Forkhead-box series expression network is associated with outcome of clear-cell renal cell carcinoma

A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma

BACKGROUND

Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions.

METHODS

RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses.

RESULTS

ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients.

CONCLUSIONS

The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients.

Clinical relevance of RNA editing profiles in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the most frequently occurring lung cancer worldwide, with increasing death rates. It belongs to the non-small cell lung cancer (NSCLC) type and has a strong association with previous smoking history. Growing evidence has demonstrated the significance of adenosine-to-inosine RNA editing (ATIRE) dysregulation in cancer. The aim of the present study was to evaluate ATIRE events that might be clinically useful or tumorigenic.

Methods: To explore survival-related ATIRE events in LUAD, its ATIRE profiles, gene expression data, and corresponding patients’ clinical information were downloaded from the Cancer Genome Atlas (TCGA) and the synapse database. We evaluated 10441 ATIRE in 440 LUAD patients from the TCGA database. ATIRE profiles were merged with TCGA survival data. We selected prognostic ATIRE sites, using a univariate Cox analysis (p < 0.001). Cox proportional hazards regression and lasso regression analysis were used to determine survival-related ATIRE sites, create risk ratings for those sites, and build a prognostic model and a nomogram for assessing overall survival (OS). Six ATIRE sites were used in the prognostic model construction and patients were randomly divided into a validation cohort (n = 176) and a training cohort (n = 264). The “Pheatmap” program was used to create risk curves that included risk score, survival time, and expression of ATIRE sites. We also determined the clinical prediction model’s discrimination. The decision curve analysis and the 1-, 2-, and 3-year corrective curves were simultaneously used to evaluate the nomogram. We also evaluated the relationship between the amount of ATIRE sites and host gene expression and the impact of ATIRE expression on transcriptome expression.

Results: The pyroglutamyl-peptidase I (PGPEP1) chr19:18476416A > I, ankyrin repeat domain 36B pseudogene 1 (ANKRD36BP1) (dist = 3,795), T-box transcription factor (TBX19) (dist = 29815) chr1:168220463A > I, Syntrophin Beta 2 (SNTB2) chr16:69338598A > I, hook microtubule-tethering protein 3 (HOOK3) chr8:42883441A > I, NADH dehydrogenase flavoprotein 3 (NDUFV3) chr21:44329452A > I, and FK506-binding protein 11 (FKBP11) chr12:49316769A > I were used in the prognostic model construction. High levels of risk score were significantly associated with worse OS and progression-free survival. Tumour stage and risk score were related to OS in LUAD patients. The predictors were among the prognostic nomogram model’s risk score, age, gender, and tumor stage. The calibration plot and C-index (0.718) demonstrated the significant accuracy of nomogram’s predictions. ATIRE level was markedly elevated in tumor tissues and was highly variable between patients.

Conclusion: Events involving ATIRE in LUAD were highly functional and clinically relevant. The RNA editing-based model provides a solid framework for further investigation of the functions of RNA editing in non-coding areas and may be used as a unique method for predicting LUAD survival.

A comprehensive analysis of the FOX family for predicting kidney renal clear cell carcinoma prognosis and the oncogenic role of FOXG1

Previous studies have confirmed that the forkhead box (FOX) superfamily of transcription factors regulates tumor progression and metastasis in multiple cancer. The purpose of this study was to develop a model based on FOX family genes for predicting kidney renal clear cell carcinom (KIRC) prognosis. We downloaded the transcriptional profiles and clinical data of KIRC patients from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. To build a new prognosis model, we screened prognosis-related FOX family genes using Lasso regression and Multivariate Cox regression analyses. Receiver operating characteristic (ROC) curves were used to evaluate model performance. Additionally, a prognostic nomogram was developed using clinical information and selected genes to improve the accuracy of prognostic prediction. We also investigated whether prognosis-related FOX family genes are related to the immune response in KIRC. Finally, we validated the oncogenic role of FOXG1 in KIRC using an in vitro tumor function assay. Six prognosis-related FOX family genes were screened: FOXO1, FOXM1, FOXK2, FOXG1, FOXA1, and FOXD1. The ROC curves indicated that our model was capable of making accurate predictions for 1-, 3-, and 5-year overall survival (OS). The nomogram further improved the accuracy of prognostic predictions. In addition, compared to those in patients with low-risk scores, high-risk scores predicted a decreased level of immune cell infiltration and a lower immune response rate. Moreover, the results of in vitro studies confirmed that FOXG1 supports the proliferation and invasion of KIRC.

