Age-specific gene expression signatures for breast tumors and cross-species conserved potential cancer progression markers in young women

Roles of DEPDC1 in various types of cancer (Review)

Dishevelled, EGL-10 and pleckstrin domain-containing 1 (DEPDC1) has been identified as a crucial factor in the development and progression of various types of cancer. This protein, which is largely undetectable in normal tissues but is highly expressed in numerous tumor types, serves a significant role in cell mitosis, proliferation, migration, invasion, angiogenesis, autophagy and apoptosis. Furthermore, DEPDC1 is implicated in several key signaling pathways, such as NF-κB, PI3K/Akt, Wnt/β-catenin and Hippo pathways, which are essential for cell proliferation and survival. The expression of DEPDC1 has been linked to poor prognosis and survival rates in multiple types of cancer, including hepatocellular carcinoma, lung adenocarcinoma, colorectal cancer and breast cancer. Notably, DEPDC1 has been suggested to have potential as a diagnostic and prognostic marker, as well as a therapeutic target. Its involvement in critical signaling pathways suggests that targeting DEPDC1 could inhibit tumor growth and metastasis, thereby improving patient outcomes. In addition, clinical trials have shown promising results for DEPDC1-derived peptide vaccines, indicating their safety and potential efficacy in cancer treatment. To the best of our knowledge, this is the first comprehensive review addressing the role of DEPDC1 in cancer. Through a critical analysis of existing studies, the present review aimed to consolidate existing knowledge and highlight gaps in understanding, paving the way for future research to elucidate the complex interactions of DEPDC1 in the context of cancer biology.

Proteogenomic analysis dissects early-onset breast cancer patients with prognostic relevance

Early-onset breast cancer is known for its aggressive clinical characteristics and high prevalence in East Asian countries, but a comprehensive understanding of its molecular features is still lacking. In this study, we conducted a proteogenomic analysis of 126 treatment-naïve primary tumor tissues obtained from Korean patients with young breast cancer (YBC) aged ≤40 years. By integrating genomic, transcriptomic, and proteomic data, we identified five distinct functional subgroups that accurately represented the clinical characteristics and biological behaviors of patients with YBC. Our integrated approach could be used to determine the proteogenomic status of HER2, enhancing its clinical significance and prognostic value. Furthermore, we present a proteome-based homologous recombination deficiency (HRD) analysis that has the potential to overcome the limitations of conventional genomic HRD tests, facilitating the identification of new patient groups requiring targeted HR deficiency treatments. Additionally, we demonstrated that protein-RNA correlations can be used to predict the late recurrence of hormone receptor-positive breast cancer. Within each molecular subtype of breast cancer, we identified functionally significant protein groups whose differential abundance was closely correlated with the clinical progression of breast cancer. Furthermore, we derived a recurrence predictive index capable of predicting late recurrence, specifically in luminal subtypes, which plays a crucial role in guiding decisions on treatment durations for YBC patients. These findings improve the stratification and clinical implications for patients with YBC by contributing to the optimal adjuvant treatment and duration for favorable clinical outcomes.

Integrated edge information and pathway topology for drug-disease associations

Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.

Role of Epstein-Barr Virus in Breast Cancer: Correlation with Clinical Outcome and Survival Analysis

Background: Breast cancer is the most prevalent cancer among women worldwide. The potential involvement of Epstein-Barr virus (EBV) in breast cancer pathogenesis has been a subject of debate, but its correlation with clinical outcomes remains uncertain. Methods: In this study, we collected 276 pathologically confirmed breast cancer tissue samples from the tissue bank of MacKay Memorial Hospital and the National Health Research Institutes in Taiwan. DNA was extracted from frozen tissue using The QIAamp DNA Mini Kit. The Taqman quantitative PCR method was employed to assess the EBV copy number per cell in these samples, using NAMALWA cells as a reference. We performed statistical analyses, including 2 × 2 contingency tables, Cox regression analysis, and Kaplan-Meier survival curves, to explore the association between clinicopathologic factors and survival outcomes in breast cancer patients. We analyzed both relapse survival, which reflects the period patients remain free from cancer recurrence post-treatment, and overall survival, which encompasses all-cause mortality. Results: Our results revealed a significant association between EBV status and relapse survival (hazard ratio: 2.75, 95% CI: 1.30, 5.86; p = 0.008) in breast cancer patients. However, no significant association was found in overall survival outcomes. Additionally, we observed significant associations between ER status and tumor histologic grade with both overall and relapse survival. Patients with EBV-positive tumors exhibited higher recurrence rates compared to those with EBV-negative tumors. Furthermore, we noted significant correlations between EBV status and HER-2 (p = 0.0005) and histological grade (p = 0.02) in our cohort of breast cancer patients. Conclusions: The presence of EBV in breast cancer tumors appears to exert an impact on patient outcomes, particularly concerning recurrence rates. Our findings highlight the significance of considering EBV status as a potential prognostic marker in breast cancer patients. Nonetheless, further research is essential to elucidate the underlying molecular mechanisms and develop novel therapeutic approaches.

A drug repurposing method based on inhibition effect on gene regulatory network

Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.

SaBrcada: Survival Intervals Prediction for Breast Cancer Patients by Dimension Raising and Age Stratification

(1) Background: Breast cancer is the second leading cause of cancer death among women. The accurate prediction of survival intervals will help physicians make informed decisions about treatment strategies or the use of palliative care. (2) Methods: Gene expression is predictive and correlates to patient prognosis. To establish a reliable prediction tool, we collected a total of 1187 RNA-seq data points from breast cancer patients (median age 58 years) in Fragments Per Kilobase Million (FPKM) format from the TCGA database. Among them, we selected 144 patients with date of death information to establish the SaBrcada-AD dataset. We first normalized the SaBrcada-AD dataset to TPM to build the survival prediction model SaBrcada. After normalization and dimension raising, we used the differential gene expression data to test eight different deep learning architectures. Considering the effect of age on prognosis, we also performed a stratified random sampling test on all ages between the lower and upper quartiles of patient age, 48 and 69 years; (3) Results: Stratifying by age 61, the performance of SaBrcada built by GoogLeNet was improved to a highest accuracy of 0.798. We also built a free website tool to provide five predicted survival periods: within six months, six months to one year, one to three years, three to five years, or over five years, for clinician reference. (4) Conclusions: We built the prediction model, SaBrcada, and the website tool of the same name for breast cancer survival analysis. Through these models and tools, clinicians will be provided with survival interval information as a basis for formulating precision medicine.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

Biological functions and therapeutic potential of SHCBP1 in human cancer

The incidence of cancer is increasing globally, and it is the most common cause of death. The identification of novel cancer diagnostic and prognostic biomarkers is important for developing cancer treatment strategies and reducing mortality. SHCSH2 domain-binding protein 1 (SHCBP1) is a protein that specifically binds to the SH2 domain of Src homology-collagen. It participates in the regulation of a variety of signal transduction pathways and can activate a variety of signaling molecules to perform a series of physiological functions. SHCBP1 is expressed in a variety of human tissues, but its abnormal expression in various systems is associated with cancer. SHCBP1 is abnormally expressed in a variety of tumors, and plays roles in almost all aspects of cancer biology (such as cell proliferation, apoptosis prevention, invasion, and metastasis) through various possible mechanisms. Its expression level is related to the clinicopathological characteristics of patients. In addition, the SHCBP1 expression pattern is closely related to cancer type, stage, and other clinical variables. Therefore, SHCBP1 is a promising tumor biomarker for cancer diagnosis and prognosis and a potential therapeutic target. This article reviews the expression, biological functions, mechanisms, and potential clinical significance of SHCBP1 in various human tumors to provide a new theoretical basis for clinical molecular diagnosis, molecular targeted therapy, and scientific research on cancer.

