High EIF2B5 mRNA expression and its prognostic significance in liver cancer: a study based on the TCGA and GEO database

Noninvasive prognostication of hepatocellular carcinoma based on cell-free DNA methylation

BACKGROUND

The current noninvasive prognostic evaluation methods for hepatocellular carcinoma (HCC), which are largely reliant on radiographic imaging features and serum biomarkers such as alpha-fetoprotein (AFP), have limited effectiveness in discriminating patient outcomes. Identification of new prognostic biomarkers is a critical unmet need to improve treatment decision-making. Epigenetic changes in cell-free DNA (cfDNA) have shown promise in early cancer diagnosis and prognosis. Thus, we aim to evaluate the potential of cfDNA methylation as a noninvasive predictor for prognostication in patients with active, radiographically viable HCC.

METHODS

Using Illumina HumanMethylation450 array data of 377 HCC tumors and 50 adjacent normal tissues obtained from The Cancer Genome Atlas (TCGA), we identified 158 HCC-related DNA methylation markers associated with overall survival (OS). This signature was further validated in 29 HCC tumor tissue samples. Subsequently, we applied the signature to an independent cohort of 52 patients with plasma cfDNA samples by calculating the cfDNA methylation-based risk score (methRisk) via random survival forest models with 10-fold cross-validation for the prognostication of OS.

RESULTS

The cfDNA-based methRisk showed strong discriminatory power when evaluated as a single predictor for OS (3-year AUC = 0.81, 95% CI: 0.68-0.94). Integrating the methRisk with existing risk indices like Barcelona clinic liver cancer (BCLC) staging significantly improved the noninvasive prognostic assessments for OS (3-year AUC = 0.91, 95% CI: 0.80-1), and methRisk remained an independent predictor of survival in the multivariate Cox model (P = 0.007).

CONCLUSIONS

Our study serves as a pilot study demonstrating that cfDNA methylation biomarkers assessed from a peripheral blood draw can stratify HCC patients into clinically meaningful risk groups. These findings indicate that cfDNA methylation is a promising noninvasive prognostic biomarker for HCC, providing a proof-of-concept for its potential clinical utility and laying the groundwork for broader applications.

EIF2B5 promotes malignant progression of hepatocellular carcinoma by activating the PI3K/AKT signaling pathway through targeting RPL6

Hepatocellular carcinoma (HCC) is a highly aggressive malignancy with limited treatment options and poor prognosis. In this study, we demonstrated the critical role of EIF2B5 in driving HCC progression. We found EIF2B5 expression is significantly upregulated in HCC tumor tissues in several bioinformatics datasets, including The Cancer Genome Atlas, and that high expression of EIF2B5 predicts poor prognosis for HCC patients. Through a series of in vitro cell biology experiments, we found that EIF2B5 knockdown significantly attenuated Hep3B and HepG2 proliferation, migration, and invasion and increased cell cycle arrest, whereas EIF2B5 overexpression promoted HCC progression. Through mass spectrometry and immunoprecipitation validation, we found that EIF2B5 directly interacted with RPL6 and that when EIF2B5 was overexpressed in HCC cells, it promoted the expression of the downstream protein RPL6, which was able to activate the phosphatidylinositol kinase (PI3K)/serine-threonine kinase (AKT)/mammalian target of rapamycin (mTOR) pathway and thereby increase the proliferation and invasion ability of HCC cell lines, as verified by second-generation sequencing analysis and western blot. We further verified these findings using the mouse ectopic tumor assay, and the results showed that EIF2B5 knockdown significantly inhibited tumor progression in HCC mice. The present study suggests that EIF2B5 promotes malignant progression of HCC by interacting with RPL6 and activating the PI3K/AKT/mTOR signaling pathway and may serve as a potential target for the treatment of HCC.

Druggable genome-wide Mendelian randomization for identifying the role of integrated stress response in therapeutic targets of bipolar disorder

For bipolar disorder (BD), the inconsistency of treatment guidelines and the long phases of pharmacological adjustment remain major challenges. BD is known to be comorbid with many medical and psychiatric conditions and they may share inflammatory and stress-related aetiologies, which could give rise to this association. The integrated stress response (ISR) responds to various stress conditions that lead to alterations in cellular homeostasis. However, as a causative mechanism underlying cognitive deficits and neurodegeneration in a broad range of brain disorders, the impact of ISR on BD is understudied. Mendelian randomization has been widely used to repurpose licensed drugs and discover novel therapeutic targets. Thus, we aimed to identify novel therapeutic targets for BD and analyze their pathophysiological mechanisms, using the summary data-based Mendelian Randomization (SMR) and Bayesian colocalization (COLOC) methods to integrate the summary-level data of the GWAS on BD and the expression quantitative trait locus (eQTL) study in blood. We utilized the GWAS data including 41,917 BD cases and 371,549 controls from the Psychiatric Genomics Consortium and the eQTL data from 31,684 participants of predominantly European ancestry from the eQTLGen consortium. The SMR analysis identified the EIF2B5 gene that was associated with BD due to no linkage but pleiotropy or causality. The COLOC analysis strongly suggested that EIF2B5 and the trait of BD were affected by shared causal variants, and thus were colocalized. Utilizing data in EpiGraphDB we find other putative causal BD genes (EIF2AK4 and GSK3B) to prioritize potential alternative drug targets.

Effect of Silymarin on Expression of micro-RNA-21 and Matrix Metalloproteinase (MMP) 2 and 9 and Tissue Inhibitors of Matrix Metalloproteinase (TIMP) 1 and 2 in Hepatocellular Carcinoma Cell Line (HepG2)

Background

Silymarin is a flavonolignan that has various medicinal properties such as liver protection, antioxidant, anti-inflammatory, anti-cancer and heart protection activities. The aim of this study was to investigate the effect of silymarin on the expression level of mir-21, matrix metalloproteinase(MMP), and their tissue inhibitors (TIMPs) in liver cancer HepG2 cell line.

Methods

An in-vitro experimental study was conducted on the human HepG2 cells prepared from Pasteur Institute, Tehran, Iran. Four concentrations of 0 (control), 50, 100, and 150 µM of silymarin were considered as the study groups according to the MTT assay. Gene expression study was performed using real-time PCR. The studied genes were mir-21, MMP-2, MMP-9, TIMP-1 and TIMP-2. In addition, some apoptosis-related genes including BAX, BCL2 and Caspase3 (CAS3) were investigated. GAPDH was used as an internal control. Relative expression was calculated by REST program using t-test on the logarithm of expression considering a significance level of 0.05.

Results

The significant up-regulations consisted of TIMP genes for doses 100 µM and 150 µM, and the apoptosis activating genes CAS3 and BAX (P < 0.05). The significant down-regulations consisted of MMP-9 in all concentrations, MMP-2 in concentration 100 µM, and the apoptosis inhibitory gene BCL2 in concentrations 50 µM and 100 µM (P < 0.05). In addition, mir-21 as an oncogenic micro-RNA showed significant down-regulation for all doses (P < 0.05). All the comparisons were with the control group.

Conclusion

The present study showed that silymarin could affect the HepG2 cell line at the gene expression level via increasing apoptosis and changing the expression of MMP-2, MMP-9, TIMP-1, TIMP-2 and mir-21. These findings were in line with each other and in favor of suppression of tumoral activity in this cell line.

Overexpression of Aurora Kinase B Is Correlated with Diagnosis and Poor Prognosis in Hepatocellular Carcinoma

Aurora kinase B (AURKB) overexpression promotes tumor initiation and development by participating in the cell cycle. In this study, we focused on the mechanism of AURKB in hepatocellular carcinoma (HCC) progression and on AURKB's value as a diagnostic and prognostic biomarker in HCC. We used data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) to analyze AURKB expression in HCC. We found that the expression levels of AURKB in HCC samples were higher than those in the corresponding control group. R packages were used to analyze RNA sequencing data to identify AURKB-related differentially expressed genes (DEGs), and these genes were found to be significantly enriched during the cell cycle. The biological function of AURKB was verified, and the results showed that cell proliferation was slowed down and cells were arrested in the G2/M phase when AURKB was knocked down. AURKB overexpression resulted in significant differences in clinical symptoms, such as the clinical T stage and pathological stage. Kaplan-Meier survival analysis, Cox regression analysis, and Receiver Operating Characteristic (ROC) curve analysis suggested that AURKB overexpression has good diagnostic and prognostic potential in HCC. Therefore, AURKB may be used as a potential target for the diagnosis and cure of HCC.

Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

Glioblastoma multiforme (GBM) patients show a variety of signs and symptoms that affect their quality of life (QOL) and self-dependence. Since most existing studies have examined prognostic factors based only on clinical factors, there is a need to consider the value of integrating multi-omics data including gene expression and proteomics with clinical data in identifying significant biomarkers for GBM prognosis. Our research aimed to isolate significant features that differentiate between short-term (≤ 6 months) and long-term (≥ 2 years) GBM survival, and between high Karnofsky performance scores (KPS ≥ 80) and low (KPS ≤ 60), using the iterative random forest (iRF) algorithm. Using the Cancer Genomic Atlas (TCGA) database, we identified 35 molecular features composed of 19 genes and 16 proteins. Our findings propose molecular signatures for predicting GBM prognosis and will improve clinical decisions, GBM management, and drug development.

