Aberrant expression of nuclear KPNA2 is correlated with early recurrence and poor prognosis in patients with small hepatocellular carcinoma after hepatectomy

Phase separation of EEF1E1 promotes tumor stemness via PTEN/AKT-mediated DNA repair in hepatocellular carcinoma

This study aimed to investigate the associations of liquid-liquid phase separation (LLPS) and tumor stemness in hepatocellular carcinomas (HCC). LLPS-related genes were extracted from DrLLPS, LLPSDB and PhaSepDB databases. Stemness index (mRNAsi) was calculated based on the data from TCGA and Progenitor Cell Biology Consortium. Through some series of bioinformatics methods, we first found that stemness index mRNAsi was associated with worse survival outcomes, immune infiltration and therapy sensitivity in HCC. G2M checkpoint and DNA repair pathways were significantly activated with high mRNAsi. Totally, 71 differentially expressed LLPS genes in HCC were correlated with mRNAsi, and a mRNAsi-associated LLPS gene signature (KPNA2, EEF1E1 and ATIC) was identified to predict prognosis for HCC patients. mRNAsi-associated LLPS genes contributed to cluster HCC patients into four molecular clusters that markedly differed on survival, immune infiltration and therapy sensitivity. Further in vivo and in vitro experiments showed that EEF1E1 was highly expressed in HepG2 and HCCLM3 cells, and EEF1E1 silencing observably inhibited tumor cell growth, liver cancer stem cells (CSCs) markers (CD133, EpCAM and SOX2) expression, enhanced DNA damage marker γH2AX expression by activating PTEN/AKT pathway. EEF1E1 could undergo LLPS condensates, and roles of EEF1E1 on tumor cells were partly reversed after inhibiting LLPS using 1, 6-hexanediol. In conclusion, EEF1E1 was identified as a phase separation protein and involves in tumor stemness and DNA damage repair in HCC. EEF1E1 and its LLPS condensate may be novel targets to elaborate the underlying mechanisms of CSCs propagation in HCC.

Inhibition of KPNA2 by ivermectin reduces E2F1 nuclear translocation to attenuate keratinocyte proliferation and ameliorate psoriasis-like lesions

Psoriasis is a chronic, immune-mediated skin disease with a significant global prevalence. Karyopherin subunit alpha 2 (KPNA2), a nuclear transport protein involved in cellular activities such as differentiation, proliferation, apoptosis, and immune response, has emerged as a potential biomarker in several diseases. Our study found that KPNA2 was significantly upregulated in psoriasis patients and in imiquimod (IMQ)-induced psoriasis mouse models by bioinformatics and molecular biotechnology. In vivo, treatment with ivermectin, a KPNA2 inhibitor, significantly improved psoriasis symptoms in mice as evidenced by reduced erythema, desquamation, and skin thickness. Histopathological staining revealed decreased expression of KPNA2, K17, and Ki67 in ivermectin-treated mice, suggesting reduced abnormal differentiation and proliferation of keratinocytes. Transcriptome data and immunoblotting analysis showed that KPNA2 inhibition reduced inflammation and keratinocyte proliferation and differentiation in IMQ-induced mice. In vitro, EdU (5-ethynyl-2'-deoxyuridine) and flow cytometry experiments demonstrated that the downregulation of KPNA2 expression in HaCaT cells was capable of inhibiting the EGF (Epidermal Growth Factor)-induced activation of AKT/STAT3 signaling and keratinocytes proliferation. In addition, nuclear-cytoplasmic protein separation and immunofluorescence localization experiments showed that KPNA2 inhibition affected the nuclear translocation of E2F transcription factor 1 (E2F1), a process critical for keratinocyte proliferation. This study elucidated the role of KPNA2 in the pathogenesis of psoriasis and highlighted its potential as a target for future psoriasis therapies. These findings provide new insights into targeted therapy for psoriasis and have significant implications for future clinical treatment.

The role of KPNA2 as a monotonically changing differentially expressed gene in the diagnosis, risk stratification, and chemotherapy sensitivity of chronic hepatitis B-liver cirrhosis-hepatocellular carcinoma

PURPOSE

Chronic hepatitis B-liver cirrhosis-hepatocellular carcinoma (CLH), commonly called the "liver cancer trilogy", is a crucial evolutionary phase in the emergence of hepatocellular carcinoma (HCC) in China. Previous studies on early diagnostic biomarkers of HCC were limited to the end-stage of HCC and did not focus on the evolutionary process of CLH.

METHODS

11 monotonically changing differentially expressed genes (MCDEGs) highly correlated with CLH were screened through bioinformatic analysis and KPNA2 was identified for further research. The serum KPNA2 expression in different CLH states was detected by Enzyme linked immunosorbent assay (ELISA). A nomogram model was constructed using univariate and multivariate Cox regression methods.

RESULTS

The single-cell RNA-seq and bulk RNA-seq revealed that KPNA2 related to immune infiltration in HCC and may participate in cell cycle pathways in HCC. The serum KPNA2 expression was monotonically upregulated in CLH and was valuable for diagnosing different CLH states. Besides, chronic hepatitis B(CHB) patients, liver cirrhosis (LC) patients, and HCC patients were classified into subgroups with distinct serum KPNA2 expressions. Accordingly, patients with different serum KPNA2 expressions displayed various clinicopathological features. The AUC value of the nomogram model was 0.959 in predicting the likelihood of developing HCC in CHB patients or LC patients. Finally, we found that KPNA2 expression was negatively correlated with the IC50 of four chemotherapeutic drugs in HCC.

