Validating RRP12 Expression and Its Prognostic Significance in HCC Based on Data Mining and Bioinformatics Methods

Identification of alternative lengthening of telomeres-related genes prognosis model in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide, characterized by high mortality. This study aimed to explore the prognostic value and function of alternative lengthening of telomeres (ALT)-related genes in HCC.

METHODS

Differentially expressed genes (DEGs) were identified based on The Cancer Genome Atlas (TCGA) and then intersected with ALT-related genes to obtain ALTDEGs. Risk score model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression and validated with Gene Expression Omnibus (GEO) datasets. The predictive efficacy of the risk score and ALTs-score was evaluated by Kaplan-Meier curves, time-ROC curves, and the nomogram analyses. The impacts of SMG5 silencing on the HCC cell behaviors were assessed by CCK-8, wound healing, and Transwell assays.

RESULTS

A total of 500 ALTDEGs were screened and 13 genes (CDCA8, SMG5, RAD54B, FOXD2, NOL10, RRP12, CCT5, CCT4, HDAC1, DDX1, HRG, HDAC2, and PPP1CB) were identified for constructing a prognostic model. The overall survival (OS) curves, time-ROC curves, and nomograms based on the risk score or ALTs-score were developed to optimally predict the survival of HCC patients. ALTs-score was correlated with immune infiltration and confirmed its value in predicting immunotherapy outcomes. Furthermore, RT-qPCR demonstrated that eight risk signature genes were up-regulated in HCC cells. SMG5 silencing suppressed the proliferation, migration, and invasion of HCC cells. It was also found that SMG5 silencing reduced C-circle level in SNU-387 cells.

CONCLUSION

We identified new ALT-related prognostic biomarkers for HCC. SMG5 knockdown inhibited the HCC progression, which might be a promising target for HCC therapy.

Knockdown of ribosome RNA processing protein 15 suppresses migration of hepatocellular carcinoma through inhibiting PATZ1-associated LAMC2/FAK pathway

BACKGROUND

Ribosomal RNA processing protein 15 (RRP15) has been found to regulate the progression of hepatocellular carcinoma (HCC). Nevertheless, the extent to which it contributes to the spread of HCC cells remains uncertain. Thus, the objective of this research was to assess the biological function of RRP15 in the migration of HCC.

METHODS

The expression of RRP15 in HCC tissue microarray (TMA), tumor tissues and cell lines were determined. In vitro, the effects of RRP15 knockdown on the migration, invasion and adhesion ability of HCC cells were assessed by wound healing assay, transwell and adhesion assay, respectively. The effect of RRP15 knockdown on HCC migration was also evaluated in vivo in a mouse model.

RESULTS

Bioinformatics analysis showed that high expression of RRP15 was significantly associated with low survival rate of HCC. The expression level of RRP15 was strikingly upregulated in HCC tissues and cell lines compared with the corresponding controls, and TMA data also indicated that RRP15 was a pivotal prognostic factor for HCC. RRP15 knockdown in HCC cells reduced epithelial-to-mesenchymal transition (EMT) and inhibited migration in vitro and in vivo, independent of P53 expression. Mechanistically, blockade of RRP15 reduced the protein level of the transcription factor POZ/BTB and AT hook containing zinc finger 1 (PATZ1), resulting in decreased expression of the downstream genes encoding laminin 5 subunits, LAMC2 and LAMB3, eventually suppressing the integrin β4 (ITGB4)/focal adhesion kinase (FAK)/nuclear factor κB kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway.

CONCLUSIONS

RRP15 promotes HCC migration by activating the LAMC2/ITGB4/FAK pathway, providing a new target for future HCC treatment.

Potential prognostic biomarkers of hepatocellular carcinoma based on 4D label-free quantitative proteomics analysis pilot investigation

BACKGROUND

Hepatocellular carcinoma carries a poor prognosis and poses a serious threat to global health. Currently, there are few potential prognostic biomarkers available for the prognosis of hepatocellular carcinoma.

METHODS

This pilot study used 4D label-free quantitative proteomics to compare the proteomes of hepatocellular carcinoma and adjacent non-tumor tissue. A total of 66,075 peptides, 6363 identified proteins, and 772 differentially expressed proteins were identified in specimens from three hepatocellular carcinoma patients. Through functional enrichment analysis of differentially expressed proteins by Gene Ontology, KEGG pathway, and protein domain, we identified proteins with similar functions.

RESULTS

Twelve differentially expressed proteins (RPL17, RPL27, RPL27A, RPS5, RPS16, RSL1D1, DDX18, RRP12, TARS2, YARS2, MARS2, and NARS1) were selected for identification and validation by parallel reaction monitoring. Subsequent Western blotting confirmed overexpression of RPL27, RPS16, and TARS2 in hepatocellular carcinoma compared to non-tumor tissue in 16 pairs of clinical samples. Analysis of The Cancer Genome Atlas datasets associated the increased expression of these proteins with poor prognosis. Tissue microarray revealed a negative association between high expression of RPL27 and TARS2 and the prognosis of hepatocellular carcinoma patients, although RPS16 was not significant.