NUSAP1 Binds ILF2 to Modulate R-Loop Accumulation and DNA Damage in Prostate Cancer

Increased expression of NUSAP1 has been identified as a robust prognostic biomarker in prostate cancer and other malignancies. We have previously shown that NUSAP1 is positively regulated by E2F1 and promotes cancer invasion and metastasis. To further understand the biological function of NUSAP1, we used affinity purification and mass spectrometry proteomic analysis to identify NUSAP1 interactors. We identified 85 unique proteins in the NUSAP1 interactome, including ILF2, DHX9, and other RNA-binding proteins. Using proteomic approaches, we uncovered a function for NUSAP1 in maintaining R-loops and in DNA damage response through its interaction with ILF2. Co-immunoprecipitation and colocalization using confocal microscopy verified the interactions of NUSAP1 with ILF2 and DHX9, and RNA/DNA hybrids. We showed that the microtubule and charged helical domains of NUSAP1 were necessary for the protein-protein interactions. Depletion of ILF2 alone further increased camptothecin-induced R-loop accumulation and DNA damage, and NUSAP1 depletion abolished this effect. In human prostate adenocarcinoma, NUSAP1 and ILF2 mRNA expression levels are positively correlated, elevated, and associated with poor clinical outcomes. Our study identifies a novel role for NUSAP1 in regulating R-loop formation and accumulation in response to DNA damage through its interactions with ILF2 and hence provides a potential therapeutic target.

Bioinformatics analyses of combined databases identify shared differentially expressed genes in cancer and autoimmune disease

BACKGROUND

Inadequate immunity caused by poor immune surveillance leads to tumorigenesis, while excessive immunity due to breakdown of immune tolerance causes autoimmune genesis. Although the function of immunity during the onset of these two processes appears to be distinct, the underlying mechanism is shared. To date, gene expression data for large bodies of clinical samples are available, but the resemblances of tumorigenesis and autoimmune genesis in terms of immune responses remains to be summed up.

METHODS

Considering the high disease prevalence, we chose invasive ductal carcinoma (IDC) and systemic lupus erythematosus (SLE) to study the potential commonalities of immune responses. We obtained gene expression data of IDC/SLE patients and normal controls from five IDC databases (GSE29044, GSE21422, GSE22840, GSE15852, and GSE9309) and five SLE databases (GSE154851, GSE99967, GSE61635, GSE50635, and GSE17755). We intended to identify genes differentially expressed in both IDC and SLE by using three bioinformatics tools including GEO2R, the limma R package, and Weighted Gene Co-expression Network Analysis (WGCNA) to perform function enrichment, protein-protein network, and signaling pathway analyses.

RESULTS

The mRNA levels of signal transducer and activator of transcription 1 (STAT1), 2'-5'-oligoadenylate synthetase 1 (OAS1), 2'-5'-oligoadenylate synthetase like (OASL), and PML nuclear body scaffold (PML) were found to be differentially expressed in both IDC and SLE by using three different bioinformatics tools of GEO2R, the limma R package and WGCNA. From the combined databases in this study, the mRNA levels of STAT1 and OAS1 were increased in IDC while reduced in SLE. And the mRNA levels of OASL and PML were elevated in both IDC and SLE. Based on Kyoto Encyclopedia of Genes and Genomes pathway analysis and QIAGEN Ingenuity Pathway Analysis, both IDC and SLE were correlated with the changes of multiple components involved in the Interferon (IFN)-Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway.

CONCLUSION

The expression levels of STAT1 and OAS1 manifest the opposite expression tendency across cancer and autoimmune disease. They are components in the IFN-JAK-STAT signaling pathway related to both tumorigenesis and autoimmune genesis. STAT1 and OAS1-associated IFN-JAK-STAT signaling could explain the commonalities during tumorigenesis and autoimmune genesis and render significant information for more precise treatment from the point of immune homeostasis.

Molecular subtypes predict the preferential site of distant metastasis in advanced breast cancer: a nationwide retrospective study

Objective

This study aimed to explore possible associations between molecular subtypes and site of distant metastasis in advanced breast cancer (ABC).

Methods

3577 ABC patients were selected from 21 hospitals of seven geographic regions in China from 2012-2014. A questionnaire was designed to collect medical information regarding demographic characteristics, risk factors, molecular subtype, recurrence/metastasis information, and disease-free survival (DFS). The cancers were classified into Luminal A, Luminal B, HER2-enriched and Triple Negative subtypes. Chi-square test and multivariate Cox proportional hazard models were performed to explore the associations between molecular subtypes and distant metastasis sites.

Results

A total of 2393 cases with molecular subtypes information were finally examined. Patients with Luminal A (51.1%) and Luminal B (44.7%) were most prone to bone metastasis, whereas liver metastasis was more frequently observed in HER2-enriched ABC patients (29.1%).The cumulative recurrence and metastasis rates of ABC patients at 36 months of DFS were the most significant within molecular types, of which Triple Negative was the highest (82.7%), while that of Luminal A was the lowest (58.4%). In the adjusted Cox regression analysis, Luminal B, HER2-enriched and Triple Negative subtypes increased the risk of visceral metastasis by 23%, 46% and 87% respectively. In addition, Triple Negative patients had a higher probability of brain metastasis (HR 3.07, 95% CI: 1.04-9.07).

Conclusion

Molecular subtypes can predict the preferential sites of distant metastasis, emphasizing that these associations were of great help in choices for surveillance, developing appropriate screening and cancer management strategies for follow-up and personalized therapy in ABC patients.

Transcriptomic data analysis coupled with copy number aberrations reveals a blood-based 17-gene signature for diagnosis and prognosis of patients with colorectal cancer

Background: Colorectal cancer (CRC) is the third most common cancer and third leading cause of cancer-associated deaths worldwide. Diagnosing CRC patients reliably at an early and curable stage is of utmost importance to reduce the risk of mortality. Methods: We identified global differentially expressed genes with copy number alterations in patients with CRC. We then identified genes that are also expressed in blood, which resulted in a blood-based gene signature. We validated the gene signature's diagnostic and prognostic potential using independent datasets of gene expression profiling from over 800 CRC patients with detailed clinical data. Functional enrichment, gene interaction networks and pathway analyses were also performed. Results: The analysis revealed a 17-gene signature that is expressed in blood and demonstrated that it has diagnostic potential. The 17-gene SVM classifier displayed 99 percent accuracy in predicting the patients with CRC. Moreover, we developed a prognostic model and defined a risk-score using 17-gene and validated that high risk score is strongly associated with poor disease outcome. The 17-gene signature predicted disease outcome independent of other clinical factors in the multivariate analysis (HR = 2.7, 95% CI = 1.3-5.3, p = 0.005). In addition, our gene network and pathway analyses revealed alterations in oxidative stress, STAT3, ERK/MAPK, interleukin and cytokine signaling pathways as well as potentially important hub genes, including BCL2, MS4A1, SLC7A11, AURKA, IL6R, TP53, NUPR1, DICER1, DUSP5, SMAD3, and CCND1. Conclusion: Our results revealed alterations in various genes and cancer-related pathways that may be essential for CRC transformation. Moreover, our study highlights diagnostic and prognostic value of our gene signature as well as its potential use as a blood biomarker as a non-invasive diagnostic method. Integrated analysis transcriptomic data coupled with copy number aberrations may provide a reliable method to identify key biological programs associated with CRC and lead to improved diagnosis and therapeutic options.

