Prognostic Gene Expression-Based Signature in Clear-Cell Renal Cell Carcinoma

Role of ONECUT family transcription factors in cancer and other diseases

Members of ONECUT transcription factor play an essential role in several developmental processes, however, the atypical expression of ONECUT proteins lead to numerous diseases, including cancer. ONECUT family proteins promote cell proliferation, progression, invasion, metastasis, angiogenesis, and stemness. This family of proteins interacts with other proteins such as KLF4, TGF-β, VEGFA, PRC2, SMAD3 and alters their expression involved in the regulation of various signaling pathways including Jak/Stat3, Akt/Erk, TGF-β, Smad2/3, and HIF-1α. Furthermore, ONECUT proteins are proposed as predictive biomarkers for pancreatic and gastric cancers. The present review summarizes the involvement of ONECUT family proteins in the development and progression of various human cancers and other diseases.

PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is associated with a high prevalence of cancer-related deaths. The survival rates of patients are significantly lower in late-stage ccRCC than in early-stage ccRCC, due to the spread and metastasis of late-stage ccRCC, surgery has not reached the goal of radical cure, and the effect of traditional radiotherapy and chemotherapy is poor. Thus, it is crucial to accurately assess the prognosis and provide personalized treatment at an early stage in ccRCC. This study aims to develop an efficient nomogram model for stratifying and predicting the survival of ccRCC patients based on tumor stage.

METHODS

We first analyzed the microarray expression data of ccRCC patients from the Gene Expression Omnibus (GEO) database and categorized them into two groups based on the disease stage (early and late stage). Subsequently, the GEO2R tool was applied to screen out the genes that were highly expressed in all GEO datasets. Finally, the clinicopathological data of the two patient groups were obtained from The Cancer Genome Atlas (TCGA) database, and the differences were compared between groups. Survival analysis was performed to evaluate the prognostic value of candidate genes (PSAT1, PRAME, and KDELR3) in ccRCC patients. Based on the screened gene PSAT1 and clinical parameters that were significantly associated with patient prognosis, we established a new nomogram model, which was further optimized to a single clinical variable-based model. The expression level of PSAT1 in ccRCC tissues was further verified by qRT-PCR, Western blotting, and immunohistochemical analysis.

RESULTS

The datasets GSE73731, GSE89563, and GSE150404 identified a total of 22, 89, and 120 over-expressed differentially expressed genes (DEGs), respectively. Among these profiles, there were three genes that appeared in all three datasets based on different stage groups. The overall survival (OS) of late-stage patients was significantly shorter than that of early-stage patients. Among the three candidate genes (PSAT1, PRAME, and KDELR3), PSAT1 was shown to be associated with the OS of patients with late-stage ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, neoadjuvant therapy, and PSAT1 level were significantly associated with patient prognosis. The concordance indices were 0.758 and 0.725 for the 3-year and 5-year OS, respectively. The new model demonstrated superior discrimination and calibration compared with the single clinical variable model. The enhancer PSAT1 used in the new model was shown to be significantly overexpressed in tissues from patients with late-stage ccRCC, as demonstrated by the mRNA level, protein level, and pathological evaluation.

CONCLUSION

The new prognostic prediction nomogram model of PSAT1 and clinicopathological variables combined was thus established, which may provide a new direction for individualized treatment for different-stage ccRCC patients.

Sirtuin 1, as a potential prognosis marker in clear cell renal cell carcinoma, regulates lipid metabolism and immune infiltration

Clear cell renal cell carcinoma (ccRCC) is a malignancy with a dismal prognosis, caused by the buildup of fat and glycogen. Sirtuin 1 (Sirt1) is a deacetylase that regulates lipid metabolism. In this study, we collected tumor and paracancer tissues from 386 ccRCC patients and followed their prognosis over an extended time period. The expression of Sirt1 in these tissues was assessed using immunohistochemistry, and LinkedOmics database analysis identified differentially expressed genes associated with Sirt1. The survival curve was generated using the Kaplan-Meier method, and immune infiltration was analyzed using the Tumor Immune Estimation Resource (TIMER) web tool. Our findings revealed that Sirt1 was expressed in tumor tissues, but not in normal tissues, and its high expression was associated with a worse prognosis. Furthermore, we observed a positive correlation between high Sirt1 expression and perirenal fat invasion and necrosis, leading to poorer survival outcomes. We established a nomogram to predict prognosis, and a correlation was observed with immune infiltration. In conclusion, our results suggest that high Sirt1 expression is associated with lipid metabolism disorder and immune infiltration, ultimately contributing to a dismal prognosis in ccRCC.

CERCAM is a prognostic biomarker associated with immune infiltration of macrophage M2 polarization in head and neck squamous carcinoma

PURPOSE

This study aimed to investigate the relevance of cerebral endothelial cell adhesion molecule (CERCAM) expression to head and neck squamous cell carcinoma (HNSCC) prognosis and immune infiltration by macrophage M2 polarization.

