Significant prognostic values of differentially expressed-aberrantly methylated hub genes in breast cancer

The 'Not-So-Famous Five' in tumorigenesis: tRNAs, tRNA fragments, and tRNA epitranscriptome in concert with AARSs and AIMPs

RNA profiling studies have revealed that ∼75% of the human genome is transcribed to RNA but only a meagre fraction of it is translated to proteins. Majority of transcribed RNA constitute a specialized pool of non-coding RNAs. Human genome contains approximately 506 genes encoding a set of 51 different tRNAs, constituting a unique class of non-coding RNAs that not only have essential housekeeping functions as translator molecules during protein synthesis, but have numerous uncharted regulatory functions. Intriguing findings regarding a variety of non-canonical functions of tRNAs, tRNA derived fragments (tRFs), esoteric epitranscriptomic modifications of tRNAs, along with aminoacyl-tRNA synthetases (AARSs) and ARS-interacting multifunctional proteins (AIMPs), envision a 'peripheral dogma' controlling the flow of genetic information in the backdrop of qualitative information wrung out of the long-live central dogma of molecular biology, to drive cells towards either proliferation or differentiation programs. Our review will substantiate intriguing peculiarities of tRNA gene clusters, atypical tRNA-transcription from internal promoters catalysed by another distinct RNA polymerase enzyme, dynamically diverse tRNA epitranscriptome, intricate mechanism of tRNA-charging by AARSs governing translation fidelity, epigenetic regulation of gene expression by tRNA fragments, and the role of tRNAs and tRNA derived/associated molecules as quantitative determinants of the functional proteome, covertly orchestrating the process of tumorigenesis, through a deregulated tRNA-ome mediating selective codon-biased translation of cancer related gene transcripts.

Human pan-cancer analysis of the predictive biomarker for the CDKN3

BACKGROUND

Cell cycle protein-dependent kinase inhibitor protein 3 (CDKN3), as a member of the protein kinase family, has been demonstrated to exhibit oncogenic properties in several tumors. However, there are no pan-carcinogenic analyses for CDKN3.

METHODS

Using bioinformatics tools such as The Cancer Genome Atlas (TCGA) and the UCSC Xena database, a comprehensive pan-cancer analysis of CDKN3 was conducted. The inverstigation encompassed the examination of CDKN3 function actoss 33 different kinds of tumors, as well as the exploration of gene expressions, survival prognosis status, clinical significance, DNA methylation, immune infiltration, and associated signal pathways.

RESULTS

CDKN3 was significantly upregulated in most of tumors and correlated with overall survival (OS) of patients. Methylation levels of CDKN3 differed significantly between tumors and normal tissues. In addition, infiltration of CD4 + T cells, cancer-associated fibroblasts, macrophages, and endothelial cells were associated with CDKN3 expression in various tumors. Mechanistically, CDKN3 was associated with P53, PI3K-AKT, cell cycle checkpoints, mitotic spindle checkpoint, and chromosome maintenance.

CONCLUSION

Our pan-cancer analysis conducted in the study provides a comprehensive understanding of the involvement of CDKN3 gene in tumorigenesis. The findings suggest that targeting CDKN3 may potentially lead to novel therapeutic strategies for the treatment of tumors.

The role of Cyclin Dependent Kinase Inhibitor 3 (CDKN3) in promoting human tumors: Literature review and pan-cancer analysis

Background

Although many experiments and clinical studies have proved the link between the expression of CDKN3 and human tumors, we have not been able to identify any bioinformatics study in which the extensive tumor-promoting effect of CDKN3 was systematically analyzed.

Objective

Explore the extensive tumor-promoting effects of CDKN3 and review the research progress of CDKN3 in cancer.

Methods

We systematically reviewed the literature on CDKN3 and tumors. We explored the potential tumor-promoting effects of CDKN3 on different tumors in the TCGA database and the GTEx database using multiple platforms and websites. We studied the expression level of CDKN3, survival, prognosis, diagnosis, genetic variation, immune infiltration, and enrichment analysis using databases such as TIMER 2.0, GEPIA2, cBioPortal, and STRING.

Results

We found that CDKN3 is highly expressed in most tumors. The expression of CDKN3 is closely related to the prognosis of some tumors. And CDKN3 may have diagnostic value. The conclusion of our literature review is roughly the same, but there are differences, which are worthy of further study. Moreover, CDKN3 may be related to immune cell infiltration in tumor tissues. The genetic alteration of LUAD, STAD, SARC, PCPG, and ESCA with "Amplification" as the main type. In addition, through enrichment analysis, we found that CDKN3 affects tumors mainly through the control of the cell cycle and mitosis.

Conclusion

CDKN3 is highly expressed in most tumor tissues and has a statistical correlation with survival prognosis. It has extensive tumor-promoting effects that may be related to mechanisms such as immune infiltration.

Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning

Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate the specific gene expression and tumor staging. However, the knowledge in this regard is still far from complete. Thus, this study aimed to explore these knowledge gaps by analyzing existing gene expression profile data from 3149 breast cancer samples, where each sample was represented by the expression of 19,644 genes and classified into Nottingham histological grade (NHG) classes (Grade 1, 2, and 3). To this end, a machine learning-based framework was designed. First, the profile data were analyzed by using seven feature ranking algorithms to evaluate the importance of features (genes). Seven feature lists were generated, each of which sorted features in accordance with feature importance evaluated from a special aspect. Then, the incremental feature selection method was applied to each list to determine essential features for classification and building efficient classifiers. Consequently, overlapping genes, such as AURKA, CBX2, and MYBL2, were deemed as potentially related to breast cancer malignancy and prognosis, indicating that such genes were identified to be important by multiple feature ranking algorithms. In addition, the study formulated classification rules to reflect special gene expression patterns for three NHG classes. Some genes and rules were analyzed and supported by recent literature, providing new references for studying breast cancer.

FEN1 Inhibition as a Potential Novel Targeted Therapy against Breast Cancer and the Prognostic Relevance of FEN1

To improve breast cancer treatment and to enable new strategies for therapeutic resistance, therapeutic targets are constantly being studied. Potential targets are proteins of DNA repair and replication and genomic integrity, such as Flap Endonuclease 1 (FEN1). This study investigated the effects of FEN1 inhibitor FEN1-IN-4 in combination with ionizing radiation on cell death, clonogenic survival, the cell cycle, senescence, doubling time, DNA double-strand breaks and micronuclei in breast cancer cells, breast cells and healthy skin fibroblasts. Furthermore, the variation in the baseline FEN1 level and its influence on treatment prognosis was investigated. The cell lines show specific response patterns in the aspects studied and have heterogeneous baseline FEN1 levels. FEN1-IN-4 has cytotoxic, cytostatic and radiosensitizing effects, expressed through increasing cell death by apoptosis and necrosis, G2M share, senescence, double-strand breaks and a reduced survival fraction. Nevertheless, some cells are less affected by the cytotoxicity and fibroblasts show a rather limited response. In vivo, high FEN1 mRNA expression worsens the prognosis of breast cancer patients. Due to the increased expression in breast cancer tissue, FEN1 could represent a new tumor and prognosis marker and FEN1-IN-4 may serve as a new potent agent in personalized medicine and targeted breast cancer therapy.

Identification of Hub genes with prognostic values in colorectal cancer by integrated bioinformatics analysis

BACKGROUND

Our study aimed to investigate the Hub genes and their prognostic value in colorectal cancer (CRC) via bioinformatics analysis.