Local production of active vitamin D3 metabolites in breast cancer cells by CYP24A1 and CYP27B1

The role of vitamin D3 and its metabolites in cancer and especially as a treatment option has been widely disputed. Clinicians noting low serum 25-hydroxyvitamin D3 [25(OH)D3] levels in their patients, recommend vitamin D3 supplementation as a method of reducing the risk of cancer; however, data supporting this are inconsistent. These studies rely on systemic 25(OH)D3 as an indicator of hormone status, but 25(OH)D3 is further metabolized in the kidney and other tissues under regulation by several factors. This study examined if breast cancer cells also possess the ability to metabolize 25(OH)D3, and if so, whether the resulting metabolites are secreted locally; if this ability reflects ERα66 status; and if they possess vitamin D receptors (VDR). To address this question, estrogen receptor alpha (ERα) positive (MCF-7) and ERα negative (HCC38 and MDA-MB-231) breast cancer cell lines were examined for expression of ERα66, ERα36, CYP24A1, CYP27B1, and VDR as well as for local production of 24,25-dihydroxyvitamin D3 [24,25(OH)2D3] and 1,25-dihydroxyvitamin D3 [1,25(OH)2D3] after treatment with 25(OH)D3. The results showed that independent of ER status, breast cancer cells express the enzymes CYP24A1 and CYP27B1, which are responsible for converting 25(OH)D3 into its dihydroxylated forms. Moreover, these metabolites are produced at levels comparable to the levels observed in blood. They are positive for VDR, indicating that they can respond to 1α,25(OH)2D3, which can upregulate CYP24A1. These findings suggest that vitamin D metabolites may contribute to the tumorigenicity of breast cancer via autocrine and/or paracrine mechanisms.

The Prognostic Value of ADAMTS8 and Its Role as a Tumor Suppressor in Breast Cancer

A disintegrin-like and metalloprotease with therombospondin type1 motif 8 (ADAMTS8) plays an important role in many malignancies. However, the clinical and biological significance of ADAMTS8 in breast cancer remain unknown. In this study, the clinical data from 1066 breast cancer patients were analyzed by The Cancer Genome Atlas (TCGA) database, and were analyzed using the correlation between ADAMTS8 expression and the clinicopathological features and prognoses. The CCK-8 assay, clone formation assay, flow cytometry and Transwell assay were used to characterize the effects of ADAMTS8 on proliferation, migration and invasion of breast cancer cells. Gene set enrichment analysis (GSEA) and western blotting were used to identify the potential molecular mechanism on how ADAMTS8 exert its biological function. ADAMTS8 overexpression correlated longer overall survival (OS) and progression-free survival (PFS). ADAMTS8 was considered as an independent prognostic factor for OS. ADAMTS8 overexpression inhibited breast cancer cell proliferation, migration and invasion in vitro, and induced G2/M cell cycle arrest. ADAMTS8 was also involved in cell cycle regulation and was associated with the EGFR/Akt signaling pathway. ADAMTS8 knockdown showed the reverse effect. Together, the results showed that ADAMTS8 functioned as a tumor suppressor gene (TGS) and could be a prognostic biomarker for breast cancer.

LncRNA KCNQ1OT1 promotes the metastasis of ovarian cancer by increasing the methylation of EIF2B5 promoter

Background

Long non-coding RNAs (lncRNAs) have emerged as regulators of human malignancies, including ovarian cancer (OC). LncRNA KCNQ1OT1 could promote OC progression, and EIF2B5 was associated with development of several tumors. This project was aimed to explore the role of lncRNA KCNQ1OT1 in OC development, as well as the involving action mechanism.

Methods

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) or Western blotting was employed to determine the expression levels of KCNQ1OT1 and EIF2B5. OC cell proliferation was evaluated by MTT and colony formation assays, and wound healing and Transwell assays were implemented to monitor cell migration and invasion, respectively. The methylation status of EIF2B5 promoter was examined by MS-PCR, to clarify whether the expression of EIF2B5 was decreased. The binding activity of KCNQ1OT1 to methyltransferases DNMT1, DNMT3A and DNMT3B was determined by dual luciferase reporter assay or RIP assay, to explore the potential of KCNQ1OT1 alters the expression of its downstream gene. ChIP assay was carried out to verify the combination between EIF2B5 promoter and above three methyltransferases.

Results

Expression of lncRNA KCNQ1OT1 was increased in OC tissues and cells. EIF2B5 expression was downregulated in OC, which was inversely correlated with KCNQ1OT1. Knockdown of KCNQ1OT1 inhibited OC cell proliferation and metastasis. KCNQ1OT1 could downregulate EIF2B5 expression by recruiting DNA methyltransferases into EIF2B5 promoter. Furthermore, interference of EIF2B5 expression rescued KCNQ1OT1 depletion-induced inhibitory impact on OC cell proliferation and metastasis.

Conclusion

Our findings evidenced that lncRNA KCNQ1OT1 aggravated ovarian cancer metastasis by decreasing EIF2B5 expression level, and provided a novel therapeutic strategy for OC.

LncRNA KCNQ1OT1 is upregulated, while EIF2B5 is downregulated in OC tissues and cells.

Knockdown of KCNQ1OT1 represses OC cell proliferation and metastasis.

KCNQ1OT1 decreases EIF2B5 expression by recruiting DNA methyltransferases into EIF2B5 promoter, thereby promoting OC progression.

Bioinformatics Study Revealed Significance of Exosome Transcriptome in Hepatocellular Carcinoma Diagnosis

Background: Hepatocellular carcinoma (HCC) is one of the fifty most common cancers globally, having a high mortality rate being the second most common cause of cancer-related deaths. However, little attention has been paid to the involvement of exosomes and ceRNA in HCC.

Method: The study aimed to explore exosome data from exoRBase database and a free online database to estimate possible binding miRNA from mRNA, lncRNA, and circRNA and discover useful exosome biomarkers for HCC therapy.

Results: The results indicated that a total of 159 mRNAs, 60 lncRNAs, and 13 circRNAs were differentially expressed, with HIST2H3C exhibiting the highest log₂FC change, CTD-2031P19 exhibiting the most relevant lncRNA, and CTD-2031P19 exhibiting the most relevant lncRNA. MARCH8, SH3PXD2A, has-circ-0014088, hsa-miR-186-5p, and hsa-miR-613 were identified as hub biomarkers used by Cytoscape. According to the KEGG pathway analysis results, the differentially expressed proteins were primarily enriched in the MAPK signaling network, central carbon metabolism in cancer, the glucagon signaling pathway, glutamatergic synapse, and spliceosome. Furthermore, immunohistochemical images from the Human Protein Atlas (HPA) online tool were used to directly evaluate the protein expression of SMARCA5, CDC42, and UBC between normal and cancer tissues, and the results showed that these three gene expressions were significantly higher in tumor tissues.

Conclusion: This study discovered atypical signature exosomes for HCC prognostic prediction based on an online database. The signals could mimic exosome microenvironmental disorders providing potential biomarkers for exosome treatment.

Therapy Trial Design in Vanishing White Matter: An Expert Consortium Opinion

Vanishing white matter (VWM) is a leukodystrophy caused by recessive variants in the genes EIF2B1-EIF2B5. It is characterized by chronic neurologic deterioration with superimposed stress-provoked episodes of rapid decline. Disease onset spans from the antenatal period through senescence. Age at onset predicts disease evolution for patients with early onset, whereas disease evolution is unpredictable for later onset; patients with infantile and early childhood onset consistently have severe disease with rapid neurologic decline and often early death, whereas patients with later onset have highly variable disease. VWM is rare, but likely underdiagnosed, particularly in adults. Apart from measures to prevent stressors that could provoke acute deteriorations, only symptomatic care is currently offered. With increased insight into VWM disease mechanisms, opportunities for treatment have emerged. EIF2B1-EIF2B5 encode the 5-subunit eukaryotic initiation factor 2B complex, which is essential for translation of mRNAs into proteins and is a principal regulator of the integrated stress response (ISR). ISR deregulation is central to VWM pathology. Targeting components of the ISR has proven beneficial in mutant VWM mouse models, and several drugs are now in clinical development. However, clinical trials in VWM pose considerable challenges: low numbers of known patients with VWM, unpredictable disease course for patients with onset after early childhood, absence of intermediate biomarkers, and novel first-in-human molecular targets. Given these challenges and considering the critical need to offer therapies, we have formulated recommendations for enhanced diagnosis, drug trial setup, and patient selection, based on our expert evaluation of molecular, laboratory, and clinical data.

A Risk-Scoring Model Based on Evaluation of Ferroptosis-Related Genes in Osteosarcoma

Background

Osteosarcoma (OS) is a bone malignancy frequently seen in pediatrics and has high mortality and incidence. Ferroptosis is an important cell death process in regulating the apoptosis and invasion of tumor cells, so constructing the risk-scoring model based on OS ferroptosis-related genes (FRGs) will benefit the evaluation of both treatment and prognosis.

Methods

The OS dataset was screened from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, and OS-related FRGs were found through the Ferroptosis Database (FerrDb) using a multivariate Cox regression model, followed by the generation of the risk scores and a risk-scoring prediction model. Further systematical exploration for immune cell infiltration and assessing the prediction of response to targeted drugs was conducted.

Results

Based on OS-related FRGs, a risk-scoring model of FRGs in OS was constructed. The six FRGs played a role in the carbon metabolism, glutathione metabolism, and pentose phosphate pathways. Results from targeted drug sensitivity analyses were concordant to pathway analyses. The response to targeted drugs statistically differed between the two groups with different risks, and the high-risk group presented a high sensitivity to targeted drugs.

Conclusions

We identified a 6-ferroptosis-gene-based prognostic signature in OS and created and verified a risk-scoring model to predict the prognosis of OS at 1, 3, and 5 years for OS patients independently.