METHODS

Timer, UALCAN and HPA databases was used to analyze the differences in mRNA and protein levels of CERCAM expression in HNSCC. The Timer database was also applied to analyze the correlation between CERCAM in HNSCC and immune infiltration. TCGA-HNSCC database was applied to analyze the correlation between CERCAM expression levels and clinicopathological features, and its diagnostic and prognostic value in HNSCC was also assessed. The cBioPortal and MethSurv databases were then applied to analyze the genetic variation and methylation status of CERCAM. In vitro cellular assays were performed to provide evidence that CERCAM promotes malignant biological behavior of tumors and promotes macrophage M2 polarization in tumors. Finally, underlying pathophysiological mechanisms of CERCAM involvement in the development of HNSCC were predicted using a bioinformatics approach.

RESULTS

CERCAM is significantly overexpressed in HNSCC and correlates with poor prognostic levels and has good performance in predicting survival status in HNSCC patients. Cox regression analysis indicates that CERCAM expression levels are independent risk factors for predicting OS, DSS, and PFI. CERCAM promotes tumor malignant biological behavior and promotes macrophage M2 polarization immune infiltration in HNSCC. In addition, CERCAM promotes tumor cell adhesion in head and neck squamous carcinoma and promotes tumor progression through several oncogenic signaling pathways.

CONCLUSION

CERCAM may serve as a new diagnostic and prognostic biomarker in HNSCC and is a promising therapeutic target for HNSCC.

Association of lncRNA and transcriptome intersections with response to targeted therapy in metastatic renal cell carcinoma

Long non-coding RNAs (lncRNAs) serve an important role in cancer progression and may be used as efficient molecular biomarkers. The present study aimed to identify lncRNAs associated with the response to the receptor tyrosine kinase inhibitor sunitinib and transcriptome profile and clinical features of metastatic renal cell carcinoma (mRCC). The gene expression of 84 cancer-associated lncRNAs in tumor and non-malignant tissue samples of 38 patients with mRCC was evaluated using quantitative PCR. In addition, the coding transcriptome was estimated using RNA sequencing in a subgroup of 20 patients and mRNA-lncRNA intersections were identified. In total, 37 and 13 lncRNAs were down- and upregulated, respectively, in tumor compared with non-malignant adjacent tissue samples. A total of 10 and 4 lncRNAs were up- and downregulated, respectively, in good responders to sunitinib compared with poor responders. High expression of HNF1A-AS1 and IPW lncRNAs was associated with prolonged progression-free survival of patients and a high expression of the TUSC7 lncRNA was associated with poor response and worse survival. Significant associations of dysregulated MEG3 and SNHG16 lncRNAs with expression of protein-coding genes representing various pathways, were identified. Furthermore, a significantly higher expression of CLIP4 gene was observed in good responders. The present study revealed promising candidates for predictive and prognostic biomarkers with further therapeutic potential.

The prognostic value of hedgehog signaling in bladder cancer by integrated bioinformatics

Bladder cancer is the second most prevalent urological malignancy. It's a big contributor to cancer-related deaths throughout the globe. Researchers discovered that the hedgehog signaling (HhS) pathway contributed to the onset and spread of many different kinds of cancer. Nevertheless, the present understanding of the function of HhS in the bladder cancer molecular landscape is incomplete. Raw data were gotten from the IMvigor210, the Gene Expression Omnibus, and The Cancer Genome Atlas databases. Bioinformatics was used to examine the HhS score of each sample, and the enrichment of differentially expressed genes (DEGs), differentiation characteristics, immunological infiltration, and metabolic activity. The HhS prognostic signature was developed with significant assistance from the least absolute shrinkage and selection operator regression and Cox regression. An HhS-related nomogram was developed to assist in the prediction of patients' survival probability. We found that HhS was linked to poor prognosis in bladder cancer, and its activation was linked to the Basal subtype of bladder cancer. Bladder cancer with high HhS activity has higher glycolysis, nucleotide metabolism, amino acid metabolism, and other cancer-promoting metabolic activities. Furthermore, HhS mediates an immunosuppressive microenvironment in bladder cancer on the basis that HhS negatively correlates with the CD8 + T cells and correlates positively with immune checkpoints and T cell exhaustion scores. Finally, an HhS-related signature was developed for predicting the prognosis of patients with bladder cancer. Targeting HhS may be a potential therapy choice for bladder cancer.

Establishing a prognostic model of chromatin modulators and identifying potential drug candidates in renal clear cell patients

BACKGROUND

Renal carcinoma is a common malignant tumor of the urinary system. Advanced renal carcinoma has a low 5-year survival rate and a poor prognosis. More and more studies have confirmed that chromatin regulators (CRs) can regulate the occurrence and development of cancer. This article investigates the functional and prognostic value of CRs in renal carcinoma patients.

