Screening, identification and validation of CCND1 and PECAM1/CD31 for predicting prognosis in renal cell carcinoma patients

Screening of differential gene expression patterns through survival analysis for diagnosis, prognosis and therapies of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of kidney cancer. Although there is increasing evidence linking ccRCC to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. Though drug therapies are the best choice after the metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving it for almost 2 years. In this case, multi-targeted different variants of therapeutic drugs are essential for effective treatment against ccRCC. To understand molecular mechanisms of ccRCC development and progression, and explore multi-targeted different variants of therapeutic drugs, it is essential to identify ccRCC-causing key genes (KGs). In order to obtain ccRCC-causing KGs, at first, we detected 133 common differentially expressed genes (cDEGs) between ccRCC and control samples based on nine (9) microarray gene-expression datasets with NCBI accession IDs GSE16441, GSE53757, GSE66270, GSE66272, GSE16449, GSE76351, GSE66271, GSE71963, and GSE36895. Then, we filtered these cDEGs through survival analysis with the independent TCGA and GTEx database and obtained 54 scDEGs having significant prognostic power. Next, we used protein-protein interaction (PPI) network analysis with the reduced set of 54 scDEGs to identify ccRCC-causing top-ranked eight KGs (PLG, ENO2, ALDOB, UMOD, ALDH6A1, SLC12A3, SLC12A1, SERPINA5). The pan-cancer analysis with KGs based on TCGA database showed the significant association with different subtypes of kidney cancers including ccRCC. The gene regulatory network (GRN) analysis revealed some crucial transcriptional and post-transcriptional regulators of KGs. The scDEGs-set enrichment analysis significantly identified some crucial ccRCC-causing molecular functions, biological processes, cellular components, and pathways that are linked to the KGs. The results of DNA methylation study indicated the hypomethylation and hyper-methylation of KGs, which may lead the development of ccRCC. The immune infiltrating cell types (CD8+ T and CD4+ T cell, B cell, neutrophil, dendritic cell and macrophage) analysis with KGs indicated their significant association in ccRCC, where KGs are positively correlated with CD4+ T cells, but negatively correlated with the majority of other immune cells, which is supported by the literature review also. Then we detected 10 repurposable drug molecules (Irinotecan, Imatinib, Telaglenastat, Olaparib, RG-4733, Sorafenib, Sitravatinib, Cabozantinib, Abemaciclib, and Dovitinib.) by molecular docking with KGs-mediated receptor proteins. Their ADME/T analysis and cross-validation with the independent receptors, also supported their potent against ccRCC. Therefore, these outputs might be useful inputs/resources to the wet-lab researchers and clinicians for considering an effective treatment strategy against ccRCC.

CPA4 as a biomarker promotes the proliferation, migration and metastasis of clear cell renal cell carcinoma cells

Clear cell renal cell carcinoma (ccRCC) is a commonly occurring and highly aggressive urological malignancy characterized by a significant mortality rate. Current therapeutic options for advanced ccRCC are limited, necessitating the discovery of novel biomarkers and therapeutic targets. Carboxypeptidase A4 (CPA4) is a zinc-containing metallocarboxypeptidase with implications in various cancer types, but its role in ccRCC remains unexplored. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized in order to investigate the differential expression patterns of CPA4. The expression of CPA4 in ccRCC patients was further verified using immunohistochemical (IHC) examination of 24 clinical specimens. A network of protein-protein interactions (PPI) was established, incorporating CPA4 and its genes that were expressed differentially. Functional enrichment analyses were conducted to anticipate the contribution of CPA4 in the development of ccRCC. To validate our earlier study, we conducted real-time PCR and cell functional tests on ccRCC cell lines. Our findings revealed that CPA4 is overexpressed in ccRCC, and the higher the expression of CPA4, the worse the clinical outcomes such as TNM stage, pathological stage, histological grade, etc. Moreover, patients with high CPA4 expression had worse overall survival, disease-specific survival and progress-free interval than patients with low expression. The PPI network analysis highlighted potential interactions contributing to ccRCC progression. Functional enrichment analysis indicated the involvement of CPA4 in the regulation of key pathways associated with ccRCC development. Additionally, immune infiltration analysis suggested a potential link between CPA4 expression and immune response in the tumour microenvironment. Finally, cell functional studies in ccRCC cell lines shed light on the molecular mechanisms underlying the role of CPA4 in promoting ccRCC formation. Overall, our study unveils CPA4 as a promising biomarker with prognostic potential in ccRCC. The identified interactions and pathways provide valuable insights into its implications in ccRCC development and offer a foundation for future research on targeted therapies. Further investigation of CPA4's involvement in immune responses may contribute to the development of immunotherapeutic strategies for ccRCC treatment.

Predictive Model of CK7 Expression in Patients With Clear Cell Renal Cell Carcinoma by Combined Multimodal Ultrasound Diagnostic Techniques: A Retrospective Study

OBJECTIVE

The aim of the work described here was to develop and validate a predictive model for cytokeratin 7 (CK7) expression in clear cell renal cell carcinoma (ccRCC) patients by combining multimodal ultrasound diagnostic techniques.