Using publicly available datasets to identify population-based transcriptomic landscape contributing to the aggressiveness of breast cancer in young women

Introduction: Although the risk of breast cancer increases with advancing age, some regions have larger number of young breast cancer patients (≤45 years-old), such as the Middle East, Eastern Asia, and North Africa, with more aggressive and poorly differentiated tumors. We aimed to conduct an in-silico analysis in an attempt to understand the aggressive nature of early-onset breast cancer, and to identify potential drivers of early-onset breast cancer using gene expression profiling datasets in a population-dependent manner. Methods: Functional genomics experiments data were acquired from cBioPortal database for cancer genomics, followed by the stratification of patients based on the age at representation of breast cancer and race. Differential gene expression analysis and gene amplification status analysis were carried out, followed by hub gene, transcription factor, and signalling pathway identification. Results: PAM50 subtype analysis revealed that young patients (≤45 years-old) had four-fold more basal tumors and worst progression-free survival (median of 101 months), compared with the 45-65 years group (median of 168 months). Fourteen genes were amplified in more than 14% of patients with an early-onset breast cancer. Interestingly, FREM2, LINC00332, and LINC00366 were exclusively amplified in younger patients. Gene expression data from three different populations (Asian, White, and African) revealed a unique transcriptomic profile of young patients, which was also reflected on the PAM50 subtype analysis. Our data indicates a higher tendency of young African patients to develop basal tumors, while young Asian patients are more prone to developing Luminal A tumors. Most genes that were found to be upregulated in younger patients are involved in important signaling pathways that promote cancer progression and metastasis, such as MAPK pathway, Reelin pathway and the PI3K/Akt pathway. Conclusion: This study provides strong evidence that the molecular profile of tumors derived from young breast cancer patients of different populations is unique and may explain the aggressiveness of these tumors, stressing the need to conduct population- based multi-omic analyses to identify the potential drivers for tumorigenesis and molecular profiles of young breast cancer patients.

NUSAP1 and PCLAF (KIA0101) Downregulation by Neoadjuvant Therapy is Associated with Better Therapeutic Outcomes and Survival in Breast Cancer

Purpose. To evaluate whether changes in genomic expression that occur beginning with breast cancer (BC) diagnosis and through to tumor resection after neoadjuvant chemotherapy (NCT) reveal biomarkers that can help predict therapeutic response and survival. Materials and Methods. We determined gene expression profiles based on microarrays in tumor samples from 39 BC patients who showed pathologic complete response (pCR) or therapeutic failure (non-pCR) after NCT (cyclophosphamide-doxorubicin/epirubicin). Based on unsupervised clustering of gene expression, together with functional enrichment analyses of differentially expressed genes, we selected NUSAP1, PCLAF, MME, and DST. We evaluated the NCT response and the expression of these four genes in BC histologic subtypes. In addition, we study the presence of tumor-infiltrating lymphocytes. Finally, we analyze the correlation between NUSAP1 and PCLAF against disease-free survival (DFS) and overall survival (OS). Results. A signature of 43 differentially expressed genes discriminated pCR from non-pCR patients (|fold change >2|, false discovery rate <0.05) only in biopsies taken after surgery. Patients achieving pCR showed downregulation of NUSAP1 and PCLAF in tumor tissues and increased DFS and OS, while overexpression of these genes correlated with poor therapeutic response and OS. These genes are involved in the regulation of mitotic division. Conclusions. The downregulation of NUSAP1 and PCLAF after NCT is associated with the tumor response to chemotherapy and patient survival.

Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images

Breast cancer tumor grade is strongly associated with patient survival. In current clinical practice, pathologists assign tumor grade after visual analysis of tissue specimens. However, different studies show significant inter-observer variation in breast cancer grading. Computer-based breast cancer grading methods have been proposed but only work on specifically selected tissue areas and/or require labor-intensive annotations to be applied to new datasets. In this study, we trained and evaluated a deep learning-based breast cancer grading model that works on whole-slide histopathology images. The model was developed using whole-slide images from 706 young (< 40 years) invasive breast cancer patients with corresponding tumor grade (low/intermediate vs. high), and its constituents nuclear grade, tubule formation and mitotic rate. The performance of the model was evaluated using Cohen's kappa on an independent test set of 686 patients using annotations by expert pathologists as ground truth. The predicted low/intermediate (n = 327) and high (n = 359) grade groups were used to perform survival analysis. The deep learning system distinguished low/intermediate versus high tumor grade with a Cohen's Kappa of 0.59 (80% accuracy) compared to expert pathologists. In subsequent survival analysis the two groups predicted by the system were found to have a significantly different overall survival (OS) and disease/recurrence-free survival (DRFS/RFS) (p < 0.05). Univariate Cox hazard regression analysis showed statistically significant hazard ratios (p < 0.05). After adjusting for clinicopathologic features and stratifying for molecular subtype the hazard ratios showed a trend but lost statistical significance for all endpoints. In conclusion, we developed a deep learning-based model for automated grading of breast cancer on whole-slide images. The model distinguishes between low/intermediate and high grade tumors and finds a trend in the survival of the two predicted groups.

FOXM1 is regulated by DEPDC1 to facilitate development and metastasis of oral squamous cell carcinoma

The Disheveled, EGL-10, Pleckstrin domain containing 1 (DEPDC1) is a new oncogene that has recently been described. The mechanisms and functions of its expression are yet to be determined in oral squamous cell carcinoma (OSCC). In the present study, the impact of DEPDC1 on the growth and development of OSCC was investigated using animal models, cell lines and human tissue samples. Elevated DEPDC1 expression within cancer cell lines and human OSCC has been identified. Mechanistic examination showed that restored DEPDC1 expression in vivo and in vitro stimulated OSCC tumour development. In addition, FOXM1 interacts with DEPDC1 as indicated by co-immunoprecipitation and immunofluorescence testing. Functionally, DEPDC1 facilitated Wnt/β-catenin signal transduction and β-catenin protein nuclear expression. In summary, the DEPDC1, interacting with FOXM1 via Wnt/β-catenin signaling, the closely regulated OSCC pathogenesis, suggesting that targeting the novel DEPDC1/FOXM1/β-catenin complex is an essential OSCC therapeutic approach.

Delineation of Pathogenomic Insights of Breast Cancer in Young Women

The prognosis of breast cancer (BC) in young women (BCYW) aged ≤40 years tends to be poorer than that in older patients due to aggressive phenotypes, late diagnosis, distinct biologic, and poorly understood genomic features of BCYW. Considering the estimated predisposition of only approximately 15% of the BC population to BC-promoting genes, the underlying reasons for an increased occurrence of BCYW, at large, cannot be completely explained based on general risk factors for BC. This underscores the need for the development of next-generation of tissue- and body fluid-based prognostic and predictive biomarkers for BCYW. Here, we identified the genes associated with BCYW with a particular focus on the age, intrinsic BC subtypes, matched normal or normal breast tissues, and BC laterality. In young women with BC, we observed dysregulation of age-associated cancer-relevant gene sets in both cancer and normal breast tissues, sub-sets of which substantially affected the overall survival (OS) or relapse-free survival (RFS) of patients with BC and exhibited statically significant correlations with several gene modules associated with cellular processes such as the stroma, immune responses, mitotic progression, early response, and steroid responses. For example, high expression of COL1A2, COL5A2, COL5A1, NPY1R, and KIAA1644 mRNAs in the BC and normal breast tissues from young women correlated with a substantial reduction in the OS and RFS of BC patients with increased levels of these exemplified genes. Many of the genes upregulated in BCYW were overexpressed or underexpressed in normal breast tissues, which might provide clues regarding the potential involvement of such genes in the development of BC later in life. Many of BCYW-associated gene products were also found in the extracellular microvesicles/exosomes secreted from breast and other cancer cell-types as well as in body fluids such as urine, saliva, breast milk, and plasma, raising the possibility of using such approaches in the development of non-invasive, predictive and prognostic biomarkers. In conclusion, the findings of this study delineated the pathogenomics of BCYW, providing clues for future exploration of the potential predictive and prognostic importance of candidate BCYW molecules and research strategies as well as a rationale to undertake a prospective clinical study to examine some of testable hypotheses presented here. In addition, the results presented here provide a framework to bring out the importance of geographical disparities, to overcome the current bottlenecks in BCYW, and to make the next quantum leap for sporadic BCYW research and treatment.