An Integrated Study on the Differential Expression of the FOX Gene Family in Cancer and Their Response to Chemotherapy Drugs

The Forkhead-box (FOX) transcription factors, as one of the largest gene families in humans, play key roles in cancer. Although studies have suggested that several FOX transcription factors have a significant impact on cancer, the functions of most of the FOX genes in cancer remain elusive. In the study, the expression of 43 FOX genes in 63 kinds of cancer diseases (including many subtypes of same cancer) and in response to 60 chemical substances was obtained from the Gene Expression Atlas database of the European Bioinformatics Institute. Based on the high degree of overlap in FOXO family members differentially expressed in various cancers and their particular responses to chemotherapeutic drugs, our data disclosed the FOX genes that played an important role in the development and progression of cancer. More importantly, we predicted the role of one or several combinatorial FOX genes in the diagnosis and prognostic assessment of a specific cancer and evaluated the potential of a certain anticancer drug therapy for this type of cancer by integrating patterns of FOX genes expression with anticancer drugs sensitivity.

Regulation and roles of FOXK2 in cancer

Forkhead box K2 (FOXK2) is a member of the forkhead box transcription factor family that contains an evolutionarily conserved winged-helix DNA-binding domain. Recently, an increasing number of studies have demonstrated that FOXK2 plays an important role in the transcriptional regulation of cancer. Here, we provide an overview of the mechanisms underlying the regulation of FOXK2 expression and function and discuss the roles of FOXK2 in tumor pathogenesis. Additionally, we evaluated the prognostic value of FOXK2 expression in patients with various cancers. This review presents an overview of the different roles of FOXK2 in tumorigenesis and will help inform the design of experimental studies involving FOXK2. Ultimately, the information presented here will help enhance the therapeutic potential of FOXK2 as a cancer target.

A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients

Simple Summary

Clear cell renal cell carcinoma is a type of kidney cancer which comprises the majority of all renal cell carcinomas. Many efforts have been made to identify biomarkers which could help healthcare professionals better treat this kind of cancer. With extensive public data available, we conducted a machine learning study to determine a gene signature that could indicate patient survival with high accuracy. Through the min-Redundancy and Max-Relevance algorithm we generated a signature of 13 genes highly correlated with patient outcomes. These findings reveal potential strategies for personalized medicine in the clinical practice.

Abstract

Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.

FOXD1 Regulates the Sensitivity of Cetuximab by Regulating the Expression of EGFR in Head and Neck Squamous Cell Cancer

Objectives

Previous experiments have shown that growth factor receptors play important role in tumor proliferation, metastasis, and therapeutic effect of chemotherapeutic drugs. At the same time, forkhead box D1 (FOXD1) plays an important role in a variety of signal transmission, but its expression profile was known little about head and neck cancer. The purpose of this experiment was to explore the regulation of FOXD1 on the tumor progression of head and neck cancer and to explore the correlation of FOXD1 on the expression of growth factor receptors (EGFR).

Methods

The bioinformatics online database analyzed the expression of FOXD1 and EGFR in tumor tissues and nontumor tissues. Real-time quantitative PCR was used to detect the FOXD1 and EGFR expression in 45 tumor tissues and 15 nontumor tissues. The plasmid was used to construct FOXD1 overexpressing head and neck squamous cell cancer lines and observe the clonal formation and invasion of tumor cells under the intervention of EGFR-specific antibody—cetuximab.