KLRK1 as a prognostic biomarker for lung adenocarcinoma cancer

Lung cancer is one of the most common malignancy worldwide and causes estimated 1.6 million deaths each year. Cancer immunosurveillance has been found to play an important role in lung cancer and may be related with its prognosis. KLRK1, encoding NKG2D, is a homodimeric lectin-like receptor. However, there has not been one research of KLRK1 as a biomarker in lung cancer. Data including patients` clinical characteristics and RNAseq information of KLRK1 from TCGA were downloaded. A total of 1019 patients with lung cancer were included in this study, among which 407 patients were female and 611 patients were male. Evaluations of mRNA expression, diagnostic value by ROC (receiver operating characteristic) curves and prognostic value by survival curve, Cox model and subgroup analysis were performed. The level of KLRK1 expression in lung adenocarcinoma cancer tissues and normal lung tissues was detected by qRT-PCR. The CCK-8 assay investigated the proliferation rate and the wound healing assay assessed the migratory ability in vitro. The expression of KLRK1 in tumor was lower than that in normal tissue. KLRK1 expression was associated with gender, histologic grade, stage, T classification and vital status. Patients with high KLRK1 expression presented an improved overall survival (P = 0.0036) and relapse free survival (P = 0.0031). KLRK1 was found to have significant prognostic value in lung adenocarcinoma (P = 0.015), stage I/II (P = 0.03), older patients (P = 0.0052), and male (P = 0.0047) by subgroup overall survival analysis, and in lung adenocarcinoma (P = 0.0094), stage I/II (P = 0.0076), older patients (P = 0.0072), and male (P = 0.0033) by subgroup relapse free survival analysis. Lung adenocarcinoma cancer patients with high KLRK1 expression presented an improved overall survival (P = 0.015) and relapse free survival (P = 0.0094). In vitro studies indicated that KLRK1 inhibited tumor cell proliferation and migration. KLRK1 was an independent prognostic factor and high KLRK1 expression indicated a better overall and relapse free survival. KLRK1 may be a prognostic biomarker for lung adenocarcinoma cancer.

Expression and clinical significance of BDH1 in liver cancer

Liver cancer is a deadly disease with generally poor patient outcomes. BDH1 is a key enzyme that regulates the metabolism and synthesis of ketone bodies. This study sought to explore the prognostic relevance of BDH1 mRNA expression in liver cancer.We utilized the Cancer Genome Atlas datasets to analyze the relationship between BDH1 expression and clinical outcomes. We used Kaplan-Meier curves and Cox analyses to explore the relevance of BDH1 mRNA levels to patient prognosis. Further gene set enrichment analysis was conducted as a means of comparing differences in gene expression as a function of BDH1 expression.Liver cancer samples exhibited significantly decreased BDH1 mRNA expression, and that this downregulation was correlated with a number of clinicopathological variables including gender, histologic grade, stage, TNM classification, and both overall and relapse-free survival. We further determined that BDH1 mRNA expression was an independent predictor of liver cancer patient prognosis. A subsequent gene set enrichment analysis found genes affected by BDH1 expression to be those enriched in pathways relating to MYC and wnt/β-catenin signaling.Our preliminary findings demonstrate for the first time that low expression of BDH1 mRNA is a potentially valuable independent prognostic indicator for liver cancer detection.

Prognostic prediction and gene regulation network of EIF2S2 in hepatocellular carcinoma based on data mining

BACKGROUND

Hepatocellular carcinoma (HCC) is a malignant tumor with a high fatality rate, predicting poor prognosis and therapeutic effect. Screening potential prognostic genes in HCC could be a creative way to advance clinical treatment. Eukaryotic translation initiation factor 2 subunit beta (EIF2S2) has reportedly been linked to several tumors, including liver cancer, but the prognostic predictions remain unknown. Therefore, we aimed to clarify the prognostic role and interaction network of EIF2S2 in HCC using bioinformatics data.

METHODS

We screened EIF2S2 using the Oncomine, Ualcan, and TCGA databases. R software was used to analyze the mRNA level and clinicopathological characteristics of hepatocellular carcinoma. Evaluation of the correlations between EIF2S2 and patients' survival was made using the Kaplan-Meier curves and Cox proportional hazards regression model. Then, the influence of EIF2S2 gene mutations on the prognosis of patients was explored by cBioPortal. The protein-protein interaction network of 50 similar genes related to EIF2S2 was implemented by GEPIA2 and Metascape. The LinkedOmics database allowed us to carry out Gene Set Enrichment Analysis. Finally, we constructed the EIF2S2 kinase, miRNA, and transcription factor target networks using GeneMANIA.

RESULTS

EIF2S2 mRNA was overexpressed in HCC and was closely associated with clinicopathological features, including gender, age, race, tumor grade, and stage. There was no correlation between EIF2S2 genetic mutations and prognostic survival. Combining Cox proportional hazards regression model analyses, high-expressed EIF2S2 predicted poor prognosis in HCC patients. Additionally, we screened the top three EIF2S2-related genes (PFDN4, HM13, and SNRPD1), the 50 similar genes, and then constructed a 50-similar-gene protein-protein interaction network identified by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using Metascape. EIF2S2 target networks in HCC were identified in kinase, miRNA, and transcription factor networks, including the mitogen-activated protein kinase 1 (MAPK1), miRNAs (Mir-144), and transcription factors (GGAANCGGAANY_UNKNOWN) using GeneMANIA.

CONCLUSIONS

EIF2S2 plays a crucial role in the gene-regulating network of HCC and may be a potential prognostic marker or therapeutic target for HCC patients.

SLC13A4 Might Serve as a Prognostic Biomarker and be Correlated with Immune Infiltration into Head and Neck Squamous Cell Carcinoma

SLC13A4 is a sodium sulfate co-transporter, which is expressed in brains, placentas, thymes and other tissues, plays an essential role in maintaining the metabolic balance of sulfate in vivo. The TCGA database shows that it is differentially expressed in a variety of tumors, but its prognostic value in tumors has not been clarified. TCGA, Oncomine and Timer databases were used to analyze SLC13A4 mRNA expression in cancer tissues and normal tissues, and its correlation with clinical prognosis in head and neck tumor. The CIBERSORT database was used to analyze the correlation between SLC13A4 expression and the infiltration of immune cells. SLC13A4 enrichment analysis was carried out by GSEA. SLC13A4 mRNA levels were significantly lower in head and neck tumors than in paracancer tissues. SLC13A4 expression in Head and neck squamous cell carcinoma (HNSCC) was closely related to tumor pathological grade and clinical stage. Decreased SLC13A4 expression was associated with poor overall survival (OS), progression free survival (PFS), disease specific survival (DSS) and recurrence free survival (RFS) in HNSCC patients. The expression of SLC13A4 was negatively correlated with Monocytes, M1 macrophages, M2 macrophages, resting CD4+ memory T cells, resting NK cells and activated NK cells, but positively correlated with neutrophils, plasma cells, T follicular helper cells, gamma delta T cells, regulatory T cells and naive B cells. In addition, the genes in SLC13A4 low-expression group were mainly concentrated in immunity-related activities, viral diseases, typical tumor pathways and metabolism. The SLC13A4 high expression group was mainly enriched in metabolic pathways. These suggest that SLC13A4 may be a potential prognostic biomarker in HNSC and correlated with immune infiltrates.

AGTRAP Is a Prognostic Biomarker Correlated With Immune Infiltration in Hepatocellular Carcinoma

Background

Recently, it has been reported that angiotensin II receptor-associated protein (AGTRAP) plays a substantial role in tumor progression. Nevertheless, the possible role of AGTRAP in hepatocellular carcinoma (HCC) remains unrecognized.

Methods

The metabolic gene rapid visualizer, Cancer Cell Line Encyclopedia, Human Protein Atlas, and Hepatocellular Carcinoma Database were used to analyze the expression of AGTRAP in HCC tissues and normal liver tissues or adjacent tissues. Kaplan-Meier plotter and UALCAN analysis were used to assess the prognostic and diagnostic value of AGTRAP. LinkedOmics and cBioPortal were used to explore the genes co-expressed with AGTRAP in HCC. To further understand the potential mechanism of AGTRAP in HCC, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment pathway analyses were performed using R software, the protein-protein interaction (PPI) network was established using the STRING database, and the immune infiltration and T-cell exhaustion related to AGTRAP were explored via Timer and GEPIA. In addition, immunohistochemistry was used to detect the expression of AGTRAP protein in HCC tissues and paired adjacent tissues from clinical specimens.

Results

This study found that the mRNA and protein levels of AGTRAP in HCC tissues were higher than those in normal liver tissues and adjacent tissues, and higher mRNA levels of AGTRAP were associated with higher histological grade and a poor overall survival in HCC patients. The area under the receiver operating characteristic curve (AUC) of AGTRAP was 0.856, suggesting that it could be a diagnostic marker for HCC. Moreover, the alteration rate of AGTRAP in HCC was 8%, and AGTRAP was involved in HCC probably through the NF-κB and MAPK signaling pathways. Furthermore, AGTRAP was positively correlated with the infiltration of CD8⁺ T cells, CD4⁺ T cells, B cells, macrophages, dendritic cells, and neutrophils, and the levels of AGTRAP were significantly correlated with T-cell exhaustion biomarkers. The immunohistochemistry results confirmed that the protein levels of AGTRAP were consistently higher in HCC tissues than in paired adjacent tissues.

Conclusion

The clinical value of AGTRAP and its correlation with immune infiltration in HCC was effectively identified in clinical data from multiple recognized databases. These findings indicate that AGTRAP could serve as a potential biomarker in the treatment of HCC, thereby informing its prognosis, diagnosis, and even immunotherapy.

High CTSL2 expression predicts poor prognosis in patients with lung adenocarcinoma

Cathepsin like 2 (CTSL2) is a lysosomal cysteine protease, and may be associated with tumor metastasis. However, CTSL2 has not been reported as a biomarker in lung adenocarcinoma (LUAD). In this study, bioinformatics analysis using data from The Cancer Genome Atlas was performed. Wilcoxon rank-sum test and chi-square test were carried out. Kaplan-Meier and Cox regression were performed to evaluate the effect of CTSL2 expression in the overall survival. Our results indicated that CTSL2 in tumor was significantly higher than that in normal tissue (P < 0.001). High CTSL2 expression was significantly associated with age (P = 0.02), vital status (P < 0.001), and T classification (P = 0.03), and correlated with poor overall survival (HR = 1.62, 95% CI = 1.21–2.18, P = 0.001). CTSL2 expression was an independent risk factor for overall survival in patients with LUAD (HR = 1.52, 95% CI = 1.12–2.05, P = 0.006). A nomogram was plotted for illustration of CTSL2 expression on the risk of LUAD. Furthermore, in vitro cell experiments showed the CTSL2 promoted the proliferation and migration of A549 cells. In summary, high CTSL2 expression predicts poor prognosis in patients with LUAD.