Diagnostic, Prognostic, and Immunological Roles of HELLS in Pan-Cancer: A Bioinformatics Analysis

Background

Inappropriate repair of DNA damage drives carcinogenesis. Lymphoid-specific helicase (HELLS) is an important component of the chromatin remodeling complex that helps repair DNA through various mechanisms such as DNA methylation, histone posttranslational modification, and nucleosome remodeling. Its role in human cancer initiation and progression has garnered recent attention. Our study aims to provide a more systematic and comprehensive understanding of the role of HELLS in the development and progression of multiple malignancies through analysis of HELLS in cancers.

Methods

We explored the role of HELLS in cancers using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. Multiple web platforms and software were used for data analysis, including R, Cytoscape, HPA, Archs4, TISIDB, cBioPortal, STRING, GSCALite, and CancerSEA.

Results

High HELLS expression was found in a variety of cancers and differentially expressed across molecular and immune subtypes. HELLS was involved in many cancer pathways. Its expression positively correlated with Th2 and Tcm cells in most cancers. It also correlated with genetic markers of immunomodulators in various cancers.

Conclusions

Our study elucidates the role HELLS plays in promotion, inhibition, and treatment of different cancers. HELLS is a potential cancer diagnostic and prognostic biomarker with immune, targeted, or cytotoxic therapeutic value. This work is a prerequisite to clinical validation and treatment of HELLS in cancers.

ANLN and UBE2T are prognostic biomarkers associated with immune regulation in breast cancer: a bioinformatics analysis

OBJECTIVES

To screen and verify differential genes affecting the prognosis of breast cancer.

METHODS

Breast cancer gene expression datasets were downloaded from the GEO database, and original data were analyzed in R. The TIMER database was used to analyze the relationship between ANLN and UBE2T and immune cell infiltration.

RESULTS

Ten hub-key genes were identified, and survival analysis showed that UBE2T and ANLN were upregulated in breast cancer and their upregulation was associated with a poor prognosis. ANLN and UBE2T upregulation was associated with the prevalence of Th1 and Th2 cells, shifting the Th1/Th2 balance to Th2 in Basal and Luminal-B breast cancers, which indicates a poor prognosis (P < 0.05).

CONCLUSION

ANLN and UBE2T are potential biomarkers for predicting the prognosis of breast cancer.

Oncobiology and treatment of breast cancer in young women

Female breast cancer emerged as the leading cancer type in terms of incidence globally in 2020. Although mortality due to breast cancer has improved during the past three decades in many countries, this trend has reversed in women less than 40 years since the past decade. From the biological standpoint, there is consensus among experts regarding the clinically relevant definition of breast cancer in young women (BCYW), with an age cut-off of 40 years. The idea that breast cancer is an aging disease has apparently broken in the case of BCYW due to the young onset and an overall poor outcome of BCYW patients. In general, younger patients exhibit a worse prognosis than older pre- and postmenopausal patients due to the aggressive nature of cancer subtypes, a high percentage of cases with advanced stages at diagnosis, and a high risk of relapse and death in younger patients. Because of clinically and biologically unique features of BCYW, it is suspected to represent a distinct biologic entity. It is unclear why BCYW is more aggressive and has an inferior prognosis with factors that contribute to increased incidence. However, unique developmental features, adiposity and immune components of the mammary gland, hormonal interplay and crosstalk with growth factors, and a host of intrinsic and extrinsic risk factors and cellular regulatory interactions are considered to be the major contributing factors. In the present article, we discuss the status of BCYW oncobiology, therapeutic interventions and considerations, current limitations in fully understanding the basis and underlying cause(s) of BCYW, understudied areas of BCYW research, and postulated advances in the coming years for the field.

Integrated Analysis of Transcriptomic and Genomic Data Reveals Blood Biomarkers With Diagnostic and Prognostic Potential in Non-small Cell Lung Cancer

Background: Lung cancer is the second most common cancer and the main leading cause of cancer-associated death worldwide. Non-small cell lung cancer (NSCLC) accounts for about 85% of lung cancer diagnoses and more than 50% of all lung cancer cases are diagnosed at an advanced stage; hence have poor prognosis. Therefore, it is important to diagnose NSCLC patients reliably and as early as possible in order to reduce the risk of mortality.

Methods: We identified blood-based gene markers for early NSCLC by performing a multi-omics approach utilizing integrated analysis of global gene expression and copy number alterations of NSCLC patients using array-based techniques. We also validated the diagnostic and the prognostic potential of the gene signature using independent datasets with detailed clinical information.

Results: We identified 12 genes that are significantly expressed in NSCLC patients’ blood, at the earliest stages of the disease, and associated with a poor disease outcome. We then validated 12-gene signature’s diagnostic and prognostic value using independent datasets of gene expression profiling of over 1000 NSCLC patients. Indeed, 12-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis (HR = 2.64, 95% CI = 1.72–4.07; p = 1.3 × 10−8). Significantly altered functions, pathways, and gene networks revealed alterations in several key genes and cancer-related pathways that may have importance for NSCLC transformation, including FAM83A, ZNF696, UBE2C, RECK, TIMM50, GEMIN7, and XPO5.

Conclusion: Our findings suggest that integrated genomic and network analyses may provide a reliable approach to identify genes that are associated with NSCLC, and lead to improved diagnosis detecting the disease in early stages in patients’ blood instead of using invasive techniques and also have prognostic potential for discriminating high-risk patients from the low-risk ones.

Increased KIAA0101 gene expression associated with poor prognosis in breast cancer

Background

Breast cancer (BC) has long been a major death threat facing women worldwide. With the development of comprehensive treatment methods, the prognosis of BC was improved but still unsatisfactory. This study was aimed to identify the key genes in BC tumorigenesis and investigate potential prognostic predictors.

Methods

Differential expression genes were analyzed in TCGA BRCA dataset using Genevestigator software. The expression profile of target gene was explored, and the correlations between selected genes with important clinical parameters were evaluated as well. The prognostic values of target genes were also carried out through Kaplan-Meier plotter OncoLnc and BC gene-expression miner.

Results

KIAA0101 gene was selected for further analysis from the differential expression genes identified. At both mRNA and protein levels, the expression of KIAA0101 in BC was higher than that in normal tissues. Further analysis indicated that overexpression of KIAA0101 was significantly correlated with worse clinical outcome parameters. KIAA0101 was highly expressed in older patients, in the luminal group, and in patients with advanced stages. Moreover, BC patients with elevated KIAA0101 expression had worse overall survival (OS), relapse-free survival (RFS), distant metastasis-free survival (DMFS) and disease-free survival (DFS).

Conclusions

Taken together, KIAA0101 could be considered as a diagnostic biomarker or predictor for BC prognosis.

gp130 Cytokines Activate Novel Signaling Pathways and Alter Bone Dissemination in ER+ Breast Cancer Cells

Breast cancer cells frequently home to the bone marrow, where they encounter signals that promote survival and quiescence or stimulate their proliferation. The interleukin-6 (IL-6) cytokines signal through the co-receptor glycoprotein130 (gp130) and are abundantly secreted within the bone microenvironment. Breast cancer cell expression of leukemia inhibitory factor (LIF) receptor (LIFR)/STAT3 signaling promotes tumor dormancy in the bone, but it is unclear which, if any of the cytokines that signal through LIFR, including LIF, oncostatin M (OSM), and ciliary neurotrophic factor (CNTF), promote tumor dormancy and which signaling pathways are induced. We first confirmed that LIF, OSM, and CNTF and their receptor components were expressed across a panel of breast cancer cell lines, although expression was lower in estrogen receptor-negative (ER^- ) bone metastatic clones compared with parental cell lines. In estrogen receptor-positive (ER⁺ ) cells, OSM robustly stimulated phosphorylation of known gp130 signaling targets STAT3, ERK, and AKT, while CNTF activated STAT3 signaling. In ER^- breast cancer cells, OSM alone stimulated AKT and ERK signaling. Overexpression of OSM, but not CNTF, reduced dormancy gene expression and increased ER⁺ breast cancer bone dissemination. Reverse-phase protein array revealed distinct and overlapping pathways stimulated by OSM, LIF, and CNTF with known roles in breast cancer progression and metastasis. In breast cancer patients, downregulation of the cytokines or receptors was associated with reduced relapse-free survival, but OSM was significantly elevated in patients with invasive disease and distant metastasis. Together these data indicate that the gp130 cytokines induce multiple signaling cascades in breast cancer cells, with a potential pro-tumorigenic role for OSM and pro-dormancy role for CNTF. © 2021 American Society for Bone and Mineral Research (ASBMR).