CONCLUSION

KPNA2 was a novel serum biomarker for diagnosing different CLH states, monitoring the dynamic evolution of CLH, and a new therapeutic target for intervening in the progression of CLH.

Novel roles of karyopherin subunit alpha 2 in hepatocellular carcinoma

Hepatocellular carcinoma is the most common type of liver cancer and associated with a high fatality rate. This disease poses a major threat to human health worldwide. A considerable number of genetic and epigenetic factors are involved in the development of hepatocellular carcinoma. However, the molecular mechanism underlying the progression of hepatocellular carcinoma remains unclear. Karyopherin subunit alpha 2 (KPNA2), also termed importin α1, is a member of the nuclear transporter family. In recent years, KPNA2 has been gradually linked to the nuclear transport pathway for a variety of tumor-associated proteins. Furthermore, it promotes tumor development by participating in various pathophysiological processes such as cell proliferation, apoptosis, immune response, and viral infection. In hepatocellular carcinoma, it has been found that KPNA2 expression is significantly higher in liver cancer tissues versus paracancerous tissues. Moreover, it has been identified as a marker of poor prognosis and early recurrence in patients with hepatocellular carcinoma. Nevertheless, the role of KPNA2 in the development of hepatocellular carcinoma remains to be determined. This review summarizes the current knowledge on the pathogenesis and role of KPNA2 in hepatocellular carcinoma, and provides new directions and strategies for the diagnosis, treatment, and prediction of prognosis of this disease.

A Five-LLPS Gene Risk Score Prognostic Signature Predicts Survival in Hepatocellular Carcinoma

Background

Primary liver cancer, dominated by hepatocellular carcinoma (HCC), is one of the most common cancer types and the third leading cause of cancer death in 2020. Previous studies have shown that liquid-liquid phase separation (LLPS) plays an important role in the occurrence and development of cancer including HCC, but its influence on the patient prognosis is still unknown. It is necessary to explore the effect of LLPS genes on prognosis to accurately forecast the prognosis of HCC patients and identify relevant targeted therapeutic sites.

Methods

Using The Cancer Genome Atlas dataset and PhaSepDB dataset, we identified LLPS genes linked to the overall survival (OS) of HCC patients. We applied Least Absolute Shrinkage and Selection Operator (LASSO) Cox penalized regression analysis to choose the best genes for the risk score prognostic signature. We then analysed the validation dataset and evaluated the effectiveness of the risk score prognostic signature. Finally, we performed quantitative real-time PCR experiments to validate the genes in the prognostic signature.

Results

We identified 43 differentially expressed LLPS genes that were associated with the OS of HCC patients. Five of these genes (BMX, FYN, KPNA2, PFKFB4, and SPP1) were selected to generate a prognostic risk score signature. Patients in the low-risk group were associated with better OS than those in the high-risk group in both the training dataset and the validation dataset. We found that BMX and FYN had lower expression levels in HCC tumour tissues, whereas KPNA2, PFKFB4, and SPP1 had higher expression levels in HCC tumour tissues. The validation demonstrated that the five-LLPS gene risk score signature has the capability of predicting the OS of HCC patients.

Conclusion

Our study constructed a five-LLPS gene risk score signature that can be applied as an effective and convenient prognostic tool. These five genes might serve as potential targets for therapy and the treatment of HCC.

Exploration of an Prognostic Signature Related to Endoplasmic Reticulum Stress in Colorectal Adenocarcinoma and Their Response Targeting Immunotherapy

Background: Endoplasmic reticulum (ER) stress plays a pro-apoptotic role in colorectal adenocarcinoma (COAD). This study aimed to develop a novel ER-stress-related prognostic risk model for COAD and provide support for COAD cohorts with different risk score responses to immune checkpoint inhibitor therapies. Methods: TCGA-COAD and GSE39582 were included in this prospective study. Univariate and multivariate Cox analyses were performed to identify prognostic ER stress-related genes (ERSGs). Accordingly, the immune infiltration landscape and immunotherapy response in different risk groups were assessed. Finally, the expression of prognostic genes in 10 normal and 10 COAD tissue samples was verified using reverse transcription-quantitative polymerase chain reaction. Results: Eight prognostic genes were selected to establish an ERSG-based signature in the training set of the TCGA-COAD cohort. The accuracy of this was confirmed using a testing set of TCGA-COAD and GSE39582 cohorts. Gene set variation analysis indicated that differential functionality in high-low-risk groups was related to immune-related pathways. Corresponding to this, CD36, TIMP1, and PTGIS were significantly associated with 19 immune cells with distinct proportions between the different risk groups, such as central memory CD4T cells and central memory CD8T cells. Moreover, the risk score was considered effective for predicting the clinical response to immunotherapy, and the immunotherapy response was significantly and negatively correlated with the risk score of individuals with COAD. Furthermore, the immune checkpoint inhibitor treatment was less effective in the high-risk group, where the expression levels of PD-L1 and tumor immune dysfunction and exclusion scores in the high-risk group were significantly increased. Finally, the experimental results demonstrated that the expression trends of prognostic genes in clinical samples were consistent with the results from public databases. Conclusion: Our study established a novel risk signature to predict the COAD prognosis of patients and provide theoretical support for the clinical treatment of COAD.