METHODS

This retrospective study enrolled 157 surgically confirmed ccRCC patients. All patients underwent pre-operative multimodal ultrasound diagnostic examinations, including B-mode ultrasound (US), color Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS). The patients were randomly divided into a training group (103 cases) and a testing group (54 cases). Univariate and multivariate logistic regression analyses were performed in the training group to identify independent indicators associated with CK7 positivity. These indicators were included in the predictive model. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the model's discriminative ability and accuracy. Decision curve analysis (DCA) and nomogram visualization were used to assess the clinical utility of the predictive model.

RESULTS

Univariate logistic regression analysis revealed that US and CDFI observations were not correlated with CK7 expression and could not predict it. Multivariate logistic regression analysis identified age (odds ratio [OR] = 0.953, 95% confidence interval [CI]: 0.909-0.999), wash-in pattern (OR = 0.180, 95% CI: 0.063-0.513) and enhancement homogeneity (OR = 11.610, 95% CI: 1.394-96.675) as independent factors related to CK7 positivity in ccRCC. Incorporating these variables into the predictive model resulted in areas under the receiver operating characteristic curve of 0.812 (95% CI: 0.711-0.913) for the training group and 0.792 (95% CI: 0.667-0.924) for the testing group. The calibration curve and DCA revealed that the model had good accuracy and clinical utility of the model.

CONCLUSION

The combination of multimodal ultrasound diagnostic techniques in constructing a predictive model for CK7 expression in ccRCC patients has significant predictive value.

Deciphering the implications of mitophagy-related signatures in clinical outcomes and microenvironment heterogeneity of clear cell renal cell carcinoma

BACKGROUND

The role of mitophagy in various cancer-associated biological processes is well recognized. Nonetheless, the comprehensive implications of mitophagy in clear cell renal cell carcinoma (ccRCC) necessitate further exploration.

METHODS

Based on the transcriptomic data encompassing 25 mitophagy-related genes (MRGs), we identified the distinct mitophage patterns in 763 ccRCC samples. Subsequently, a mitophage-related predictive signature with machine learning algorithms was constructed, designated as RiskScore, to quantify the individual mitophagy status in ccRCC patients. Employing multispectral immunofluorescence (mIF) and immunohistochemistry (IHC) staining, we detected the effect of PTEN-induced putative kinase 1 (PINK1) in the prognosis and immune microenvironment of ccRCC.

RESULTS

Our analysis initially encompassed a comprehensive assessment of the expression profiling, genomic variations, and interactions among the 25 MRGs in ccRCC. Subsequently, the consensus clustering algorithm was applied to stratify ccRCC patients into three clusters with distinct prognostic outcomes, tumor microenvironment (TME) characteristics, and underlying biological pathways. We screened eight pivotal genes (CLIC4, PTPRB, SLC16A12, ENPP5, FLRT3, HRH2, PDK4, and SCD5) to construct a mitophagy-related predictive signature, which showed excellent prognostic value for ccRCC patients. Moreover, patient subgroups divided by the RiskScore showed contrasting expression levels of immune checkpoints (ICPs), abundance of immune cells, and immunotherapy response. Additionally, a nomogram was established with robust predictive power integrating the RiskScore and clinical features. Notably, we observed that PINK1 expression markedly correlated with favorable treatment response and advanced maturation stages of tertiary lymphoid structures, which potentially shed light on enhancing anti-tumor immunity of ccRCC.

CONCLUSION

Collectively, this study initially developed a signature associated with mitophagy, which demonstrated an excellent ability to predict the clinical prognosis, TME characterization, and responsiveness to targeted therapy and immunotherapy for ccRCC patients. Of particular note is the pivotal role of PINK1 in mediating the treatment response and immune microenvironment for ccRCC patients.

TREM-1 as a potential prognostic biomarker associated with immune infiltration in clear cell renal cell carcinoma

BACKGROUND

The tumor immune microenvironment plays a crucial role in the efficacy of various therapeutics. However, their correlation is not yet completely understood in Clear cell renal cell carcinoma (ccRCC). This study aimed to investigate the potential of TREM-1 as a potential novel biomarker for ccRCC.

METHODS

We constructed a ccRCC immune prognostic signature. The clinical characteristics, the status of the tumor microenvironment, and immune infiltration were analyzed through the ESTIMATE and CIBERSORT algorithms for the hub gene, while the Gene Set Enrichment Analysis and PPI analysis were performed to predict the function of the hub gene. Immunohistochemical staining was used to detect the expression of TREM-1 in renal clear cell carcinoma tissues.

RESULTS

The CIBERSORT and ESTIMATE algorithms revealed that TREM-1 was correlated with the infiltration of 12 types of immune cells. Therefore, it was determined that TREM-1 was involved in numerous classical pathways in the immune response via GSEA analysis. In Immunohistochemical staining, we found that the expression of TREM-1 was significantly upregulated with increasing tumor grade in renal clear cell carcinoma, and elevated TREM-1 expression was associated with poor prognosis.

CONCLUSIONS

The results suggest that TREM-1 may act as an implicit novel prognostic biomarker in ccRCC that could be utilized to facilitate immunotherapeutic strategy.