CONCLUSIONS

These data suggest that RPL27 and TARS2 play an important role in hepatocellular carcinoma progression and may be potential prognostic biomarkers of overall survival in hepatocellular carcinoma patients.

The significance of HAUS1 and its relationship with immune microenvironment in hepatocellular carcinoma

Background: HAUS Augmin-like complex subunit 1(HAUS1), as a controlling gene, which affected the production of spindle was firstly discovered in Drosophila cells. Although HAUS1 has been intensively studied, but its significance and relationship with the immune microenvironment in Hepatocellular carcinoma (HCC) remain unclear. Materials and Methods: All data of HCC in this paper were obtained from The Cancer Genome Atlas(TCGA), Genotype-Tissue Expression (GTEx), Gene Expression Omnibus (GEO) and the Human Protein Atlas(HPA) database. The role and potential value of HAUS1 in the tumorigenesis and development of HCC were studied by applying plenty of bioinformatics analysis methods. Knocked down the expression of HAUS1 through siRNA and further investigated the function of HAUS1 in HCC Results: HAUS1 was highly expressed in HCC, which led to a poor prognosis. ROC curve analysis showed that HAUS1 had a excellent diagnostic value. It was also associated with clinical stage, pathological grade and AFP of HCC. Univariate and multivariate COX regression analysis showed that HAUS1 was an independent prognostic factor for HCC patients. HAUS1 was associated with immune cells infiltrate and immune checkpoints in HCC, and it could generate significative therapeutic results when combined with anti-CTLA4 and anti-CD274 treatment. In vitro experiments, HAUS1 was found to promote the proliferation, invasion and metastasis, participated in cell cycle regulation and inhibited apoptosis of HCC. Conclusion: These results suggested that HAUS1 might serve as a potential therapeutic target, as well as a diagnostic, prognostic, and survival biomarker for HCC.

Identification of common genes and pathways underlying imatinib and nilotinib treatment in CML: a Bioinformatics Study

Imatinib (IMA) and nilotinib are the first and second generations of BCR-ABL tyrosine kinase inhibitors, which widely applied in chronic myeloid leukemia (CML) treatment. Here we aimed to provide new targets for CML treatment by transcriptome analysis. Microarray data GSE19567 was downloaded and analyzed from Gene Expression Omnibus (GEO) to identify common genes, which are downregulated or upregulated in K562-imatinib and K562-nilotinib treated cells. The differentially expressed genes (DEGs) were assessed, and STRING and Cytoscape were used to create the protein-protein interaction (PPI) network. In imatinib and nilotinib treated groups' comparison, there were common 626 upregulated and 268 downregulated genes, which were differentially expressed. The GO analysis represented the enrichment of DEGs in iron ion binding, protein tyrosine kinase activity, transcription factor activity, ATP binding, sequence-specific DNA binding, cytokine activity, the mitochondrion, sequence-specific DNA binding, plasma membrane and cell-cell adherens junction. KEGG pathway analysis revealed that downregulated DEGs were associated with pathways including microRNAs in cancer and PI3K-Akt signaling pathway. Furthermore, upregulated DEGs were involved in hematopoietic cell lineage, lysosome and chemical carcinogenesis. Among the upregulated genes, MYH9, MYH14, MYL10, MYL7, MYL5, RXRA, CYP1A1, FECH, AKR1C3, ALAD, CAT, CITED2, CPT1A, CYP3A5, CYP3A7, FABP1, HBD, HMBS and PPOX genes were found as hub genes. Moreover, 20 downregulated genes, YARS, AARS, SARS, GARS, CARS, IARS, RRP79, CEBPB, RRP12, UTP14A, PNO1, CCND1, DDX10, MYC, WDR43, CEBPG, DDIT3, VEGFA, PIM1 and TRIB3 were identified as hub genes. These genes have the potential to become target genes for diagnosis and therapy of CML patients.

RRP12 suppresses cell migration and invasion in colorectal cancer cell via regulation of epithelial-mesenchymal transition

Background

The survival of patients with advanced colorectal cancer (CRC) is variable. The high rates of recurrence, metastasis, and drug resistance make clinical treatment difficult, which needs to further develop therapeutic and prognostic targets. Ribosomal RNA processing 12 homolog (RRP12), as a nucleolar protein involved in ribosome subunit maturation and export, plays important roles in cell cycle-related processes and the response to DNA damage, and regulates the occurrence and development of various cancers. The primary aim of this study was to identify the function of RRP12 in the process of epithelial-mesenchymal transition (EMT) in CRC.