Identification of Three Key Genes Associated with Hepatocellular Carcinoma Progression Based on Co-expression Analysis

Hepatocellular carcinoma (HCC) is the fifth most common cancer and one of the leading causes of cancer-related death in the world. Due to the recurrence of HCC, its survival rate is still low. Therefore, it is vital to seek prognostic biomarkers for HCC. In this study, differential analysis was conducted on gene expression data in The Cancer Genome Atlas -LIHC, and 4482 differentially expressed genes in tumor tissue were selected. Then, weighted gene co-expression network analysis was used to analyze the co-expression of the gained differential genes. By module-trait correlation analysis, the turquoise gene module that was significantly related to tumor grade, pathologic_T stage, and clinical stage was identified. Thereafter, enrichment analysis of genes in this module uncovered that the genes were mainly enriched in the signaling pathways involved in spliceosome and cell cycle. After that, through correlation analysis, 18 hub genes highly correlated with tumor grade, clinical stage, pathologic_T stage, and the turquoise module were selected. Meanwhile, protein-protein interaction (PPI) network was constructed by using genes in the module. Finally, three key genes, heterogeneous nuclear ribonucleoprotein L, serrate RNA effector molecule, and cyclin B2, were identified by intersecting the top 30 genes with the highest connectivity in PPI network and the previously obtained 18 hub genes in the turquoise module. Further survival analysis revealed that high expression of the three key genes predicted poor prognosis of HCC. These results indicated the direction for further research on clinical diagnosis and prognostic biomarkers of HCC.

Integrated analysis of the functions and prognostic values of RNA binding proteins in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC), one of the most common malignant tumors worldwide, ranks as the fifth most common cancer and has been the second most frequent cause of cancer-related death. RNA binding proteins (RBPs) are proteins that interact with different classes of RNA and are commonly detected in cells.

Methods

We used RNA sequencing data from TCGA to display dysfunctional RBPs microenvironments and provide potential useful biomarkers for HCC diagnosis and prognosis.

Results

330 differently expressed RBPs (208 upregulated and 122 downregulated) were identified. KEGG were mainly enriched in RNA degradation, Influenza A, Hepatitis C, RIG-I-like receptor signaling pathway, Herpes simplex virus 1 infection and RNA transport. CBioPortal results demonstrated that these genes were altered in 50 samples out of 357 HCC patients (14%) and the amplification of BRCA1 was the largest frequent copy-number alteration.

Conclusion

Based on the online database, we identified novel RBPs markers for the prognosis of hepatocellular carcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-01843-0.

Single sample scoring of hepatocellular carcinoma: A study based on data mining

Hepatocellular carcinoma (HCC) is a high mortality malignancy and the second leading cause of cancer-related deaths. Because the immune system plays a dual role by assisting the host barrier and tumor progression, there are complex interactions with considerable prognostic significance. Herein, we performed single-sample gene set enrichment (ssGSEA) to explore the tumor microenvironment (TME) and quantify the tumor-infiltrating immune cell (TIIC) subgroups of immune responses based on the HCC cohort of The Cancer Genome Atlas (TCGA) database. We evaluate molecular subpopulations, survival, function, and expression differential associations, as well as reveal potential targets, and biomarkers for immunotherapy. We combined the TME score and the 29 immune cell types in the low, medium, and high immunity groups. The stromal score, immune score, and ESTIMATE score were positively correlated with immune activity but negatively correlated with the tumor purity. There were 23 human leukocyte antigen (HLA)-related genes that were significantly different. However, KIAA1429 was not significant among the different immunity groups. Besides, programmed death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) expression increased with the increase of immune activity. This may provide valuable information for HCC immunotherapy. We also found that there was no significant difference in naïve B cells, macrophages M1, activated mast cells, resting natural killer (NK) cells, and T cells gamma delta among the different immunity groups. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the differential proteins were mainly enriched in alpha-linolenic acid (ALA) metabolism, cytokine-cytokine receptor interaction, glycosaminoglycan biosynthesis-heparan sulfate/heparin, glycosphingolipid biosynthesis-ganglio series and proteasome. Our findings provide a deeper understanding of the immune scene, uncovering remarkable immune infiltration patterns of various subtypes of HCC using ssGSEA. This study advances the understanding of immune response and provides a basis for research to enhance immunotherapy.

Bioinformatics Analysis and Validation Identify CDK1 and MAD2L1 as Prognostic Markers of Rhabdomyosarcoma

Purpose

The goal of the current study was to identify potential prognostic biomarkers of rhabdomyosarcoma (RMS).

Materials and Methods

We screened chip sequencing datasets of RMS through the gene expression omnibus (GEO) database. A total of 74 RMS patient tissues and 39 normal muscle cell tissues were analyzed. Limma R software was used to identify the differentially expressed genes (DEGs) between RMS tissues and normal controls. The GO plot R package was used to visualize the results of the GO analysis. We screened for pathaffy package enrichment of DEGs by the Kyoto Encyclopedia of Genes and Genomes (KEGG). The cutoff criterion was a P-value <0.05. Immunohistochemistry (IHC) was applied to validate the expression of CDK1 (cyclin-dependent kinases 1) and MAD2L1 (Mitotic Arrest Deficient 2 Like 1) in RMS.

Results

We obtained a total of 498 up- and 480 down-regulated DEGs. The hub genes are mainly involved in the cell cycle and P53 singling pathway. CDK1 expression was associated with tumor size and COG-STS (Children's Oncology Group-soft tissue sarcoma) staging of RMS. For the low CDK1 expression group and high CDK1 expression group, the 5-year overall survival (OS) rate was 83.0% vs 63.5% (P = 0.004), and the 5-year event-free survival (EFS) rate was 47.5% vs 27.5% (P = 0.049) respectively. When compared low MAD2L1 expression group with high MAD2L1 expression group, the 5-year OS rate was 80.0% vs 43.2% (P = 0.001), and the 5-year EFS rate was 45.1% vs 21.8% (P = 0.038), respectively. If patients were divided into three groups: low CDK1 and low MAD2L1 expression group, high CDK1 or high MAD2L1 expression group, and high CDK1 and high MAD2L1 expression group, the 5-year OS rate was 87.1%, 58.6%, 39.6% (P = 0.001), while the 5-year EFS rate of RMS patients was 54.2%, 23.2%, 21.7% (P = 0.028), respectively.

Conclusion

This study has identified that CDK1 and MAD2L1 were adverse prognostic factors of RMS.

Immunoglobulin superfamily member 10 is a novel prognostic biomarker for breast cancer

Background

Immunoglobulin superfamily member 10 (IGSF10) is a member of the immunoglobulin superfamily that is expressed at high levels in both the gallbladder and ovary. Currently, the role and possible mechanism of IGSF10 in breast cancer remain unclear.

Method

By applying real-time quantitative polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC), the expression of IGSF10 in breast cancer cells and tissues was detected. We collected the clinical information from 700 patients with breast cancer in The Cancer Genome Atlas (TCGA), and analyzed the relationship between IGSF10 expression and the clinicopathological features and survival outcomes of these patients. The potential mechanisms and pathways associated with IGSF10 in breast cancer were explored by performing a gene set enrichment analysis (GSEA).

Results

According to TCGA data, qRT-PCR and IHC experiments, levels of the IGSF10 mRNA and protein were significantly decreased in breast cancer tissues. IGSF10 expression was significantly correlated with age, tumor size, and tumor stage. Moreover, shorter overall survival (OS) and relapse-free survival (RFS) correlated with lower IGSF10 expression, according to the survival analysis. The multivariate analysis identified that IGSF10 as an independent prognostic factor for the OS (hazard ratio (HR) = 1.793, 95% confidence interval (CI) [1.141–2.815], P = 0.011) and RFS (HR = 2.298, 95% CI [1.317–4.010], P = 0.003) of patients with breast cancer. Based on the GSEA, IGSF10 was involved in DNA repair, cell cycle, and glycolysis. IGSF10 was also associated with the PI3K/Akt/mTOR and mTORC1 signaling pathways.

Conclusions

This study revealed a clear relationship between IGSF10 expression and the tumorigenesis of breast cancer for the first time. Therefore, further studies are needed to understand the mechanism of IGSF10 in breast cancer.

Long non-coding RNA BACE1-AS is an independent unfavorable prognostic factor in liver cancer

Liver cancer is one of the leading causes of cancer-associated deaths with incidence rates continuously on the rise. Biomarkers are urgently required for early diagnosis and better prognostic classification, which is essential for risk stratification and optimizing treatment strategies in clinical settings. By analyzing the data extracted from The Cancer Genome Atlas database using R, the long noncoding RNA (lncRNA) β-site APP-cleaving enzyme 1 antisense (BACE1-AS) was discovered to have both high diagnostic and prognostic values in liver cancer, which could serve as a promising biomarker in clinical settings. Precisely, lncRNA BACE1-AS is significantly overexpressed in liver cancer and its levels vary within different subgroups, suggesting its tumorigenic role. Furthermore, higher BACE1-AS predicts poorer overall survival and relapse-free survival outcomes. Overall, the present study demonstrated that BACE1-AS may be involved in liver cancer progression and could serve as a promising biomarker for diagnosis and prognostic evaluation.