Establishment and validation of a novel anoikis-related prognostic signature of clear cell renal cell carcinoma

Background

Despite progression in its treatment, the clinical outcome of patients with clear cell renal cell carcinoma (ccRCC) remains not ideal. Anoikis is a unique form of programmed apoptosis, owing to insufficient cell-matrix interactions. Anoikis plays a crucial role in tumor migration and invasion, and tumor cells could protect themselves through the capacity of anoikis resistance.

Methods

Anoikis-related genes (ARGs) were obtained from Genecards and Harmonizome portals. The ARGs related to ccRCC prognosis were identified through univariate Cox regression analysis, then we utilized these ARGs to construct a novel prognostic model for ccRCC patients. Moreover, we explored the expression profile of ARGs in ccRCC using the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. We also conducted Real-Time Polymerase Chain Reaction (RT-PCR) to probe ARGs expression of the risk score. Finally, we performed correlation analysis between ARGs and tumor immune microenvironment.

Results

We identified 17 ARGs associated with ccRCC survival, from which 7 genes were chosen to construct a prognostic model. The prognostic model was verified as an independent prognostic indicator. The expression of most ARGs was higher in ccRCC samples. These ARGs were closely correlated with immune cell infiltration and immune checkpoint members, and had independent prognostic value respectively. Functional enrichment analysis demonstrated that these ARGs were significantly associated with multiple types of malignances.

Conclusion

The prognostic signature was identified to be highly efficient in predicting ccRCC prognosis, and these ARGs were closely related to tumor microenvironment.

Core genes in lung adenocarcinoma identified by integrated bioinformatic analysis

This study aims to identify potential core genes of lung adenocarcinoma (LUAD). Three datasets (GSE32863, GSE43458, and GSE116959) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between LUAD and normal tissues were filtrated by GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed via Metascape database. The protein-protein interaction (PPI) network was constructed and core genes were identified using STRING and Cytoscape. Core genes expressions and their relevant clinical characteristics were performed via Oncomine and UALCAN databases respectively. The correlation between core genes and immune infiltrates was investigated by TIMER database. Kaplan-Meier plotter was performed for survival analysis. The signal pathway network of core genes was mapped by KEGG Mapper analysis tool. In this study, ten core genes were significantly related to overall survival (OS) of LUAD patients, which can provide clues for prognosis of LUAD.

Inhibition of Wnt7b reduces the proliferation, invasion, and migration of colorectal cancer cells

OBJECTIVE

Colorectal cancer is one of the most common gastrointestinal tumors. The role of Wnt7b as a ligand of the Wnt signaling pathway in colorectal cancer remains to be studied. Through bioinformatics online analysis, we found that Wnt7b is abnormally highly expressed in a variety of gastrointestinal tumors. This study mainly explored the effects of Wnt7b regulating the Wnt/β-catenin signaling pathway on the proliferation, migration, and invasion of SW480 cells in colorectal cancer.

METHODS AND RESULTS

Applying the TCGA data set, Wnt7b was found to be highly expressed in most gastrointestinal tumor samples. Real-time quantitative PCR(q-PCR), Western blotting(WB) results showed that Wnt7b was significantly higher expressed in colorectal cancer cell lines compared with normal intestinal epithelial cells. SW480 cells transfected with the sh-Wnt7b showed successful knockdown of Wnt7b. MTT colorimetry showed the proliferation ability of sh-Wnt7b group decreased significantly compared with the non-transfected group. The results of double staining flow cytometry showed that the sh-Wnt7b group had more apoptosis. Cell scratch test showed that the cell migration rate of sh-wnt7b group considerably reduced. The Transwell invasion experiment demonstrated that the number of cell invasions in the sh-Wnt7b group decreased significantly. After SW480 cells was transfected with sh-Wnt7b, the protein levels of β-catenin, CCND1, and CD44 in this group of cells were detected to be reduced by WB, and the same results were obtained by q-PCR detection of mRNA.

CONCLUSION

Wnt7b is highly expressed in colorectal cancer cells, which may affect the proliferation, migration, and invasion of colorectal cancer cells by activating the Wnt/β-catenin signaling pathway.

Renal Carcinoma and Angiogenesis: Therapeutic Target and Biomarkers of Response in Current Therapies

Due to the aberrant hypervascularization and the high immune infiltration of renal tumours, current therapeutic regimens of renal cell carcinoma (RCC) target angiogenic or immunosuppressive pathways or both. Tumour angiogenesis plays an essential role in tumour growth and immunosuppression. Indeed, the aberrant vasculature promotes hypoxia and can also exert immunosuppressive functions. In addition, pro-angiogenic factors, including VEGF-A, have an immunosuppressive action on immune cells. Despite the progress of treatments in RCC, there are still non responders or acquired resistance. Currently, no biomarkers are used in clinical practice to guide the choice between the different available treatments. Considering the role of angiogenesis in RCC, angiogenesis-related markers are interesting candidates. They have been studied in the response to antiangiogenic drugs (AA) and show interest in predicting the response. They have been less studied in immunotherapy alone or combined with AA. In this review, we will discuss the role of angiogenesis in tumour growth and immune escape and the place of angiogenesis-targeted biomarkers to predict response to current therapies in RCC.