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer

The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.

Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses

Background: Hepatocellular carcinoma (HCC) is considered the most common type of liver cancer and the fourth leading cause of cancer-related deaths in the world. Since the disease is usually diagnosed at advanced stages, it has poor prognosis. Therefore, reliable biomarkers are urgently needed for early diagnosis and prognostic assessment. Methods: We used genome-wide gene expression profiling datasets from human and rat early HCC (eHCC) samples to perform integrated genomic and network-based analyses, and discovered gene markers that are expressed in blood and conserved in both species. We then used independent gene expression profiling datasets for peripheral blood mononuclear cells (PBMCs) for eHCC patients and from The Cancer Genome Atlas (TCGA) database to estimate the diagnostic and prognostic performance of the identified gene signature. Furthermore, we performed functional enrichment, interaction networks and pathway analyses. Results: We identified 41 significant genes that are expressed in blood and conserved across species in eHCC. We used comprehensive clinical data from over 600 patients with HCC to verify the diagnostic and prognostic value of 41-gene-signature. We developed a prognostic model and a risk score using the 41-geneset that showed that a high prognostic index is linked to a worse disease outcome. Furthermore, our 41-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis. Our data reveals a number of cancer-related pathways and hub genes, including EIF4E, H2AFX, CREB1, GSK3B, TGFBR1, and CCNA2, that may be essential for eHCC progression and confirm our gene signature's ability to detect the disease in its early stages in patients' biological fluids instead of invasive procedures and its prognostic potential. Conclusion: Our findings indicate that integrated cross-species genomic and network analysis may provide reliable markers that are associated with eHCC that may lead to better diagnosis, prognosis, and treatment options.

Excavating the pathogenic gene of breast cancer based on high throughput data of tumor and somatic reprogramming

Breast cancer (BC) is one of the most common malignancies in female, and has a high mortality rate. The mechanisms of tumorigenesis and reprogramming of somatic cells have a certain degree of similarity. Here, we focus on the relationship between gene expression, signaling pathways and functions in BC compared to induced pluripotent stem cells (iPSCs). We first identified differentially expressed genes (DEGs) common to BC and iPSCs in datasets from GEO and TCGA. We found 22 DEGs that were significantly associated with clinicopathological features and prognosis by performing Kaplan-Meier survival analysis and one-way ANOVA. The results of protein mass spectrometry of tumor stem cells (Mcfips) demonstrated that the proteins encoded by 8 of these DEGs were also differentially expressed. The functional enrichment analysis showed that most of the 30 DEGs were related to collagen and chromatin functions. Our results might offer targets for future studies into the mechanisms underlying tumor occurrence and progression, and our studies could provide valuable data for both basic research and clinical applications of BC.

The Unique Biology behind the Early Onset of Breast Cancer

Breast cancer commonly affects women of older age; however, in developing countries, up to 20% of breast cancer cases present in young women (younger than 40 years as defined by oncology literature). Breast cancer in young women is often defined to be aggressive in nature, usually of high histological grade at the time of diagnosis and negative for endocrine receptors with poor overall survival rate. Several researchers have attributed this aggressive nature to a hidden unique biology. However, findings in this aspect remain controversial. Thus, in this article, we aimed to review published work addressing somatic mutations, chromosome copy number variants, single nucleotide polymorphisms, differential gene expression, microRNAs and gene methylation profile of early-onset breast cancer, as well as its altered pathways resulting from those aberrations. Distinct biology behind early-onset of breast cancer was clear among estrogen receptor-positive and sporadic cases. However, further research is needed to determine and validate specific novel markers, which may help in customizing therapy for this group of patients.

The BIRC Family Genes Expression in Patients with Triple Negative Breast Cancer

The BIRC (baculoviral IAP repeat-containing; BIRC) family genes encode for Inhibitor of Apoptosis (IAP) proteins. The dysregulation of the expression levels of the genes in question in cancer tissue as compared to normal tissue suggests that the apoptosis process in cancer cells was disturbed, which may be associated with the development and chemoresistance of triple negative breast cancer (TNBC). In our study, we determined the expression level of eight genes from the BIRC family using the Real-Time PCR method in patients with TNBC and compared the obtained results with clinical data. Additionally, using bioinformatics tools (Ualcan and The Breast Cancer Gene-Expression Miner v4.5 (bc-GenExMiner v4.5)), we compared our data with the data in the Cancer Genome Atlas (TCGA) database. We observed diverse expression pattern among the studied genes in breast cancer tissue. Comparing the expression level of the studied genes with the clinical data, we found that in patients diagnosed with breast cancer under the age of 50, the expression levels of all studied genes were higher compared to patients diagnosed after the age of 50. We observed that in patients with invasion of neoplastic cells into lymphatic vessels and fat tissue, the expression levels of BIRC family genes were lower compared to patients in whom these features were not noted. Statistically significant differences in gene expression were also noted in patients classified into three groups depending on the basis of the Scarff-Bloom and Richardson (SBR) Grading System.

Overexpression of β-Arrestins inhibits proliferation and motility in triple negative breast cancer cells

β-Arrestins (βArrs) are intracellular signal regulating proteins. Their expression level varies in some cancers and they have a significant impact on cancer cell function. In general, the significance of βArrs in cancer research comes from studies examining GPCR signalling. Given the diversity of different GPCR signals in cancer cell regulation, contradictory results are inevitable regarding the role of βArrs. Our approach examines the direct influence of βArrs on cellular function and gene expression profiles by changing their expression levels in breast cancer cells, MDA-MB-231 and MDA-MB-468. Reducing expression of βArr1 or βArr2 tended to increase cell proliferation and invasion whereas increasing their expression levels inhibited them. The overexpression of βArrs caused cell cycle S-phase arrest and differential expression of cell cycle genes, CDC45, BUB1, CCNB1, CCNB2, CDKN2C and reduced HER3, IGF-1R, and Snail. Regarding to the clinical relevance of our results, low expression levels of βArr1 were inversely correlated with CDC45, BUB1, CCNB1, and CCNB2 genes compared to normal tissue samples while positively correlated with poorer prognosis in breast tumours. These results indicate that βArr1 and βArr2 are significantly involved in cell cycle and anticancer signalling pathways through their influence on cell cycle genes and HER3, IGF-1R, and Snail in TNBC cells.

FGF13 interaction with SHCBP1 activates AKT-GSK3α/β signaling and promotes the proliferation of A549 cells

FGF13, a member of the FGF subfamily, has been found to be highly expressed in cancer cells such as prostate cancer, melanoma, glioma and multiple myeloma. However, the mechanism of FGF13 function during cancer cell proliferation remains to be unexplored, especially Non-small cell lung cancer (NSCLC). In this study, the cell proliferation effect of FGF13 on A549 cells was checked by CCK-8, clone formation, Ki67 immunofluorescence staining and Flow Cytometry assay. Localization of FGF13 within A549 cells was performed with confocal laser scanning microscope. The protein variations and interaction were measured by western blotting and co-immunoprecipitation analysis. It showed that FGF13 was mainly distributed in the cytoplasm and exhibited a high expression level in A549 cells. High expression of FGF13 activated AKT-GSK3 signaling pathway, and inhibited the activity of p21 and p27. Thus, FGF13 enhanced the process of transition from G1 to S phase and promoted A549 cells proliferation. Furthermore, the interaction between FGF13 and SHCBP1 was confirmed. Meanwhile, FGF13 and SHCBP1 had a cooperative effect to accelerate the cell cycle progression, especially the ability to promote cell proliferation is significantly enhanced via protein interaction. Hence, we conclude that FGF13 played a positive regulation role during A549 cells proliferation. FGF13 interacted with SHCBP1 to facilitate cell cycle progression, providing new insights into deep understanding of non-small cell lung cancer mechanisms of proliferation and regulation function of FGF13.

