Characterization of starvation response-related genes for predicting prognosis in breast cancer.

Breast cancer (BRCA) cells typically exist in nutrient-deficient microenvironments and quickly adapt to states with fluctuating nutrient levels. The tumor microenvironment of starvation is intensely related to metabolism and the malignant progression of BRCA. However, the potential molecular mechanism has not been thoroughly scrutinized. As a result, this study aimed to dissect the prognostic implications of mRNAs involved in the starvation response and construct a signature for forecasting the outcomes of BRCA. In this research, we investigated how starvation could affect BRCA cells' propensities for invasion and migration. The effects of autophagy and glucose metabolism mediated by starved stimulation were examined through transwell assays, western blot, and the detection of glucose concentration. A starvation response-related gene (SRRG) signature was ultimately generated by integrated analysis. The risk score was recognized as an independent risk indicator. The nomogram and calibration curves revealed that the model had excellent prediction accuracy. Functional enrichment analysis indicated this signature was significantly enriched in metabolic-related pathways and energy stress-related biological processes. Furthermore, phosphorylated protein expression of the model core gene EIF2AK3 increased after the stimulus of starvation, and EIF2AK3 may play an essential role in the progression of BRCA in the starved microenvironment. To sum up, we constructed and validated a novel SRRG signature that could accurately predict outcomes and may be developed as a therapeutic target for the precise treatment of BRCA.

Systematic pan-cancer analysis identifies transmembrane protein 158 as a potential therapeutic, prognostic and immunological biomarker.

The purpose of this study was to investigate the expression significance, predictive value, immunologic function, and biological role of transmembrane protein 158 (TMEM158) in the development of pan-cancer. To achieve this, we utilized data from multiple databases, including TCGA, GTEx, GEPIA, and TIMER, to collect gene transcriptome, patient prognosis, and tumor immune data. We evaluated the association of TMEM158 with patient prognosis, tumor mutational burden (TMB), and microsatellite instability (MSI) in pan-cancer samples. We performed immune checkpoint gene co-expression analysis and gene set enrichment analysis (GSEA) to better understand the immunologic function of TMEM158. Our findings revealed that TMEM158 was significantly differentially expressed between most types of cancer tissues and their adjacent normal tissues and was associated with prognosis. Moreover, TMEM158 was significantly correlated with TMB, MSI, and tumor immune cell infiltration in multiple cancers. Co-expression analysis of immune checkpoint genes showed that TMEM158 was related to the expression of several common immune checkpoint genes, especially CTLA4 and LAG3. Gene enrichment analysis further revealed that TMEM158 was involved in multiple immune-related biological pathways in pan-cancer. Overall, this systematic pan-cancer analysis suggests that TMEM158 is generally highly expressed in various cancer tissues and is closely related to patient prognosis and survival across multiple cancer types. TMEM158 may serve as a significant predictor of cancer prognosis and modulate immune responses to various types of cancer.

Low expression of SLC34A1 is associated with poor prognosis in clear cell renal cell carcinoma.