Development and Validation of a Novel Mitochondrion and Ferroptosis-Related Long Non-Coding RNA Prognostic Signature in Hepatocellular Carcinoma

Mitochondrion and ferroptosis are related to tumorigenesis and tumor progression of hepatocellular carcinoma (HCC). Therefore, this study focused on exploring the participation of lncRNAs in mitochondrial dysfunction and ferroptosis using public datasets from The Cancer Genome Atlas (TCGA) database. We identified the mitochondrion- and ferroptosis-related lncRNAs by Pearson's analysis and lasso-Cox regression. Moreover, real-time quantitative reverse transcription PCR (RT-qPCR) was utilized to further confirm the abnormal expression of these lncRNAs. Based on eight lncRNAs, the MF-related lncRNA prognostic signature (LPS) with outstanding stratification ability and prognostic prediction capability was constructed. In addition, functional enrichment analysis and immune cell infiltration analysis were performed to explore the possible functions of lncRNAs and their impact on the tumor microenvironment. The pathways related to G2M checkpoint and MYC were activated, and the infiltration ratio of regulatory T cells and M0 and M2 macrophages was higher in the high-risk group. In conclusion, these lncRNAs may affect mitochondria functions, ferroptosis, and immune cell infiltration in HCC through specific pathways, which may provide valuable insight into the progression and therapies of HCC.

Bone Marrow Mesenchymal Stem Cell (BMSC)-Derived Exosomal miR-168-5p Inhibits Proliferation and Chemotherapy Resistance in Gastric Cancer by Downregulation of Cyclin D1 and P Glycoprotein

miR-168-5p is indicated as an upstream effector of the tumor suppressor signal pathway in ovarian cancer and bladder cancer, but the role in gastric cancer (GC) remains unknown. This study aims to reveal the expression and significance of miR-168-5p in GC. RT-qPCR analysis was used to detect the expression of miR-168-5p in GC tissues and plasma, and the relationship of miR-168-5p and CCND1 was evaluated. GC cells were co-cultured with BMSCs or transfected with miR-168-5p mimic. CCK-8 assay and flow cytometry were conducted to assess the effect of miR-168-5p in GC and the interaction between BMSCs and cancer cell progression. Animal experiment was established to explore the in vivo effect of miR-168-5p. miR-168-5p is poorly expressed in gastric cancer cells and the plasma of patients with gastric cancer. BMSC co-culture is similar to miR-168-5p mimic induced miR-168-5p expression increase. miR-168-5p overexpression decreased the proliferative, invasive and migratory capacities of GC cells, and promoted apoptosis. Mechanically, miR-168-5p targeted and decreased the expression of CCND1. Additionally, the low miR-168-5p expression in GC was closely related to poor prognosis and malignant transformation. BMSC exosomes carrying miR-168-5p suppress cell progression in GC when inhibiting the expression of CCND1 and P glycoprotein, which indicates potential diagnostic and prognostic value of miR-168-5p and helps the development of miR-168-5p-based treatment for drug-resistant GC.

Identification of heterogeneity and prognostic key genes associated with uveal melanoma using single-cell RNA-sequencing technology

Uveal melanoma (UM) is the most common intraocular malignancy in adults. The prognosis is poor once metastasis has developed. The treatment of metastatic UM remains challenging nowadays due to lacking a deep understanding of the biological characteristics of this disease. Here, we revealed the cell subpopulations with distinct functional status and the existence of cells with high invasive potential within heterogeneous primary and metastatic UM. The single-cell sequencing data were retrieved from GSE139829 and GSE138433, through which we identified a new cell cluster related to metastatic UM as a unique type of immune cell. The cell-cell communication was conducted by 'Cellchat' to understand the cell crosstalk between these immune cells and their surrounding cells. The crucial signals contributing most to outgoing or incoming signaling of this cell group were identified to reveal the crucial pathway genes. Furthermore, we judged the prognostic value of these candidates on the basis of the data downloaded from The Cancer Genome Atlas. The results demonstrated that the increased IL10, SELPLG, EPHB and ITGB2 signaling pathways could be promising predicting factors for the patient prognosis in UM. Conclusively, we discover the potential key signals of UM for occurrence and metastasis, and also provide a theoretical basis for judging whether there is a high risk of metastasis or recurrence.

Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis

Lower-grade glioma (LGG) is a common type of central nervous system tumor. Due to its complicated pathogenesis, the choice and timing of adjuvant therapy after tumor treatment are controversial. This study explored and identified potential therapeutic targets for lower-grade. The bioinformatics method was employed to identify potential biomarkers and LGG molecular mechanisms. Firstly, we selected and downloaded GSE15824, GSE50161, and GSE86574 from the GEO database, which included 40 LGG tissue and 28 normal brain tissue samples. GEO and VENN software identified of 206 codifference expressed genes (DEGs). Secondly, we applied the DAVID online software to investigate the DEG biological function and KEGG pathway enrichment, as well as to build the protein interaction visualization network through Cytoscape and STRING website. Then, the MCODE plug is used in the analysis of 22 core genes. Thirdly, the 22 core genes were analyzed with UNCLA software, of which 18 genes were associated with a worse prognosis. Fourthly, GEPIA was used to analyze the 18 selected genes, and 14 genes were found to be a significantly different expression between LGGs and normal brain tumor samples. Fifthly, hierarchical gene clustering was used to examine the 14 important gene expression differences in different histologies, as well as analysis of the KEGG pathway. Five of these genes were shown to be abundant in the natural killer cell-mediated cytokines (NKCC) and phagosome pathways. The five key genes that may be affected by the immune microenvironment play a crucial role in LGG development.