METHODS

mRNA expression and clinical information were obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis and LASSO regression analysis were used to select prognostic chromatin-regulated genes and use them to construct a risk model for predicting the prognosis of renal cancer. Differences in prognosis between high-risk and low-risk groups were compared using Kaplan-Meier analysis. In addition, we analyzed the relationship between chromatin regulators and tumor immune infiltration, and explored differences in drug sensitivity between risk groups.

RESULTS

We constructed a model consisting of 11 CRs to predict the prognosis of renal cancer patients. We not only successfully validated its feasibility, but also found that the 11 CR-based model was an independent prognostic factor. Functional analysis showed that CRs were mainly enriched in cancer development-related signalling pathways. We also found through the TIMER database that CR-based models were also associated with immune cell infiltration and immune checkpoints. At the same time, the genomics of drug sensitivity in cancer database was used to analyze the commonly used drugs of renal clear cell carcinoma patients. It was found that patients in the low-risk group were sensitive to medicines such as axitinib, pazopanib, sorafenib, and gemcitabine. In contrast, those in the high-risk group may be sensitive to sunitinib.

CONCLUSION

The chromatin regulator-related prognostic model we constructed can be used to assess the prognostic risk of patients with clear cell renal cell carcinoma. The results of this study can bring new ideas for targeted therapy of clear cell renal carcinoma, helping doctors to take corresponding measures in advance for patients with different risks.

Incremental value of radiomics with machine learning to the existing prognostic models for predicting outcome in renal cell carcinoma

Purpose

To systematically evaluate the potential of radiomics coupled with machine-learning algorithms to improve the predictive power for overall survival (OS) of renal cell carcinoma (RCC).

Methods

A total of 689 RCC patients (281 in the training cohort, 225 in the validation cohort 1 and 183 in the validation cohort 2) who underwent preoperative contrast-enhanced CT and surgical treatment were recruited from three independent databases and one institution. 851 radiomics features were screened using machine-learning algorithm, including Random Forest and Lasso-COX Regression, to establish radiomics signature. The clinical and radiomics nomogram were built by multivariate COX regression. The models were further assessed by Time-dependent receiver operator characteristic, concordance index, calibration curve, clinical impact curve and decision curve analysis.

Result

The radiomics signature comprised 11 prognosis-related features and was significantly correlated with OS in the training and two validation cohorts (Hazard Ratios: 2.718 (2.246,3.291)). Based on radiomics signature, WHOISUP, SSIGN, TNM Stage and clinical score, the radiomics nomogram has been developed. Compared with the existing prognostic models, the AUCs of 5 years OS prediction of the radiomics nomogram were superior to the TNM, WHOISUP and SSIGN model in the training cohort (0.841 vs 0.734, 0.707, 0.644) and validation cohort2 (0.917 vs 0.707, 0.773, 0.771). Stratification analysis suggested that the sensitivity of some drugs and pathways in cancer were observed different for RCC patients with high-and low-radiomics scores.

Conclusion

This study showed the application of contrast-enhanced CT-based radiomics in RCC patients, creating novel radiomics nomogram that could be used to predict OS. Radiomics provided incremental prognostic value to the existing models and significantly improved the predictive power. The radiomics nomogram might be helpful for clinicians to evaluate the benefit of surgery or adjuvant therapy and make individualized therapeutic regimens for patients with renal cell carcinoma.

The Association of Tumor Immune Microenvironment of the Primary Lesion with Time to Metastasis in Patients with Renal Cell Carcinoma: A Retrospective Analysis

Biological or immunological differences in primary lesions between synchronous and metachronous metastatic renal cell carcinoma (mRCC) have been reported. However, the association between the tumor immune microenvironment (TIME) of primary lesions and time to metastasis remains unknown. We investigated the differences in the TIME of primary lesions based on time intervals to metastasis, mainly between the synchronous group (SG; metastasis within 3 months) and metachronous group (MG; metastasis after 3 months), and its association with clinicopathological parameters in patients with mRCC. Overall, 568 patients treated first-line with vascular endothelial growth factor receptor inhibitors comprised the analysis population (SG: N = 307 [54.0%]; MG: N = 261 [46.0%]). SG had a higher proportion of patients with poor prognostic pathological feature tumors: WHO/ISUP grade 4, necrosis, lymphovascular invasion, infiltrative growth pattern, and sarcomatoid differentiation. Regarding the TIME, more immunogenic features were seen in SG than MG, with a higher PD-L1 positivity and a lower proportion of the desert phenotype. This is the first study to examine the differences in the TIME of primary lesions in patients with mRCC based on the time intervals to metastasis. The TIME of primary lesions could affect the time to metastasis.