NUF2 is a valuable prognostic biomarker to predict early recurrence of hepatocellular carcinoma after surgical resection

Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking

Background

Left ventricular hypertrophy (LVH) is a common consequence of hypertension and can lead to heart failure. The immune response plays an important role in hypertensive LVH; however, there is no comprehensive method to investigate the mechanistic relationships between immune response and hypertensive LVH or to find novel therapeutic targets. This study aimed to screen hub immune-related genes involved in hypertensive LVH as well as to explore immune target-based therapeutic drugs.

Materials and methods

RNA-sequencing data from a mouse model generated by angiotensin II infusion were subjected to weighted gene co-expression network analysis (WGCNA) to identify core expression modules. Machine learning algorithms were applied to screen immune-related LVH characteristic genes. Heart structures were evaluated by echocardiography and cardiac magnetic resonance imaging (CMRI). Validation of hub genes was conducted by RT-qPCR and western blot. Using the Connectivity Map database and molecular docking, potential small-molecule drugs were explored.

Results

A total of 1215 differentially expressed genes were obtained, most of which were significantly enriched in immunoregulation and collagen synthesis. WGCNA and multiple machine learning strategies uncovered six hub immune-related genes (Ankrd1, Birc5, Nuf2, C1qtnf6, Fcgr3, and Cdca3) that may accurately predict hypertensive LVH diagnosis. Immune analysis revealed that fibroblasts and macrophages were closely correlated with hypertensive LVH, and hub gene expression was significantly associated with these immune cells. A regulatory network of transcription factor-mRNA and a ceRNA network of miRNA-lncRNA was established. Notably, six hub immune-related genes were significantly increased in the hypertensive LVH model, which were positively linked to left ventricle wall thickness. Finally, 12 small-molecule compounds with the potential to reverse the high expression of hub genes were ruled out as potential therapeutic agents for hypertensive LVH.

Conclusion

This study identified and validated six hub immune-related genes that may play essential roles in hypertensive LVH, providing new insights into the potential pathogenesis of cardiac remodeling and novel targets for medical interventions.

Emerging Prognostic Markers in Patients Undergoing Liver Resection for Hepatocellular Carcinoma: A Narrative Review

In patients with hepatocellular carcinoma (HCC), liver resection is potentially curative. Nevertheless, post-operative recurrence is common, occurring in up to 70% of patients. Factors traditionally recognized to predict recurrence and survival after liver resection for HCC include pathologic factors (i.e., microvascular and capsular invasion) and an increase in alpha-fetoprotein level. During the past decade, many new markers have been reported to correlate with prognosis after resection of HCC: liquid biopsy markers, gene signatures, inflammation markers, and other biomarkers, including PIVKA-II, immune checkpoint molecules, and proteins in urinary exosomes. However, not all of these new markers are readily available in clinical practice, and their reproducibility is unclear. Liquid biopsy is a powerful and established tool for predicting long-term outcomes after resection of HCC; the main limitation of liquid biopsy is represented by the cost related to its technical implementation. Numerous patterns of genetic expression capable of predicting survival after curative-intent hepatectomy for HCC have been identified, but published findings regarding these markers are heterogenous. Inflammation markers in the form of prognostic nutritional index and different blood cell ratios seem more easily reproducible and more affordable on a large scale than other emerging markers. To select the most effective treatment for patients with HCC, it is crucial that the scientific community validate new predictive markers for recurrence and survival after resection that are reliable and widely reproducible. More reports from Western countries are necessary to corroborate the evidence.

NUF2 regulated the progression of hepatocellular carcinoma through modulating the PI3K/AKT pathway via stabilizing ERBB3

Hepatocellular carcinoma (HCC) is among the most prevalent and lethal cancers worldwide. The NDC80 kinetochore complex component NUF2 has been previously identified as up-regulating in HCC and associated with patient prognosis. However, the pathophysiological effects and molecular mechanisms of NUF2 in tumorigenesis remain unclear. In this study, we confirmed a significant increase in NUF2 expression in HCC tissues and established a correlation between high NUF2 expression and adverse outcomes in HCC patients. Through in vitro and in vivo experiments, we demonstrated that genetic inhibition of NUF2 suppressed the proliferation of HCC cells and disrupted the cell cycle. Further investigation into the molecular mechanisms revealed that NUF2 interacted with ERBB3, inhibiting its ubiquitination degradation, thus activating the PI3K/AKT signaling pathway and influencing cell cycle regulation. Overall, this study revealed the crucial role of NUF2 in promoting the malignant progression of HCC, suggesting its potential as both a prognostic biomarker and a therapeutic target for HCC.