Bioinformatics Analysis of Prognostic Value of SPC24 in ccRCC and Pan-Cancer

Purpose

Clear cell renal cell carcinoma (ccRCC) is one of the most common diseases in the world, with high morbidity and mortality. Recent studies have revealed the important role of SPC24, a subunit of the Ndc80 complex, in the occurrence and development of carcinoma. However, the latent effect of SPC24 in the progress of ccRCC remains to be further explored. The intent of this research is to investigate whether SPC24 can be used as an index to predict the progression of ccRCC and to explore its relationship with the immune microenvironment and pan-cancer.

Materials and Methods

Based on data from public databases, we determined the expression level and clinical value of SPC24 in ccRCC and human pan-cancer. RT-qPCR analysis was carried out to detect the expression level of SPC24 in the OSRC/786O (human ccRCC cells) cell lines and HK2 (human normal kidney cells) cell line. The signal transduction pathways activated by different levels of SPC24 expression were explored by Gene Set Enrichment Analysis (GSEA), and the CIBERSORT algorithm was applied to analyze the relationship between infiltrating immune cells and SPC24 expression in ccRCC and pan-cancer tissues.

Results

SPC24 is overexpressed in ccRCC and several types of tumors, which is associated with poor prognosis. GSEA and CIBERSORT algorithms suggested that the high expression group of SPC24 enriched various pathways including immune-related pathways, and the several infiltrated immunocytes were related to the expression of SPC24.

Conclusion

Our study revealed that SPC24 is a prognostic factor in ccRCC related to immunomodulation and has generalized value in pan-cancer.

Screening and identification of key biomarkers of papillary renal cell carcinoma by bioinformatic analysis

Background

Papillary renal cell carcinoma (PRCC) is the most common type of renal cell carcinoma after clear cell renal cell carcinoma (ccRCC). Its pathological classification is controversial, and its molecular mechanism is poorly understood. Therefore, the identification of key genes and their biological pathways is of great significance to elucidate the molecular mechanisms of PRCC occurrence and progression.

Methods

The PRCC-related datasets GSE7023, GSE48352 and GSE15641 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and gene ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape and STRING were used to construct the protein-protein interaction network (PPI) and perform module analysis to identify hub genes and key pathways. A heatmap of hub genes was constructed using the UCSC cancer genomics browser. Overall survival and recurrence-free survival of patients stratified by the expression levels of hub genes were analysed using Kaplan-Meier Plotter. The online database UALCAN was applied to analyse gene expression based on tissue type, stage, subtype and race.

Results

A total of 214 DEGs, specifically, 205 downregulated genes and 9 upregulated genes, were identified. The DEGs were mainly enriched in angiogenesis, kidney development, oxidation-reduction process, metabolic pathways, etc. The 17 hub genes identified were mainly enriched in the biological processes of angiogenesis, cell adhesion, platelet degranulation, and leukocyte transendothelial migration. Survival analysis showed that EGF, KDR, CXCL12, REN, PECAM1, CDH5, THY1, WT1, PLAU and DCN might be related to the carcinogenesis, metastasis or recurrence of PRCC. UALCAN analysis showed that low expression of PECAM1 and PLAU in PRCC tissues was related to stage, subtype and race.

Conclusions

The DEGs and hub genes identified in the present study provide insight into the specific molecular mechanisms of PRCC occurrence and development and may be potential molecular markers and therapeutic targets for the accurate classification and efficient diagnosis and treatment of PRCC.

Aurora kinase A (AURKA) promotes the progression and imatinib resistance of advanced gastrointestinal stromal tumors

BACKGROUND

Gastrointestinal stromal tumor (GIST) is a common tumor that originates from the alimentary system mesenchyme. Compared to typical gastrointestinal carcinomas, GISTs exhibit unique malignant behaviors. Bioinformatic tools and subsequent experiments were applied to investigate novel targets involved in GIST progression and imatinib resistance.

METHODS

Differences in gene expression profiles between advanced and nonadvanced GISTs were comprehensively analyzed based on the Gene Expression Omnibus (GEO) dataset GSE136755. A protein-protein interaction (PPI) network was constructed to identify the potential target gene. Gene set enrichment analysis (GSEA) was used to elucidate relevant biological events related to the target gene based on the GSE47911 dataset. Subsequently, immunohistochemistry and Kaplan-Meier analysis were performed to validate the prognostic value of the target gene in GISTs. Overexpression of the target gene was conducted to analyze its function in the proliferation, apoptosis, and imatinib resistance of GIST/T1 cells.