OBJECTIVE: Clear cell renal cell carcinoma (ccRCC) is a malignant renal tumor that is highly prone to metastasis and recurrence. The exact pathogenesis of this cancer is still not well understood. This study aimed to identify novel hub genes in renal clear cell carcinoma and determine their diagnostic and prognostic value. METHODS: Intersection genes were obtained from multiple databases, and protein-protein interaction analysis and functional enrichment analysis were performed to identify key pathways related to the intersection genes. Hub genes were identified using the cytoHubba plugin in Cytoscape. GEPIA and UALCAN were utilized to observe differences in mRNA and protein expression of hub genes between KIRC and adjacent normal tissues. The Wilcoxon rank sum test was used to analyze hub gene levels between paired KIRC and matched non-cancer samples. IHC results were obtained from the HPA online database, and according to the median gene expression level, they were divided into a high-expression group and a low-expression group. The correlation of these groups with the prognosis of KIRC patients was analyzed. Logistic regression and the Wilcoxon rank sum test were used to test the relationship between SLC34A1 level and clinicopathological features. The diagnostic value of SLC34A1 was evaluated by drawing the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Cox regression analysis was used to analyze the relationship between clinicopathological features, SLC34A1 expression, and KIRC survival rate. LinkedOmics was used to obtain the genes most related to SLC34A1 and their functional enrichment. Genetic mutations and methylation levels of SLC34A1 in KIRC were obtained from the cBioPortal website and the MethSurv website, respectively. RESULTS: Fifty-eight ccRCC differential genes were identified from six datasets, and they were mainly enriched in 10 functional items and 4 pathways. A total of 5 hub genes were identified. According to the GEPIA database analysis, low expression of SLC34A1, CASR, and ALDOB in tumors led to poor prognosis. Low expression of SLC34A1 mRNA was found to be related to clinicopathological features of patients. SLC34A1 expression in normal tissues could accurately identify tumors (AUC 0.776). SLC34A1 was also found to be an independent predictor of ccRCC in univariate and multivariate Cox analyses. The mutation rate of the SLC34A1 gene was 13%. Eight of the 10 DNA methylated CpG sites were associated with the prognosis of ccRCC. SLC34A1 expression in ccRCC was positively correlated with B cells, eosinophils, neutrophils, T cells, TFH, and Th17 cells, and negatively correlated with Tem, Tgd, and Th2 cells. CONCLUSION: The expression level of SLC34A1 in KIRC samples was found to be decreased, which predicted a decreased survival rate of KIRC. SLC34A1 may serve as a molecular prognostic marker and therapeutic target for KIRC patients.

Tripartite-motif family genes associated with cancer stem cells affect tumor progression and can assist in the clinical prognosis of kidney renal clear cell carcinoma.

Ubiquitination is presently a hot topic in the field of oncology. The tripartite-motif (TRIM) family of proteins represents one of the largest classes of putative single protein RING-finger E3 ubiquitin ligases, which play an essential role in the ubiquitination of proteins in the body. At the same time, research related to cancer stem cells (CSCs) is increasing in popularity in the field of oncology. CSCs are potentially chemically resistant and can be selectively enriched in patients receiving chemotherapy, ultimately leading to adverse outcomes, such as treatment failure and cancer recurrence. There is a close relationship between multiple TRIM family genes and CSCs. Accumulating evidence suggests that TRIM family proteins are expressed in diverse human cancers and act as regulators of oncoproteins or tumor suppressor proteins. In this study, we used biological information to explore the potential function of TRIM family genes related to CSCs in the development of pan-cancer. Kidney renal clear cell carcinoma (KIRC) is one of the deadliest malignant tumors in the world. Owing to its complex molecular and cellular heterogeneity, the effectiveness of existing KIRC-related risk prediction models is not satisfactory at present. Therefore, we focused on the potential role of these TRIM family genes in KIRC and used seven TRIM family genes to establish a prognostic risk model. This model includes TRIM16, TRIM32, TRIM24, TRIM8, TRIM27, PML, and TRIM11. In conclusion, this study provides further insight into the prognosis of KIRC, which may guide treatment.

Micro-Opto-Electro-Mechanical Systems for High-Precision Displacement Sensing: A Review.

High-precision displacement sensing has been widely used across both scientific research and industrial applications. The recent interests in developing micro-opto-electro-mechanical systems (MOEMS) have given rise to an excellent platform for miniaturized displacement sensors. Advancement in this field during past years is now yielding integrated high-precision sensors which show great potential in applications ranging from photoacoustic spectroscopy to high-precision positioning and automation. In this review, we briefly summarize different techniques for high-precision displacement sensing based on MOEMS and discuss the challenges for future improvement.

GBP2 enhances paclitaxel sensitivity in triple­negative breast cancer by promoting autophagy in combination with ATG2 and inhibiting the PI3K/AKT/mTOR pathway.