Molecular characterization-based multi-omics analyses in primary liver cancer using the Japanese version of the genome atlas

BACKGROUND

Primary liver cancer (PLC) is classified into hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular and intrahepatic cholangiocarcinoma (CHC). We investigated the genomic landscape of PLC according to the histological classification and established a cross-histological molecular subtyping for PLC by a multi-omics analysis.

METHODS

We analyzed 265 PLC cases with whole-exome sequencing and DNA copy number analyses and 251 cases with gene expression profiling.

RESULTS

The cohort included HCC (n = 223, 84%), ICC (n = 34, 13%), and CHC (n = 8, 3%). Mutation analyses identified histological type-specific driver genes, such as CTNNB1 in HCC and KRAS, IDH1, and PIK3CA in ICC, and ARID1A and KMT2C in CHC. The tumor suppressor gene TP53 mutation was detected in 21.1% of HCC, 16.1% of ICC, and 25.0% of CHC cases. Other well-characterized tumor suppressor genes included RB1, which was mutated in 2.8% of HCC and 3.2% of ICC; and PTEN, which was mutated in 1.4% of HCC, 3.2% of ICC, and 12.5% of CHC cases. DNA copy number analyses identified focal amplifications, with NUF2 (1q23.3) the most frequently detected as an amplified gene in all 3 types (HCC, 3.8%; CHC, 12.5%, ICC, 3.2%). Molecular subtyping for PLC based on the multi-omics analysis identified three subtypes, one of which was associated with recurrence after resection and amplified genes located at chromosome 8q.

CONCLUSIONS

Our dataset serves as a fundamental resource for genomic medicine for PLC in Japan and identified amplified genes located at chromosome 8q as promising therapeutic targets for the subgroup with a poor prognosis.

NUF2 Promotes Breast Cancer Development as a New Tumor Stem Cell Indicator

Multiple new subtypes of breast cancer (BRCA) are identified in women each year, rendering BRCA the most common and rapidly expanding form of cancer in females globally. NUF2 has been identified as a prognostic factor in various human cancers, regulating cell apoptosis and proliferation. However, its role in BRCA prognosis has not been clarified. This study explored the role of NUF2 in breast cancer development and prognosis using informatic analysis combined with in vivo intracellular studies. Through the online website TIMER, we evaluated the transcription profile of NUF2 across a variety of different cancer types and found that NUF2 mRNA was highly expressed in BRCA patients. Its transcription level was found to be related to the subtype, pathological stage, and prognosis of BRCA. The R program analysis showed a correlation of NUF2 with cell proliferation and tumor stemness in the BRCA patient samples. Subsequently, the association between the NUF2 expression level and immune cell infiltration was analyzed using the XIANTAO and TIMER tools. The results revealed that NUF2 expression was correlated with the responses of multiple immune cells. Furthermore, we observed the effect of NUF2 expression on tumor stemness in BRCA cell lines in vivo. The experimental results illuminated that the overexpression of NUF2 statistically upregulated the proliferation and tumor stemness ability of the BRCA cell lines MCF-7 and Hs-578T. Meanwhile, the knockdown of NUF2 inhibited the abilities of both cell lines, a finding which was verified by analyzing the subcutaneous tumorigenic ability in nude mice. In summary, this study suggests that NUF2 may play a key role in the development and progression of BRCA by affecting tumor stemness. As a stemness indicator, it has the potential to be one of the markers for the diagnosis of BRCA.

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

A 9-gene prognostic signature for kidney renal clear cell carcinoma overall survival based on co-expression and regression analyses