Decreased Expression of Karyopherin-α 1 is Related to the Malignant Degree of Cervical Cancer and is Critical for the Proliferation of Hela Cells

Karyopherin α (KPNA) proteins are involved in nucleocytoplasmic trafficking and are critical for protein subcellular localization. Recent studies have suggested that KPNA proteins are abnormally expressed in various solid tumors. The objective of this study was to investigate the expression of KPNA1 and KPNA2 in cervical cancer tissue with different histologic grades and cell lines, as well as the effects of the KPNA1 expression level on Hela cell proliferation. We collected the medical data of 106 patients with cervical cancer and investigated the protein expression of KPNA1 and KPNA2 by immunohistochemistry and western blot. The results revealed a significantly lower expression of KPNA1 in cervical cancer compared to normal tissue. Conversely, stronger staining intensity for KPNA2 was observed in cervical tumor samples. The expression levels of KPNA1 and KPNA2 were significantly associated with the tumor histologic grade. The weakest KPNA1 expression and strongest staining for KPNA2 were observed in grade III tumor tissue. The expression levels of KPNA1 were lower in Hela and C33A cells compared with normal human cervical epithelial cells; however, the expression of KPNA2 exhibited an opposite trend. The up-regulation of KPNA1 significantly suppressed the proliferation of Hela cells and relevant proteins expression, as well as promoted transportation of IRF3 into nucleus. Our results suggest the downregulation of KPNA1 expression is related to the malignant degree of cervical cancer and is closely associated with the proliferation of cervical cancer cells.

Liquid-Liquid Phase Separation-Related Genes Associated with Tumor Grade and Prognosis in Hepatocellular Carcinoma: A Bioinformatic Study

Aim

The aim of the present study was to identify the association between tumor grade and liquid-liquid phase separation (LLPS)-related genes, and to generate a LLPS-related gene-based risk index (LLPSRI) as a prognostic tool for hepatocellular carcinoma (HCC).

Methods

Weighted gene correlation network analysis was performed to test whether the LLPS-related gene modules were associated with tumor grade of HCC. The candidate modules were subjected to functional enrichment analysis. We generated a LLPSRI using the expression profiles of the hub genes among the candidate modules in order to identify patients at high risk. Then, the biological characteristics of the high-risk patients were revealed using gene set enrichment analysis. Additionally, an independent external data set was used to validate the LLPSRI.

Results

Four gene modules showed a significant positive correlation with tumor grade and involved various cancer-related pathways. Among the hub genes, six were selected to generate the LLPSRI, which was significantly associated with prognosis of HCC patients. The LLPSRI could successfully divide patients with HCC into high- and low-risk groups, and patients in the high-risk group showed shorter overall survival than those in the low-risk group. E2F, MYC, and mTORC1 signaling may be important determinants of survival in the high-risk group. The prognostic value of the LLPSRI was validated with the independent external data set.

Conclusion

We identified LLPS-related gene modules that are associated with HCC tumor grade. The LLPSRI may be useful as a prognostic marker of HCC, and it may reliably stratify patients into groups at low or high risk of worse survival. Our analysis also suggests that certain biological characteristics of HCC may be associated with high risk of worse survival.

A Cell Cycle Progression-Derived Gene Signature to Predict Prognosis and Therapeutic Response in Hepatocellular Carcinoma

Objective

Dysregulation of cell cycle progression (CCP) is one of the hallmarks of cancer. Here, our study is aimed at developing a CCP-derived gene signature for predicting high-risk population of hepatocellular carcinoma (HCC).

Methods

Our study retrospectively analyzed the transcriptome profiling and clinical information of HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) projects. Uni- and multivariate cox regression models were conducted for identifying which hallmarks of cancer were risk factors of HCC. CCP-derived gene signature was developed with LASSO method. The predictive efficacy was verified by ROC curves and subgroup analyses. A nomogram was then generated and validated by ROC, calibration, and decisive curves. Immune cell infiltration was estimated with ssGSEA method. Potential small molecular compounds were predicted via CTRP and CMap analyses. The response to chemotherapeutic agents was evaluated based on the GDSC project.

Results

Among hallmarks of cancer, CCP was identified as a dominant risk factor for HCC prognosis. CCP-derived gene signature displayed the favorable predictive efficacy in HCC prognosis independent of other clinicopathological parameters. A nomogram was generated for optimizing risk stratification and quantifying risk evaluation. CCP-derived signature was in relation to immune cell infiltration, HLA, and immune checkpoint expression. Combining CTRP and CMap analyses, fluvastatin was identified as a promising therapeutic agent against HCC. Furthermore, CCP-derived signature might be applied for predicting the response to doxorubicin and gemcitabine.

Conclusion

Collectively, CCP-derived gene signature was a promising marker in prediction of survival outcomes and therapeutic responses for HCC patients.

Multiomics-based analyses of KPNA2 highlight its multiple potentials in hepatocellular carcinoma

Dysregulation and prognostic roles of Karyopherin α2 (KPNA2) were reported in many malignancies including hepatocellular carcinoma (HCC). A multi-omics analysis of KPNA2 is needed to gain a deeper understanding of its multilevel molecular characteristics and provide novel clues for HCC diagnosis, prognosis, and target therapy. Herein multi-omic alterations of KPNA2 were analyzed at genetic, epigenetic, transcript, and protein levels with evaluation of their relevance with clinicopathological features of HCC by integrative analyses. The significant correlations of KPNA2 expression with its gene copy number variation (CNV) and methylation status were shown through Spearman correlation analyses. With Cox regression, Kaplan-Meier survival, and receiver operating characteristic (ROC) analyses, based on the factors of KPNA2 CNV, methylation, expression, and tumor stage, risk models for HCC overall survival (OS) and disease-free survival (DFS) were constructed which could discriminate the 1-year, 3-year, and 5-year OS/DFS status effectively. With Microenvironment Cell Populations-counter (MCP-counter), the immune infiltrations of HCC samples were evaluated and their associations with KPNA2 were shown. KPNA2 expression in liver was found to be influenced by low fat diet and presented significant correlations with fatty acid metabolism and fatty acid synthase activity in HCC. KPNA2 was detected lowered in HCC patient's plasma by enzyme linked immunosorbent assay (ELISA), consistent with its translocation to nuclei of HCC cells. In conclusion, KPNA2 multilevel dysregulation in HCC and its correlations with immune infiltration and the fatty acid metabolism pathway indicated its multiple roles in HCC. The clinicopathological significance of KPNA2 was highlighted through the in-depth analyses at multilevels.