RESULTS

In the current study, a total of 606 differentially expressed genes (DEGs) were screened based on the GSE136755 dataset, and the upregulated DEGs in advanced GISTs were mainly involved in cell division through functional annotations. The intersecting hub gene, Aurora kinase A (AURKA), was identified by degree and bottleneck algorithms. GSEA revealed that AURKA was involved in cell cycle-related biological processes. Analysis of the Oncomine and GEPIA databases revealed a pattern of elevated AURKA expression in most human malignances. Clinical assays demonstrated that AURKA could be an independent prognostic factor for GISTs. Additionally, overexpression of AURKA was experimentally demonstrated to promote cell proliferation, inhibit cell apoptosis, and enhance imatinib resistance in GIST/T1 cells.

CONCLUSIONS

These findings indicated that overexpression of AURKA promoted GIST progression and enhanced imatinib resistance, implying that AURKA is a potential therapeutic target for GISTs.

Molecular and Functional Analysis of Sunitinib-Resistance Induction in Human Renal Cell Carcinoma Cells

Resistance in clear cell renal cell carcinoma (ccRCC) against sunitinib is a multifaceted process encompassing numerous molecular aberrations. This induces clinical complications, reducing the treatment success. Understanding these aberrations helps us to select an adapted treatment strategy that surpasses resistance mechanisms, reverting the treatment insensitivity. In this regard, we investigated the dominant mechanisms of resistance to sunitinib and validated an optimized multidrug combination to overcome this resistance. Human ccRCC cells were exposed to single or chronic treatment with sunitinib to obtain three resistant clones. Upon manifestation of sunitinib resistance, morphometric changes in the cells were observed. At the molecular level, the production of cell membrane and extracellular matrix components, chemotaxis, and cell cycle progression were dysregulated. Molecules enforcing the cell cycle progression, i.e., cyclin A, B1, and E, were upregulated. Mass spectrometry analysis revealed the intra- and extracellular presence of N-desethyl sunitinib, the active metabolite. Lysosomal sequestration of sunitinib was confirmed. After treatment with a synergistic optimized drug combination, the cell metabolic activity in Caki-1-sunitinib-resistant cells and 3D heterotypic co-cultures was reduced by >80%, remaining inactive in non-cancerous cells. These results demonstrate geno- and phenotypic changes in response to sunitinib treatment upon resistance induction. Mimicking resistance in the laboratory served as a platform to study drug responses.

Identification of Potential miRNA-mRNA Regulatory Network Contributing to Hypertrophic Cardiomyopathy (HCM)

Background: Hypertrophic cardiomyopathy (HCM) is a myocardial disease with unidentified pathogenesis. Increasing evidence indicated the potential role of microRNA (miRNA)-mRNA regulatory network in disease development. This study aimed to explore the miRNA-mRNA axis in HCM. Methods: The miRNA and mRNA expression profiles obtained from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed miRNAs (DEMs) and genes (DEGs) between HCM and normal samples. Target genes of DEMs were determined by miRTarBase. Gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to identify biological functions of the DEGs and DEMs. miRNA-mRNA regulatory network was constructed to identify the hub genes and miRNAs. Logistic regression model for HCM prediction was established basing on the network. Results: A total of 224 upregulated and 366 downregulated DEGs and 10 upregulated and 14 downregulated DEMs were determined. We identified 384 DEM-targeted genes, and 20 of them were overlapped with the DEGs. The enriched functions include extracellular structure organization, organ growth, and phagosome and melanoma pathways. The four miRNAs and three mRNAs, including hsa-miR-373, hsa-miR-371-3p, hsa-miR-34b, hsa-miR-452, ARHGDIA, SEC61A1, and MYC, were identified through miRNA-mRNA regulatory network to construct the logistic regression model. The area under curve (AUC) values over 0.9 suggested the good performance of the model. Conclusion: The potential miRNA-mRNA regulatory network and established logistic regression model in our study may provide promising diagnostic methods for HCM.

Identification of hub driving genes and regulators of lung adenocarcinoma based on the gene Co-expression network

Lung adenocarcinoma (LUAD) remains the leading cause of cancer-related deaths worldwide. Increasing evidence suggests that circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs) can regulate target gene expression and participate in tumor genesis and progression. However, hub driving genes and regulators playing a potential role in LUAD progression have not been fully elucidated yet. Based on data from The Cancer Genome Atlas database, 2837 differentially expressed genes, 741 DE-regulators were screened by comparing cancer tissues with paracancerous tissues. Then, 651 hub driving genes were selected by the topological relation of the protein-protein interaction network. Also, the target genes of DE-regulators were identified. Moreover, a key gene set containing 65 genes was obtained from the hub driving genes and target genes intersection. Subsequently, 183 hub regulators were selected based on the analysis of node degree in the ceRNA network. Next, a comprehensive analysis of the subgroups and Wnt, mTOR, and MAPK signaling pathways was conducted to understand enrichment of the subgroups. Survival analysis and a receiver operating characteristic curve analysis were further used to screen for the key genes and regulators. Furthermore, we verified key molecules based on external database, LRRK2, PECAM1, EPAS1, LDB2, and HOXA11-AS showed good results. LRRK2 was further identified as promising biomarker associated with CNV alteration and various immune cells' infiltration levels in LUAD. Overall, the present study provided a novel perspective and insight into hub driving genes and regulators in LUAD, suggesting that the identified signature could serve as an independent prognostic biomarker.