The Construction of Bone Metastasis-Specific Prognostic Model and Co-expressed Network of Alternative Splicing in Breast Cancer

Background

Breast cancer (BRCA) ranks among the top most common female malignancies and was regarded as incurable when combined with bone and distant metastasis. Alternative splicing events (ASEs) together with splicing factors (SFs) were considered responsible for the development and progression of tumors.

Methods

Datasets including RNA sequencing and ASEs of BRCA samples were achieved from TCGA and TCGASpliceSeq databases. Then, a survival model was built including 15 overall-survival-associated splicing events (OS-SEs) by Cox regression and Lasso regression. The co-expressed SFs of each bone-and-distant-metastasis-related OS-SE were discovered by Pearson correlation analysis. Additionally, Gene Set Variation Analysis (GSVA) was performed to identify the downstream mechanisms of the key OS-SEs. Finally, the results were validated in different online platforms.

Results

A reliable survival model was established (the area under ROC = 0.856), and CIRBP was found co-expressed with FAM110B (R = 0.320, P < 0.001) associated with the fatty acid metabolism pathway.

Conclusion

Aberrant SF, CIRBP, regulated a specific ASE, exon skip (ES) of FAM110B, during which the fatty acid metabolism pathway played an essential part in tumorigenesis and prognosis of BRCA.

The Role of Shcbp1 in Signaling and Disease

Src homolog and collagen homolog (Shc) proteins have been identified as adapter proteins associated with cell surface receptors and have been shown to play important roles in signaling and disease. Shcbp1 acts as a Shc SH2-domain binding protein 1 and is involved in the regulation of signaling pathways, such as FGF, NF-κB, MAPK/ERK, PI3K/AKT, TGF-β1/Smad and β -catenin signaling. Shcbp1 participates in T cell development, the regulation of downstream signal transduction pathways, and cytokinesis during mitosis and meiosis. In addition, Shcbp1 has been demonstrated to correlate with Burkitt-like lymphoma, breast cancer, lung cancer, gliomas, synovial sarcoma, human hepatocellular carcinoma and other diseases. Shcbp1 may play an important role in tumorigenesis and progression. Accordingly, recent studies are reviewed herein to discuss and interpret the role of Shcbp1 in normal cell proliferation and differentiation, tumorigenesis and progression, as well as its interactions with proteins.

SHCBP1 promotes tumor cell proliferation, migration, and invasion, and is associated with poor prostate cancer prognosis

OBJECTIVE

Prostate cancer (PCa) is an aggressive tumor. SHC SH2-domain-binding protein 1 (SHCBP1) has been identified frequently upregulated in various cancers, in addition to PCa. The aims of this study were to determine the relationships between SHCBP1 and clinicopathological characteristics of PCa and to explore the role of SHCBP1 in PCa proliferation and progression.

METHODS

Tissue microarray and immunohistochemistry were used to determine the prognostic significance of SHCBP1. The relationship between clinicopathological characteristics of PCa and SHCBP1 was then analyzed using Cox regression analyses. To investigate SHCBP1 functions in vitro and in vivo, we knocked down SHCBP1 in PCa cell lines and established xenograft mice models. A series of cytological function assays were utilized to determine the role of SHCBP1 in cell proliferation, migration, invasion, and apoptosis.

RESULTS

SHCBP1 was significantly upregulated in PCa tissues compared with BPH tissues. Patients with a higher expression of SHCBP1 were associated with poor survival outcomes than those with a lower expression of SHCBP1. Lentivirus-mediated shRNA knockdown of SHCBP1 in prostate cancer cell lines diminished cell growth, migration, and invasion dramatically both in vitro and in vivo, accompanied by an enhanced expression of large tumor suppressor 1 (LATS1) and tumor protein P53 (TP53) and inhibition of MDM2 proto-oncogene (MDM2), which suggested that SHCBP1 may promote proliferation and invasion in vitro via the LATS1-MDM2-TP53 pathway. The results of cycloheximide (CHX) and MG-132 assays indicated that SHCBP1 knockdown could attenuate the degradation of TP53 by the proteasome, prolong the half-life of TP53, and enhance the stabilization of TP53.

CONCLUSION

These findings suggest that SHCBP1 overexpression contributes to PCa progression and that targeting SHCBP1 might be therapeutically beneficial to patients with PCa.

Centrosomal protein 55: A new paradigm in tumorigenesis

Centrosomal Protein 55 (Cep55), also known as c10orf3 and FLJ10540, was initially discovered as a major player in abscission, the final stage of cytokinesis. Subsequent studies have described its role in regulating the PI3K/AKT pathway, increasing cancer cell stemness, and promoting tumor formation. Clinically, Cep55 has been found to be overexpressed in many cancer types. Cep55 overexpression has been notably associated with tumor stage, tumor aggressiveness, poor prognosis, and metastasis. The present review discusses the role of Cep55 as a crucial biomarker and model in tumorigenesis.

Upregulation of CEP55 Predicts Dismal Prognosis in Patients with Liver Cancer

Purpose

This study was performed to investigate the association of CEP55 expression with liver cancer and explore potential underlying mechanisms. Materials and Methods. Data obtained from The Cancer Genome Atlas (TCGA) was used to investigate CEP55 expression, its prognostic value, the potential mechanisms of its upregulation, CEP55-related pathways, and its biological functions in liver cancer. Data from Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) was used to validate survival analysis. The correlation between CEP55 and tumor-infiltrating immune cells (TIICs) in liver cancer was determined by using Tumor Immune Estimation Resource (TIMER).

Results

CEP55 was significantly overexpressed in the liver tumor sample compared to the adjacent normal liver sample. High CEP55 expression was significantly associated with histological grade, advanced stages, histological type, high T classification, and survival status. High CEP55 expression was significantly related to dismal prognosis compared with low CEP55 expression, which was validated by the GSE54236 dataset and ICGC database. Meanwhile, CEP55 was identified as the risk factor to independently predict overall survival (OS) for patients with liver cancer upon multivariate analysis. Enrichment analysis indicated that cell cycle, DNA replication, pathways in cancer, mTOR signaling pathway, and VEGF signaling pathway were significantly enriched in the high CEP55 expression group. In addition, the CEP55 expression was significantly related to the infiltration level of B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells in hepatocellular carcinoma (HCC). CEP55 methylation level was negatively correlated to its mRNA expression. And patients with CEP55 hypermethylation and low expression can achieve a better prognosis than those with CEP55 hypomethylation and high expression.

Conclusion

CEP55 may serve as a candidate treatment target for it is a determinant of prognosis and immune infiltration in liver cancer patients. DNA hypomethylation might contribute to the overexpression of CEP55 in liver cancer.

Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review

Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.

Annotation and cluster analysis of long noncoding RNA linked to male sex and estrogen in cancers

The sex difference in cancer occurrence is a consistent finding in cancer epidemiology. Several solid tumors, including lung cancer, colorectal cancer, hepatic carcinoma, and renal carcinoma, are generally more common in males. Although sexual dimorphism is attributed to hormonal or behavioral differences, evidence for the function of lncRNA is lacking in sex-specific cancers. We show here that LINC00263 is one of the most dysregulated lncRNAs in lung adenocarcinomas and is upregulated in lung adenocarcinoma, colorectal cancer, and renal carcinoma, especially in male patients compared to females. LINC00263 functions as an oncogene by promoting translocation of p65 into the nucleus to activate the NF-κB-signaling pathway through interaction with IKKα in the cytoplasm. The expression of LINC00263 is strongly correlated with ESR1, and it is decreased after treatment with estrogen. Ligand-activated ER could inhibit the function of LINC00263 by inhibiting NF-κB from cytoplasmic translocation into the nucleus. The inhibitory effect of estrogen on LINC00263 indicates its differential expression in male and female patients. Our findings indicate that LINC00263 is linked to male sex and estrogen as an oncogene, and these findings might help in the exploration of the mechanisms of differential gene regulation in sex-specific cancers.