Comprehensive analysis of the competing endogenous circRNA-lncRNA-miRNA-mRNA network and identification of a novel potential biomarker for hepatocellular carcinoma

BACKGROUND

The competing endogenous RNAs (ceRNAs) hypothesis has received increasing attention as a novel explanation for tumorigenesis and cancer progression. However, there is still a lack of comprehensive analysis of the circular RNA (circRNA)-long non-coding RNA (lncRNA)-miRNA-mRNA ceRNA network in hepatocellular carcinoma (HCC).

METHODS

RNA sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were employed to identify Differentially Expressed mRNAs (DEmRNAs), DElncRNAs, and DEcircRNAs between HCC and normal tissues. Candidates were identified to construct networks through a comprehensive bioinformatics strategy. A prognostic mRNA signature was established based on data from TCGA database and validated using data from the GEO database. Then, the HCC prognostic circRNA-lncRNA-miRNA-mRNA ceRNA network was established. Finally, the expression and function of an unexplored hub gene, deoxythymidylate kinase (DTYMK), was explored through data mining. The results were examined using clinical samples and in vitro experiments.

RESULTS

We constructed a prognostic signature with seven target mRNAs by univariate, lasso and multivariate Cox regression analyses, which yielded 1, 3 and 5-year AUC values of 0.797, 0.733 and 0.721, respectively, indicating its sensitivity and specificity in the prognosis of HCC. Moreover, the prognostic signature could be validated in GSE14520. The prognostic ceRNA network of 21 circRNAs, 15 lncRNAs, 5 miRNAs, and 7 mRNAs was established according to the targeting relationship between 7 hub mRNAs and other RNAs. Our experiment results indicated that the depletion of DTYMK inhibited liver cancer cell proliferation and invasion.

CONCLUSIONS

The network revealed in this study may help comprehensively elucidate the ceRNA mechanisms driving HCC, and provide novel candidate biomarkers for evaluating the prognosis of HCC.

Integrative bioinformatics analysis of KPNA2 in six major human cancers

Background

Malignant tumors were considered as the leading causes of cancer-related mortality globally. More and more studies found that dysregulated genes played an important role in carcinogenesis. The aim of this study was to explore the significance of KPNA2 in human six major cancers including non-small cell lung cancer (NSCLC), gastric cancer, colorectal cancer, breast cancer, hepatocellular carcinoma, and bladder cancer based on bioinformatics analysis.

Methods

The data were collected and comprehensively analyzed based on multiple databases. KPNA2 mRNA expression in six major cancers was investigated in Oncomine, the human protein atlas, and GEPIA databases. The mutation status of KPNA2 in the six major cancers was evaluated by online data analysis tool Catalog of Somatic Mutations in Cancer (COSMIC) and cBioPortal. Co-expressed genes with KPNA2 were identified by using LinkedOmics and made pairwise correlation by Cancer Regulome tools. Protein-protein interaction (PPI) network relevant to KPNA2 was constructed by STRING database and KEGG pathway of the included proteins of the PPI network was explored and demonstrated by circus plot. Survival analysis-relevant KPNA2 of the six cancers was performed by GEPIA online data analysis tool based on TCGA database.

Results

Compared with paired normal tissue, KPNA2 mRNA was upregulated in all of the six types of cancers. KPNA2 mutations, especially missense substitution, were widely identified in six cancers and interact with different genes in different cancer types. Genes involved in PPI network were mainly enriched in p53 signaling pathway, cell cycle, viral carcinogenesis, and Foxo signaling pathway. KPNA2 protein was mainly expressed in nucleoplasm and cytosol in cancer cells. Immunohistochemistry assay indicated that KPNA2 protein was also positively expressed in nucleoplasm with brownish yellow staining. Overall survival (OS) and progression free survival (PFS) were different between KPNA2 high and low expression groups.

Conclusions

KPNA2 was widely dysregulated and mutated in carcinomas and correlated with the patients prognosis which may be potential target for cancer treatment and biomarker for prognosis.

Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis

Background

Hepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis.

Methods

A bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003).

Results

Firstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05).

Conclusion

MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.

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.

KPNA2 expression is a potential marker for differential diagnosis between osteosarcomas and other malignant bone tumor mimics

BACKGROUND

Karyopherin α2 (KPNA2), a member of the karyopherin α family, has been studied in several cancers but has not yet been substantially investigated in malignant bone tumors. The purpose of the current study was to evaluate the KPNA2 expression level and its utility as a novel diagnostic biomarker in osteosarcomas and malignant bone tumor mimics, such as chondrosarcomas and Ewing sarcomas (ESs).

METHOD

We investigated the expression of KPNA2 protein by immunohistochemistry on paraffin-embedded surgical specimens from 223 patients with malignant and benign bone tumors, including 81 osteosarcomas, 42 chondrosarcomas, 15 ESs, 28 osteoid osteomas, 20 osteochondromas and 37 chondroblastomas. Immunoreactivity was scored semiquantitatively based on staining extent and intensity.