This research attempted to screen potential signatures associated with KIRC progression and overall survival by weighted gene co-expression network analysis (WGCNA) and Cox regression. The KIRC-associated mRNA expression and clinical data were accessed from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened by differential analysis. A co-expression network was constructed by "WGCNA". Based on WGCNA module, GO and KEGG analyses were performed. Protein-protein interaction (PPI) network was constructed. Prognostic signatures were screened by Lasso-Cox regression. Prognostic model was evaluated by Receiver Operating Characteristic (ROC) and Kaplan-Meier (K-M) curves. Multivariate Cox and nomogram were introduced to examine whether risk score could be an independent marker. qRT-PCR was introduced to determine expression of 9 hub genes in KIRC clinical tumor tissues and adjacent tissues, respectively. Genes in the green module were highly associated with clinical status, and green module genes were significantly enriched in mitotic nuclear division, cell cycle, and p53 signaling pathway. Twenty-six candidates were subsequently screened out from the green module. Next, a 9-gene prognostic model (DLGAP5, NUF2, TOP2A, RRM2, HJURP, PLK1, AURKB, KIF18A, CCNB2) was constructed. The predicting ability of the model was optimal. Some cancer-related signaling pathways were differently activated between two risk score groups. Additionally, under-expression of some signature genes (AURKB, CCNB2, PLK1, RRM2, TOP2A) was associated with better survival rate for KIRC patients. Meanwhile, all 9 hub genes were substantially overexpressed in KIRC patients. A KIRC prognostic signature was screened in this study, contributing valuable findings to KIRC biomarker development.

NUF2 Expression Promotes Lung Adenocarcinoma Progression and Is Associated With Poor Prognosis

Aberrant expression of the gene encoding the Ndc80 kinetochore complex component (NUF2) reportedly contributes to the progression of several human cancers. However, the functional roles of NUF2 and their underlying mechanisms in lung adenocarcinoma (LUAD) are largely unknown. The current study aimed to investigate the role of NUF2 in LUAD tumorigenesis. Here, TCGA, ONCOMINE, the Human Protein Atlas, UALCAN, and the results of our cohort were used to analyze the expression of NUF2 in LUAD. A Kaplan–Meier analysis and univariate and multivariate Cox regression analyses were performed to estimate the prognostic values of NUF2 expression in the Cancer Genome Atlas cohort. We studied the effects of NUF2 expression on proliferation, migration, invasion, and tumor growth using LUAD cell lines. Gene set enrichment analysis (GSEA) was used to analyze the pathways and biological function enrichment of NUF2 in LUAD. The ssGSEA database was used to analyze the relationship between NUF2 expression and immune cell infiltration in LUAD. Results revealed elevated expression of NUF2 in LUAD specimens. Patients overexpressing NUF2 had poor prognoses relative to those with low NUF2 expression. Knockdown of NUF2 suppressed the proliferation, migration, invasion, epithelial-mesenchymal transition, and colony formation of LUAD cells. Moreover, NUF2 knockdown induced cell cycle arrest at the G0/G1 phase. Gene Ontology and GSEA analyses suggested that NUF2 may be involved in immunity, proliferation, and apoptosis-related pathways. NUF2 overexpression was positively correlated with differential immune cell infiltration. In conclusion, NUF2 expression was associated with the clinical phenotype of LUAD and hence has potential implications in LUAD treatment.

Prognostic Value of Postoperative Circulating Tumor DNA in Patients With Early- and Intermediate-Stage Hepatocellular Carcinoma

Majority of patients with resected early- and intermediate-stage liver cancer will experience postoperative recurrence. This study aimed to investigate the application of ctDNA sequencing in the postoperative period of hepatocellular carcinoma. A total of 96 patients with liver cancer were enrolled in this study. Postoperative peripheral blood samples were collected from all patients after surgery and analyzed using hybridization capture-based next-generation sequencing. Identification of at least one somatic mutation in the peripheral blood was defined as ctDNA+. Five genetic features in tumor tissues were associated with disease-free survival (DFS) using Lasso-Cox model. The area under the receiver operating characteristic curve was 0.813 and 0.882 in training and validation cohorts, respectively. The recurrence rate in ctDNA+ and ctDNA- groups was 60.9% and 27.8%, respectively. Multivariate Cox regression analysis showed that the postoperative ctDNA was an independent prognostic predictor of DFS (HR [hazard ratio]: 6.074, 95% Cl [confidence interval]: 2.648-13.929, P<0.001) and overall survival (OS) (HR: 4.829, 95% CI: 1.508-15.466, P=0.008). Combined ctDNA with AFP improved prediction performance. The median DFS was 2.0, and 8.0 months in ctDNA+/AFP-H and ctDNA+/AFP-L groups, respectively; while ctDNA-/AFP-H and ctDNA-/AFP-L groups had not reached the median time statistically (Log-rank test, P < 0.0001). Furthermore, ctDNA- patients had better prognosis than ctDNA+ patients irrespective of tumor stage. Postoperative ctDNA sequencing has great prognostic value in patients with liver cancer. Patients with positive ctDNA should receive more intensive disease monitoring and more aggressive treatment strategies to improve the survival time.