Chemoresistance is a major challenge in treating triple­negative breast cancer (TNBC); chemotherapy remains the primary approach. The present study aimed to elucidate the role of guanylate­binding protein 2 (GBP2) in activating autophagy in TNBC and its impact on the sensitivity of TNBC cells to paclitaxel (PTX). Transfection with lentivirus was performed to establish TNBC cell lines with stable, high GBP2 expression. The mRNA and protein levels of GBP2 expression were evaluated utilizing reverse transcription­quantitative PCR and western blotting, respectively. Autophagy in TNBC cells was evaluated using immunoblotting, transmission electron microscopy and fluorescence microscopy. The PI3K/AKT/mTOR pathway proteins and their phosphorylation were detected by immunoblotting, and fluorescence co­localization analysis was performed to evaluate the association between GBP2 and autophagy­related protein 2 (ATG2). BALB/c NUDE mice were subcutaneously injected with GBP2 wild­type/overexpressing MDA­MB­231 cells. Low GBP2 expression was detected in TNBC, which was associated with a poor prognosis. Overexpression of GBP2 suppressed cell growth, and especially enhanced autophagy in TNBC. Forced expression of GBP2 significantly increased the PTX sensitivity of TNBC cells, and the addition of autophagy inhibitors reversed this effect. GBP2 serves as a prognostic marker and exerts a notable inhibitory impact on TNBC. It functions as a critical regulator of activated autophagy by co­acting with ATG2 and inhibiting the PI3K/AKT/mTOR pathway, which contributes to increasing sensitivity of TNBC cells to PTX. Therefore, GBP2 is a promising therapeutic target for enhancing TNBC treatment.

USP46 enhances tamoxifen resistance in breast cancer cells by stabilizing PTBP1 to facilitate glycolysis.

Tamoxifen (TAM) is the primary drug for treating estrogen receptor alpha-positive (ER+) breast cancer (BC). However, resistance to TAM can develop in some patients, limiting its therapeutic efficacy. The ubiquitin-specific protease (USP) family has been associated with the development, progression, and drug resistance of various cancers. To explore the role of USPs in TAM resistance in BC, we used qRT-PCR to compare USP expression between TAM-sensitive (MCF-7 and T47D) and TAM-resistant cells (MCF-7R and T47DR). We then modulated USP46 expression and examined its impact on cell proliferation, drug resistance (via CCK-8 and EdU experiments), glycolysis levels (using a glycolysis detection assay), protein interactions (confirmed by co-IP), and protein changes (analyzed through Western blotting). Our findings revealed that USP46 was significantly overexpressed in TAM-resistant BC cells, leading to the inhibition of the ubiquitin degradation of polypyrimidine tract-binding protein 1 (PTBP1). Overexpression of PTBP1 increased the PKM2/PKM1 ratio, promoted glycolysis, and intensified TAM resistance in BC cells. Knockdown of USP46 induced downregulation of PTBP1 protein by promoting its K48-linked ubiquitination, resulting in a decreased PKM2/PKM1 ratio, reduced glycolysis, and heightened TAM sensitivity in BC cells. In conclusion, this study highlights the critical role of the USP46/PTBP1/PKM2 axis in TAM resistance in BC. Targeted therapy against USP46 may represent a promising strategy to improve the prognosis of TAM-resistant patients.

Low expression of fatty acid oxidation related gene ACADM indicates poor prognosis of renal clear cell carcinoma and is related to tumor immune infiltration.

This research aims to identify the key fatty acid beta-oxidation (FAO) genes that are altered in kidney renal clear cell carcinoma (KIRC) and to analyze the role of these genes in KIRC. The Gene Expression Omnibus (GEO) and FAO datasets were used to identify these key genes. Wilcoxon rank sum test was used to assess the levels of acyl-CoA dehydrogenase medium chain (ACADM) between KIRC and non-cancer samples. The logistic regression and Wilcoxon rank sum test were used to explore the association between ACADM and clinical features. The diagnostic performance of ACADM for KIRC was assessed using a diagnostic receiver operating characteristic (ROC) curve. The co-expressed genes of ACADM were identified in LinkedOmics database, and their function and pathway enrichment were analyzed. The correlation between ACADM expression level and immune infiltration was analyzed by Gene Set Variation Analysis (GSVA) method. Additionally, the proliferation, migration, and invasion abilities of KIRC cells were assessed after overexpressing ACADM. Following differential analysis and intersection, we identified six hub genes, including ACADM. We found that the expression level of ACADM was decreased in KIRC tissues and had a better diagnostic effect (AUC = 0.916). Survival analysis suggested that patients with decreased ACADM expression had a worse prognosis. According to correlation analysis, a variety of clinical features were associated with the expression level of ACADM. By analyzing the infiltration level of immune cells, we found that ACADM may be related to the enrichment of immune cells. Finally, ACADM overexpression inhibited proliferation, migration, and invasion of KIRC cells. In conclusion, our findings suggest that reduced ACADM expression in KIRC patients is indicative of poor prognosis. These results imply that ACADM may be a diagnostic and prognostic marker for individuals with KIRC, offering a reference for clinicians in diagnosis and treatment.