RESULTS

Sixty-seven of 81 (82.7%) osteosarcoma, zero of 42 (0%) chondrosarcoma and one of 15 (6.7%) ES samples showed immunoreactivity for KPNA2. Negative KPNA2 expression was observed in all benign bone tumors. The expression of KPNA2 in osteosarcoma samples was much higher than that in chondrosarcoma and ES samples (P < 0.001). The sensitivity and specificity of KPNA2 immunoexpression for detecting osteosarcoma were 82.7 and 100%, respectively. Several subtypes of osteosarcoma were analyzed, and immunostaining of KPNA2 was frequent in osteoblastic samples (90.9%), with 39 samples (70.9%) showing strong-intensity staining. KPNA2 positivity was observed in ten of 13 (76.9%) chondroblastic, two of 6 (33.3%) fibroblastic, three of 4 (75%) telangiectatic and two of 3 (66.7%) giant cell-rich osteosarcoma samples. The strongest intensity staining was observed in osteoblastic osteosarcoma.

CONCLUSION

KPNA2 is frequently expressed in osteosarcomas, particularly in osteoblastic and chondroblastic tumors, but is rarely positive in chondrosarcomas and ESs. This feature may aid in distinguishing between osteosarcoma and other bone sarcoma mimics. This report supports KPNA2 as a novel marker for the diagnosis of osteosarcoma.

KPNA2-Associated Immune Analyses Highlight the Dysregulation and Prognostic Effects of GRB2, NRAS, and Their RNA-Binding Proteins in Hepatocellular Carcinoma

Karyopherin α2 (KPNA2) was reported to be overexpressed and have unfavorable prognostic effects in many malignancies including hepatocellular carcinoma (HCC). Although its contributions to inflammatory response were reported in many studies, its specific associations with immune infiltrations and immune pathways during cancer progression were unclear. Here, we aimed to identify new markers for HCC diagnosis and prognosis through KPNA2-associated immune analyses. RNA-seq expression data of HCC datasets were downloaded from The Cancer Genome Atlas and International Cancer Genome Consortium. The gene expressions were counts per million normalized. The infiltrations of 24 kinds of immune cells in the samples were evaluated with ImmuCellAI (Immune Cell Abundance Identifier). The Spearman correlations of the immune infiltrations with KPNA2 expression were investigated, and the specific positive correlation of B-cell infiltration with KPNA2 expression in HCC tumors was identified. Fifteen genes in KEGG (Kyoto Encyclopedia of Genes and Genomes) B-cell receptor signaling pathway presented significant correlations with KPNA2 expression in HCC. Among them, GRB2 and NRAS were indicated to be independent unfavorable prognostic factors for HCC overall survival. Clinical Proteomic Tumor Analysis Consortium HCC dataset was investigated to validate the results at protein level. The upregulation and unfavorable prognostic effects of KPNA2 and GRB2 were confirmed, whereas, unlike its mRNA form, NRAS protein was presented to be downregulated and have favorable prognostic effects. Through receiver operating characteristic curve analysis, the diagnostic potential of the three proteins was shown. The RNA-binding proteins (RBPs) of KPNA2, NRAS, and GRB2, downloaded via The Encyclopedia of RNA Interactomes, were investigated for their clinical significance in HCC at protein level. An eight-RBP signature with independent prognostic value and dysregulations in HCC was identified. All the RBPs were significantly correlated with MKI67 expression and at least one of KPNA2, GRB2, and NRAS at protein level in HCC, indicating their roles in HCC progression and the regulation of the three proteins. We concluded that KPNA2, GRB2, NRAS, and their RBPs might have coordinating roles in HCC immunoregulation and progression. They might be new markers for HCC diagnosis and prognosis predication and new targets for HCC immunotherapy.

Potential of modern circulating cell-free DNA diagnostic tools for detection of specific tumour cells in clinical practice

Personalized medicine is a developing field of medicine that has gained in importance in recent decades. New diagnostic tests based on the analysis of circulating cell-free DNA (cfDNA) were developed as a tool of diagnosing different cancer types. By detecting the subpopulation of mutated DNA from cancer cells, it is possible to detect the presence of a specific tumour in early stages of the disease. Mutation analysis is performed by quantitative polymerase chain reaction (qPCR) or the next generation sequencing (NGS), however, cfDNA protocols need to be modified carefully in preanalytical, analytical, and postanalytical stages. To further improve treatment of cancer the Food and Drug Administration approved more than 20 companion diagnostic tests that combine cancer drugs with highly efficient genetic diagnostic tools. Tools detect mutations in the DNA originating from cancer cells directly through the subpopulation of cfDNA, the circular tumour DNA (ctDNA) analysis or with visualization of cells through intracellular DNA probes. A large number of ctDNA tests in clinical studies demonstrate the importance of new findings in the field of cancer diagnosis. We describe the innovations in personalized medicine: techniques for detecting ctDNA and genomic DNA (gDNA) mutations approved Food and Drug Administration companion genetic diagnostics, candidate genes for assembling the cancer NGS panels, and a brief mention of the multitude of cfDNA currently in clinical trials. Additionally, an overview of the development steps of the diagnostic tools will refresh and expand the knowledge of clinics and geneticists for research opportunities beyond the development phases.