METHODS

The data set of colorectal cancer was downloaded from the GEO database (GSE21510, GSE110224 and GSE74602) for differential expression analysis using the GEO2R tool. Hub genes were screened by protein-protein interaction (PPI) comprehensive analysis. GEPIA was used to verify the expression of Hub genes and evaluate its prognostic value. The protein expression of Hub gene in CRC was analyzed using the Human Protein Atlas database. The cBioPortal was used to analyze the type and frequency of Hub gene mutations, and the effects of mutation on the patients' prognosis. The TIMER database was used to study the correlation between Hub genes and immune infiltration in CRC. Gene set enrichment analysis (GSEA) was used to explore the biological function and signal pathway of the Hub genes and corresponding co-expressed genes.

RESULTS

We identified 346 differentially expressed genes (DEGs), including 117 upregulated and 229 downregulated. Four Hub genes (AURKA, CCNB1, EXO1 and CCNA2) were selected by survival analysis and differential expression validation. The protein and mRNA expression levels of AURKA, CCNB1, EXO1 and CCNA2 were higher in CRC tissues than in adjacent tissues. There were varying degrees of immune cell infiltration and gene mutation of Hub genes, especially B cells and CD8+ T cells. The results of GSEA showed that Hub genes and their co-expressed genes mainly participated in chromosome segregation, DNA replication, translational elongation and cell cycle.

CONCLUSION

Overexpression of AURKA, CCNB1, CCNA2 and EXO1 had a better prognosis for CRC and this effect was correlation with gene mutation and infiltration of immune cells.

Ribonucleotide reductase M2 (RRM2): Regulation, function and targeting strategy in human cancer

Ribonucleotide reductase M2 (RRM2) is a small subunit in ribonucleotide reductases, which participate in nucleotide metabolism and catalyze the conversion of nucleotides to deoxynucleotides, maintaining the dNTP pools for DNA biosynthesis, repair, and replication. RRM2 performs a critical role in the malignant biological behaviors of cancers. The structure, regulation, and function of RRM2 and its inhibitors were discussed. RRM2 gene can produce two transcripts encoding the same ORF. RRM2 expression is regulated at multiple levels during the processes from transcription to translation. Moreover, this gene is associated with resistance, regulated cell death, and tumor immunity. In order to develop and design inhibitors of RRM2, appropriate strategies can be adopted based on different mechanisms. Thus, a greater appreciation of the characteristics of RRM2 is a benefit for understanding tumorigenesis, resistance in cancer, and tumor microenvironment. Moreover, RRM2-targeted therapy will be more attention in future therapeutic approaches for enhancement of treatment effects and amelioration of the dismal prognosis.

Comprehensive Analysis Reveals the Potential Roles of CDKN3 in Pancancer and Verification in Endometrial Cancer

Background

Cyclin-dependent kinase inhibitor 3 (CDKN3) has been studied in many cancers. However, the comprehensive and systematic pancancer analysis of CDKN3 genes is still lacking.

Methods

Data were downloaded from online databases. R was used for analysis of the differential expression and gene alteration of CDKN3 and of the associations between CDKN3 expression and survival, signaling pathways, and drug sensitivity. Clinical samples and in vitro experiments were selected for verification.

Results

CDKN3 expression was higher in most types of cancers, and this phenotype was significantly correlated with poor survival. CDKN3 showed gene alterations and copy number alterations in many cancers and associated with some immune-related pathways and factors. Drug sensitivity analysis elucidated that CDKN3 could be a useful marker for therapy selection. Clinical samples elucidated CDKN3 expressed high in endometrial cancer tissue. In vitro studies showed that CDKN3 induced pro-tumor effect in immune environment and facilitated endometrial cancer cell proliferation and G1/S phase transition.

Conclusion

CDKN3 has been shown to be highly expressed in most types of cancers and promoted cancer cell progression. CDKN3 may serve as a novel marker in clinical diagnosis, treatment, and prognosis prediction in future.

Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods

BACKGROUND

Targeted therapy has revolutionized cancer treatment, greatly improving patient outcomes and quality of life. Lung cancer, specifically non-small cell lung cancer, is frequently driven by the G12C mutation at the KRAS locus. The development of KRAS inhibitors has been a breakthrough in the field of cancer research, given the crucial role of KRAS mutations in driving tumor growth and progression. However, over half of patients with cancer bypass inhibition show limited response to treatment. The mechanisms underlying tumor cell resistance to this treatment remain poorly understood.

METHODS

To address above gap in knowledge, we conducted a study aimed to elucidate the differences between tumor cells that respond positively to KRAS (G12C) inhibitor therapy and those that do not. Specifically, we analyzed single-cell gene expression profiles from KRAS G12C-mutant tumor cell models (H358, H2122, and SW1573) treated with KRAS G12C (ARS-1620) inhibitor, which contained 4297 cells that continued to proliferate under treatment and 3315 cells that became quiescent. Each cell was represented by the expression levels on 8687 genes. We then designed an innovative machine learning based framework, incorporating seven feature ranking algorithms and four classification algorithms to identify essential genes and establish quantitative rules.

RESULTS

Our analysis identified some top-ranked genes, including H2AFZ, CKS1B, TUBA1B, RRM2, and BIRC5, that are known to be associated with the progression of multiple cancers.

CONCLUSION

Above genes were relevant to tumor cell resistance to targeted therapy. This study provides important insights into the molecular mechanisms underlying tumor cell resistance to KRAS inhibitor treatment.

Expression profiling of luminal B breast tumor in Indian women

PURPOSE

In this study, we aimed at profiling of luminal B breast cancer specific gene expression pattern in Indian women using mRNA-seq and validation based on TCGA expression data.

METHODS

RNA isolated from luminal B tumor and adjacent normal tissues was used for library construction and sequencing. Reference-based assemblies of these reads were used for differential gene expression analysis using DeSeq2. The DEGs were evaluated using TCGA expression data. Kaplan-Meier survival method was used to evaluate association between genes showing luminal B specific differential expression pattern and breast cancer prognosis and statistical significance was assessed using log-rank test. Alternate splicing analysis was done using rmats.

RESULTS

Differential expression analysis identified 2371 differentially expressed genes (DEGs) in luminal B breast tumors in comparison with adjacent normal tissues of Indian Women. Of them, 1692 DEGs were validated using TCGA luminal B paired samples. Integration of this data with the DEGs obtained by comparative analysis of unpaired luminal B with luminal A unpaired samples from TCGA resulted in 291 DEGs showing luminal B specific expression pattern. Further, 26 genes of prognostic value were identified. Differential splicing analysis between luminal B tumors and adjacent normal tissues in our cohort led to the identification of 687 genes showing significant differential alternate splicing events.

CONCLUSION

This study profiled gene expression pattern of luminal B tumors of Indian women and identified 26 key genes of prognostic value for luminal B breast cancer. This study also profiled differential alternate splicing and identified important alternate splicing events in luminal B breast cancer.