Comprehensive pan-cancer analysis reveals CDC6 as a potential immunomodulatory agent and promising therapeutic target in pancreatic cancer.

Background: CDC6 is critical in DNA replication initiation, but its expression patterns and clinical implications in cancer are underexplored. This study uses multi-omics data from The Cancer Genome Atlas (TCGA) to comprehensively analyze CDC6 across various cancers, aiming to evaluate its potential as a prognostic biomarker and explore its role in immunotherapy. Methods: By leveraging multi-omics data from TCGA, we conducted a comprehensive analysis of CDC6 expression across a variety of cancer types. Least absolute shrinkage and selection operator (LASSO) regression was employed to assess the association of CDC6 with key molecules implicated in pancreatic cancer. Results: CDC6 expression was found to be significantly upregulated across a broad spectrum of cancers. High levels of CDC6 expression were associated with poor prognosis in several cancer types. Notable associations were observed between CDC6 expression and tumor mutational burden (TMB), microsatellite instability (MSI), as well as immune cell infiltration. Co-expression analysis revealed significant associations between CDC6 and prevalent immune checkpoint genes. A risk model incorporating CDC6-related genes, including CCNA1, CCNA2, CCND1, CCND2, CDC25B, CDC6, and CDK2, was developed for pancreatic cancer. Conclusions: CDC6 emerges as a promising prognostic biomarker and a potential target for immunotherapy across various cancers, including pancreatic cancer. It appears to modulate immune responses across cancer types, highlighting its regulatory role. Further exploration into the biological functions and clinical implications of CDC6 is warranted.

An Angiogenesis-Related lncRNA Signature Is Associated with Prognosis and Tumor Immune Microenvironment in Breast Cancer.

Angiogenesis is crucial in the development and progression of tumors. This study examined the relationship between angiogenesis-related lncRNAs (AR-lncRNAs) and breast cancer (BC) immunity and prognosis. We used univariate Cox regression analysis to obtain AR-lncRNAs closely related to BC prognosis. Cluster analysis of BC patients was performed using non-negative matrix factorization (NMF) analysis according to the expression of AR-lncRNAs that were prognostically relevant. An AR-lncRNA risk model (AR-lncM) was created using LASSO regression analysis to predict the prognosis and survival of BC patients. Subsequently, the effect of LINC01614 on cell migration and invasion was verified by Transwell and Western blot assays, and the CCK-8 assay detected its impact on cell sensitivity to tamoxifen. Finally, we obtained 17 AR-lncRNAs from the TCGA database that were closely associated with the prognosis of BC patients. Based on the expression of these AR-lncRNAs, BC patients were divided into five clusters using NMF analysis. Cluster 1 was found to have a better prognosis, higher expression of immune checkpoints, and higher levels of immune cell infiltration. Furthermore, an AR-LncM model was created using ten prognostic-related AR-lncRNAs. The model's risk predictive performance was validated using survival analysis, timeROC curves, and univariate and multivariate Cox analysis. The most interesting gene in the model, LINC01614, was found to regulate epithelial-mesenchymal transition (EMT) and tamoxifen sensitivity in BC cells, implying that LINC01614 could be a potential therapeutic target for BC patients.

High C1QTNF1 expression mediated by potential ncRNAs is associated with poor prognosis and tumor immunity in kidney renal clear cell carcinoma.