Big Data-Based Identification of Multi-Gene Prognostic Signatures in Liver Cancer

Simultaneous identification of multiple single genes and multi-gene prognostic signatures with higher efficacy in liver cancer has rarely been reported. Here, 1,173 genes potentially related to the liver cancer prognosis were mined with Coremine, and the gene expression and survival data in 370 samples for overall survival (OS) and 319 samples for disease-free survival (DFS) were retrieved from The Cancer Genome Atlas. Numerous survival analyses results revealed that 39 genes and 28 genes significantly associated with DFS and OS in liver cancer, including 18 and 12 novel genes that have not been systematically reported in relation to the liver cancer prognosis, respectively. Next, totally 9,139 three-gene combinations (including 816 constructed by 18 novel genes) for predicting DFS and 3,276 three-gene combinations (including 220 constructed by 12 novel genes) for predicting OS were constructed based on the above genes, and the top 15 of these four parts three-gene combinations were selected and shown. Moreover, a huge difference between high and low expression group of these three-gene combination was detected, with median survival difference of DFS up to 65.01 months, and of OS up to 83.57 months. The high or low expression group of these three-gene combinations can predict the longest prognosis of DFS and OS is 71.91 months and 102.66 months, and the shortest is 6.24 months and 13.96 months. Quantitative real-time polymerase chain reaction and immunohistochemistry reconfirmed that three genes F2, GOT2, and TRPV1 contained in one of the above combinations, are significantly dysregulated in liver cancer tissues, low expression of F2, GOT2, and TRPV1 is associated with poor prognosis in liver cancer. Overall, we discovered a few novel single genes and multi-gene combinations biomarkers that are closely related to the long-term prognosis of liver cancer, and they can be potential therapeutic targets for liver cancer.

Identification of hub genes in hepatocellular carcinoma using integrated bioinformatic analysis

The molecular mechanisms underlying hepatocellular carcinoma (HCC) progression remain largely undefined. Here, we identified 176 commonly upregulated genes in HCC tissues based on three Gene Expression Omnibus datasets and The Cancer Genome Atlas (TCGA) cohort. We integrated survival and methylation analyses to further obtain 12 upregulated genes for validation. These genes were overexpressed in HCC tissues at the transcription and protein levels, and increased mRNA levels were related to higher tumor grades and cancer stages. The expression of all markers was negatively associated with overall and disease-free survival in HCC patients. Most of these hub genes can promote HCC proliferation and/or metastasis. These 12 hub genes were also overexpressed and had strong prognostic value in many other cancer types. Methylation and gene copy number analyses indicated that the upregulation of these hub genes was probably due to hypomethylation or increased gene copy numbers. Further, the methylation levels of three genes, KPNA2, MCM3, and LRRC1, were associated with HCC clinical features. Moreover, the levels of most hub genes were related to immune cell infiltration in HCC microenvironments. Finally, we identified three upregulated genes (KPNA2, TARBP1, and RNASEH2A) that could comprehensively and accurately provide diagnostic and prognostic value for HCC patients.

The emerging roles of KPNA2 in cancer

Karyopherin α2 (KPNA2, also known as importinα-1), a member of the nuclear transporter family, is involved in the nucleocytoplasmic transport pathway of a variety of tumor-associated proteins. Recent studies have found that KPNA2 is overexpressed in various cancers, which is associated with poor prognosis. In addition, it has been shown to promote tumor formation and progression by participating in cell differentiation, proliferation, apoptosis, immune response, and viral infection. It is indicated that KPNA2 also plays an important role in the diagnosis, treatment and prognosis of tumors. Herein, we provide an overview of the function and mechanism of KPNA2 in cancer and the prospects in the diagnosis and treatment of cancer. In the future, KPNA2 provides new ideas for the early diagnosis of malignant tumors, the development of molecularly targeted drugs, and prognosis evaluation.

Identification of key genes and long non-coding RNA associated ceRNA networks in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Although multiple efforts have been made to understand the development of HCC, morbidity, and mortality rates remain high. In this study, we aimed to discover the mRNAs and long non-coding RNAs (lncRNAs) that contribute to the progression of HCC. We constructed a lncRNA-related competitive endogenous RNA (ceRNA) network to elucidate the molecular regulatory mechanism underlying HCC.

METHODS

A microarray dataset (GSE54238) containing information about both mRNAs and lncRNAs was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and lncRNAs (DElncRNAs) in tumor tissues and non-cancerous tissues were identified using the limma package of the R software. The miRNAs that are targeted by DElncRNAs were predicted using miRcode, while the target mRNAs of miRNAs were retrieved from miRDB, miRTarBas, and TargetScan. Functional annotation and pathway enrichment of DEGs were performed using the EnrichNet website. We constructed a protein-protein interaction (PPI) network of DEGs using STRING, and identified the hub genes using Cytoscape. Survival analysis of the hub genes and DElncRNAs was performed using the gene expression profiling interactive analysis database. The expression of molecules with prognostic values was validated on the UALCAN database. The hepatic expression of hub genes was examined using the Human Protein Atlas. The hub genes and DElncRNAs with prognostic values as well as the predictive miRNAs were selected to construct the ceRNA networks.

RESULTS

We found that 10 hub genes (KPNA2, MCM7, CKS2, KIF23, HMGB2, ZWINT, E2F1, MCM4, H2AFX, and EZH2) and four lncRNAs (FAM182B, SNHG6, SNHG1, and SNHG3) with prognostic values were overexpressed in the hepatic tumor samples. We also constructed a network containing 10 lncRNA-miRNA-mRNA pathways, which might be responsible for regulating the biological mechanisms underlying HCC.

CONCLUSION

We found that the 10 significantly overexpressed hub genes and four lncRNAs were negatively correlated with the prognosis of HCC. Further, we suggest that lncRNA SNHG1 and the SNHG3-related ceRNAs can be potential research targets for exploring the molecular mechanisms of HCC.