Overexpression of TICRR and PPIF confer poor prognosis in endometrial cancer identified by gene co-expression network analysis

The incidence of endometrial cancer (EC) is intensively increasing. However, due to the complexity and heterogeneity of EC, the molecular targeted therapy is still limited. The reliable and accurate biomarkers for tumor progression are urgently demanded. After normalizing the data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we utilized limma and WGCNA packages to identify differentially expressed genes (DEGs). The copy number variations of candidate genes were investigated by cBioPortal. Enrichment pathways analysis was performed by ClueGO and CluePedia. The methylation status was explored by UALCAN. ROC curve and survival analysis were conducted by SPSS and Kaplan–Meier. Infiltration immune cells in microenvironment were analyzed by TISIDB. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were applied to explore potential biological pathways. Immunohistochemistry staining (IHC), cell proliferation, cell apoptosis, colony formation, migration, invasion and scratch-wound assays were performed to investigate the function of key genes in vitro. In this study, four expression profile datasets were integrated to identify candidate genes. Combined with WGCNA analysis, the top ten candidates were screened out, whose abnormal methylation patterns were extremely correlated with their expression level and they were associated with tumor grades and predicted poor survival. GSEA and GSVA demonstrated they were involved in DNA replication and cell cycle transition in EC. Gene silencing of TICRR and PPIF dramatically inhibited cell growth, migration and epithelial-mesenchymal transition (EMT) and enhanced progesterone sensitivity. Additionally, from DrugBank database, cyclosporine may be effective for PPIF targeted therapy. By integrative bioinformatics analysis and in vitro experiments, our study shed novel light on the molecular mechanisms of EC. TICRR and PPIF may promise to be potential therapeutic targets for endometrial cancer.

Identification of RNA-binding protein SNRPA1 for prognosis in prostate cancer

Prostate cancer is one of the deadliest cancers in men. RNA-binding proteins play a critical role in human cancers; however, whether they have a significant effect on the prognosis of prostate cancer has yet to be elucidated. In the present study, we performed a comprehensive analysis of RNA sequencing and clinical data from the Cancer Genome Atlas dataset and obtained differentially expressed RNA-binding proteins between prostate cancer and benign tissues. We constructed a protein-protein interaction network and Cox regression analyses were conducted to identify prognostic hub RNA-binding proteins. SNRPA1 was associated with the highest risk of poor prognosis and was therefore selected for further analysis. SNRPA1 expression was positively correlated with Gleason score and pathological TNM stage in prostate cancer patients. Furthermore, the expression profile of SNRPA1 was validated using the Oncomine, Human Protein Atlas, and Cancer Cell Line Encyclopedia databases. Meanwhile, the prognostic profile of SNRPA1 was successfully verified in GSE70769. Additionally, the results of molecular experiments revealed the proliferative role of SNRPA1 in prostate cancer cells. In summary, our findings evidenced a relationship between RNA-binding proteins and prostate cancer and indicated the prognostic significance of SNRPA1 in prostate cancer.

C3, C3AR1, HLA-DRA, and HLA-E as potential prognostic biomarkers for renal clear cell carcinoma

Background

Prognostic biomarkers play a vital role in the early detection of the cancer and assessment of prognosis. With advances in technology, a large number of biomarkers of kidney renal clear cell carcinoma (KIRC) have been discovered, but their prognostic value has not been fully investigated, and thus have not been widely used in clinical practice. We aimed to identify the reliable markers associated with the prognosis of KIRC patients.

Methods

We obtained 72 normal samples and 539 tumor samples from The Cancer Genome Atlas (TCGA), and 23 normal samples and 32 tumor samples from the Gene Expression Omnibus (GEO). Overlapping differentially expressed genes (ODEGs) were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, followed by construction of a protein-protein interaction (PPI) network to screen hub genes. Kaplan-Meier analysis, univariate Cox analysis, multivariate Cox analysis, Wilcoxon signed-rank test, Kruskal-Wallis test, and gene set enrichment analysis (GSEA) were performed to verify the prognostic value and function of the markers we selected. The relationships among gene expression level, tumor immune cell infiltration, and immune-checkpoints were also analyzed.

Results

A total of 910 genes were screened out, and C3, C3AR1, HLA-DRA, and HLA-E were identified as potential tumor markers. The expression of each gene was closely associated with tumor immune cell infiltration, survival rate, and the patients’ clinical characteristics (P<0.05). C3AR1, HLA-DRA, and HLA-E were also verified as independent prognostic factors of KIRC (P<0.05), and all these potential biomarkers had a close correlation with immune checkpoints.

Conclusions

C3, C3AR1, HLA-DRA, and HLA-E could be reliable biomarkers of KIRC and may have a significant contribution to make in immunotherapy, thus playing an important role in the improvement of prognosis.