Prediction of Poor Prognosis of HCC by Early Warning Model for Co-Expression of miRNA and mRNA Based on Bioinformatics Analysis

Objective:

Hepatocellular Carcinoma (HCC) has the highest mortality rate worldwide with the intractability of its extremely complicated pathogenesis and unclear mechanism. The limited survival highlights the need for the further detection of prognosis for HCC. MicroRNAs (miRNAs) and messenger RNAs (mRNAs) have been identified as regulatory factors and target genes in human cancers, while some studies also found post-transcriptional modification plays a crucial role in the occurrence and development of HCC. The present study aimed to elucidate the prognostic significance of miRNA and mRNA models in HCC.

Methods:

Data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The miRNA and mRNA expressions were tested by the Wilcoxon and used funrich software to predict mRNA that might be related to miRNA. Then we determined the intersection with overlapped mRNA and miRNA Venn diagram, and screened out hub gene by using Degree algorithm in Cytoscape software. The COX models, with TCGA data as the training set and ICGC data as the test set, were constructed. All patients were divided into high-risk and low-risk groups. Data on overall survival of different groups were collected and analyzed by Kaplan-Meier method, and independent risk factors affecting prognosis were assessed by Cox analysis.

Results:

The miRNA and mRNA polygenic risk model showed a good true positive rate. Kaplan-Meier curve and Cox analysis suggested that the high-risk group was associated with poor prognosis, and the risk score could be used as an independent risk factor for HCC.

Conclusion:

Tumor risk models constructed in this study could effectively predict the prognosis of patients, which is expected to provide a reference for the prognostic stratification and treatment strategy development of HCC.

Diagnostic and prognostic value of HABP2 as a novel biomarker for endometrial cancer

BACKGROUND

Endometrial cancer is the fifth most common malignant disorder in women, with its incidence increasing. A biomarker with diagnostic and prognostic value remains to be found. The HABP2 protein, or Factor VII-activating protease, encodes a hyaluronic acid-binding protein.

METHODS

Patient data including clinical characteristics and RNAseq information of HABP2 was obtained from The Cancer Genome Atlas (TCGA), and analyzed by R statistic packages. A total of 370 women with endometrial cancer were enrolled in the study. To study the diagnostic value of HABP2 in patients with endometrial cancer, receiver operating characteristic (ROC) curves were plotted by the pROC package. To study the prognostic value of HABP2 in patients with endometrial cancer, the survival package in R was used and the Cox model was established.

RESULTS

HABP2 expression was lower in endometrial cancer compared with normal endometrial tissues. HABP2 showed moderate diagnostic value for endometrial cancer, with HBP2 expression associated with vital status, histologic grade, and residual tumor. HABP2 was an independent prognostic factor, with low HABP2 expression indicating a better overall survival.

CONCLUSIONS

HABP2 has diagnostic and prognostic value and maybe a novel biomarker for endometrial cancer.

High CENPM mRNA expression and its prognostic significance in hepatocellular carcinoma: a study based on data mining

Background

Hepatocellular carcinoma (HCC) is a high mortality disease, the fifth most general cancer worldwide, and the second leading to cancer-related deaths, with more than 500,000 new patients diagnosed each year. First, the high expression of centromere M (CENPM) in mammary gland tissue of b-catenin transformed mice was identified.

Materials and methods

In our study, we evaluated the expression of CENPM in hepatocellular carcinoma based on data obtained from an online database. Multivariate analysis showed that the expression of CENPM and M classification was an independent prognostic factor for patients with hepatocellular carcinoma.

Results

Survival analysis showed that patients with high CENPM had a worse prognosis than patients with low CENPM (P < 0.01). A multivariate Cox regression hazard model showed that B cells, CD8+ T cells, macrophages, and dendritic cells infiltrated by immune cells were statistically significant in liver cancer (P < 0.05). Using the network, the 50 most frequently changed neighbor genes of CENPM were shown, and the most common change was RAD21 (18.3%).

Conclusion

Our study found that the expression of CENPM was significantly increased in patients with hepatocellular carcinoma, and it was related to a variety of clinical characteristics, its correlation with the level of immune infiltration and poor prognosis, so CENPM can be used as a useful prognosis for patients' markers and HCC.

Identification of a protein signature for predicting overall survival of hepatocellular carcinoma: a study based on data mining

BACKGROUND

Hepatocellular carcinoma (HCC), is the fifth most common cancer in the world and the second most common cause of cancer-related deaths. Over 500,000 new HCC cases are diagnosed each year. Combining advanced genomic analysis with proteomic characterization not only has great potential in the discovery of useful biomarkers but also drives the development of new diagnostic methods.

METHODS

This study obtained proteomic data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) and validated in The Cancer Proteome Atlas (TCPA) and TCGA dataset to identify HCC biomarkers and the dysfunctional of proteogenomics.

RESULTS

The CPTAC database contained data for 159 patients diagnosed with Hepatitis-B related HCC and 422 differentially expressed proteins (112 upregulated and 310 downregulated proteins). Restricting our analysis to the intersection in survival-related proteins between CPTAC and TCPA database revealed four coverage survival-related proteins including PCNA, MSH6, CDK1, and ASNS.

CONCLUSION

This study established a novel protein signature for HCC prognosis prediction using data retrieved from online databases. However, the signatures need to be verified using independent cohorts and functional experiments.

HN1 as a diagnostic and prognostic biomarker for liver cancer

BACKGROUND

The present study aimed to examine the diagnostic and prognostic value of HN1 in terms of overall survival (OS) and recurrence-free survival (RFS) in liver cancer and its potential regulatory signaling pathway.

METHODS

We obtained clinical data and HN1 RNA-seq expression data of liver cancer patients from The Cancer Genome Atlas database, and analyzed the differences and clinical association of HN1 expression in different clinical features. We uesd receiver-operating characteristic curve to evaluate the diagnosis capability of HN1. We analyzed and evaluated the prognostic significance of HN1 by Kaplan-Meier curves and Cox analysis. Gene Set Enrichment Analysis (GSEA) was used to identify signaling pathways related to HN1 expression.

RESULTS

HN1 mRNA was up-regulated in liver cancer, and was associated with age, histologic grade, stage, T classification, M classification, and vital status. HN1 mRNA had ideal specificity and sensitivity for the diagnosis (AUC = 0.855). Besides, the analysis of Kaplan-Meier curves and Cox model showed that HN1 mRNA was strongly associated with the overall survival and could be well-predicted liver cancer prognosis, as an independent prognostic variable. GSEA analysis identified three signaling pathways that were enriched in the presence of high HN1 expression.

CONCLUSION

HN1 serves as a biomarker of diagnosis and prognosis in liver cancer.

Discovering Biomarkers in Peritoneal Metastasis of Gastric Cancer by Metabolomics

BACKGROUND AND OBJECTIVE

Metabolomics has recently been applied in the field of oncology. In this study, we aimed to use metabolomics to explore biomarkers in peritoneal metastasis of gastric cancer.

METHODS

Peritoneal lavage fluid (PLF) of 65 gastric cancer patients and related clinical data were collected from the First Hospital of Jilin University. The metabolic components were identified by liquid chromatography-mass spectrometry (LC-MS). Total ion current (TIC) spectra, principal component analysis (PCA), and the Student's t-test were used to identify differential metabolites in PLF. A support vector machine (SVM) was used to screen the differential metabolites in PLF with a weight of 100%. Cluster analysis was used to evaluate the similarity between samples. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic ability of the metabolites. Univariate and multivariate logistic regression analyses were used to identify potential risk factors for peritoneal metastasis of gastric cancer.

RESULTS

We found the differential levels of PLF metabolites by LC-MS, TIC spectra, PCA and the t-test. Cluster analysis showed the co-occurrence of metabolites in the peritoneal metastasis group (p<0.05). ROC analysis showed the diagnostic ability of metabolites (p<0.05). Univariate and multivariate logistic regression analyses showed the potential independent risk factors for peritoneal metastasis in gastric cancer patients (p<0.05).

CONCLUSION

Through the statistical analysis of metabolomics, we found that TG (54:2), G3P, α-aminobutyric acid, α-CEHC, dodecanol, glutamyl alanine, 3-methylalanine, sulfite, CL (63:4), PE-NMe (40:5), TG (53:4), retinol, 3-hydroxysterol, tetradecanoic acid, MG (21:0/0:0/0:0), tridecanoic acid, myristate glycine and octacosanoic acid may be biomarkers for peritoneal metastasis of gastric cancer.

SLC25A11 serves as a novel prognostic biomarker in liver cancer

Liver cancer is a disease with high mortality; it is often diagnosed at intermediate and advanced stages and has a high recurrence rate. ROS restriction and adequate energy supply play significant roles in liver cancer. SLC25A11, a member of the malate-aspartate shuttle (MAS), regulates electroneutral exchange between 2-oxoglutarate and other dicarboxylates. It transports glutathione (GSH) from the cytoplasm into mitochondria to maintain GSH levels to limit ROS production. Moreover, SLC25A11 is essential for ATP generation in cancers as it regulates NADH transportation from the cytoplasm to mitochondria. The purpose of this research was to investigate the prognostic value of SLC25A11 in liver cancer. The Cancer Genome Atlas database was used to analyze the levels of SLC25A11 in liver cancer. Fisher's exact and chi-square tests were used to evaluate the relationship between SLC25A11 expression and clinical characteristics. Finally, we explored the value of SLC25A11 in prognosis by Cox analysis and Kaplan-Meier curves. Our results revealed that SLC25A11 was downregulated in liver cancer compared to normal controls. Low expression of SLC25A11 was associated with clinical stage, vital status, histologic grade, overall survival (OS) and relapse-free survival (RFS). Liver cancer patients with low SLC25A11 expression had shorter OS and RFS than patients with high SLC25A11 expression. Multivariate analysis showed that the expression of SLC25A11 was an independent predictor of RFS and OS. In conclusion, this study identified that SLC25A11 serves as a new prognostic marker for liver cancer.