Novel Gene Signatures as Prognostic Biomarkers for Predicting the Recurrence of Hepatocellular Carcinoma

Simple Summary

A high percentage of patients who undergo surgical resection for hepatocellular carcinoma (HCC) experience recurrence. Therefore, identification of accurate molecular markers for predicting recurrence of HCC is important. We analyzed recurrence and non-recurrence HCC tissues using two public omics datasets comprising microarray and RNA-sequencing and found novel gene signatures associated with recurrent HCC. These molecules might be used to not only predict for recurrence of HCC but also act as potential prognostic indicators for patients with HCC.

Abstract

Hepatocellular carcinoma (HCC) has a high rate of cancer recurrence (up to 70%) in patients who undergo surgical resection. We investigated prognostic gene signatures for predicting HCC recurrence using in silico gene expression analysis. Recurrence-associated gene candidates were chosen by a comparative analysis of gene expression profiles from two independent whole-transcriptome datasets in patients with HCC who underwent surgical resection. Five promising candidate genes, CETN2, HMGA1, MPZL1, RACGAP1, and SNRPB were identified, and the expression of these genes was evaluated using quantitative reverse transcription PCR in the validation set (n = 57). The genes CETN2, HMGA1, RACGAP1, and SNRPB, but not MPZL1, were upregulated in patients with recurrent HCC. In addition, the combination of HMGA1 and MPZL1 demonstrated the best area under the curve (0.807, 95% confidence interval [CI] = 0.681–0.899) for predicting HCC recurrence. In terms of clinicopathological correlation, CETN2, MPZL1, RACGAP1, and SNRPB were upregulated in patients with microvascular invasion, and the expression of MPZL1 and SNRPB was increased in proportion to the Edmonson tumor differentiation grade. Additionally, overexpression of CETN2, HMGA1, and RACGAP1 correlated with poor overall survival (OS) and disease-free survival (DFS) in the validation set. Finally, Cox regression analysis showed that the expression of serum alpha-fetoprotein and RACGAP1 significantly affected OS, whereas platelet count, microvascular invasion, and HMGA1 expression significantly affected DFS. In conclusion, HMGA1 and RACGAP1 may be potential prognostic biomarkers for predicting the recurrence of HCC after surgical resection.

Role of chromosome 1q copy number variation in hepatocellular carcinoma

Chromosome 1q often has been observed to be amplified in hepatocellular carcinoma. This review summarizes literature reports of multiple genes that have been proposed as possible 1q amplification drivers. These largely fall within 1q21-1q23. In addition, publicly available copy number alteration data from The Cancer Genome Atlas project were used to identify additional candidate genes involved in carcinogenesis. The most frequent location for gene amplification was 1q22, consistent with the results of the literature search. The genes TPM3 and NUF2 were found to be candidates whose amplification and/or mRNA up-regulation was most highly associated with poorer hepatocellular carcinoma outcomes.

Integrative Multi-Omics Analysis of Identified NUF2 as a Candidate Oncogene Correlates With Poor Prognosis and Immune Infiltration in Non-Small Cell Lung Cancer

Background

Lung cancer is one of the most common malignant tumors and the leading causes of cancer-related deaths worldwide. As a component of the nuclear division cycle 80 complex, NUF2 is a part of the conserved protein complex related to the centromere. Although the high expression of NUF2 has been reported in many different types of human cancers, the multi-omics analysis in non-small cell lung cancer (NSCLC) of NUF2 remains to be elucidated.

Methods

In this analysis, NUF2 expression difference analysis in non-small cell lung cancer was evaluated by Oncomine, TIMER, GEO, and TCGA database. And the prognosis analysis of NUF2 based on Kaplan-Meier was performed. R language was used to analyze the differential expression genes, functional annotation and protein-protein interaction (PPI). GSEA analysis of differential expression genes was also carried out. Mechanism analysis about exploring the characteristic of NUF2, multi-omics, and correlation analysis was carried out using UALCAN, cBioportal, GEPIA, TIMER, and TISIDB, respectively.