Background: Kidney renal clear cell carcinoma (KIRC) originates from proximal tubular cells and is the most common subtype of renal cell carcinoma. KIRC is characterized by changes in lipid metabolism, and obesity is a risk factor for it. C1q And TNF Related 1 (C1QTNF1), a novel adipokine and member of the C1q and TNF-related protein (CTRP) family, has been shown to affect the progression of various cancers. However, the role of C1QTNF1 in KIRC has not been studied. Methods: The Wilcoxon rank sum test was used to analyze the expression of C1QTNF1 in KIRC tissues and normal tissues. The relationship between clinicopathological features and C1QTNF1 levels was also examined by logistic regression and the Wilcoxon rank sum test. In addition, the effect of C1QTNF1 on the prognosis of KIRC patients was analyzed by Kaplan-Meier (KM). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the potential signaling pathways and biological functions of differential genes. A nomogram was constructed to predict the prognosis of KIRC patients. Spearman correlation analysis was performed to determine the association between C1QTNF1 expression and immune cell infiltration and immune checkpoint genes. The upstream miRNAs and lncRNAs of C1QTNF1 were predicted by the ENCORI online tool. Finally, we examined the proliferation, invasion, and migration abilities of KIRC cells after C1QTNF1 knockdown. Results: The expression of C1QTNF1 in KIRC tissues was significantly higher than in normal renal tissues. Patients with higher C1QTNF1 expression had a poor prognosis, a finding supported by Kaplan-Meier survival analysis. C1QTNF1 expression was significantly correlated with TNM and pathologic stages, age, and gender (p < 0.05). The C1QTNF1 expression level was significantly correlated with immune cell infiltration and immune checkpoint genes in KIRC. Additionally, high C1QTNF1 expression was associated with poor prognosis in stage I and II, T1 and T2, T3 and T4, N0, and M0 patients (HR > 1, p < 0.05). The calibration diagram shows that the C1QTNF1 model has effective predictive performance for the survival of KIRC patients. Knockdown of C1QTNF1 inhibited KIRC cell proliferation, cell migration, and cell invasion. In addition, CYTOR and AC040970.1/hsa-miR-27b-3p axis were identified as the most likely upstream ncRNA-related pathways of C1QTNF1 in KIRC. Conclusion: In conclusion, our study suggests that high expression of C1QTNF1 is associated with KIRC progression and immune infiltration. The increased expression of C1QTNF1 suggests a poor prognosis in KIRC patients.

A mitochondrial function-related LncRNA signature predicts prognosis and immune microenvironment for breast cancer.

Mitochondrial function, as the core of the cell's energy metabolism, is firmly connected to cancer metabolism and growth. However, the involvement of long noncoding RNAs (lncRNAs) related to mitochondrial function in breast cancer (BRCA) has not been thoroughly investigated. As a result, the objective of this research was to dissect the prognostic implication of mitochondrial function-related lncRNAs and their link to the immunological microenvironment in BRCA. The Cancer Genome Atlas (TCGA) database was used to acquire clinicopathological and transcriptome information for BRCA samples. Mitochondrial function-related lncRNAs were recognized by coexpression analysis of 944 mitochondrial function-related mRNAs obtained from the MitoMiner 4.0 database. A novel prognostic signature was built in the training cohort using integrated analysis of mitochondrial function-related lncRNA and the corresponding clinical information through univariate analysis, lasso regression, and stepwise multivariate Cox regression analysis. The prognostic worth was judged in the training cohort and validated in the test cohort. In addition, functional enrichment and immune microenvironment analyses were performed to explore the risk score on the basis of the prognostic signature. An 8-mitochondrial function-related lncRNA signature was generated by integrated analysis. Individuals within the higher-risk category had a worse overall survival rate (OS) (training cohort: P < 0.001; validation cohort: P < 0.001; whole cohort: P < 0.001). The risk score was identified as an independent risk factor by multivariate Cox regression analysis (training cohort: HR 1.441, 95% CI 1.229-1.689, P < 0.001; validation cohort: HR 1.343, 95% CI 1.166-1.548, P < 0.001; whole cohort: HR 1.241, 95% CI 1.156-1.333, P < 0.001). Following that, the predictive accuracy of the model was confirmed by the ROC curves. In addition, nomograms were generated, and the calibration curves revealed that the model had excellent prediction accuracy for 3- and 5-year OS. Besides, the higher-risk BRCA individuals have relatively decreased amounts of infiltration of tumor-killing immune cells, lower levels of immune checkpoint molecules, and immune function. We constructed and verified a novel mitochondrial function-related lncRNA signature that might accurately predict the outcome of BRCA, play an essential role in immunotherapy, and might be exploited as a therapeutic target for precise BRCA therapy.

Comprehensive analysis of the role of CXCL family members in clear cell renal cell carcinoma.