Phosphocode-dependent glutamyl-prolyl-tRNA synthetase 1 signaling in immunity, metabolism, and disease

Ubiquitously expressed aminoacyl-tRNA synthetases play essential roles in decoding genetic information required for protein synthesis in every living species. Growing evidence suggests that they also function as crossover mediators of multiple biological processes required for homeostasis. In humans, eight cytoplasmic tRNA synthetases form a central machinery called the multi-tRNA synthetase complex (MSC). The formation of MSCs appears to be essential for life, although the role of MSCs remains unclear. Glutamyl-prolyl-tRNA synthetase 1 (EPRS1) is the most evolutionarily derived component within the MSC that plays a critical role in immunity and metabolism (beyond its catalytic role in translation) via stimulus-dependent phosphorylation events. This review focuses on the role of EPRS1 signaling in inflammation resolution and metabolic modulation. The involvement of EPRS1 in diseases such as cancer is also discussed.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Silencing LCN2 enhances RSL3-induced ferroptosis in T cell acute lymphoblastic leukemia

BACKGROUND

T-cell acute lymphoblastic leukemia (T-ALL) is a life-threatening malignancy and therapeutic toxicity remains a huge challenge for survival rates. A novel iron-dependent form of cell death, ferroptosis, shows potentials in cancer therapy. This study aimed to identify ferroptosis-associated hub genes within a proteinprotein interaction (PPI) network.

METHODS

We screened differential expressed genes (DEGs) in GSE46170 dataset and obtained ferroptosis-related genes from FerrDb database. Through overlapping between DEGs and ferroptosis-related genes, ferroptosis-associated DEGs were identified for further PPI network construction. Molecular complex detection (MCODE) algorithm in Cytoscape was employed to determine tightly connected protein clusters. Chord diagram of Gene Ontology (GO) was generated to reveal the potential biological process of hub genes. Through transfection with siRNA of lipocalin 2 (LCN2) into TALL cells, the regulatory role of LCN2 in ferroptosis was investigated.

RESULTS

Venn diagram identified a total of 37 ferroptosis-associated DEGs between GSE46170 and ferroptosis-associated genes, which were mainly enriched in ferroptosis and necroptosis. Based on PPI network analysis, 5 hub genes (LCN2, LTF, HP, SLC40A1 and TFRC) were found. These hub genes were involved in iron ion transport and could distinguish T-ALL from normal individuals. Further experimental studies demonstrated that LCN2 was highly expressed in T-ALL, while silencing LCN2 promoted RSL3-induced ferroptotic cell death in T-ALL cells.

CONCLUSION

This study identified novel ferroptosis-associated hub genes, which shed new insights into the underlying mechanism of ferroptosis in T-ALL and also provide promising therapeutic targets for T-ALL.

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers

Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common modules were evaluated by several biological metrics adapted to cancer. Then, a new prognostic scoring method was developed by weighting mRNA expression, methylation, and mutation status of genes. The survival analysis pointed out statistically significant results for GNG11, CBX2, CDKN3, ARHGEF10, CLN8, SEC61G and PTDSS1 genes. The literature search reveals that the identified biomarkers are associated with the same or different types of cancers. Our method does not only identify known cancer-specific biomarker genes, but also proposes new potential biomarkers. Thus, this study provides a rationale for identifying new gene targets and expanding treatment options across cancer types.

MicroRNA-455-3p accelerate malignant progression of tumor by targeting H2AFZ in colorectal cancer

Colorectal cancer (CRC) becomes the second leading cause of cancer-related deaths in 2020. Emerging studies have indicated that microRNAs (miRNAs) play a key role in tumorigenesis and progression. The dysfunctions of miR-455-3p are observed in many cancers. However, its biological function in CRC remains to be confirmed. By sequencing serum sample, miR-455-3p was found to be up-regulated in CRC patients. RT-qPCR demonstrated that the miR-455-3p expression was both higher in the serum and tumor tissues of CRC patients. Furthermore, it indicated that miR-455-3p had the ability in promoting cell proliferation, suppressing cell apoptosis, and stimulating cell migration. In vivo experiments also showed that miR-455-3p promoted tumor growth. Additionally, H2AFZ was proved as the direct gene target of miR-455-3p by dual-luciferase assay. Taken together, miR-455-3p functioned as a tumor promoter in CRC development by regulating H2AFZ directly. Thus, it has enormous potential as a biomarker in the diagnosis of CRC.

The Roles of EXO1 and RPA1 Polymorphisms in Prognosis of Lung Cancer Patients Treated with Platinum-Based Chemotherapy

Background. Lung cancer is one of the major causes of cancer-related mortality worldwide. DNA repair and damage response contribute to genomic instability that accompanies tumor progression. In this study, we focus on evaluating association between DNA repair polymorphisms of EXO1, RPA1, and prognosis in lung cancer patients whom received platinum-based chemotherapy. Methods. 593 lung cancer patients were recruited in this study. We performed genotyping of 19 single nucleotide polymorphisms (SNPs) by Sequenom MassARRAY. Cox regression analysis was used to assess overall survival (OS) and progression-free survival (PFS) among SNP genotypes. Results. Significant differences in PFS and OS were observed in RPA1 rs5030740, EXO1 rs1776148, and rs1047840. Results showed that patients with CC genotype in rs5030740 (recessive model: $P=0.034$ ) had a better PFS. Patients with AA or/and AG genotypes in rs1776148 (additive model: $P=0.004$ ; dominant model: $P=0.048$ ) and AA genotype in rs1047840 (recessive model: $P=0.023$ ) had longer OS. We also demonstrated differences in subgroup analysis between rs5030740, rs1776148, rs1047840, and prognosis. Conclusions. Our results indicated that EXO1 rs1776148, rs1047840, and RPA1 rs5030740 were significantly associated with prognosis of lung cancer. Rs1776148, rs1047840, and rs5030740 may act as prognosis markers in lung cancer patients with platinum-based chemotherapy.

Breast cancer-associated SNP rs72755295 is a cis-regulatory variation for human EXO1

Breast cancer is the most common malignant tumor in women. A previous genome-wide association study reports that rs72755295, a SNP locating at intron of EXO1 (exonuclease 1), is associated with breast cancer. Due to the complete linkage disequilibrium between rs72755295 and rs4149909, a nonsynonymous mutation for EXO1, rs4149909 is supposed to be the causal SNP. Since EXO1 is overexpressed in breast carcinoma samples, we hypothesized that the genetic variations in this locus might confer breast cancer risk by regulating EXO1 expression. To substantiate this, a functional genomics study was performed. The dual luciferase assay indicated that G of rs72755295 presents significantly higher relative enhancer activity than A, thus verifying that this SNP can influence gene expression in breast cell. Through chromosome conformation capture it was disclosed that the enhancer containing rs72755295 can interact with the EXO1 promoter. RNA-seq analysis indicated that EXO1 expression is dependent on the rs72755295 genotype. By chromatin immunoprecipitation, the transcription factor PAX6 (paired box 6) was recognized to bind the region spanning rs72755295. In electrophoretic mobility shift assay, G of rs72755295 displays obviously higher binding affinity with nuclear protein than A. Our results indicated that rs72755295 is a cis-regulatory variation for EXO1 and might confer breast cancer risk besides rs4149909.

Downregulation of the enhancer of zeste homolog 1 transcriptional factor predicts poor prognosis of triple-negative breast cancer patients

Background

Triple-negative breast cancer (TNBC) is the most malignant subtype of breast cancer and lacks effective biomarkers. This study seeks to unravel the expression status and the prospective transcriptional mechanisms of EZH1/EZH2 in TNBC tissue samples. Moreover, another objective of this study is to reveal the prognostic molecular signatures for risk stratification in TNBC patients.