Results

The expression of NUF2 in NSCLC, both lung adenocarcinoma (LUAD) and squamous lung cancer (LUSC), was significantly higher than that in normal tissues. The analysis of UALCAN database samples proved that NUF2 expression was connected with stage and smoking habits. Meanwhile, the overall survival curve also validated that high expression of NUF2 has a poorer prognosis in NSCLC. GO, KEGG, GSEA, subcellular location from COMPARTMENTS indicated that NUF2 may regulate the cell cycle. Correlation analysis also showed that NUF2 was mainly positively associated with cell cycle and tumor-related genes. NUF2 altered group had a poorer prognosis than unaltered group in NSCLC. Immune infiltration analysis showed that the NUF2 expression mainly have negatively correlation with immune cells and immune subtypes in LUAD and LUSC. Furthermore, quantitative PCR was used to validate the expression difference of NUF2 in LUAD and LUSC.

Conclusion

Our findings elucidated that NUF2 may play an important role in cell cycle, and significantly associated with tumor-related gene in NSCLC; we consider that NUF2 may be a prognostic biomarkers in NSCLC.

The roles of the cell division cycle-associated gene family in hepatocellular carcinoma

Background

The members of the cell division cycle-associated (CDCA) gene family are significant regulators of cell proliferation known to play key roles in various cancers. However, the function of CDCA genes in hepatocellular carcinoma (HCC) is unclear. The aim of this research was to clarify the roles of CDCA family members in HCC using bioinformatics analysis tools.

Methods

We studied data on the mRNA and protein expression of CDCA genes and survival in patients with HCC using the Oncomine, UALCAN, HPA, CCLE, LinkedOmics, cBioPortal, and Metascape databases.

Results

Significant overexpression of all CDCA members was found in HCC tissues. The expression levels of CDCAs were related to the tumor stage, and high expression levels were correlated with a low survival rate in patients with HCC. Also, we observed a high mutation rate (45%) of CDCAs in the HCC samples, which manifested as deep deletion, amplification, or increased mRNA expression. In the correlation analysis, we found that any 2 CDCA members were significantly positively correlated with each other. Cycle-related genes including AHCTF1, AKT1, BIRC5, CENPF, CENPL, and CENPQ were closely associated with CDCA gene alterations.

Conclusions

The findings of this study indicate that CDCAs may be potential therapeutic targets and prognostic indicators for patients with HCC.

Constructing the Logical Regression Model to Predict the Target of Jianpi Jiedu Decoction in the Treatment of Hepatocellular Carcinoma

Objectives

The purpose of this study was to identify the molecular mechanism and prognosis-related genes of Jianpi Jiedu decoction in the treatment of hepatocellular carcinoma.

Methods

The gene expression data of hepatocellular carcinoma samples and normal tissue samples were downloaded from TCGA database, and the potential targets of drug composition of Jianpi Jiedu decoction were obtained from TCMSP database. The genes were screened out in order to obtain the expression of these target genes in patients with hepatocellular carcinoma. The differential expression of target genes was analyzed by R software, and the genes related to prognosis were screened by univariate Cox regression analysis. Then, the LASSO model was constructed for risk assessment and survival analysis between different risk groups. At the same time, independent prognostic analysis, GSEA analysis, and prognostic analysis of single gene in patients with hepatocellular carcinoma were performed.

Results

174 compounds of traditional Chinese medicine were screened by TCMSP database, corresponding to 122 potential targets. 39 upregulated genes and 9 downregulated genes were screened out. A total of 20 candidate prognostic related genes were screened out by univariate Cox analysis, of which 12 prognostic genes were involved in the construction of the LASSO regression model. There was a significant difference in survival time between the high-risk group and low-risk group (p < 0.05). Among the genes related to prognosis, the expression levels of CCNB1, NQO1, NUF2, and CHEK1 were high in tumor tissues (p < 0.05). Survival analysis showed that the high expression levels of these four genes were significantly correlated with poor prognosis of HCC (p < 0.05). GSEA analysis showed that the main KEGG enrichment pathways were lysine degradation, folate carbon pool, citrate cycle, and transcription factors.

Conclusions

In the study, we found that therapy target genes of Jianpi Jiedu decoction were mainly involved in metabolism and apoptosis in hepatocellular carcinoma, and there was a close relationship between the prognosis of hepatocellular carcinoma and the genes of CCNB1, NQO1, NUF2, and CHEK1.