Impact of inflammation and immunotherapy in renal cell carcinoma

Substantial research attention has been directed at exploring the mechanisms and treatment of renal cell carcinoma (RCC). Indeed, the association between inflammation and tumor phenotypes has been at the center of cancer research. Concomitant with research on the inflammation response and inflammatory molecules involved in RCC, new breakthroughs have emerged. A large body of knowledge now shows that treatments targeting inflammation and immunity in RCC provide substantial clinical benefits. Adequate analysis and a better understanding of the mechanisms of inflammatory factors in the occurrence and progression of RCC are highly desirable. Currently, numerous RCC treatments targeted at inflammation and immunotherapy are available. The current review describes in detail the link between inflammation and RCC.

Overexpression of PKMYT1 Facilitates Tumor Development and Is Correlated with Poor Prognosis in Clear Cell Renal Cell Carcinoma

BACKGROUND Protein kinase membrane-associated tyrosine/threonine (PKMYT1) has been found in many tumors, but its association with clear cell renal cell carcinoma (ccRCC) remains unclear. MATERIAL AND METHODS PKMYT1 expression in ccRCC was examined in the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Tumor Immune Estimation Resource databases. The correlation between PKMYT1 expression and clinicopathological parameters was explored via the chi-square test. Receiver operating characteristic curves were used to estimate the diagnostic performance of PKMYT1. Kaplan-Meier curves, a Cox model, nomogram, time-dependent receiver operating characteristic curves, and decision curve analysis (DCA) were used to evaluate the prognostic value and clinical utility of PKMYT1. Genes coexpressed with PKMYT1 in ccRCC were identified based on TCGA, the gene expression profiling interactive, and cBioPortal. Gene Set Enrichment Analysis revealed biological pathways associated with PKMYT1 in ccRCC. RESULTS Weighted gene coexpression network analysis identified PKMYT1 as one of the genes most significantly correlated with progression of histological grade. PKMYT1 was significantly upregulated in ccRCC compared with normal tissue (P<0.001), with a trend toward differentiating between individuals with ccRCC and those who were healthy (area under the curve=0.942). High PKMYT1 expression was correlated with unsatisfactory survival (hazard ratio=1.67, P=0.001), indicating that it is a risk factor for ccRCC. A nomogram incorporating PKMYT1 level was created and showed a clinical net benefit. PKMYT1 was strongly positively correlated with the anti-silencing function of 1B histone chaperone (ASF1B) gene in ccRCC. CONCLUSIONS PKMYT1 is upregulated in ccRCC and its presence indicates poor prognosis, making it a potential therapeutic target for ccRCC.

Identification and validation of novel metastasis-related signatures of clear cell renal cell carcinoma using gene expression databases

Patients with clear cell renal cell carcinoma (ccRCC) typically face aggressive disease progression when metastasis occurs. Here, we screened and identified differentially expressed genes in three microarray datasets from the Gene Expression Omnibus database. We identified 112 differentially expressed genes with functional enrichment as candidate prognostic biomarkers. Lasso Cox regression suggested 10 significant oncogenic hub genes involved in earlier recurrence and poor prognosis of ccRCC. Receiver operating characteristic curves validated the specificity and sensitivity of the Cox regression penalty used to predict prognosis. The area under the curve indexes of the integrated genes scores were 0.758 and 0.772 for overall and disease-free survival, respectively. The prognostic values of ADAMTS9, C1S, DPYSL3, H2AFX, MINA, PLOD2, RUNX1, SLC19A1, TPX2, and TRIB3 were validated through an analysis of 10 hub genes in 380 patients with ccRCC from a real-world cohort. The expression levels of were of high prognostic value for predicting metastatic potential. These findings will likely significantly contribute to our understanding of the underlying mechanisms of ccRCC, which will enhance efforts to optimize therapy.

Identification of potential key genes and key pathways related to clear cell renal cell carcinoma through bioinformatics analysis

Clear cell renal cell carcinoma (ccRCC) is a common malignancy of the genitourinary system and is associated with high mortality rates. However, the molecular mechanism of ccRCC pathogenesis is still unclear, which translates to few effective diagnostic and prognostic biomarkers. In this study, we conducted a bioinformatics analysis on three Gene Expression Omnibus datasets and identified 437 differentially expressed genes (DEGs) related to ccRCC development and prognosis, of which 311 and 126 genes are respectively down-regulated and up-regulated. The protein-protein interaction network of these DEGs consists of 395 nodes and 1872 interactions and 2 prominent modules. The Staphylococcus aureus infection and complement and coagulation cascades are significantly enriched in module 1 and are likely involved in ccRCC progression. Forty-two hub genes were screened, of which von Willebrand factor, TIMP metallopeptidase inhibitor 1, plasminogen, formimidoyltransferase cyclodeaminase, solute carrier family 34 member 1, hydroxyacid oxidase 2, alanine-glyoxylate aminotransferase 2, phosphoenolpyruvate carboxykinase 1, and 3-hydroxy-3-methylglutaryl-CoA synthase 2 are possibly related to the prognosis of ccRCC. The differential expression of all nine genes was confirmed by quantitative real-time polymerase chain reaction analysis of the ccRCC and normal renal tissues. These key genes are potential biomarkers for the diagnosis and prognosis of ccRCC and warrant further investigation.