Methods

To determine the expression status of EZH1/EZH2 in TNBC tissue samples, microarray analysis and immunohistochemistry were performed on in house breast cancer tissue samples. External mRNA expression matrices were used to verify its expression patterns. Furthermore, the prospective transcriptional mechanisms of EZH1/EZH2 in TNBC were explored by performing differential expression analysis, co-expression analysis, and chromatin immunoprecipitation sequencing analysis. Kaplan-Meier survival analysis and univariate Cox regression analysis were utilized to detect the prognostic molecular signatures in TNBC patients. Nomogram and time-dependent receiver operating characteristic curves were plotted to predict the risk stratification ability of the prognostic-signatures-based Cox model.

Results

In-house TMAs (66 TNBC vs. 106 non-TNBC) and external gene microarrays, as well as RNA-seq datasets (1,135 TNBC vs. 6,198 non-TNBC) results, confirmed the downregulation of EZH1 at both the protein and mRNA levels (SMD = -0.59 [-0.80, -0.37]), as is opposite to that of EZH2 (SMD = 0.74 [0.40, 1.08]). The upregulated transcriptional target genes of EZH1 were significantly aggregated in the cell cycle pathway, where CCNA2, CCNB1, MAD2L1, and PKMYT1 were determined as key transcriptional targets. Additionally, the downregulated transcriptional targets of EZH2 were enriched in response to the hormone, where ESR1 was identified as the hub gene. The six-signature-based prognostic model produced an impressive performance in this study, with a training AUC of 0.753, 0.981, and 0.977 at 3-, 5-, and 10-year survival probability, respectively.

Conclusion

EZH1 downregulation may be a key modulator in the progression of TNBC through negative transcriptional regulation by targeting CCNA2, CCNB1, MAD2L1, and PKMYT1.

Upregulation of EXO1 caused by homology-dependent repair confers cisplatin resistance to gastric cancer cells

The homology-dependent repair (HDR) pathway is involved in DNA damage response (DDR), which is crucial to cancer cell survival after treatment with DNA damage agents, including cisplatin (CDDP). Here, we explored the interactions between EXO1, a core gene in the HDR pathway, and CDDP resistance in gastric cancer (GC). Using bioinformatics analysis, we identified the HDR pathway as the most amplified pathway in DDR in GC. In addition, EXO1 was the core gene in the HDR pathway and showed the most significant amplification in GC. The amplification of EXO1 resulted in higher EXO1 expression in cancerous tissues, with malignant prognostic effects. Moreover, we upregulated or downregulated EXO1 in GC cells to examine its effects on the cell malignant phenotype and CDDP resistance in vitro and in vivo. Depletion of EXO1 inhibited cell proliferatory, migratory and invasive activities, and provided apoptosis resistance to GC cells. EXO1 expression was elevated in CDDP-resistant cells. Ectopic expression of EXO1 increased the resistance of GC cells to CDDP, while downregulation of EXO1 increased the sensitivity of GC cells. Taken together, our study indicates that the HDR pathway is an important player in CDDP resistance in GC through the regulation of EXO1.

Hes-related family BHLH transcription factor with YRPW motif 1-activated proteasome 26S subunit, non-ATPase 14 regulates trophoblast function and endometrial angiogenesis

Proteasome 26S subunit, non-ATPase 14 (PSMD14) expression has been previously reported to be reduced in patients with pre-eclampsia (PE). The present study investigated the interaction network associated with the role of PSMD14 in PE. Reverse transcription-quantitative PCR (RT-qPCR) and western blotting were performed to determine the transfection efficacy following plasmid-based gene transfer of PSMD14 into HTR-8/SVneo cells. Cell proliferation was measured using an MTT assay and 5-ethynyl-2'-deoxyuridine staining. The expression of proliferation-related proteins, including Ki67 and PCNA, was determined using western blotting. Wound healing and Transwell assays were performed to measure cell invasion and migration, whilst the expression of migration-related proteins, including MMP2 and MMP9, was measured using western blotting. The angiogenesis of HUVECs following treatment with the HTR-8/SVneo cell culture supernatant was examined using tube formation assay. Following overexpression of Hes-related family BHLH transcription factor with YRPW motif 1 (HEY1) by transfection of pcDNA3.1 expression vector containing full-length human HEY1 or knockdown by transfection of shRNA plasmids targeting HEY1, the expression of HEY1 and PSMD14 was detected using RT-qPCR and western blotting. The potential interaction between HEY1 and the PSMD14 promoter was examined using dual-luciferase reporter and chromatin immunoprecipitation assays. PSMD14 overexpression was found to promote the proliferation, invasion, migration of HTR-8/SVneo cells and the angiogenesis of HUVECs following treatment with the HTR-8/SVneo cell culture supernatant, accompanied by enhanced expression of proliferation and migration-related proteins. Furthermore, the transcription factor HEY1 activated the expression of PSMD14. Knocking down HEY1 expression partially reversed the promoting effects of PSMD14 overexpression on the proliferation, invasion, migration, angiogenesis, proliferation and migration-related protein expression in trophoblasts. In conclusion, HEY1-activated PSMD14 promoted trophoblast proliferation, invasion and angiogenesis. Therefore, HEY1 and PSMD14 can be potential targets for PE treatment.

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.

A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers

The main hallmarks of cancer include sustaining proliferative signaling and resisting cell death. We analyzed the genes of the WNT pathway and seven cross-linked pathways that may explain the differences in aggressiveness among cancer types. We divided six cancer types (liver, lung, stomach, kidney, prostate, and thyroid) into classes of high (H) and low (L) aggressiveness considering the TCGA data, and their correlations between Shannon entropy and 5-year overall survival (OS). Then, we used principal component analysis (PCA), a random forest classifier (RFC), and protein-protein interactions (PPI) to find the genes that correlated with aggressiveness. Using PCA, we found GRB2, CTNNB1, SKP1, CSNK2A1, PRKDC, HDAC1, YWHAZ, YWHAB, and PSMD2. Except for PSMD2, the RFC analysis showed a different list, which was CAD, PSMD14, APH1A, PSMD2, SHC1, TMEFF2, PSMD11, H2AFZ, PSMB5, and NOTCH1. Both methods use different algorithmic approaches and have different purposes, which explains the discrepancy between the two gene lists. The key genes of aggressiveness found by PCA were those that maximized the separation of H and L classes according to its third component, which represented 19% of the total variance. By contrast, RFC classified whether the RNA-seq of a tumor sample was of the H or L type. Interestingly, PPIs showed that the genes of PCA and RFC lists were connected neighbors in the PPI signaling network of WNT and cross-linked pathways.

Aminoacyl-tRNA synthetases of the multi-tRNA synthetase complex and their role in tumorigenesis

In mammalian cells, 20 aminoacyl-tRNA synthetases (AARS) catalyze the ligation of amino acids to their cognate tRNAs to generate aminoacylated-tRNAs. In higher eukaryotes, 9 of the 20 AARSs, along with 3 auxiliary proteins, join to form the cytoplasmic multi-tRNA synthetase complex (MSC). The complex is absent in prokaryotes, but evolutionary expansion of MSC constituents, primarily by addition of novel interacting domains, facilitates formation of subcomplexes that join to establish the holo-MSC. In some cases, environmental cues direct the release of constituents from the MSC which enables the execution of non-canonical, i.e., "moonlighting", functions distinct from their essential activities in protein translation. These activities are generally beneficial, but can also be deleterious to the cell. Elucidation of the non-canonical activities of several AARSs residing in the MSC suggest they are potential therapeutic targets for cancer, as well as metabolic and neurologic diseases. Here, we describe the role of MSC-resident AARSs in cancer progression, and the factors that regulate their release from the MSC. Also, we highlight recent developments in therapeutic modalities that target MSC AARSs for cancer prevention and treatment.