Decreased Expression of NUSAP1 Predicts Poor Overall Survival in Cervical Cancer

Background: Nucleolar and spindle-associated protein 1 (NUSAP1) was previously reported to be associated with poor prognosis in multiple cancers. In the present study, we comprehensively investigated the clinicopathological features and potential prognostic value of NUSAP1 in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC).

Methods: The expression profiles of the genes were extracted from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Cancer Cell Line Encyclopedia (CCLE), Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas databases. The association between clinicopathological characteristics and NUSAP1 was analyzed using logistic regression in TCGA patients and receiver operating characteristic (ROC) curve analysis for GSE7803, GSE9750, and GSE63514 datasets. The prognostic value of NUSAP1 in TCGA patients was evaluated using the Kaplan-Meier method and Cox regression. Gene set enrichment analysis (GSEA) was conducted using TCGA dataset.

Results: A total of 68 differentially expressed genes (DEGs) were identified in CESC. ROC analysis of NUSAP1 suggested that the area under the ROC curve was 0.968. Kaplan-Meier survival analysis indicated that CESC with low expression of NUSAP1 has a worse prognosis than CESC with high NUSAP1 expression (P = 0.005). The logistic regression revealed that low NUSAP1 expression in CESC was related to advanced tumor stage in TCGA database. Moreover, Cox regression analysis showed that NUSAP1 expression correlated significantly with prognosis in the case of patients in TCGA database. GSEA demonstrated that CESC patients with high expression of NUSAP1 were enriched in the G2M checkpoint, MYC targets, and breast cancer ZNF217.

Conclusion: The results suggest that identification of DEGs might enhance our understanding of the causes and molecular mechanisms underlying the development of CESC. Moreover, NUSAP1 may play an important role in CESC progression and prognosis and may serve as a valuable indicator of poor survival in CESC.

Cancer progression is mediated by proline catabolism in non-small cell lung cancer

Dysregulated metabolism contributes to cancer initiation and progression, but the key drivers of these pathways are just being discovered. Here, we report a critical role for proline catabolism in non-small cell lung cancer (NSCLC). Proline dehydrogenase (PRODH) is activated to reduce proline levels by the chromatin remodeling factor lymphoid-specific helicase (LSH), an epigenetic driver of NSCLC. PRODH promotes NSCLC tumorigenesis by inducing epithelial to mesenchymal transition (EMT) and IKKα-dependent inflammatory genes, including CXCL1, LCN2, and IL17C. Consistently, proline addition promotes the expression of these inflammatory genes, as well as EMT, tumor cell proliferation, and migration in vitro and tumor growth in vivo, while the depletion or inhibition of PRODH blocks these phenotypes. In summary, we reveal an essential metabolic pathway amenable to targeting in NSCLC.

Identification of potential key genes for HER-2 positive breast cancer based on bioinformatics analysis

BACKGROUNDS

HER-2 positive breast cancer is a subtype of breast cancer with poor clinical outcome. The aim of this study was to identify differentially expressed genes (DEGs) for HER-2 positive breast cancer and elucidate the potential interactions among them.

MATERIAL AND METHODS

Three gene expression profiles (GSE29431, GSE45827, and GSE65194) were derived from the Gene Expression Omnibus (GEO) database. GEO2R tool was applied to obtain DEGs between HER-2 positive breast cancer and normal breast tissues. Gene ontology (GO) annotation analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery (David) online tool. Protein-protein interaction (PPI) network, hub gene identification and module analysis was conducted by Cytoscape software. Online Kaplan-Meier plotter survival analysis tool was also used to investigate the prognostic values of hub genes in HER-2 positive breast cancer patients.

RESULTS

A total of 54 upregulated DEGs and 269 downregulated DEGs were identified. Among them, 10 hub genes including CCNB1, RAC1, TOP2A, KIF20A, RRM2, ASPM, NUSAP1, BIRC5, BUB1B, and CEP55 demonstrated by connectivity degree in the PPI network were screened out. In Kaplan-Meier plotter survival analysis, the overexpression of RAC1 and RRM2 were shown to be associated with an unfavorable prognosis in HER-2 positive breast cancer patients.

CONCLUSIONS

This present study identified a number of potential target genes and pathways which might impact the oncogenesis and progression of HER-2 positive breast cancer. These findings could provide new insights into the detection of novel diagnostic and therapeutic biomarkers for this disease.

Breast Cancer in Young Women: Status Quo and Advanced Disease Management by a Predictive, Preventive, and Personalized Approach

Why does healthcare of breast cancer (BC) patients, especially in a young population, matter and why are innovative strategies by predictive, preventive, and personalized medicine (PPPM) strongly recommended to replace current reactive medical approach in BC management? Permanent increase in annual numbers of new BC cases with particularly quick growth of premenopausal BC patients, an absence of clearly described risk factors for those patients, as well as established screening tools and programs represent important reasons to focus on BC in young women. Moreover, "young" BC cases are frequently "asymptomatic", difficult to diagnose, and to treat effectively on time. The objective of this article is to update the knowledge on BC in young females, its unique molecular signature, newest concepts in diagnostics and therapy, and to highlight the concepts of predictive, preventive, and personalized medicine with a well-acknowledged potential to advance the overall disease management.

PREX1 drives spontaneous bone dissemination of ER+ breast cancer cells

A significant proportion of breast cancer patients develop bone metastases, but the mechanisms regulating tumor cell dissemination from the primary site to the skeleton remain largely unknown. Using a novel model of spontaneous bone metastasis derived from human ER+ MCF7 cells, molecular profiling revealed increased PREX1 expression in a cell line established from bone-disseminated MCF7 cells (MCF7b), which were more migratory, invasive, and adhesive in vitro compared to parental MCF7 cells, and this phenotype was mediated by PREX1. MCF7b cells grew poorly in the primary tumor site when re-inoculated in vivo, suggesting these cells are primed to grow in the bone, and were enriched in skeletal sites of metastasis over soft tissue sites. Skeletal dissemination from the primary tumor was reversed with PREX1 knockdown, indicating that PREX1 is a key driver of spontaneous dissemination of tumor cells from the primary site to the bone marrow. In breast cancer patients, PREX1 levels are significantly increased in ER+ tumors and associated with invasive disease and distant metastasis. Together, these findings implicate PREX1 in spontaneous bone dissemination and provide a significant advance to the molecular mechanisms by which breast cancer cells disseminate from the primary tumor site to bone.

Identification of an eight-lncRNA prognostic model for breast cancer using WGCNA network analysis and a Cox‑proportional hazards model based on L1-penalized estimation

An ever‑increasing number of long noncoding (lnc)RNAs has been identified in breast cancer. The present study aimed to establish an lncRNA signature for predicting survival in breast cancer. RNA expression profiling was performed using microarray gene expression data from the National Center for Biotechnology Information Gene Expression Omnibus, followed by the identification of breast cancer‑related preserved modules using weighted gene co‑expression network (WGCNA) network analysis. From the lncRNAs identified in these preserved modules, prognostic lncRNAs were selected using univariate Cox regression analysis in combination with the L1‑penalized (LASSO) Cox‑proportional Hazards (Cox‑PH) model. A risk score based on these prognostic lncRNAs was calculated and used for risk stratification. Differentially expressed RNAs (DERs) in breast cancer were identified using MetaDE. Gene Set Enrichment Analysis pathway enrichment analysis was conducted for these prognostic lncRNAs and the DERs related to the lncRNAs in the preserved modules. A total of five preserved modules comprising 73 lncRNAs were mined. An eight‑lncRNA signature (IGHA1, IGHGP, IGKV2‑28, IGLL3P, IGLV3‑10, AZGP1P1, LINC00472 and SLC16A6P1) was identified using the LASSO Cox‑PH model. Risk score based on these eight lncRNAs could classify breast cancer patients into two groups with significantly different survival times. The eight‑lncRNA signature was validated using three independent cohorts. These prognostic lncRNAs were significantly associated with the cell adhesion molecules pathway, JAK‑signal transducer and activator of transcription 5A pathway, and erbb pathway and are potentially involved in regulating angiotensin II receptor type 1, neuropeptide Y receptor Y1, KISS1 receptor, and C‑C motif chemokine ligand 5. The developed eight‑lncRNA signature may have clinical implications for predicting prognosis in breast cancer. Overall, this study provided possible molecular targets for the development of novel therapies against breast cancer.