Background CXCLs are a group of low-molecular-weight growth factors secreted by cells, mainly through G protein-coupled receptors for signal transduction and induction of cell chemotactic motility. Their abnormal expression is linked to immune cell activity in cancer and tumor growth and progression. However, the differential expressions of CXCLs in ccRCC, prognostic prospects, and immune infiltration have not been clearly explored. Objective This study aimed to analyze the expression profile of CXCL family members in clear cell renal cell carcinoma, its prognostic significance, and the correlation between CXCL family members and tumor immunity. Methods The expression difference of CXCLs between ccRCC and normal renal tissues was analyzed by the TCGA database. The prognostic value of CXCLs in ccRCC was analyzed by the Kaplan-Meier Plotter. The copy number variation (CNV) of CXCLs in ccRCC was explored through the GSCA website. The cBioPortal online tool was used to screen out 355 co-expressed genes significantly related to CXCLs. The protein-protein interaction network of co-expressed genes was constructed using the STRING database, and the pathways that significantly enriched these genes were explored using Metascape. We then used the least absolute shrinkage and selection operator (LASSO) regression analysis to develop a predictive risk model for ccRCC patients. The relationship between CXCLs and tumor immune cell infiltration was analyzed. Finally, drugs interacting with CXCLs were analyzed using the DGIdb database. Results It was observed that mRNA expression levels of CXCL-2,-3,-4,-5,-9,-10,-11,-13, and -16 in the tissue of KIRC were higher than normal KIRC tissue. In contrast, CXCL12 expression decreased. Furthermore, CXCL5,-9,-10,-11,-12, and -13 mRNA expression was significantly correlated with the clinical stage. In KIRC patients, elevated CXCL1,-2,-5, and -13 expression was associated with shorter overall survival, while elevated CXCL14 expression was associated with a better prognosis. Through LASSO regression analysis, we obtained a 5-gene prognostic signature. This prognostic feature is associated with the infiltration of multiple immune cells. Conclusion In this study, we evaluated the expression levels of CXCL genes in KIRC and their prognostic potential in KIRC. CXCL-5,-9,-10,-11,-12, and -13 may be associated with ccRCC progression, and CXCL-1,-2,-5,-13, and -14 may be potential prognostic markers.

Establishment of a new prognostic risk model of MAPK pathway-related molecules in kidney renal clear cell carcinoma based on genomes and transcriptomes analysis.

Purpose: The mitogen-activated protein kinase (MAPK) signaling pathway is often studied in oncology as the most easily mentioned signaling pathway. This study aims to establish a new prognostic risk model of MAPK pathway related molecules in kidney renal clear cell carcinoma (KIRC) based on genome and transcriptome analysis. Methods: In our study, RNA-seq data were acquired from the KIRC dataset of The Cancer Genome Atlas (TCGA) database. MAPK signaling pathway-related genes were obtained from the gene enrichment analysis (GSEA) database. We used "glmnet" and the "survival" extension package for LASSO (Least absolute shrinkage and selection operator) regression curve analysis and constructed a prognosis-related risk model. The survival curve and the COX regression analysis were used the "survival" expansion packages. The ROC curve was plotted using the "survival ROC" extension package. We then used the "rms" expansion package to construct a nomogram plot. We performed a pan-cancer analysis of CNV (copy number variation), SNV (single nucleotide variant), drug sensitivity, immune infiltration, and overall survival (OS) of 14 MAPK signaling pathway-related genes using several analysis websites, such as GEPIA website and TIMER database. Besides, the immunohistochemistry and pathway enrichment analysis used The Human Protein Atlas (THPA) database and the GSEA method. Finally, the mRNA expression of risk model genes in clinical renal cancer tissues versus adjacent normal tissues was further verified by real-time quantitative reverse transcription (qRT-PCR). Results: We performed Lasso regression analysis using 14 genes and created a new KIRC prognosis-related risk model. High-risk scores suggested that KIRC patients with lower-risk scores had a significantly worse prognosis. Based on the multivariate Cox analysis, we found that the risk score of this model could serve as an independent risk factor for KIRC patients. In addition, we used the THPA database to verify the differential expression of proteins between normal kidney tissues and KIRC tumor tissues. Finally, the results of qRT-PCR experiments suggested large differences in the mRNA expression of risk model genes. Conclusions: This study constructs a KIRC prognosis prediction model involving 14 MAPK signaling pathway-related genes, which is essential for exploring potential biomarkers for KIRC diagnosis.