Correlation of NUF2 Over-expression with Poorer Patient Survival in Multiple Cancers

Purpose

NUF2 has been implicated in multiple cancers recently, suggesting NUF2 may play a role in the common tumorigenesis process. In this study, we aim to perform comprehensive meta-analysis of NUF2 expression in the cancer types included in the Cancer Genome Atlas (TCGA).

Materials and Methods

RNA-sequencing data in 31 cancer types in the TCGA data and 11 independent datasets were used to examine NUF2 expression. Silencing NUF2 using targeting shRNAs in hepatocellular carcinoma (HCC) cell lines was used to evaluate NUF2’s role in HCC in vitro and in vivo.

Results

NUF2 up-regulation is significantly observed in 23 out of the 31 cancer types in the TCGA datasets and validated in 13 major cancer types using 11 independent datasets. NUF2 overexpression was clinically important as high NUF2 was significantly associated with tumor stages in eight different cancers. High NUF2 was also associated with significantly poorer patient overall survival and disease-free survival in eight and six cancers, respectively. We proceeded to validate NUF2 overexpression and its negative association with overall survival at the protein level in an independent cohort of 40 HCC patients. Compared to the non-targeting controls, NUF2 knockdown cells showed significantly reduced ability to grow, migrate into a scratch wound and invade the 8 μm porous membrane in vitro. Moreover, NUF2 knockdown cells also formed significantly smaller tumors than control cells in mouse xenograft assays in vivo.

Conclusion

NUF2 up-regulation is a common feature of many cancers. The prognostic potential and functional impact of NUF2 up-regulation warrant further studies.

Establishment of a Genomic-Clinicopathologic Nomogram for Predicting Early Recurrence of Hepatocellular Carcinoma After R0 Resection

BACKGROUND

A high rate of postoperative recurrence, especially early recurrence (ER) occurring within 1 year, seriously impedes patients with hepatocellular carcinoma (HCC) from achieving long-term survival. This study aimed to establish a genomic-clinicopathologic nomogram for precisely predicting ER in HCC patients after R0 resection.

METHODS

Two reliable datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were selected as the training and validation cohorts, respectively. The prognostic genes related to ER were screened out by univariate Cox regression analysis and differential expression analysis. The gene-based prognostic index was constructed using LASSO and Cox regression analyses, and its independent prognostic value was assessed by Kaplan-Meier and multivariate Cox analyses. Gene set enrichment analysis (GSEA) was performed to explore the biological pathways related to the prognostic index. Finally, the nomogram integrating all the independent prognostic factors was established and comprehensively evaluated by calibration plots, the C-index, receiver operating characteristic curves, and decision curve analysis.

RESULTS

Nine dysregulated and prognostic genes related to ER (ZNF131, TATDN2, TXN, DDX55, KPNA2, ZNF30, TIMELESS, SFRP1, and COLEC11) were identified (all P < 0.05). The prognostic index model based on the 9 genes was successfully constructed using the TCGA cohort and showed a certain capability to discriminate the ER group from the non-ER group (P < 0.05) and good independent prognostic value in terms of predicting poor early recurrence-free survival (P < 0.05). Eight biological pathways significantly related to ER were identified by GSEA, such as "cell cycle", "homologous recombination" and "p53 signaling pathway." The genomic-clinicopathologic nomogram integrating the 9-gene-based prognostic index and TNM stage displayed significantly higher predictive accuracy and clinical application value than that of TNM stage model both in the training and validation cohorts (all P < 0.05).

CONCLUSIONS

The novel genomic-clinicopathologic nomogram may be a convenient and powerful tool for accurately predicting ER in HCC patients after R0 resection.

Identification of novel transcription factor-microRNA-mRNA co-regulatory networks in pulmonary large-cell neuroendocrine carcinoma

Background

Large cell neuroendocrine carcinoma (LCNEC) of the lung is a rare neuroendocrine neoplasm. Previous studies have shown that microRNAs (miRNAs) are widely involved in tumor regulation through targeting critical genes. However, it is unclear which miRNAs play vital roles in the pathogenesis of LCNEC, and how they interact with transcription factors (TFs) to regulate cancer-related genes.

