 168-gene deep learning model for the differentiation of cancer from normalcy achieved an externally validated accuracy of 97.96% (99.01% sensitivity and 95.65% specificity). Survival analysis revealed that ZNF281 and EPHB6 were the two most promising prognostic genetic markers for CxCa among others. Our findings open new opportunities to enhance current understanding of the characteristics of CxCa pathobiology. In addition, the combination of transcriptomics-based signatures and deep learning classification may become an important approach to improve CxCa diagnosis and management in clinical practice.

The Bidirectional Regulation between MYL5 and HIF-1α Promotes Cervical Carcinoma Metastasis

Myosin light chains (MLC) serve important regulatory functions in a wide range of cellular and physiological processes. Recent research found that MLC are also chromatin-associated nuclear proteins which regulate gene transcription. In this study, the MLC member myosin regulatory light chain 5 (MYL5) expression was upregulated in late stage cervical cancer patients, positively correlated with pelvic lymph node metastasis, and identified as a poor survival indicator. MYL5 overexpression promoted metastasis in cervical cancer in vitro and in vivo models, whereas MYL5 silencing had the converse effect. We demonstrated a bidirectional regulation between MYL5 and hypoxia inducible factor-1α (HIF-1α). HIF-1α activates MYL5 via binding to the hypoxia response element (HRE) in the promoter of MYL5, and MYL5 could sustain HIF-1α expression by tethering to recognition sequence AGCTCC in the HIF-1α promoter region. Clinical data confirmed a positive correlation between MYL5 and HIF-1α. In summary, our data show that MYL5 may act as a prognosis predictive factor in cervical carcinoma, and strategies that inhibit the interaction of MYL5 and HIF-1α may benefit the cervical carcinoma patients with metastasis.

Multi-Scale Genomic, Transcriptomic and Proteomic Analysis of Colorectal Cancer Cell Lines to Identify Novel Biomarkers

Selecting colorectal cancer (CRC) patients likely to respond to therapy remains a clinical challenge. The objectives of this study were to establish which genes were differentially expressed with respect to treatment sensitivity and relate this to copy number in a panel of 15 CRC cell lines. Copy number variations of the identified genes were assessed in a cohort of CRCs. IC50's were measured for 5-fluorouracil, oxaliplatin, and BEZ-235, a PI3K/mTOR inhibitor. Cell lines were profiled using array comparative genomic hybridisation, Illumina gene expression analysis, reverse phase protein arrays, and targeted sequencing of KRAS hotspot mutations. Frequent gains were observed at 2p, 3q, 5p, 7p, 7q, 8q, 12p, 13q, 14q, and 17q and losses at 2q, 3p, 5q, 8p, 9p, 9q, 14q, 18q, and 20p. Frequently gained regions contained EGFR, PIK3CA, MYC, SMO, TRIB1, FZD1, and BRCA2, while frequently lost regions contained FHIT and MACROD2. TRIB1 was selected for further study. Gene enrichment analysis showed that differentially expressed genes with respect to treatment response were involved in Wnt signalling, EGF receptor signalling, apoptosis, cell cycle, and angiogenesis. Stepwise integration of copy number and gene expression data yielded 47 candidate genes that were significantly correlated. PDCD6 was differentially expressed in all three treatment responses. Tissue microarrays were constructed for a cohort of 118 CRC patients and TRIB1 and MYC amplifications were measured using fluorescence in situ hybridisation. TRIB1 and MYC were amplified in 14.5% and 7.4% of the cohort, respectively, and these amplifications were significantly correlated (p≤0.0001). TRIB1 protein expression in the patient cohort was significantly correlated with pERK, Akt, and Caspase 3 expression. In conclusion, a set of candidate predictive biomarkers for 5-fluorouracil, oxaliplatin, and BEZ235 are described that warrant further study. Amplification of the putative oncogene TRIB1 has been described for the first time in a cohort of CRC patients.

Receptor-type protein tyrosine phosphatases in cancer

Protein tyrosine phosphatases (PTPs) play an important role in regulating cell signaling events in coordination with tyrosine kinases to control cell proliferation, apoptosis, survival, migration, and invasion. Receptor-type protein tyrosine phosphatases (PTPRs) are a subgroup of PTPs that share a transmembrane domain with resulting similarities in function and target specificity. In this review, we summarize genetic and epigenetic alterations including mutation, deletion, amplification, and promoter methylation of PTPRs in cancer and consider the consequences of PTPR alterations in different types of cancers. We also summarize recent developments using PTPRs as prognostic or predictive biomarkers and/or direct targets. Increased understanding of the role of PTPRs in cancer may provide opportunities to improve therapeutic approaches.

Expression profiling of cervical cancers in Indian women at different stages to identify gene signatures during progression of the disease

Cervical cancer is the second most common cancer among women worldwide, with developing countries accounting for >80% of the disease burden. Although in the West, active screening has been instrumental in reducing the incidence of cervical cancer, disease management is hampered due to lack of biomarkers for disease progression and defined therapeutic targets. Here we carried out gene expression profiling of 29 cervical cancer tissues from Indian women, spanning International Federation of Gynaecology and Obstetrics (FIGO) stages of the disease from early lesion (IA and IIA) to progressive stages (IIB and IIIA–B), and identified distinct gene expression signatures. Overall, metabolic pathways, pathways in cancer and signaling pathways were found to be significantly upregulated, while focal adhesion, cytokine–cytokine receptor interaction and WNT signaling were downregulated. Additionally, we identified candidate biomarkers of disease progression such as SPP1, proliferating cell nuclear antigen (PCNA), STK17A, and DUSP1 among others that were validated by quantitative real-time polymerase chain reaction (qRT-PCR) in the samples used for microarray studies as well in an independent set of 34 additional samples. Integrative analysis of our results with other cervical cancer profiling studies could facilitate the development of multiplex diagnostic markers of cervical cancer progression.

The RB tumor suppressor positively regulates transcription of the anti-angiogenic protein NOL7

The expression of the angiogenic phenotype is regulated by a balance of pro-angiogenic and anti-angiogenic factors released into the tumor microenvironment. Nuclear protein 7 (NOL7), a novel tumor suppressor, acts as a master regulator of angiogenesis by downregulating pro-angiogenic factors and upregulating anti-angiogenic factors. Using cervical cancer as a model of investigation, we have previously shown that loss of NOL7 mRNA and protein expression is observed as early as the premalignant phase. Analysis of the gene failed to identify tumor-specific promoter methylation or coding region mutations, suggesting that NOL7 loss may be mediated by aberrant expression of its upstream regulators. In this study, we show that the RB tumor suppressor gene (RB) positively regulates NOL7 at the transcriptional level by recruiting transcription factors and transcription machinery proteins to its promoter region. Conversely, the loss of RB represses NOL7 transcription by inhibiting assembly of these proteins. This loss of NOL7 expression is also observed in RB-deficient human malignancies. Together, this work further characterizes the transcriptional activator function of RB and defines a potential role for RB in regulating angiogenesis through activation of NOL7. Current anti-angiogenic therapies lack long-term efficacy, as they are unable to target the diverse angiogenic signals generated by tumors. Our data provide evidence to support the hypothesis that reactivation of pRB can potentially modulate the expression of the angiogenic phenotype through regulation of NOL7. Therefore, this knowledge may be employed to design more comprehensive and effective therapies.