NEK2 is associated with poor prognosis of clear cell renal cell carcinoma and promotes tumor cell growth and metastasis.

BACKGROUND: Never in Mitosis gene-A(NIMA)-related Kinase 2 (NEK2) is a critical player in themitotic processes. NEK2 is highly expressed in many kindsof human cancers and has been shown toparticipatein drug resistance, tumorigenesis, and tumor progression. However, the expression or function of NEK2 in clear cell renal cell carcinoma (ccRCC)hasnot yet been investigated. METHODS: Weused TCGA databaseto study the NEK2 expression in ccRCC. The expression of NEK2 in tumor tissuesand adjacent tissueswas examined by immunohistochemistry. We also analysed the correlation between NEK2 expression and clinical parametersofccRCC. The mRNA and protein level of NEK2 expression were semi-quantifiedby qRT-PCR and western blotting analysis. Following NEK2 knockdown by RNA interference in Caki-1cells, whileNEK2 overexpression in A489 cells, CCK8and transwell assay was used to confirmtheproliferation, migration and invasion, respectively.Finally, our in vivo study were carried out using nudemice to establish mouse model for kidney cancer. RESULTS: We observed elevated expression of NEK2 both in ccRCCtumor tissues and cell lines. Together with clinical and pathological features, our analysis indicated a clear association of clinical outcomes between ccRCC patients with high and lowNEK2expression. Our in vitro studies demonstratedthat NEK2 knockdowninhibits the proliferation,migrationand invasion of Caki-1cells, oppositely, overexpressionof NEK2 promotes the proliferation, migrationand invasion of A489cells.In the end, our animal study demonstrated that deletion of NEK2 expression could impair tumor growth. CONCLUSION: Our data suggestedthat NEK2wasimportant inregulating ccRCC cell proliferation and metastasis, and indicated NEK2as a potentially important target for the treatment ofccRCC.

Sphingosine kinase 1 regulates lipid metabolism to promote progression of kidney renal clear cell carcinoma.

PURPOSE: To detect the expression of sphingosine kinase 1 (SPHK1) in clear cell renal cell carcinoma (ccRCC) and explore its biological role in the occurrence and development of ccRCC through regulation of fatty acid metabolism. METHODS: Using the Cancer Genome Atlas database, SPHK1 expression and its clinical significance were detected in clear cell renal cell carcinoma. Immunohistochemistry was performed to detect SPHK1 expression in RCC samples in our hospital. The connection between the SPHK1 levels and clinicopathological features of patients was assessed. Nile Red was used to detect fatty acids in cells. Cell Counting Kit-8 and 5-ethynyl-2'-deoxyuridine assays were performed to determine the effect of SPHK1 on renal cell viability and proliferation, respectively. Additionally, the effects of SPHK1 on the proliferation and metastasis of ccRCC were studied using wound healing and Transwell assays. Fatty acids were added exogenously in recovery experiments and western blotting was performed to determine the effect of SPHK1 on fatty acid metabolism in ccRCC. Finally, the effects of SPHK1 on tumor growth were investigated in a xenograft model. RESULTS: Bioinformatics analysis revealed that SPHK1 expression was upregulated in kidney RCC. OverSPHK1 expression was associated with poor prognosis for ccRCC patients. High SPHK1 expression was detected in human ccRCC. SPHK1 expression was related to clinicopathological features, such as tumor size and Furman grade. Additionally, cell proliferation, migration, and invasion were inhibited in ccRCC cells with low SPHK1 expression. In rescue experiments, proliferation, migration, and invasion were restored. In vivo, reduced SPHK1 levels correlated with lower expression of fatty acid synthase, stearoyl-CoA desaturase 1, and acetyl CoA carboxylase, and slowed tumor growth. CONCLUSIONS: SPHK1 is abnormally overexpressed in human ccRCC. Patients with ccRCC may benefit from treatments that target SPHK1, which may also serve as a prognostic indicator.

Development of a Novel Sphingolipid Signaling Pathway-Related Risk Assessment Model to Predict Prognosis in Kidney Renal Clear Cell Carcinoma.