Methods

To determine the novel TF-miRNA-target gene feed-forward loop (FFL) model of LCNEC, we integrated multi-omics data from Gene Expression Omnibus (GEO), Transcriptional Regulatory Relationships Unraveled by Sentence-Based Text Mining (TRRUST), Transcriptional Regulatory Element Database (TRED), and The experimentally validated microRNA-target interactions database (miRTarBase database). First, expression profile datasets for mRNAs (GSE1037) and miRNAs (GSE19945) were downloaded from the GEO database. Overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified through integrative analysis. The target genes of the FFL were obtained from the miRTarBase database, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed on the target genes. Then, we screened for key miRNAs in the FFL and performed gene regulatory network analysis based on key miRNAs. Finally, the TF-miRNA-target gene FFLs were constructed by the hypergeometric test.

Results

A total of 343 DEGs and 60 DEMs were identified in LCNEC tissues compared to normal tissues, including 210 down-regulated and 133 up-regulated genes, and 29 down-regulated and 31 up-regulated miRNAs. Finally, the regulatory network of TF-miRNA-target gene was established. The key regulatory network modules included ETS1-miR195-CD36, TAOK1-miR7-1-3P-GRIA1, E2F3-miR195-CD36, and TEAD1-miR30A-CTHRC1.

Conclusions

We constructed the TF-miRNA-target gene regulatory network, which is helpful for understanding the complex LCNEC regulatory mechanisms.

Six Novel Biomarkers for Diagnosis and Prognosis of Esophageal squamous cell carcinoma: validated by scRNA-seq and qPCR

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide. ESCC has a generally poor prognosis and there is a lack of available biomarkers for diagnosis and prognosis. The aim of the study was to identify novel biomarkers for ESCC. We screened the overlapping differentially expressed genes (DEGs) acquired from six Gene Expression Omnibus (GEO) ESCC datasets and The Cancer Genome Atlas (TCGA) ESCC datasets. Subsequently, protein-protein interaction network analysis was performed to identify the key hub genes. Then, Kaplan Meier survival and receiver operating curve (ROC) analysis were utilized to clarify the diagnostic and prognostic role of these hub genes. The UALCAN database, single cell RNA sequencing (scRNA-seq) and real-time quantitative PCR (qPCR) were performed to confirm the expression levels of identified hub genes. Finally, immune infiltration analysis was conducted to investigate the role of these genes in the pathogenesis of ESCC. The results showed that PBK, KIF2C, NUF2, KIF20A, RAD51AP1, and DEPDC1 effectively distinguish ESCC tissues from normal samples, and all of them were significantly correlated with overall survival. The results of scRNA-seq and qPCR indicated that the expression levels of hub genes in ESCC were significantly higher than in normal cells or tissues. Further immune infiltration analysis showed that infiltration of dendritic cells was significantly negatively correlated with PBK, KIF2C, NUF2, RAD51AP1, and DEPDC1 expression levels. In conclusion, our results suggest that PBK, KIF2C, NUF2, KIF20A, RAD51AP1 and DEPDC1 are all potential biomarkers for ESCC diagnosis and prognosis may also be potential therapeutic targets for ESCC.

Identification of Potential Hub Genes and Signal Pathways Promoting the Distinct Biological Features of Cord Blood-Derived Endothelial Progenitor Cells Via Bioinformatics

Background: Numerous studies, ranging from the alleviation of tissue ischemia to the assessment of cancer prognosis, have demonstrated the fundamental biological differences between human umbilical cord blood-derived endothelial progenitor cells (CB-EPCs) and adult peripheral blood-derived endothelial progenitor cells (PB-EPCs). However, the underlying molecular mechanisms that produce these differences are not clear.The purpose of this study was to identify potential hub genes, key protein interactive networks, and correlated signal pathways unique to CB-EPC biology via bioinformatic methods. Materials and Methods: We selected the microarray dataset GSE39763 and identified the differentially expressed genes (DEGs) using the "limma" package in the RStudio software. These DEGs were annotated by gene ontology enrichment analyses and signal pathway analyses. A protein-protein interaction (PPI) analysis was then performed to construct PPI networks and identify a hub protein module. We further validated candidate DEGs from the selected module in the gene expression profiling interactive analysis (GEPIA) database because the DEGs were enriched in cancer pathways. Results: Setting an adjusted p-value <0.01 and |Log₂ fold change (FC)| ≥ 2 as cutoff criteria, a total of 346 DEGs, including 314 upregulated genes and 32 downregulated genes in CB-EPCs, were identified. Expression of the genes encoding the AT-Hook Containing Transcription Factor 1 (AHCTF1), the Cancer Susceptibility Candidate 5 (CASC5), the Centromere Protein C (CENPC), the Centromere Protein E (CENPE), the Centromere Protein F (CENPF), the NUF2 Component of NDC80 Kinetochore Complex (NUF2), the RAN-Binding Protein 2 (RANBP2), the Shugoshin-like 2 (SGOL2), the Structural Maintenance of Chromosomes 3 (SMC3), and the Spindle Apparatus Coiled-Coil Protein 1 (SPDL1) proteins were specifically associated with CB-EPCs. Except for CENPC, the other nine genes' expression are all associated with a poorer overall survival rate in cancers. The expression levels of the CENPF and NUF2 genes in tumor patients were significantly higher than those in the controls. Conclusion: The CB-EPCs express genes with greater potential for proliferation and increased migration compared to PB-EPCs; in this regard they are similar to cancer cells.