Results

The expression of FOXD1 and EGFR in tumor tissues was higher than that in nontumor tissues. The higher expression of FOXD1 and EGFR was not conducive to the prognosis of patients. The expression of FOXD1 and EGFR was positively correlated, and immunohistochemical analysis showed the high expression of FOXD1 and EGFR has close relation to the advanced stage of the tumor. In vitro cell experiments proved that overexpression of FOXD1 can partially offset the cloning ability of cetuximab on head and neck tumor cells.

Conclusion

FOXD1 has an important regulatory role in the progression of head and neck cancer, and its abnormally high expression was not conducive to the prognosis of cancer patients. FOXD1 can regulate the expression of growth factor receptors in head and neck cancer, which provides a new idea for the better use of tumor growth factor receptor-specific antibodies for collaborative therapy.

Sonic Hedgehog signaling pathway in gynecological and genitourinary cancer (Review)

Cancers of the urinary tract, as well as those of the female and male reproductive systems, account for a large percentage of malignancies worldwide. Mortality is frequently affected by late diagnosis or therapeutic difficulties. The Sonic Hedgehog (SHH) pathway is an evolutionary conserved molecular cascade, which is mainly associated with the development of the central nervous system in fetal life. The present review aimed to provide an in‑depth summary of the SHH signaling pathway, including the characterization of its major components, the mechanism of its upstream regulation and non‑canonical activation, as well as its interactions with other cellular pathways. In addition, the three possible mechanisms of the cellular SHH cascade in cancer tissue are discussed. The aim of the present review was to summarize significant findings with regards to the expression of the SHH pathway components in kidney, bladder, ovarian, cervical and prostate cancer. Reports associated with common deficits and de‑regulations of the SHH pathway were summarized, despite the differences in molecular and histological patterns among these malignancies. However, currently, neither are SHH pathway elements included in panels of prognostic/therapeutic molecular patterns in any of the discussed cancers, nor have the drugs targeting SMO or GLIs been approved for therapy. The findings of the present review may support future studies on the treatment of and/or molecular targets for gynecological and genitourinary cancers.

Forkhead box F2 as a novel prognostic biomarker and potential therapeutic target in human cancers prone to bone metastasis: a meta-analysis

OBJECTIVE

To undertake a systematic review and meta-analysis to evaluate the prognostic value of Forkhead box F2 (FOXF2) levels in different types of cancers prone to bone metastasis.

METHODS

A systematic search of publications listed in electronic databases (The Web of Science, EMBASE®, PubMed®, PMC, Science Direct and CNKI) from inception to 5 November 2020 was conducted. The hazard ratios (HRs) and 95% confidence intervals (95% CIs) were used to assess the relationship between FOXF2 levels and patient prognosis including overall survival (OS) and disease-free survival (DFS).

RESULTS

Sixteen studies enrolling 8461 participants were included in the meta-analysis. High levels of FOXF2 were a predictor of OS (HR: 0.66; 95% CI 0.51, 0.86) and DFS (HR: 0.60; 95% CI 0.48, 0.76). The trim-and-fill analysis, sensitivity analysis and subgroup analyses stratified by the study characteristics confirmed the robustness of the results.

CONCLUSION

These current findings indicate that high FOXF2 levels could be an indicator of a good prognosis in cancer patients with tumours that are prone to bone metastasis. FOXF2 levels might be a clinically important prognostic biomarker.

A Risk Score Model Based on Nine Differentially Methylated mRNAs for Predicting Prognosis of Patients with Clear Cell Renal Cell Carcinoma

Purpose

DNA methylation alterations play important roles in initiation and progression of clear cell renal cell carcinoma (ccRCC). In this study, we attempted to identify differentially methylated mRNA signatures with prognostic value for ccRCC.