This study aimed to explore underlying mechanisms by which sphingolipid-related genes play a role in kidney renal clear cell carcinoma (KIRC) and construct a new prognosis-related risk model. We used a variety of bioinformatics methods and databases to complete our exploration. Based on the TCGA database, we used multiple R-based extension packages for data transformation, processing, and statistical analyses. First, on analyzing the CNV, SNV, and mRNA expression of 29 sphingolipid-related genes in various types of cancers, we found that the vast majority were protective in KIRC. Subsequently, we performed cluster analysis of patients with KIRC using sphingolipid-related genes and successfully classified them into the following three clusters with significant prognostic differences: Cluster 1, Cluster 2, and Cluster 3. We performed differential analyses of transcription factor activity, drug sensitivity, immune cell infiltration, and classical oncogenes to elucidate the unique roles of sphingolipid-related genes in cancer, especially KIRC, and provide a reference for clinical treatment. After analyzing the risk rates of sphingolipid-related genes in KIRC, we successfully established a risk model composed of seven genes using LASSO regression analysis, including SPHK1, CERS5, PLPP1, SGMS1, SGMS2, SERINC1, and KDSR. Previous studies have suggested that these genes play important biological roles in sphingolipid metabolism. ROC curve analysis results showed that the risk model provided good prediction accuracy. Based on this risk model, we successfully classified patients with KIRC into high- and low-risk groups with significant prognostic differences. In addition, we performed correlation analyses combined with clinicopathological data and found a significant correlation between the risk model and patient's M, T, stage, grade, and fustat. Finally, we developed a nomogram that predicted the 5-, 7-, and 10-year survival in patients with KIRC. The model we constructed had strong predictive ability. In conclusion, we believe that this study provides valuable data and clues for future studies on sphingolipid-related genes in KIRC.

Analysis of genomes and transcriptomes of clear cell renal cell carcinomas identifies mutations and gene expression changes in the TGF-beta pathway.

The occurrence of clear cell renal cell carcinoma (ccRCC) is related to changes in the transforming growth factor-ß (TGF-ß) signaling pathway. In this study, we adopted an integrated approach to identify and verify the effects of changes in this pathway on ccRCC and provide a guide for identifying new therapeutic targets. We performed transcriptome analysis of 539 ccRCC cases from The Cancer Genome Atlas (TCGA) and divided the samples into different TGF-ß clusters according to unsupervised hierarchical clustering. We found that 76 of the 85 TGF-ß pathway genes were dysregulated, and 55 genes were either protective or risk factors affecting the prognosis of ccRCC. The survival time of patients with tumors with low TGF-ß scores was shorter than that of patients with tumors with high TGF-ß scores. The overall survival (OS) of patients with ccRCC with high TGF-ß scores was better than that of patients with low TGF-ß scores. The TGF-ß score correlated with the expression of key ccRCC and deacetylation genes. The sensitivity of tumor patients to targeted drugs differed between the high and low TGF-ß score groups. Therefore, a prognostic model based on the TGF-ß gene pathway can predict the prognosis of ccRCC patients. Grouping patients with ccRCC according to their TGF-ß score is of great significance for evaluating the prognosis of patients, selecting targeted drugs, and identifying new therapeutic targets.

Comprehensive Multiomics Analysis Reveals Potential Diagnostic and Prognostic Biomarkers in Adrenal Cortical Carcinoma.

Adrenal cortical carcinoma (ACC) is a severe malignant tumor with low early diagnosis rates and high mortality. In this study, we used a variety of bioinformatic analyses to find potential prognostic markers and therapeutic targets for ACC. Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data sets were used to perform differential expressed analysis. WebGestalt was used to perform enrichment analysis, while String was used for protein-protein analysis. Our study first detected 28 up-regulation and 462 down-regulation differential expressed genes through the GEO and TCGA databases. Then, GO functional analysis, four pathway analyses (KEGG, REACTOME, PANTHER, and BIOCYC), and protein-protein interaction network were performed to identify these genes by WebGestalt tool and KOBAS website, as well as String database, respectively, and finalize 17 hub genes. After a series of analyses from GEPIA, including gene mutations, differential expression, and prognosis, we excluded one candidate unrelated to the prognosis of ACC and put the remaining genes into pathway analysis again. We screened out CCNB1 and NDC80 genes by three algorithms of Degree, MCC, and MNC. We subsequently performed genomic analysis using the TCGA and cBioPortal databases to better understand these two hub genes. Our data also showed that the CCNB1 and NDC80 genes might become ACC biomarkers for future clinical use.