Bioinformatics Analysis and Experimental Study of Exonuclease 1 Gene in Lung Adenocarcinoma

The objective of this study is to examine the role of Human Exonuclease 1(EXO1) gene in the diagnosis and prognosis of lung adenocarcinoma (LUAD), and predict the signal pathways EXO1 involved in. The clinical parameters and EXO1 expression datasets of LUAD patients were obtained from The Cancer Genome Atlas (TCGA), Oncomine and Gene Expression Omnibus (GEO) database. Wilcoxon rank-sum test was performed to determine whether EXO1 expression was upregulated in LUAD. The correlation between EXO1 expression and clinicopathological parameters was analyzed by Chi-square test, and Kaplan-Meier survival analysis and COX regression models were adopted to analyze and verify the correlation of EXO1 expression with OS of LUAD patients for the exploration of prognostic value of EXO1 in LUAD patients. The signaling pathway related to EXO1 was predicted by gene set enrichment analysis (GSEA). In addition, sera from LUAD patients and healthy subjects were collected, and real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was conducted to detect EXO1 expression. EXO1 expression was upregulated in LUAD patients with respect to normal individuals. EXO1 expression was negatively correlated with the prognosis and thus could independently predict the prognosis of LUAD patients. EXO1 gene was involved in 128 signal pathways, of which 9 pathways may be closely related. EXO1 was highly expressed in the blood of LUAD patients. High EXO1 expression can serve as an independent risk factor for poor prognosis, and the expression of serum EXO1 has certain diagnostic value for LUAD.

Novel Genetic Prognostic Signature for Lung Adenocarcinoma Identified by Differences in Gene Expression Profiles of Low- and High-Grade Histological Subtypes

The 2021 WHO classification proposed a pattern-based grading system for early-stage invasive non-mucinous lung adenocarcinoma. Lung adenocarcinomas with high-grade patterns have poorer outcomes than those with lepidic-predominant patterns. This study aimed to establish genetic prognostic signatures by comparing differences in gene expression profiles between low- and high-grade adenocarcinomas. Twenty-six (9 low- and 17 high-grade adenocarcinomas) patients with histologically “near-pure” patterns (predominant pattern comprising >70% of tumor areas) were selected retrospectively. Using RNA sequencing, gene expression profiles between the low- and high-grade groups were analyzed, and genes with significantly different expression levels between these two groups were selected for genetic prognostic signatures. In total, 196 significant candidate genes (164 upregulated and 32 upregulated in the high- and low-grade groups, respectively) were identified. After intersection with The Cancer Genome Atlas–Lung Adenocarcinoma prognostic genes, three genes, exonuclease 1 (EXO1), family with sequence similarity 83, member A (FAM83A), and disks large-associated protein 5 (DLGAP5), were identified as prognostic gene signatures. Two independent cohorts were used for validation, and the areas under the time-dependent receiver operating characteristic were 0.784 and 0.703 in the GSE31210 and GSE30219 cohorts, respectively. Our result showed the feasibility and accuracy of this novel three-gene prognostic signature for predicting the clinical outcomes of lung adenocarcinoma.

Characterization of Cancer Stem Cell Characteristics and Development of a Prognostic Stemness Index Cell-Related Signature in Oral Squamous Cell Carcinoma

Objective

Cancer stem cells (CSCs) with self-renewal and plasticity contribute to tumor initiation and progression. This study developed an mRNA expression-based stemness index- (mRNAsi-) associated signature and validated biological functions of stem cell-related genes in oral squamous cell carcinoma (OSCC).

Methods

Here, mRNAsi was measured for OSCC samples from TCGA cohort, and prognosis and tumor microenvironment (stromal/immune scores, tumor purity) in high- and low-mRNAsi samples were evaluated with survival analyses and ESTIMATE algorithm. Based on prognostic mRNAsi-related genes, a risk score model was constructed by the LASSO method. The predictive accuracy was evaluated by uni- and multivariate Cox analyses and ROC curves. Among the genes in the model, the functions of H2AFZ on proliferation, apoptosis, invasion, and EMT were investigated in OSCC cells.

Results

High mRNAsi was distinctly associated with undesirable prognosis, increased stromal and immune scores, and lowered tumor purity. The mRNAsi-associated signature containing 11 genes was developed, and high-risk score was distinctly related to poor survival outcomes. Moreover, this signature was an independent and robust risk factor. H2AFZ upregulation significantly enhanced proliferative and invasive capacities and facilitated EMT as well as lowered apoptotic levels in Cal-27 and HSC-3 cells.

Conclusion

Our study characterized cancer stem cell characteristics that were closely related to tumor microenvironment and developed a stemness index cell-related signature that could assist prognosis prediction and risk stratification for OSCC. H2AFZ could become a potential therapeutic target against OSCC.

Prognostic Value Estimation of BRIP1 in Breast Cancer by Exploiting Transcriptomics Data Through Bioinformatics Approaches

BRIP1 (Breast Cancer 1 Interacting Helicase 1) is a tumor suppressor gene that has vital function in preserving the genetic stability by repairing DNA damage though have significant associations with the onset of breast cancer (BC) if mutated or overexpressed. In this study, the prognostic value of BRIP1 gene was evaluated and validated through bioinformatics approaches utilizing transcriptomic (mRNA expression) data from several BC databases. To determine the prognostic value, the expression level of mRNA transcript was analyzed in context of comparison between breast tumor and normal tissues regarding clinical features, breast tumor subtypes, promoter methylation status, correlation level, mutation frequency, and survival of BC patients. BRIP1 expression was found to be significantly overexpressed in various BC molecular subtypes (e.g. PAM50, Sorlie’s) and clinical status (estrogen and progesterone receptor) than associated normal tissues which correlated with prognosis. Also, in promoter methylation level, its expression was observed as upregulated-hypomethylated regarding various clinicopathological features. Multiple data mining exhibited positive correlation between BRIP1 and INTS2 (Integrator Complex Subunit 2) expressions in BC. Further, mutation analysis revealed that BRIP1 gene was altered by acquiring both somatic and germline mutations. In addition, a total of 42 mutations; 24 missense, 8 fusion, 7 truncating, and 3 inframe mutations in BC patients was detected in BRIP1 protein. Moreover, higher BRIP1 expression was found to be correlated with poor disease-specific, disease metastasis-free, relapse-free, and overall survivals of BC patients. Since, overexpression of BRIP1 was identified to be associated with different clinical features, breast tumor subtypes, promoter methylation status, and survival of BC patients that may provide a risk of ensuing malignant transformation. Thus, lower expression of BRIP1 might hinder BC prognosis. We consider that this analysis will present a proof for BRIP1 gene to be a noteworthy molecular biomarker for BC prognosis.

Periostin gene expression in neu-positive breast cancer cells is regulated by a FGFR signaling cross talk with TGFβ/PI3K/AKT pathways

Background

Breast cancer is a highly heterogeneous disease with multiple drivers and complex regulatory networks. Periostin (Postn) is a matricellular protein involved in a plethora of cancer types and other diseases. Postn has been shown to be involved in various processes of tumor development, such as angiogenesis, invasion, cell survival and metastasis. The expression of Postn in breast cancer cells has been correlated with a more aggressive phenotype. Despite extensive research, it remains unclear how epithelial cancer cells regulate Postn expression.