Cancer-Testis Gene Expression in Hepatocellular Carcinoma: Identification of Prognostic Markers and Potential Targets for Immunotherapy

BACKGROUND

Cancer-testis genes can serve as prognostic biomarkers and valuable targets for immunotherapy in multiple tumors because of their restricted expression in testis and cancer. However, their expression pattern in hepatocellular carcinoma is still not well understood. The purpose is to comprehensively characterize the cancer-testis gene expression in hepatocellular carcinoma as well as identify prognostic markers and potential targets for immunotherapy.

METHODS

Cancer-testis database and publicly available data sets reporting new cancer-testis genes were integrated, and then restricted them in a testis and hepatocellular carcinoma expression pattern. Pathway enrichment analysis and survival analysis were conducted to evaluate the biological function and prognostic effect of cancer-testis genes. Clustering analysis and coexpression analysis were performed to illustrate cancer-testis gene expression patterns in hepatocellular carcinoma. The association of gene expression of each cancer-testis gene to the corresponding methylation status was detected. Finally, we explored the associations between cancer-testis genes and CD8⁺ T-cell infiltration in hepatocellular carcinoma by TISIDB, and then validated it in an independent hepatocellular carcinoma cohort with 72 patients.

RESULTS

A total of 59 testis-specific genes were identified highly expressed in hepatocellular carcinoma. Pathway enrichment analysis revealed that cancer-testis genes in hepatocellular carcinoma significantly involves in the process of cell cycle regulation. Most of the cancer-testis genes were coexpressed, and cluster analysis suggested that cancer-testis gene expressed in hepatocellular carcinoma is independent of sex, hepatitis status, and histology type. We also found that demethylation might be a regulatory mechanism of cancer-testis gene expression in hepatocellular carcinoma. Survival analysis indicated that cancer-testis genes could predict the prognosis of patients with hepatocellular carcinoma. Furthermore, BUB1B was identified contributing to the resistance of CD8⁺ T-cell infiltration in hepatocellular carcinoma and was an independent prognostic factor both for overall survival and disease-free survival.

CONCLUSIONS

Our analysis enables better understanding of cancer-testis genes in hepatocellular carcinoma and provides potential targets for hepatocellular carcinoma treatment. Experimental and clinical studies are needed for further validations.

Prognostic potential of PRPF3 in hepatocellular carcinoma

pre-mRNA processing factor 3 (PRPF3) is an RNA binding protein in a core component of the exon junction complex. Abnormal PRPF3 expression is potentially associated with carcinogenesis. However, the biological role of PRPF3 in hepatocellular carcinoma (HCC) remains to be determined. We analyzed PRPF3 expression via multiple gene expression databases and identified its genetic alterations and functional networks using cBioPortal. Co-expressed genes with PRPF3 and its regulators were identified using LinkedOmics. The correlations between PRPF3 and cancer immune infiltrates were investigated via Tumor Immune Estimation Resource (TIMER). PRPF3 was found up-regulated with amplification in tumor tissues in multiple HCC cohorts. High PRPF3 expression was associated with poorer overall survival (OS) and disease-free survival (DFS). Functional network analysis suggested that PRPF3 regulates spliceosome, DNA replication, and cell cycle signaling via pathways involving several cancer-related kinases and E2F family. Notably, PRPF3 expression was positively correlated with infiltrating levels of CD4+ T and CD8+ T cells, macrophages, neutrophils, and dendritic cells. PRPF3 expression showed strong correlations with diverse immune marker sets in HCC. These findings suggest that PRPF3 is correlated with prognosis and immune infiltrating in HCC, laying a foundation for further study of the immune regulatory role of PRPF3 in HCC.