Involvement of the centrosomal protein 55 (cep55) gene in zebrafish head formation

Mammalian CEP55 (centrosomal protein 55 kDa) is a coiled-coil protein localized to the centrosome in interphase cells and is required for cytokinesis. A homozygous non-sense mutation in human CEP55 has been recently identified in perinatal lethal MARCH (multinucleated neurons, anhydramnios, renal dysplasia, cerebellar hypoplasia and hydranencephaly) syndrome. We have isolated zebrafish cep55 mutants defective in head morphology. The zebrafish cep55 gene was expressed in the head including the retina and the pectoral fin at 1 day post-fertilization (dpf), and extensive cell death was widely observed in the head and tail of the cep55 mutant. In the cep55 mutant, the anterior-posterior distance of the ventral pharyngeal arches was short, and retinal lamination was disorganized. Neural cells, such as islet1-positive cells and pax2-positive cells, and fli1b-positive vascular cells were reduced in the head of the cep55 mutant. Thus, we propose that the zebrafish cep55 mutant is a model organism for human MARCH syndrome.

E2 ubiquitin-conjugating enzymes in cancer: Implications for immunotherapeutic interventions

Despite the medical advances of the 21st century, the incidence of cancer continues to increase and the search for a universal cure remains a major health challenge. Our lack of understanding the complex pathophysiology of the tumor microenvironment has hindered the development and efficiency of anti-cancer therapeutic strategies. The tumor microenvironment, composed of multiple cellular and non-cellular components, enables tumor-promoting processes such as proliferation, angiogenesis, migration and invasion, metastasis, and drug resistance. The ubiquitin-mediated degradation system is involved in several physiologic processes including cell cycling, signal transduction, receptor downregulation, endocytosis and transcriptional regulation. Ubiquitination includes attachment of ubiquitin to target proteins via E1 (activating), E2 (conjugating) and E3 (ligating) enzymes. Several studies have shown that E2 enzymes are dysregulated in variety of cancers. Multiple investigations have demonstrated the involvement of E2s in various tumor-promoting processes including DNA repair, cell cycle progression, apoptosis and oncogenic signaling. E2 enzymes consist of 40 members that facilitate ubiquitin-substrate conjugation thereby modulating the stability and interaction of various proteins. As such, E2s are potential biomarkers as diagnostic, prognostic and therapeutic tools. In this review, we discuss the role of E2s in modulating various types of cancer.

Overexpression of UHRF1 and its potential role in the development of invasive ductal breast cancer validated by integrative bioinformatics and immunohistochemistry analyses

BACKGROUND

Increasing evidence has highlighted the role of ubiquitin-like PHD and RING finger domain-containing protein 1 (UHRF1) in the development of cancers, including hepatocellular carcinoma, pancreatic cancer, and bladder cancer. However, the correlation between UHRF1 and breast cancer remains unclear. The present study aimed to analyze the expression of UHRF1 and its role in the development of invasive ductal breast cancer (IDC) by integrating multilevel expression data and immunohistochemistry analysis.

METHODS

The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to gather UHRF1 expression data on IDC. Additionally, immunohistochemistry analysis was used to investigate the correlations between UHRF1 expression and the clinical characteristics of IDC.

RESULTS

The GEO and TCGA databases indicated that UHRF1 was up-regulated in IDC. Consistently, the immunohistochemical specimens showed that the significant overexpression of UHRF1 in IDC, and its expression level showed an increasing trend from ductal carcinomas in situ to IDC. Notably, the increased levels of UHRF1 were closely correlated with estrogen receptor expression, pathological grade, and the prognosis of the disease. In addition, patients with a high UHRF1 expression had a poorer prognosis.

CONCLUSIONS

In conclusion, our findings suggested that UHRF1 plays a promoting role in breast tumorigenesis, and the over-expression of UHRF1 could serve as a biomarker for the prognosis in invasive ductal carcinomas in breast cancer.

High Expression of DEPDC1 Promotes Malignant Phenotypes of Breast Cancer Cells and Predicts Poor Prognosis in Patients With Breast Cancer

DEP domain containing 1 (DEPDC1) is a novel tumor-associated gene, which is aberrantly expressed in multiple types of cancer and involves in tumorigenesis and cancer progression. Here, we examined the functional involvement and underlying mechanism of DEPDC1 in breast cancer. In this study, the immunohistochemistry results demonstrated that DEPDC1 was high-expressed in breast cancer tissues compared with the paired adjacent normal breast tissues, and its tendency at protein level was consistent with mRNA level from TCGA data. Moreover, DEPDC1 mRNA level revealed the strongest association with poor prognosis and development in breast cancer. In vitro assays showed that DEPDC1 overexpression resulted in significant promotion of proliferation by regulating cell cycle in MCF-7 cells, whilst an opposite effect was found in the MDA-MB-231 cells with DEPDC1 deletion. Notably, further investigation indicated DEPDC1's ability of promoting breast cancer cells migration and invasion. In addition, we discovered that DEPDC1 caused hyper-activation of PI3K/AKT/mTOR signaling in breast cancer cells. Therefore, the increased DEPDC1 expression in breast cancer is correlated with disease progression and poor survival, which suggested that DEPDC1 might be a potential therapeutic target against this disease.

Grade-specific diagnostic and prognostic biomarkers in breast cancer

An integrative approach is presented to identify grade-specific biomarkers for breast cancer. Grade-specific molecular interaction networks were constructed with differentially expressed genes (DEGs) of cancer grade 1, 2, and 3. We observed that the molecular network of grade3 is predominantly associated with cancer-specific processes. Among the top ten connected DEGs in the grade3, the increase in the expression of UBE2C and CCNB2 genes was statistically significant across different grades. Along with UBE2C and CCNB2 genes, the CDK1, KIF2C, NDC80, and CCNB2 genes are also profoundly expressed in different grades and reduce the patient's survival. Gene set enrichment analysis of these six genes reconfirms their role in metastatic phenotype. Moreover, the coexpression network shows a strong association of these six genes promotes cancer specific biological processes and possibly drives cancer from lower to a higher grade. Collectively the identified genes can act as potential biomarkers for breast cancer diagnosis and prognosis.

CEP55 promotes the proliferation and invasion of tumour cells via the AKT signalling pathway in osteosarcoma

The molecular mechanisms underlying the development of osteosarcoma (OS) are not fully understood. In this study, we investigated for the first time the clinical significance and biological activity of centrosomal protein 55 (CEP55) in OS. We found that CEP55 was overexpressed in OS, and the CEP55 expression level in OS was correlated with metastasis and poor prognosis. Through in vitro experiments, we confirmed that CEP55 knockdown significantly induced cell cycle arrest at G1 phase and suppressed OS cell proliferation, migration and invasion. In addition, CEP55 knockdown suppressed OS tumour growth in nude mice. Global gene expression profiling of CEP55-silenced MNNG/HOS cells showed that the AKT pathway might be involved in the regulation of OS cell activity. Two downstream factors of AKT signalling, CCND1 and FN1, were found to have significantly higher expression in tumour tissues, and their mRNA expression levels were strongly correlated with CEP55 expression. To conclude, our data suggest that CEP55 can be used as a prognostic marker for OS, highlighting the significance of CEP55 signalling as a putative therapeutic target.