Methods

Using murine tumor models and human TMAs, we have assessed the proportion of tumor samples that have acquired Postn expression in tumor cells. Using biochemical approaches and tumor cell lines derived from Neu+ murine primary tumors, we have identified major regulators of Postn gene expression in breast cancer cell lines.

Results

Here, we show that, while the stromal compartment typically always expresses Postn, about 50% of breast tumors acquire Postn expression in the epithelial tumor cells. Furthermore, using an in vitro model, we show a cross-regulation between FGFR, TGFβ and PI3K/AKT pathways to regulate Postn expression. In HER2-positive murine breast cancer cells, we found that basic FGF can repress Postn expression through a PKC-dependent pathway, while TGFβ can induce Postn expression in a SMAD-independent manner. Postn induction following the removal of the FGF-suppressive signal is dependent on PI3K/AKT signaling.

Conclusion

Overall, these results reveal a novel regulatory mechanism and shed light on how breast tumor cells acquire Postn expression. This complex regulation is likely to be cell type and cancer specific as well as have important therapeutic implications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-021-01487-8.

CDC6: A novel canine tumour biomarker detected in circulating extracellular vesicles

Circulating nucleic acids and extracellular vesicles (EV) represent novel biomarkers to diagnose cancer. The non‐invasive nature of these so‐called liquid biopsies provides an attractive alternative to tissue biopsy‐based cancer diagnostics. This study aimed to investigate if circulating cell cycle‐related E2F target transcripts can be used to diagnose tumours in canine tumour patients with different types of tumours. Furthermore, we assessed if these mRNAs are localised within circulating EV. We isolated total RNA from the plasma of 20 canine tumour patients and 20 healthy controls. Four E2F target genes (CDC6, DHFR, H2AFZ and ATAD2) were selected based on the analysis of published data of tumour samples available in public databases. We performed reverse transcription and quantitative real‐time PCR to analyse the plasma levels of selected E2F target transcripts. All four E2F target transcripts were detectable in the plasma of canine tumour patients. CDC6 mRNA levels were significantly higher in the plasma of canine tumour patients compared to healthy controls. A subset of canine tumour patient and healthy control plasma samples (n = 7) were subjected to size exclusion chromatography in order to validate association of the E2F target transcripts to circulating EV. For CDC6, EV analysis enhanced their detectability compared to total plasma analysis. In conclusion, our study reveals circulating CDC6 as a promising non‐invasive biomarker to diagnose canine tumours.

Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis

Liver cancer is one of the most common cancers and the top leading cause of cancer death globally. However, the molecular mechanisms of liver tumorigenesis and progression remain unclear. In the current study, we investigated the hub genes and the potential molecular pathways through which these genes contribute to liver cancer onset and development. The weighted gene co-expression network analysis (WCGNA) was performed on the main data attained from the GEO (Gene Expression Omnibus) database. The Cancer Genome Atlas (TCGA) dataset was used to evaluate the association between prognosis and these hub genes. The expression of genes from the black module was found to be significantly related to liver cancer. Based on the results of protein-protein interaction, gene co-expression network, and survival analyses, DNA topoisomerase II alpha (TOP2A), ribonucleotide reductase regulatory subunit M2 (RRM2), never in mitosis-related kinase 2 (NEK2), cyclin-dependent kinase 1 (CDK1), and cyclin B1 (CCNB1) were identified as the hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the differentially expressed genes (DEGs) were enriched in the immune-associated pathways. These hub genes were further screened and validated using statistical and functional analyses. Additionally, the TOP2A, RRM2, NEK2, CDK1, and CCNB1 proteins were overexpressed in tumor liver tissues as compared to normal liver tissues according to the Human Protein Atlas database and previous studies. Our results suggest the potential use of TOP2A, RRM2, NEK2, CDK1, and CCNB1 as prognostic biomarkers in liver cancer.

Prediction of Disease Genes Based on Stage-Specific Gene Regulatory Networks in Breast Cancer

Breast cancer is one of the most common malignant tumors in women, which seriously endangers women’s health. Great advances have been made over the last decades, however, most studies predict driver genes of breast cancer using biological experiments and/or computational methods, regardless of stage information. In this study, we propose a computational framework to predict the disease genes of breast cancer based on stage-specific gene regulatory networks. Firstly, we screen out differentially expressed genes and hypomethylated/hypermethylated genes by comparing tumor samples with corresponding normal samples. Secondly, we construct three stage-specific gene regulatory networks by integrating RNA-seq profiles and TF-target pairs, and apply WGCNA to detect modules from these networks. Subsequently, we perform network topological analysis and gene set enrichment analysis. Finally, the key genes of specific modules for each stage are screened as candidate disease genes. We obtain seven stage-specific modules, and identify 20, 12, and 22 key genes for three stages, respectively. Furthermore, 55%, 83%, and 64% of the genes are associated with breast cancer, for example E2F2, E2F8, TPX2, BUB1, and CKAP2L. So it may be of great importance for further verification by cancer experts.

Highly heterogeneous-related genes of triple-negative breast cancer: potential diagnostic and prognostic biomarkers

Background

Triple-negative breast cancer (TNBC) is a highly heterogeneous subtype of breast cancer, showing aggressive clinical behaviors and poor outcomes. It urgently needs new therapeutic strategies to improve the prognosis of TNBC. Bioinformatics analyses have been widely used to identify potential biomarkers for facilitating TNBC diagnosis and management.

Methods

We identified potential biomarkers and analyzed their diagnostic and prognostic values using bioinformatics approaches. Including differential expression gene (DEG) analysis, Receiver Operating Characteristic (ROC) curve analysis, functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, survival analysis, multivariate Cox regression analysis, and Non-negative Matrix Factorization (NMF).

Results

A total of 105 DEGs were identified between TNBC and other breast cancer subtypes, which were regarded as heterogeneous-related genes. Subsequently, the KEGG enrichment analysis showed that these genes were significantly enriched in ‘cell cycle’ and ‘oocyte meiosis’ related pathways. Four (FAM83B, KITLG, CFD and RBM24) of 105 genes were identified as prognostic signatures in the disease-free interval (DFI) of TNBC patients, as for progression-free interval (PFI), five genes (FAM83B, EXO1, S100B, TYMS and CFD) were obtained. Time-dependent ROC analysis indicated that the multivariate Cox regression models, which were constructed based on these genes, had great predictive performances. Finally, the survival analysis of TNBC subtypes (mesenchymal stem-like [MSL] and mesenchymal [MES]) suggested that FAM83B significantly affected the prognosis of patients.

Conclusions

The multivariate Cox regression models constructed from four heterogeneous-related genes (FAM83B, KITLG, RBM24 and S100B) showed great prediction performance for TNBC patients’ prognostic. Moreover, FAM83B was an important prognostic feature in several TNBC subtypes (MSL and MES). Our findings provided new biomarkers to facilitate the targeted therapies of TNBC and TNBC subtypes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08318-1.

Exonuclease 1 (EXO1) is a Potential Prognostic Biomarker and Correlates with Immune Infiltrates in Lung Adenocarcinoma

Background

Exonuclease 1 (EXO1) has been identified to be highly expressed in different human malignancies, but its expression and prognostic role in lung adenocarcinoma (LUAD) remain unknown.