Methods

The mRNA methylation and expression profiling data of 306 ccRCC tumors were downloaded from The Cancer Genome Atlas (TCGA) to screen differentially methylated lncRNAs and mRNAs (DMLs and DMMs) between bad and good prognosis patients. Uni- and multivariable Cox regression analyses and LASSO Cox-PH regression analysis were used to select prognostic lncRNAs and mRNAs. Corresponding risk scores were calculated and compared for predictive performance in the training set using Kaplan-Meier OS and ROC curve analyses. The optimal risk score was then identified and validated in the validation set. Function enrichment analysis was conducted.

Results

This study screened 461 DMMs and 63 DMLs between good prognosis and bad prognosis patients, and furthermore, nine mRNAs and six lncRNAs were identified as potential prognostic molecules. Compared to nine-mRNA status risk score model, six-lncRNA methylation risk score model, and six-lncRNA status risk score model, the nine-mRNA methylation risk score model showed superiority for prognosis stratification of ccRCC patients in the training set. The prognostic ability of the nine-mRNA methylation risk score model was validated in the validation set. The nine prognostic mRNAs were functionally associated with neuroactive ligand receptor interaction and inflammation-related pathways.

Conclusion

The nine-mRNA methylation signature (DMRTA2, DRGX, FAM167A, FGGY, FOXI2, KRTAP2-1, TCTEX1D1, TTBK1, and UBE2QL1) may be a useful prognostic biomarker and tool for ccRCC patients. The present results would be helpful to elucidate the possible pathogenesis of ccRCC.

FOXK2 downregulation suppresses EMT in hepatocellular carcinoma

Forkhead box K2 (FOXK2) was first identified as an NFAT-like interleukin-binding factor. FOXK2 has been reported to act as either oncogene or tumor suppressor. However, functional and regulating mechanisms of FOXK2 in epithelial–mesenchymal transition (EMT) in hepatocellular carcinoma (HCC) remain unclear. An FOXK2-specific siRNA was employed to decrease the endogenous expression of FOXK2. MTT assay, colony formation and transwell assay were used to evaluate proliferation, migration and invasion of Hep3B and HCCLM3 cells, respectively. The protein expression associated with EMT and Akt signaling pathways was evaluated using western blot. FOXK2 downregulation could inhibit cell proliferation and colony formation and suppress migration and invasion in Hep3B and HCCLM3 cells. The expression of E-cadherin was significantly upregulated, and the expression of snail and p-Akt was significantly downregulated in siFOXK2-transfected cells compared with control cells. SF1670 induced the expression of p-Akt and snail and suppressed the expression of E-cadherin in Hep3B and HCCLM3 cells. SF1670 promoted the invasion and colony formation of Hep3B and HCCLM3 cells. SF1670 partly inhibited the effect of FOXK2 suppression on Hep3B and HCCLM3 cells. In conclusion, this study revealed that FOXK2 downregulation suppressed the EMT in HCC partly through inhibition of the Akt signaling pathway.

FOXK2 Transcription Factor and Its Emerging Roles in Cancer

Forkhead box (FOX) transcription factors compose a large family of regulators of key biological processes within a cell. FOXK2 is a member of FOX family, whose biological functions remain relatively unexplored, despite its description in the early nineties. More recently, growing evidence has been pointing towards a role of FOXK2 in cancer, which is likely to be context-dependent and tumour-specific. Here, we provide an overview of important aspects concerning the mechanisms of regulation of FOXK2 expression and function, as well as its complex interactions at the chromatin level, which orchestrate how it differentially regulates the expression of gene targets in pathophysiology. Particularly, we explore the emerging functions of FOXK2 as a regulator of a broad range of cancer features, such as cell proliferation and survival, DNA damage, metabolism, migration, invasion and metastasis. Finally, we discuss the prognostic value of assessing FOXK2 expression in cancer patients and how it can be potentially targeted for future anticancer interventions.