A 4-gene prognostic signature predicting survival in hepatocellular carcinoma

OBJECTIVE

To develop an independent prognostic signature for patients with hepatocellular carcinoma (HCC).

METHODS

HCC gene expression profile the cancer genome atlas-liver hepatocellular carcinoma and GSE14520 were used as discovery and test set, respectively. Differentially expressed genes (DEGs) were identified between HCC tissues and adjacent normal liver tissues. Univariate Cox proportional hazards regression analysis was performed to identify DEGs correlated with survival of HCC patients. A 4-gene-based signature was constructed based on a least absolute shrinkage and selection operator Cox penalized regression model. The predictive value of the signature was analyzed and validated.

RESULTS

Two hundred sixty-three DEGs were identified between HCC and adjacent liver tissues. After univariate survival analysis, 90 DEGs were found to be significantly correlated with the overall survival (OS) of HCC patients, of which 4 genes (KPNA2, CDC20, SPP1, and TOP2A) with non-zero coefficient were used to construct a prognostic signature. The 4-gene signature was significantly associated with the age (P = 0.046), grade ( P = 0.022), and T stage ( P = 0.023) of HCC patients in the discovery set and it also significantly associated with TNM stage ( P = 0.033), and serum alpha-fetoprotein lever ( P = 0.034). Patients in the 4-gene low-risk group were associated with better OS and recurrence-free survival (RFS) than those in the high-risk group in the discovery and test set. Meanwhile, the 4-gene signature is an independent prognostic factor regarding OS and RFS in the discovery and test set.

CONCLUSION

We developed a 4-gene-based signature, which could be a candidate prognostic factor for patients with HCC.

Downregulation of RFX1 predicts poor prognosis of patients with small hepatocellular carcinoma

OBJECTIVE

Regulatory factor X1 (RFX1) deletion has been reported to be correlated with poor prognosis of some types of cancer. The present study aimed to investigate the prognostic value of RFX1 in HCC, especially in small hepatocellular carcinoma.

METHODS

Immunohistochemical assay was used to investigate RFX1 expression in 221 HCC tissues and another validation cohort of 71 small HCC samples. We also performed in vitro experiments to investigate if RFX1 regulated invasive capacity of HCC cells and expression of epithelial-mesenchymal transition (EMT) markers.

RESULTS

We found that RFX1 expression was significantly lower in HCC tissues compared to the corresponding non-tumor tissues. Further survival analysis suggested that the downregulation of RFX1 correlated with poor prognosis and a high recurrence risk in HCC patients, particularly in small HCC patients. Furthermore, another validation cohort of small HCC samples confirmed that downregulation of RFX1 in HCC tissues predicted high recurrence risk and poor prognosis for early stage HCC patients. In vitro studies suggested that knocking down RFX1 facilitated HCC cell invasion, while overexpression of RFX1 reduced the invasion of HCC cells. Western blot assays also indicated that RFX1 regulated expression of some EMT markers. Knocking down RFX1 decreased E-cadherin and increased vimentin expression, while RFX1 overexpression enhanced E-cadherin and decreased vimentin expression.

CONCLUSIONS

Our study demonstrated that RFX1 downregulation is a new predictive marker of high recurrence risk and poor prognosis of HCC; It has potential to help guide treatment for postoperative HCC patients, especially for small HCC patients.

Association of overexpressed karyopherin alpha 2 with poor survival and its contribution to interleukin-1β-induced matrix metalloproteinase expression in oral cancer

BACKGROUND

The purpose of this study was to elucidate the clinicopathological associations and molecular mechanisms of karyopherin alpha 2 (KPNA2) in oral cavity squamous cell carcinoma (SCC) progression.

METHODS

The KPNA2 expressions were analyzed by immunohistochemistry and enzyme-linked immunosorbent assay in 209 tissues and 181 saliva samples, respectively. The functions of KPNA2 in migration and invasion were examined in KPNA2-knowdown cells. The matrix metalloproteinase (MMP) levels were determined by real-time quantitative polymerase chain reaction (qPCR). The subcellular fraction was used to obtain the nuclear distribution of nuclear factor-kappa B (NF-κB).

RESULTS

The KPNA2 overexpression was associated with extranodal extension (P < .05) and poor disease-specific survival in patients with oral cavity SCC (P < .05). The salivary KPNA2 levels were elevated in patients with oral cavity SCC (P < .05). The KPNA2 knockdown reduced cell migration and invasion. This knockdown also suppressed the interleukin (IL)-1β-induced nuclear import of NF-κB and MMP (MMP-1, MMP-3, and MMP-9) transcription.

CONCLUSION

The KPNA2 overexpression is an independent biomarker for poor prognosis of oral cavity SCC and is required for MMP-mediated metastasis.

Prognostic value of increased KPNA2 expression in some solid tumors: A systematic review and meta-analysis

Background

Karyopherin α2 (KPNA2), a member of the Karyopherin α family, has recently been reported to play an important role in tumor progression. However, the association between KPNA2 expression and prognosis in cancer remains controversial. So we performed this meta-analysis to evaluate whether expression of KPNA2 was associated with prognosis in patients with solid tumor.