Lipocalin-1 Expression as a Prognosticator Marker of Survival in Breast Cancer Patients

PURPOSE

LCN1 (lipocalin-1), a gene that encodes tear lipocalin (or von Ebner's gland protein), is mainly expressed in secretory glands and tissues, such as the lachrymal and lingual gland, and nasal, mammary, and tracheobronchial mucosae. Analysis of the Cancer Genome Atlas (TCGA) Breast Carcinoma (BRCA) level 3 data revealed a relationship between LCN1 expression and survival in breast cancer patients.

METHODS

The χ2 test and Fisher exact test were applied to analyze the clinical data and RNA sequencing expression data, and the association between LCN1 expression and clinicopathologic features was determined. The receiver-operating characteristic (ROC) curve of LCN1 was drawn to assess its ability as a diagnostic marker, and the optimal cutoff value was obtained from the ROC curve to distinguish groups with high and low LCN1 expression. Cox regression was used to compare both groups, and a log-rank test was applied to calculate p values and compare the -Kaplan-Meier curves. Furthermore, GEO datasets were employed for external data validation.

RESULTS

Analysis of 1,104 breast cancer patients with a primary tumor revealed that LCN1 was overexpressed in breast cancer. High LCN1 expression was associated with clinicopathologic features and poor survival. Analyzing the area under the ROC curve (AUC) of LCN1, it was found that its diagnostic ability was limited. Multivariate analysis indicated that LCN1 expression is an independent predictor of survival in breast cancer patients. Through validation in GEO datasets, LCN1 expression was higher in tumor than normal tissue of the breast. High LCN1 expression was associated with poor survival in breast cancer patients.

CONCLUSIONS

High LCN1 expression is an independent prognosticator of a poor prognosis in breast cancer.

Expression of eukaryotic translation initiation factor 3 subunit B in liver cancer and its prognostic significance

Liver cancer is one of the major malignancies with the worst prognosis among all solid tumor types. It is therefore ponderable to explore prognostic biomarkers and therapeutic targets for liver cancer. Eukaryotic translation initiation factor 3 subunit B (EIF3B) is closely linked to the transcription initiation of cancer-associated genes. In the present study, EIF3B was indicated to be a potential prognostic biomarker of liver cancer. The mRNA expression level of EIF3B in liver cancer was assessed by analyzing the Cancer Genome Atlas dataset. χ² and Fisher's exact tests were used to assess the association of EIF3B expression with clinical parameters. Receiver-operating characteristic curve analysis was used for evaluating the diagnostic value of EIF3B. Overall and relapse-free survival were assessed using Kaplan-Meier curves to determine the association between EIF3B expression and survival. Univariate and multivariate Cox regression analysis were performed to identify the factors affecting overall/relapse-free survival. Gene set enrichment analysis (GSEA) was used to identify signaling pathways associated with EIF3B in liver cancer. It was revealed that EIF3B was highly expressed in liver cancer tissues and it had a promising diagnostic ability. Furthermore, the survival analysis indicated that patients with high EIF3B expression generally had shorter overall as well as relapse-free survival. Univariate and multivariate Cox analysis suggested that high EIF3B mRNA expression may serve as an independent biomarker for the prognostication of patients with liver cancer. GSEA suggested that MYC-V1 (HALLMARK_MYC_TARGETS_V1 geneset; P=0.009), MYC-V2 (HALLMARK_MYC_TARGETS_V2 geneset; P=0.004) and DNA repair pathways (HALLMARK_DNA_REPAIR geneset; P<0.001) were differentially enriched in high EIF3B expression and low EIF3B expression groups. In conclusion, high EIF3B expression was indicated to be an independent prognostic biomarker for patients with liver cancer.

Bioinformatics analysis reveals meaningful markers and outcome predictors in HBV-associated hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of malignant neoplasm of the liver with high morbidity and mortality. Extensive research into the pathology of HCC has been performed; however, the molecular mechanisms underlying the development of hepatitis B virus-associated HCC have remained elusive. Thus, the present study aimed to identify critical genes and pathways associated with the development and progression of HCC. The expression profiles of the GSE121248 dataset were downloaded from the Gene Expression Omnibus database and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses were performed by using the Database for Annotation, Visualization and Integrated Discovery. Subsequently, protein-protein interaction (PPI) networks were constructed for detecting hub genes. In the present study, 1,153 DEGs (777 upregulated and 376 downregulated genes) were identified and the PPI network yielded 15 hub genes. GO analysis revealed that the DEGs were primarily enriched in ‘protein binding’, ‘cytoplasm’ and ‘extracellular exosome’. KEGG analysis indicated that DEGs were accumulated in ‘metabolic pathways’, ‘chemical carcinogenesis’ and ‘fatty acid degradation’. After constructing the PPI network, cyclin-dependent kinase 1, cyclin B1, cyclin A2, mitotic arrest deficient 2 like 1, cyclin B2, DNA topoisomerase IIα, budding uninhibited by benzimidazoles (BUB)1, TTK protein kinase, non-SMC condensin I complex subunit G, NDC80 kinetochore complex component, aurora kinase A, kinesin family member 11, cell division cycle 20, BUB1B and abnormal spindle microtubule assembly were identified as hub genes based on the high degree of connectivity by using Cytoscape software. In addition, overall survival (OS) and disease-free survival (DFS) analyses were performed using the Gene Expression Profiling Interactive Analysis online database, which revealed that the increased expression of all hub genes were associated with poorer OS and DFS outcomes. Receiver operating characteristic curves were constructed using GraphPad prism 7.0 software. The results confirmed that 15 hub genes were able to distinguish HCC form normal tissues. Furthermore, the expression levels of three key genes were analyzed in tumor and normal samples of the Human Protein Atlas database. The present results may provide further insight into the underlying mechanisms of HCC and potential therapeutic targets for the treatment of this disease.

The Prediction and Prognostic Significance of INPP5K Expression in Patients with Liver Cancer

Liver cancer is a devastating disease for humans with poor prognosis. Although the survival rate of patients with liver cancer has improved in the past decades, the recurrence and metastasis of liver cancer are still obstacles for us. Inositol polyphosphate-5-phosphatase K (INPP5K) belongs to the family of phosphoinositide 5-phosphatases (PI 5-phosphatases), which have been reported to be associated with cell migration, polarity, adhesion, and cell invasion, especially in cancers. However, there have been few studies on the correlation of INPP5K and liver cancer. In this study, we explored the prognostic significance of INPP5K in liver cancer through bioinformatics analysis of data collected from The Cancer Genome Atlas (TCGA) database. Chi-square and Fisher exact tests were used to evaluate the relationship between INPP5K expression and clinical characteristics. Our results showed that low INPP5K expression was correlated with poor outcomes in liver cancer patients. Univariate and multivariate Cox analyses demonstrated that low INPP5K mRNA expression played a significant role in shortening overall survival (OS) and relapse-free survival (RFS), which might serve as the useful biomarker and prognostic factor for liver cancer. In conclusion, low INPP5K mRNA expression is an independent risk factor for poor prognosis in liver cancer.

Rab22a is a novel prognostic marker for cell progression in breast cancer

Breast cancer (BC) is the most common female malignant tumor worldwide. The mechanism of tumorigenesis is still unclear. Ras‑related proteins in brain (Rab)22a belongs to the Ras superfamily, which may act as an oncogene and participate in carcinogenesis. The present study aims to identify whether Rab22a could be a novel biomarker of prognosis and determine the effects of Rab22a on BC cell progression. A total 258 BC and 56 para‑tumor or non‑tumor formalin fixed paraffin embedded tissues were stained through immunohistochemistry. The association between Rab22a expression and clinicopathological features, as well as overall survival status were analyzed. The expression level of Rab22a in breast cell lines were detected using reverse transcription‑quantitative PCR and western blotting. SK‑BR‑3 cells were infected with Rab22a short hairpin RNA lenti‑virus and the ability of cell proliferation, migration and invasion were measured. Gene Set Enrichment Analysis (GSEA) was employed to analyze the pathways involved in the Rab22a mRNA high level group. Rab22a was found to be overexpressed in BC tissues and upregulated in BC cells. High expression of Rab22a was related to a poor prognosis of patients with BC. Knockdown of Rab22a decreased the proliferation, migration and invasion ability of BC cells. GSEA indicated that certain pathways, including mammalian target of rapamycin complex 1 and protein secretion were upregulated, while pathways, such as hypoxia and KRas were downregulated in the Rab22a high level group. Rab22a is of prognostic value for BC and necessary for BC cell proliferation.

Ubiquitin-like modifier-activating enzyme 7 as a marker for the diagnosis and prognosis of breast cancer

Ubiquitin-like modifier-activating enzyme 7 (UBA7) is a specific E1-like ubiquitin-activating enzyme involved in interferon-stimulated gene 15 (ISG15) conjugation. UBA7 expression has been reported to be notably decreased in lung cancer. The present study aimed to investigate the changes in UBA7 expression in breast cancer and the association between UBA7 expression and clinical characteristics, and to elucidate the diagnostic and prognostic significance of UBA7 in breast cancer. The clinical data and RNA-sequencing expression values of 1,104 patients with breast cancer were downloaded from The Cancer Genome Atlas database. The associations between UBA7 expression and clinical characteristics were determined using χ2 and Fisher's exact tests. UBA7 expression values were divided into low and high groups using the optimal cut-off value, as determined by the overall survival (OS) value identified via a receiver operating characteristic (ROC) curve analysis, to further study the association between UBA7 expression and clinical characteristics. The diagnostic capability of UBA7 was assessed via ROC analysis, and Kaplan-Meier curve and Cox regression analyses were performed to determine the prognostic value of UBA7. The results demonstrated that UBA7 expression was decreased in breast cancer, and significant differences were observed between groups with regards to vital status, tumor classification, metastasis classification, histological type, sex, molecular subtype, and expression levels of progesterone receptor, estrogen receptor (ER) and human epidermal growth factor receptor 2. Low and high UBA7 expression levels were associated with age, ER expression, menopause status, Tumor-Node-Metastasis classification stage, margin status, vital status, radiation therapy use, OS and relapse-free survival. Furthermore, patients with low UBA7 expression levels had a poor prognosis. UBA7 expression also demonstrated an ability to diagnose patients at all clinical stages. Taken together, the results indicated that UBA7 expression was significantly decreased in breast cancer, and was associated with clinical characteristics and prognosis. Thus, UBA7 can be deemed as a potential biomarker in breast cancer, and may serve as a target in treatment.