Materials and Methods

Two independent cohorts extracted from public databases and one cohort from our center were analyzed in this study. Expression levels of EXO1 in LUAD tissues and paired para-cancer tissues were detected. The prognostic value of EXO1 in LUAD patients was evaluated in the three cohorts. Enrichment analyses were performed to explore the possible underlying biological pathways. Moreover, we also explored the correlations between EXO1 and tumor-infiltrating immune cells and evaluated the impact of EXO1 knock-down on the migration of lung cancer cells.

Results

In this study, we found that EXO1 was highly expressed in LUAD tissues compared with para-cancerous tissues in public databases (p < 0.01), which was consistent with our data (p < 0.01). Survival analysis indicated that high expression of EXO1 was associated with poor prognosis in LUAD (p < 0.01). Enrichment analyses indicated that biological pathways like cell cycle regulation, DNA damage and repair, immune response, neuroactive ligand-receptor interaction, may be associated with EXO1 aberrant expression. Moreover, high expression of EXO1 was correlated with decreased infiltrating B cells (p < 0.01) and CD4+ T cells (p < 0.01) levels, and low infiltrating levels of B cells (p < 0.01) and dendritic cells (DCs) (p < 0.05) indicated poor overall survival (OS) in LUAD. Additionally, in vitro experiments suggested that knockdown of EXO1 may inhibit the migratory ability of lung cancer cells.

Conclusion

In conclusion, EXO1 is a potential prognostic biomarker in LUAD, and correlates with infiltrating levels of immune cells in the tumor microenvironment. Further prospective validation of EXO1 in lung cancer is warranted.

Key Genes and Prognostic Analysis in HER2+ Breast Cancer

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes (CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

Abnormal promoter DNA hypermethylation of the integrin, nidogen, and dystroglycan genes in breast cancer

Cell transmembrane receptors and extracellular matrix components play a pivotal role in regulating cell activity and providing for the concerted integration of cells in the tissue structures. We have assessed DNA methylation in the promoter regions of eight integrin genes, two nidogen genes, and the dystroglycan gene in normal breast tissues and breast carcinomas (BC). The protein products of these genes interact with the basement membrane proteins LAMA1, LAMA2, and LAMB1; abnormal hypermethylation of the LAMA1, LAMA2, and LAMB1 promoters in BC has been described in our previous publications. In the present study, the frequencies of abnormal promoter hypermethylation in BC were 13% for ITGA1, 31% for ITGA4, 4% for ITGA7, 39% for ITGA9, 38% for NID1, and 41% for NID2. ITGA2, ITGA3, ITGA6, ITGB1, and DAG1 promoters were nonmethylated in normal and BC samples. ITGA4, ITGA9, and NID1 promoter hypermethylation was associated with the HER2 positive tumors, and promoter hypermethylation of ITGA1, ITGA9, NID1 and NID2 was associated with a genome-wide CpG island hypermethylated BC subtype. Given that ITGA4 is not expressed in normal breast, one might suggest that its abnormal promoter hypermethylation in cancer is non-functional and is thus merely a passenger epimutation. Yet, this assumption is not supported by our finding that it is not associated with a hypermethylated BC subtype. ITGA4 acquires expression in a subset of breast carcinomas, and methylation of its promoter may be preventive against expression in some tumors. Strong association of abnormal ITGA4 hypermethylation with the HER2 positive tumors (p = 0.0025) suggests that simultaneous presence of both HER2 and integrin α4 receptors is not beneficial for tumor cells. This may imply HER2 and integrin α4 signaling pathways interactions that are yet to be discovered.

Identification of potential prognostic biomarkers for breast cancer using WGCNA and PPI integrated techniques

In this study, we aimed to detect promising prognostic factors of breast cancer and interpreted the relevant mechanisms using an integrated bioinformatics analysis. RNA sequencing profile of breast cancer was downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, which were combined as a group (TCGA_GTEx). GSE70947 dataset was from Gene Expression Omnibus. Blue and turquoise modules, respectively identified in TCGA_GTEx database and GSE70947 dataset using weighted co-expression network analysis (WGCNA), were both notably associated with breast cancer. By comparing genes in the two significant modules with differentially expressed genes (DEGs), we obtained a set of 40 shared genes, which were mainly enriched in chromosome segregation and mismatch repair pathway. After protein-protein interaction (PPI) network and overall survival analysis, two hub genes EXO1 and KIF4A were extracted from the set of 40 shared genes, which were up-regulated and associated with the dismal outcome of breast cancer patients. There was a notable negative correlation between EXO1 and KIF4A expression and age of breast cancer patients, whereas a positive relationship with two another clinical traits stage and tumor category was detected. Univariate and multivariate Cox regression analysis revealed that the two hub genes could be independent prognostic factors of breast cancer. Mechanistically, gene correlation analysis suggested that EXO1 and KIF4A exerted their oncogenic role via promoting breast cancer cell proliferation. Overall, our findings identify two promising individual prognostic predictors of breast cancer and pave the new way for diagnosis and therapy strategy of breast cancer.

Roles of Aminoacyl-tRNA Synthetases in Cancer

Aminoacyl-tRNA synthetases (ARSs) catalyze the ligation of amino acids to their cognate transfer RNAs (tRNAs), thus playing an important role in protein synthesis. In eukaryotic cells, these enzymes exist in free form or in the form of multi-tRNA synthetase complex (MSC). The latter contains nine cytoplasmic ARSs and three ARS-interacting multifunctional proteins (AIMPs). Normally, ARSs and AIMPs are regarded as housekeeping molecules without additional functions. However, a growing number of studies indicate that ARSs are involved in a variety of physiological and pathological processes, especially tumorigenesis. Here, we introduce the roles of ARSs and AIMPs in certain cancers, such as colon cancer, lung cancer, breast cancer, gastric cancer and pancreatic cancer. Furthermore, we particularly focus on their potential clinical applications in cancer, aiming at providing new insights into the pathogenesis and treatment of cancer.

Identification of potential target genes and crucial pathways in small cell lung cancer based on bioinformatic strategy and human samples

Small cell lung cancer (SCLC) is a carcinoma of the lungs with strong invasion, poor prognosis and resistant to multiple chemotherapeutic drugs. It has posed severe challenges for the effective treatment of lung cancer. Therefore, searching for genes related to the development and prognosis of SCLC and uncovering their underlying molecular mechanisms are urgent problems to be resolved. This study is aimed at exploring the potential pathogenic and prognostic crucial genes and key pathways of SCLC via bioinformatic analysis of public datasets. Firstly, 117 SCLC samples and 51 normal lung samples were collected and analyzed from three gene expression datasets. Then, 102 up-regulated and 106 down-regulated differentially expressed genes (DEGs) were observed. And then, functional annotation and pathway enrichment analyzes of DEGs was performed utilizing the FunRich. The protein-protein interaction (PPI) network of the DEGs was constructed through the STRING website, visualized by Cytoscape. Finally, the expression levels of eight hub genes were confirmed in Oncomine database and human samples from SCLC patients. It showed that CDC20, BUB1, TOP2A, RRM2, CCNA2, UBE2C, MAD2L1, and BUB1B were upregulated in SCLC tissues compared to paired adjacent non-cancerous tissues. These suggested that eight hub genes might be viewed as new biomarkers for prognosis of SCLC or to guide individualized medication for the therapy of SCLC.