Methods

In this study, the expression of RRP12 in tissue samples and the association with clinicopathological features in CRC was evaluated, and the correlation between RRP12 expression and aggressiveness of CRC was detected. After knockdown of RRP12 gene, the relationship between RRP12 and EMT-related indicators was verified in vivo and in vitro of CRC cells. Identification of RRP12-related genes and pathways through bioinformatic-based analyses was performed to find its potential mechanism.

Results

RRP12 is highly expressed in CRC cell lines and clinical samples and is associated with poor survival in CRC patients. RRP12 expression was positively associated with lymph node metastasis, tumor-node-metastasis (TNM) stage, and poor differentiation. Knockdown of RRP12 was found to suppress migration and invasion of CRC cells. RRP12 contributed to the EMT process of CRC cell lines in a ZEB1-mediated manner. RRP12 knockdown was found to reverse metastasis of CRC cells in vivo. Bioinformatic-based analyses indicated that RRP12 could serve as a potential biomarker for prognostic assessment of CRC patients.

Conclusions

RRP12 is involved in the tumorigenesis and metastasis of CRC by regulating the EMT process through ZEB1. Thus, RRP12 could be a potential therapeutic target for CRC therapy.

Identification of ZMYND19 as a novel biomarker of colorectal cancer: RNA-sequencing and machine learning analysis

Colorectal cancer (CRC) is the third most common cause of cancer-related deaths. The five-year relative survival rate for CRC is estimated to be approximately 90% for patients diagnosed with early stages and 14% for those diagnosed at an advanced stages of disease, respectively. Hence, the development of accurate prognostic markers is required. Bioinformatics enables the identification of dysregulated pathways and novel biomarkers. RNA expression profiling was performed in CRC patients from the TCGA database using a Machine Learning approach to identify differential expression genes (DEGs). Survival curves were assessed using Kaplan-Meier analysis to identify prognostic biomarkers. Furthermore, the molecular pathways, protein-protein interaction, the co-expression of DEGs, and the correlation between DEGs and clinical data have been evaluated. The diagnostic markers were then determined based on machine learning analysis. The results indicated that key upregulated genes are associated with the RNA processing and heterocycle metabolic process, including C10orf2, NOP2, DKC1, BYSL, RRP12, PUS7, MTHFD1L, and PPAT. Furthermore, the survival analysis identified NOP58, OSBPL3, DNAJC2, and ZMYND19 as prognostic markers. The combineROC curve analysis indicated that the combination of C10orf2 -PPAT- ZMYND19 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.98, 1.00, and 0.99, respectively. Eventually, ZMYND19 gene was validated in CRC patients. In conclusion, novel biomarkers of CRC have been identified that may be a promising strategy for early diagnosis, potential treatment, and better prognosis.

Elevated expression of LIF predicts a poor prognosis and promotes cell migration and invasion of clear cell renal cell carcinoma

Background

Renal cell carcinoma (RCC) is the seventh most common cancer in humans, of which clear cell renal cell carcinoma (ccRCC) accounts for the majority. Recently, although there have been significant breakthroughs in the treatment of ccRCC, the prognosis of targeted therapy is still poor. Leukemia inhibitory factor (LIF) is a pleiotropic protein, which is overexpressed in many cancers and plays a carcinogenic role. In this study, we explored the expression and potential role of LIF in ccRCC.

Methods

The expression levels and prognostic effects of the LIF gene in ccRCC were detected using TCGA, GEO, ICGC, and ArrayExpress databases. The function of LIF in ccRCC was investigated using a series of cell function approaches. LIF-related genes were identified by weighted gene correlation network analysis (WGCNA). GO and KEGG analyses were performed subsequently. Cox univariate and LASSO analyses were used to develop risk signatures based on LIF-related genes, and the prognostic model was validated in the ICGC and E-MTAB-1980 databases. Then, a nomogram model was constructed for survival prediction and validation of ccRCC patients. To further explore the drug sensitivity between LIF-related genes, we also conducted a drug sensitivity analysis based on the GDSC database.

Results

The mRNA and protein expression levels of LIF were significantly increased in ccRCC patients. In addition, a high expression of LIF has a poor prognostic effect in ccRCC patients. LIF knockdown can inhibit the migration and invasion of ccRCC cells. By using WGCNA, 97 LIF-related genes in ccRCC were identified. Next, a prognostic risk prediction model including eight LIF-related genes (TOB2, MEPCE, LIF, RGS2, RND3, KLF6, RRP12, and SOCS3) was developed and validated. Survival analysis and ROC curve analysis indicated that the eight LIF-related-gene predictive model had good performance in evaluating patients’ prognosis in different subgroups of ccRCC.

Conclusion

Our study revealed that LIF plays a carcinogenic role in ccRCC. In addition, we firstly integrated multiple LIF-related genes to set up a risk-predictive model. The model could accurately predict the prognosis of ccRCC, which offers clinical implications for risk stratification, drug screening, and therapeutic decision.