MicroRNA hsa-mir-3923 serves as a diagnostic and prognostic biomarker for gastric carcinoma

Gastric carcinoma (GC) refers to a common digestive system disease that exhibits a very high incidence. MicroRNA hsa-mir-3923 belongs to a type of miRNA, of which the function has been merely investigated in breast, pancreatic cancers and pre-neoplasic stages of gastric cancer. It has not been studied or reported in gastric carcinoma, so the relationship between gastric hsa-mir-3923 expression and the clinics feature and pathology of GC cases was examined. This study employed data mining for analyzing gastric carcinoma data in The Cancer Genome Atlas database. A Chi squared test was performed for assessing the relations of hsa-mir-3923 expression with clinics-related and pathology-regulated variables. This study conducted the assessment of the role of hsa-mir-3923 in prognostic process using Kaplan-Meier curves, Receiver operating characteristic (ROC) analysis and proportional hazards model (Cox) study. With the use of Gene Expression Omnibus, this study carried out gene set enrichment analysis (GSEA). In the meantime, the common miRNA database was compared to predict potential target genes; as revealed by co-expression analysis, a regulatory network probably existed, containing hsa-mir-3923. For the analysis of the most tightly associated cytological behavior and pathway in GC, this study adopted the databases for Annotation, Visualization and Integrated Discovery (David) and KO-Based Annotation System (KOBAS). Cytoscape, R and STRING were employed for mapping probable regulatory networks displaying relations to hsa-mir-3923. Lastly, we obtained 69 genes most tightly associated with hsa-mir-3923 and described their relationship with Circos plot. As revealed from the results, hsa-mir-3923 displayed up-regulation in gastric carcinoma, and it displayed associations with vital status, N stage and histologic grade when being expressed. The predicted results of miRNA target genes suggested that there may be a close relationship between 66 genes and hsa-mir-3923 in gastric cancer. As indicated from co-expression data, a small regulating network of 4 genes probably existed. Our results elucidated that hsa-mir-3923 high-expression reveals poor prognosis of GC patients.

Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

Image processing plays a vital role in many areas such as healthcare, military, scientific and business due to its wide variety of advantages and applications. Detection of computed tomography (CT) liver disease is one of the difficult tasks in the medical field. Hand crafted features and classifications are the two types of methods used in the previous approaches, to classify liver disease. But these classification results are not optimal. In this article, we propose a novel method utilizing deep belief network (DBN) with grasshopper optimization algorithm (GOA) for liver disease classification. Initially, the image quality is enhanced by preprocessing techniques and then features like texture, color and shape are extracted. The extracted features are reduced by utilizing the dimensionality reduction method like principal component analysis (PCA). Here, the DBN parameters are optimized using GOA for recognizing liver disease. The experiments are performed on the real time and open source CT image datasets which embraces normal, cyst, hepatoma, and cavernous hemangiomas, fatty liver, metastasis, cirrhosis, and tumor samples. The proposed method yields 98% accuracy, 95.82% sensitivity, 97.52% specificity, 98.53% precision, and 96.8% F‐1 score in simulation process when compared with other existing techniques.

Upregulation of exosomal microRNA‑21 in pancreatic stellate cells promotes pancreatic cancer cell migration and enhances Ras/ERK pathway activity

Pancreatic stellate cells (PSCs) are typically activated in pancreatic ductal adenocarcinoma (PDAC) and release exosomes containing high levels of microRNA‑21 (miR‑21). However, the specific roles of exosomal miR‑21 in regulating the PDAC malignant phenotype remain unknown. The present study aimed to determine the effects of exosomal miR‑21 on the migratory ability of PDAC cells and explore the potential underlying molecular mechanism. Weighted gene correlation network and The Cancer Genome Atlas database analysis revealed that high miR‑21 levels were associated with a poor prognosis in patients with pancreatic adenocarcinoma, and that the Ras/ERK signaling pathway may be a potential target of miR‑21. In vitro, PDAC cells were demonstrated to internalize the PSC-derived exosome, resulting in high miR‑21 levels, which subsequently promoted cell migration, induced epithelial‑to‑mesenchymal transition (EMT) and increased matrix metalloproteinase‑2/9 activity. In addition, exosomal miR‑21 increased the levels of ERK1/2 and Akt phosphorylation in PDAC cells. Collectively, these results suggested that PSC‑derived exosomal miR‑21 may promote PDAC cell migration and EMT and enhance Ras/ERK signaling activity. Thus, miR‑21 may be a potential cause of poor prognosis in patients with pancreatic cancer and a new treatment target.

Comprehensive evaluation of SPATS2 expression and its prognostic potential in liver cancer

Spermatogenesis associated serine rich 2 (SPATS2) has been reported to be dysregulated in few types of cancer; however, no reports have investigated SPATS2 in liver cancer. The aim of the present study was to investigate SPATS2 expression in liver cancer and to analyze its association with the prognosis of liver cancer patients.We examined the differential expression of SPATS2 in liver cancer by exploring The Cancer Genome Atlas (TCGA) database. The diagnostic efficiency of SPATS2 was obtained by Receiver Operating Characteristic (ROC) curve. The Chi-Squared test was used to assess clinical relevance. Survival analysis and Cox regression model were used to detect the effect of SPATS2 on the survival of liver cancer patients. Gene Set Enrichment Analysis (GSEA) was used to identify signaling pathways related to SPATS2 expression.SPATS2 is highly expressed in liver cancer (P < 2.2e-16) and has the high diagnostic ability (AUC = 0.964). Survival analysis showed that patients with high SPATS2 expression have an apparently shorter overall survival (OS, P < .0001) and relapse-free survival (RFS, P < .0001). Cox regression analysis showed that high SPATS2 expression might be an independent risk factor for liver cancer (OS, HR = 2.41, P = .000; RFS, HR = 1.90, P < .001). GSEA analysis identified 3 signaling pathways (Mitotic spindle, G2 M checkpoint, E2F targets) that were enriched in the presence of high SPATS2 expression.SPATS2 expression could be a novel diagnostic and prognostic biomarker in liver cancer.

The prognostic value of lncRNA SNHG4 and its potential mechanism in liver cancer

BACKGROUND AND OBJECT

Emerging evidence shows that non-coding RNA functions as new gene regulators and prognostic markers in several cancers, including liver cancer. Here, we focused on the small nucleolar RNA host gene 4 (SNHG4) in liver cancer prognosis based on The Cancer Genome Atlas (TCGA) data.

METHODS

The expression data and clinical information were downloaded from TCGA. Chi-square tests evaluated the correlation between SNHG4 expression and clinical parameters. Differences in survival between high and low expression groups (optic cutoff value determined by ROC) from Cox regression analysis were compared, and P-value was calculated by a log-rank test. Kaplan-Meier curves were compared with the log-rank test. GSEA and ceRNA network were conducted to explore the potential mechanism.

RESULTS

Data mining of lncRNA expression data for 371 patients with primary tumor revealed overexpression of SNHG4 in liver cancer. High SNHG4 expression was correlated with histological type (P = 0.01), histologic grade (P = 0.001), stage (P = 0.01), T classification (P = 0.004) and survival status (P = 0.013). Patients with high SNHG4 expression had poor overall survival and relapse-free survival compared with those with low SNHG4 expression. Multivariate analysis identified SNHG4 as an independent prognostic factor of poor survival in liver cancer. GSEA revealed related signaling pathway and ceRNA network explored the further mechanism.

CONCLUSION

High SNHG4 expression is an independent predictor of poor prognosis in liver cancer.

Clinical characteristics and prognostic value of MEX3A mRNA in liver cancer

BACKGROUND

MEX3A is an RNA-binding proteins (RBPs) that promotes the proliferation, invasion, migration and viability of cancer cells. The aim of this study was to explore the clinicopathological characteristics and prognostic significance of MEX3A mRNA expression in liver cancer.

METHODS

RNA-Seq and clinical data were collected from The Cancer Genome Atlas (TCGA). Boxplots were used to represent discrete variables of MEX3A. Chi-square tests were used to analyze the correlation between clinical features and MEX3A expression. Receiver operating characteristic (ROC) curves were used to confirm diagnostic ability. Independent prognostic ability and values were assessed using Kaplan-Meier curves and Cox analysis.

RESULTS

We acquired MEX3A RNA-Seq from 50 normal liver tissues and 373 liver cancer patients along with clinical data. We found that MEX3A was up-regulated in liver cancer which increased according to histological grade (p < 0.001). MEX3A showed moderate diagnostic ability for liver cancer (AUC = 0.837). Kaplan-Meier curves and Cox analysis revealed that the high expression of MEX3A was significantly associated with poor survival (OS and RFS) (p < 0.001). Moreover, MEX3A was identified as an independent prognostic factor of liver cancer (p < 0.001).

CONCLUSIONS

MEX3A expression shows promise as an independent predictor of liver cancer prognosis.

Elevated high mobility group A2 expression in liver cancer predicts poor patient survival

BACKGROUND

liver cancer is a malignant tumor with a high morbidity and mortality that endangers human health. High mobility group A2 (HMGA2) is a chromosome associated protein that participates in embryogenesis, tissue development, tumorigenesis and development.

OBJECTIVE

to explore the relationship between HMGA2 expression and the clinicopathological parameters and survival of liver cancer patients using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (HCC) data.

METHODS

RNA-sequencing data and the corresponding clinical characteristics of the patients were downloaded from the Atlas database. The Chi-squared test was used to assess the relationship between HMGA2 expression and clinical variables. Cox regression analysis was used to compare survival rates between the high- and low-expressing groups; the p-values and Kaplan-Meier survival curves were compared using the log-rank test.