Biological and clinical significance of flap endonuclease‑1 in triple‑negative breast cancer: Support of metastasis and a poor prognosis

Flap endonuclease‑1 (FEN1), a structure‑specific nuclease participating in DNA replication and repair processes, has been confirmed to promote the proliferation and drug resistance of tumor cells. However, the biological functions of FEN1 in cancer cell migration and invasion have not been defined. In the present study, using online database analysis and immunohistochemistry of the specimens, it was found that FEN1 expression was associated with a highly invasive triple‑negative breast cancer (TNBC) subtype in both breast cancer samples from the Oncomine database and from patients recruited into the study. Furthermore, FEN1 was an important biomarker of lymph node metastasis and poor prognosis in patients with TNBC. FEN1 promoted migration of TNBC cell lines and FEN1 knockdown reduced the number of spontaneous lung metastasis in vivo. Ingenuity Pathway Analysis of FEN1‑related transcripts in 198 patients with TNBC demonstrated that the polo‑like kinase family may be the downstream target of FEN1. PLK4 was further identified as a critical target of FEN1 mediating TNBC cell migration, by regulating actin cytoskeleton rearrangement. The results of the present study validate FEN1 as a therapeutic target in patients with TNBC and revealed a new role for FEN1 in regulating TNBC invasion and metastasis.

Genome-Wide Analysis of Methylation-Driven Genes and Identification of an Eight-Gene Panel for Prognosis Prediction in Breast Cancer

BACKGROUND

Aberrant DNA methylation is a crucial epigenetic regulator that is closely related to the occurrence and development of various cancers, including breast cancer (BC). The present study aimed to identify a novel methylation-based prognosis biomarker panel by integrally analyzing gene expression and methylation patterns in BC patients.

METHODS

DNA methylation and gene expression data of breast cancer (BRCA) were downloaded from The Cancer Genome Atlas (TCGA). R packages, including ChAMP, SVA, and MethylMix, were applied to identify the unique methylation-driven genes. Subsequently, these genes were subjected to Metascape for GO analysis. Univariant Cox regression was used to identify survival-related genes among the methylation-driven genes. Robust likelihood-based survival modeling was applied to define the prognosis markers. An independent data set (GSE72308) was used for further validation of our risk score system.

RESULTS

A total of 879 DNA methylation-driven genes were identified from 765 BC patients. In the discovery cohort, we identified 50 survival-related methylation-driven genes. Finally, we built an eight-methylation-driven gene panel that serves as prognostic predictors.

CONCLUSIONS

Our analysis of transcriptome and methylome variations associated with the survival status of BC patients provides a further understanding of basic biological processes and a basis for the genetic etiology in BC.

Estrogen-regulated PTTG1 promotes breast cancer progression by regulating cyclin kinase expression

BACKGROUND

The present study aims to investigate the effects of pituitary tumor transforming gene (PTTG) 1 on breast cancer and its underlying mechanism.

METHODS

GEO data set was applied to analyze the relationship between PTTG1 and survival status and the TCGA breast cancer dataset was used to explore its possible targets. The stable cell lines including PTTG1 knockdown cells, estrogen receptor (ESR) 1 knockdown cells, and PTTG1 overexpression cells were constructed. MTT was used to determine cell viabilities. Propidium iodide (PI) staining and flow cytometry were used to analyze the cell cycle. Quantitative polymerase chain reaction (qPCR) was employed to determine the mRNA expressions. Points mutations and luciferase reporter assays were used to determine the binding sites of estrogen.

RESULTS

PTTG1 was associated with poor survival rates in breast cancer. In vitro study demonstrated that PTTG1 affected cell viabilities of MCF7 and T47D cells. Besides, PTTG1 affected cell cycle arrest of breast cancer cells. Overexpression of PTTG1 led to more breast cancer cells distributed in S phase. The levels of PTTG1 were associated with estrogen and further results showed that the levels of PTTG1 were positively correlated to tamoxifen resistance. Two genes including CCNA2 and CCNB2 were identified to be possible targets of PTTG1.

CONCLUSION

Estrogen-regulated PTTG1 promotes the development of breast cancer cells by the regulation of the cell cycle.

Potential mechanism of RRM2 for promoting Cervical Cancer based on weighted gene co-expression network analysis

Cervical cancer is the most common gynecologic malignant tumor, with a high incidence in 50-55-year-olds. This study aims to investigate the potential molecular mechanism of RRM2 for promoting the development of cervical cancer based on The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). RRM2 was found to be significant upregulated in cervical tissue (P<0.05) by extracting the expression of RRM2 from TCGA, GSE63514, GSE7410, GSE7803 and GSE9750. Survival analysis indicated that the overall survival was significantly worse in the patients with high-expression of RRM2 (P<0.05). The top 1000 positively/negatively correlated genes with RRM2 by Pearson Correlation test were extracted. The gene co-expression network by Weighted Gene Co-Expression Network Analysis (WGCNA) with these genes and the clinical characteristics (lymphocyte infiltration, monocyte infiltration, necrosis, neutrophil infiltration, the number of normal/stromal/tumor cells and the number of tumor nuclei) was constructed. By screening the hub nodes from the co-expression network, results suggested that RRM2 may co-express with relevant genes to regulate the number of stromal/tumor cells and the process of lymphocyte infiltration to promote the progression of cervical cancer. RRM2 is likely to become a novel potential diagnostic and prognostic biomarker of cervical cancer and provide evidence to support the study of mechanisms for cervical cancer.

miR-140-3p impedes the proliferation of human cervical cancer cells by targeting RRM2 to induce cell-cycle arrest and early apoptosis

Cervical cancer is a critically malignant tumor with the second mortality of females worldwide. MicroRNAs (miRNAs) are short but regulatory non-coding RNAs playing a pivotal role in many biological processes including tumorigenesis. However, the exact role of miR-140-3p in cervical cancer remains to be elucidated. Here we identified that miR-140-3p was significantly reduced in cervical cancer tissues by comprehensive analysis of TCGA data, hinting that higher expression level of miR-140-3p predicted a good clinical prognosis. Quantitative real-time PCR (RT-qPCR) assay was performed to confirm the negative correlation between miR-140-3p expression level and human cervical cancer tissues as well as various cervical cancer cell lines. To clarify the certain role of miR-140-3p, forced expression by microRNA mimics was applied in Caski and C33A cells, showing that miR-140-3p overexpression significantly impeded the proliferation of cervical cancer cells by cell count kit (CCK-8) assay. Western blot analysis of cell cycle-related proteins Cyclin A, Cyclin B1 and Cyclin D1 have further confirmed the cell cycle arrest was induced by the ectopic expression of miR-140-3p. Annexin-V based FACS analysis also found the simultaneous appearance of early apoptotic cell population in miR-140-3p overexpression cells. The protein level of BCL-2 was attenuated in accompany with elevated Bax and Cleaved caspase-3 protein, indicating miR-140-3p overexpression induced early apoptosis. Mechanistically, we demonstrated that miR-140-3p could target the 3'UTR of RRM2 which has been proved to be highly involved in the onset of cancer. Furthermore, upregulation of miR-140-3p and RRM2 failed to inhibit the proliferation of human cervical cancer cells, revealing that RRM2 served as the target downstream gene of miR-140-3p abolishing its ability as a tumor suppressor. Overall, we figured out the new role of miR-140-3p in cervical cancer and concluded that miR-140-3p was a candidate of cancer control in preclinical.