Methods/Findings

24 published eligible studies, including 6164 cases, were identified and included in this meta-analysis through searching of PubMed, EMBASE and Web of Science. We found that KPNA2 expression was an independent predictor for the prognosis of solid tumor with primary outcome (overall survival [OS]: pooled HR=1.767, 95% CI=1.503-2.077, P<0.001) and secondary outcomes (time to recurrence [TTR], recurrence free survival [RFS] and progression free survival [PFS]). However, the association between KPNA2 overexpression and disease free survival [DFS] in solid tumors was not significant (pooled HR=1.653, 95% CI=0.903-3.029, P=0.104). Furthermore, the subgroup analysis revealed that KPNA2 overexpression was associated with poor OS in East-Asian patients and European patients, as well as patients with gastric and colorectal cancer.

Conclusion

KPNA2 expression may be a useful prognostic biomarker to monitor cancer prognosis. Further prospective studies with larger sample sizes are required to confirm our findings.

mTOR regulates proteasomal degradation and Dp1/E2F1- mediated transcription of KPNA2 in lung cancer cells

Karyopherin subunit alpha-2 (KPNA2) is overexpressed in various human cancers and is associated with cancer invasiveness and poor prognosis in patient. Nevertheless, the regulation of KPNA2 expression in cancers remains unclear. We herein applied epidermal growth factor (EGF) and five EGF receptor (EGFR)-related kinase inhibitors to investigate the role of EGFR signaling in KPNA2 expression in non-small cell lung cancer (NSCLC) cells. We found that EGFR signaling, particularly the mammalian target of rapamycin (mTOR) activity was positively correlated with KPNA2 protein levels in NSCLC cells. The mTOR inhibitors and mTOR knockdown reduced the protein and mRNA levels of KPNA2 in NSCLC and breast cancer cells. Specifically, rapamycin treatment induced proteasome-mediated KPNA2 protein decay and attenuated the transcriptional activation of KPNA2 by decreasing Dp1/E2F1 level in vivo. Immunoprecipitation assay further revealed that KPNA2 physically associated with the phospho-mTOR/mTOR and this association was abolished by rapamycin treatment. Collectively, our results show for the first time that KPNA2 is transcriptionally and post-translationally regulated by the mTOR pathway and provide new insights into targeted therapy for NSCLC.

Reduced RCE1 expression predicts poor prognosis of colorectal carcinoma

Background

As an end-proteolytic enzyme that cleaves the last three residues of proteins with a terminal CAAX, Ras-converting enzyme 1 (RCE1) has an essential role in multiple signaling pathways and take part in the process of differentiation, proliferation and carcinogenesis. The aim of the study is to investigate expression pattern of RCE1 and its prognosis in colorectal carcinoma (CRC).

Methods

The expression of RCE1 and phospho-MAPK family members was confirmed by immunohistochemical staining of CRC tissues. miR-RCE1 lentiviral vectors were transduced into HCT116 and SW489 cells. Reverse transcription PCR (RT-PCR) and western blot were conducted to measure the transfection efficiency. Transwell assays were used to detect the invasiveness of CRC cells.

Results

In the present study, we assessed RCE1 expression in 244 CRC specimens and matching adjacent, non-tumorous tissues by immunohistochemistry (IHC). Compared with the matched adjacent non-tumor tissue samples, the RCE1 reduced in the tumor tissue samples (p < 0.001). RCE1 expression was significantly decreased in 106 of 244 (43.4%) CRC cases. In univariate and multivariate analyses, Decreasing expression of RCE1 independently predicts poor prognosis for patients in both overall survival and disease-free survival. Further study indicated that RCE1 influenced tumor invasion through the p38 pathway. Knockdown of RCE1 reduced phosphorylation and significantly increased the invasive capacity of CRC cells.

Conclusion

Taken together, the outcomes of this study indicate that RCE1 acts as a tumor suppressor in CRC, as its reduced expression may increase CRC cell invasion and independently predict an unsatisfactory prognosis in CRC patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3393-3) contains supplementary material, which is available to authorized users.

Silencing of karyopherin α2 inhibits cell growth and survival in human hepatocellular carcinoma

Karyopherin α2 (KPNA2), involved in nucleocytoplasmic transport, has been reported to be upregulated in hepatocellular carcinoma and considered as a biomarker for poor prognosis. However, comprehensive studies of KPNA2 functions in hepatocellular carcinogenesis are still lacking. Our study examine the roles and related molecular mechanisms of KPNA2 in hepatocellular carcinoma development. Results show that KPNA2 knockdown inhibited the proliferation and growth of hepatocellular carcinoma cells in vitro and in vivo. KPNA2 knockdown also inhibited colony formation ability, induced cell cycle arrest and cellular apoptosis in two hepatocellular carcinoma cell lines, HepG2 and SMMC-7721. Furthermore, gene expression microarray analysis in HepG2 cells with KPNA2 knockdown revealed that critical signaling pathways involved in cell proliferation and survival were deregulated. In conclusion, this study provided systematic evidence that KPNA2 was an essential factor promoting hepatocellular carcinoma and unraveled potential molecular pathways and networks underlying KPNA2-induced hepatocellular carcinogenesis.

Targeting nuclear transporters in cancer: Diagnostic, prognostic and therapeutic potential

The Karyopherin superfamily is a major class of soluble transport receptors consisting of both import and export proteins. The trafficking of proteins involved in transcription, cell signalling and cell cycle regulation among other functions across the nuclear membrane is essential for normal cellular functioning. However, in cancer cells, the altered expression or localization of nuclear transporters as well as the disruption of endogenous nuclear transport inhibitors are some ways in which the Karyopherin proteins are dysregulated. The value of nuclear transporters in the diagnosis, prognosis and treatment of cancer is currently being elucidated with recent studies highlighting their potential as biomarkers and therapeutic targets.