RESULTS

RNA-seq data from 373 cases of liver cancer cases were analyzed. HMGA2 was overexpressed in liver cancer and significantly associated with gender (p = 0.0357), T classification (p = 0.0063), clinical classification (p = 0.0026) and overall survival (p = 0.0386). According to the multivariate analysis, HMGA2 could independently predict overall survival in liver cancer.

CONCLUSIONS

HMGA2 independently predicts poor prognosis in liver cancer and serves as a molecular marker to determine disease prognosis.

LncRNA ROR1‑AS1 high expression and its prognostic significance in liver cancer

Hepatocellular carcinoma (HCC) is a common disease of the digestive system with no curative treatments. Long noncoding RNA tyrosine protein kinase transmembrane receptor 1 antisense RNA 1 (lncRNA ROR1‑AS1) is an lncRNA whose functions have been predicted in human diseases; however, its important role in cancer has been probed only in mantle cell lymphoma, not in HCC. Therefore, the present study aimed to elucidate the prognostic significance of lncRNA ROR1‑AS1 in HCC. The Cancer Genome Atlas Liver Hepatocellular Carcinoma was used to analyze the expression of ROR1‑AS1 in liver cancer. χ2 tests were performed to evaluate associations between clinical characteristics and ROR1‑AS1 expression. The role of ROR1‑AS1 in HCC prognosis was assessed using Kaplan‑Meier curves and proportional hazards model (Cox) analysis. Gene set enrichment analysis was performed by using a Gene Expression Omnibus dataset. At the same time, Multi Experiment Matrix was used to predict genes that may be co‑expressed with ROR1‑AS1. The Database for Annotation, Visualization and Integrated Discovery and KO‑Based Annotation System were used to analyze the most closely associated cytological behaviors and pathways in HCC. Then, the genes in the three databases were integrated to screen mRNAs, microRNAs and lncRNAs that had co‑expression relationships with ROR1‑AS1. Cytoscape, Search Tool for the Retrieval of Interacting Genes/Proteins and Molecular Evolutionary Genetics Analysis were used to map potential regulatory networks and developmental relationships associated with ROR1‑AS1. Finally, 12 genes most closely associated with ROR1‑AS1 were identified, and their relationship was described using a Circos plot. The results showed that ROR1‑AS1 was upregulated in HCC, and its expression was related to clinical stage, T stage and N stage. Furthermore, Kaplan‑Meier curves and Cox analysis indicated that high expression of ROR1‑AS1 was associated with poor prognosis, and that ROR1‑AS1 was an independent risk factor for HCC. Co‑expression data suggested that there may be a large regulatory network of 45 genes with indirect associations with ROR1‑AS1, a small regulatory network of 15 genes with direct or indirect regulatory relationships, and a special regulatory network containing 12 genes directly associated with ROR1‑AS1. The present findings indicated that high expression of ROR1‑AS1 suggests poor prognosis in patients with HCC.

GPSM2 Serves as an Independent Prognostic Biomarker for Liver Cancer Survival

BACKGROUND AND OBJECTIVE

Liver cancer is a malignancy with a poor prognosis. G protein signaling modulator 2 is mainly related to cell division and cell cycle regulation. In this review, the relationship between G protein signaling modulator 2 and clinical characteristics of patients with liver cancer has been explored, especially with respect to its prognostic value.

METHODS

G protein signaling modulator 2 messenger RNA expression and clinicopathological characteristics of patients with liver cancer were obtained from The Cancer Genome Atlas. The expression level of G protein signaling modulator 2 RNA-Seq was validated by using Gene Expression Omnibus. Chi-square test was performed to evaluate the relationship between G protein signaling modulator 2 expression and clinical characteristics. The threshold value of G protein signaling modulator 2 in the diagnosis of liver cancer was evaluated by a receiver-operating characteristic curve. Cox regression analysis and Kaplan-Meier curves were performed to evaluate the relationship between G protein signaling modulator 2 and liver cancer prognosis, which included overall and residual-free survival, and explored the prognostic value of G protein signaling modulator 2. Liver cancer survival analyses were validated by using the data of G protein signaling modulator 2 RNA-Seq from the International Cancer Genome Consortium.

RESULTS

The expression level of G protein signaling modulator 2 messenger RNA was remarkably higher in liver cancer than that in healthy tissues (P < 2.2 × e-16), which was also validated by data from the GSE14520 database. In addition, high G protein signaling modulator 2 expression significantly correlated with histological grade (P = .020), vital status (P < .001), clinical (P = .001), and T stage (P = .001). The receiver-operating characteristic curves showed G protein signaling modulator 2 to be an advantageous diagnostic molecule for liver cancer (area under curve = 0.893). Furthermore, the results of Cox analysis and Kaplan-Meier curves suggested that the upregulation of G protein signaling modulator 2 expression is linked to poor prognosis and G protein signaling modulator 2 messenger RNA could be an independent predictor for liver cancer, which was validated by data from the International Cancer Genome Consortium database.

CONCLUSIONS

G protein signaling modulator 2 messenger RNA was overexpressed in liver cancer, and G protein signaling modulator 2 is an independent prognostic factor. G protein signaling modulator 2 is expected to be a treatment target for cancer.

Low EIF2B5 expression predicts poor prognosis in ovarian cancer

Ovarian cancer has the highest mortality among gynecological cancers. Although ovarian cancer usually responds well to chemotherapy, most patients still have a poor prognosis. EIF2B5 is a crucial molecule in posttranscriptional modifications involved in tumor progression, and here we investigated the prognostic role of EIF2B5 in ovarian cancer. We examined the differential expression of EIF2B5 mRNA in ovarian cancer by exploring The Cancer Genome Atlas (TCGA) database. The chi square test was used to identify a clinical correlation. Survival analysis and Cox regression model were performed to determine the association between EIF2B5 expression and overall survival (OS) in ovarian cancer patients. As a result, Low EIF2B5 expression was found in ovarian cancer tissues and correlated with survival status. Survival analysis showed that ovarian cancer patients with low EIF2B5 expression had a short OS. Moreover, Cox regression analysis indicated that low EIF2B5 expression was an independent risk factor for a poor prognosis in ovarian cancer. Additionally, according to gene set enrichment analysis, mesenchymal transition, angiogenesis, coagulation, and bile acid metabolism were differentially enriched in ovarian cancer with high EIF2B5 expression. In conclusion, Low EIF2B5 expression is an independent risk factor for a poor prognosis in ovarian cancer patients.

Comprehensive multi-omics analysis identified core molecular processes in esophageal cancer and revealed GNGT2 as a potential prognostic marker

BACKGROUND

Esophageal cancer is one of the most poorly diagnosed and fatal cancers in the world. Although a series of studies on esophageal cancer have been reported, the molecular pathogenesis of the disease remains elusive.

AIM

To investigate comprehensively the molecular process of esophageal cancer.

METHODS

Differential expression analysis was performed to identify differentially expressed genes (DEGs) in different stages of esophageal cancer from The Cancer Genome Atlas data. Exacting gene interaction modules were generated, and hub genes in the module interaction network were found. Further, through survival analysis, methylation analysis, pivot analysis, and enrichment analysis, some important molecules and related functions/pathways were identified to elucidate potential mechanisms in esophageal cancer.

RESULTS

A total of 7457 DEGs and 14 gene interaction modules were identified. These module genes were significantly involved in the positive regulation of protein transport, gastric acid secretion, insulin-like growth factor receptor binding, and other biological processes as well as p53 signaling pathway, epidermal growth factor signaling pathway, and epidermal growth factor receptor signaling pathway. Transcription factors (including hypoxia inducible factor 1A) and non-coding RNAs (including colorectal differentially expressed and hsa-miR-330-3p) that significantly regulate dysfunction modules were identified. Survival analysis showed that G protein subunit gamma transducin 2 (GNGT2) was closely related to survival of esophageal cancer. DEGs with strong methylation regulation ability were identified, including SST and SH3GL2. Furthermore, the expression of GNGT2 was evaluated by quantitative real time polymerase chain reaction, and the results showed that GNGT2 expression was significantly upregulated in esophageal cancer patient samples and cell lines. Moreover, cell counting kit-8 assay revealed that GNGT2 could promote the proliferation of esophageal cancer cell lines.

CONCLUSION

This study not only revealed the potential regulatory factors involved in the development of esophageal cancer but also deepens our understanding of its underlying mechanism.

High MAST2 mRNA expression and its role in diagnosis and prognosis of liver cancer

Liver cancer is a high morbidity and low survival disease all over the world. Chromosomal instability is hallmark of liver cancer. Microtubule-associated serine and threonine kinase 2 (MAST2), as a microtubule associated protein, may involve in tumorous chromosomal instability and plays important roles in cell proliferation and survival. The role of MAST2 in liver cancer has not been well elucidated, which is the aim of our study. In this study, The Cancer Genome Atlas database was used to study the MAST2 mRNA expression in liver cancer, and Chi-squared tests were performed to test the correlation between clinical features and MAST2 expression. ROC curve was performed to examined the diagnostic capacity. The prognostic value of MAST2 in liver cancer was assessed through Kaplan-Meier curves as well as Cox analysis. Our results showed MAST2 was upregulated in liver cancer, and the area under the curve (AUC) was 0.925 and indicated powerful diagnostic capability. High MAST2 expression was associated with advanced clinical status such as histological type (p = 0.0059), histologic grade (p = 0.0142), stage (p = 0.0008), T classification (p = 0.0028), N classification (p = 0.0107), survival status (p = 0.0062), and poor prognosis of patients. Importantly, MAST2 was an independent risk factor for patients' prognosis after adjusting for other risk factors including stage, T classification, and residual tumor. In total, MAST2 is a potential diagnostic and prognostic biomarker of liver cancer.