Expression Atlas of FGF and FGFR Genes in Pancancer Uncovered Predictive Biomarkers for Clinical Trials of Selective FGFR Inhibitors

Network Dynamics Caused by Genomic Alteration Determine the Therapeutic Response to FGFR Inhibitors for Lung Cancer

Recently, FGFR inhibitors have been highlighted as promising targeted drugs due to the high prevalence of FGFR1 amplification in cancer patients. Although various potential biomarkers for FGFR inhibitors have been suggested, their functional effects have been shown to be limited due to the complexity of the cancer signaling network and the heterogenous genomic conditions of patients. To overcome such limitations, we have reconstructed a lung cancer network model by integrating a cell line genomic database and analyzing the model in order to understand the underlying mechanism of heterogeneous drug responses. Here, we identify novel genomic context-specific candidates that can increase the efficacy of FGFR inhibitors. Furthermore, we suggest optimal targets that can induce more effective therapeutic responses than that of FGFR inhibitors in each of the FGFR-resistant lung cancer cells through computational simulations at a system level. Our findings provide new insights into the regulatory mechanism of differential responses to FGFR inhibitors for optimal therapeutic strategies in lung cancer.

Enhancing Characteristic Gene Selection and Tumor Classification by the Robust Laplacian Supervised Discriminative Sparse PCA

Characteristic gene selection and tumor classification of gene expression data play major roles in genomic research. Due to the characteristics of a small sample size and high dimensionality of gene expression data, it is a common practice to perform dimensionality reduction prior to the use of machine learning-based methods to analyze the expression data. In this context, classical principal component analysis (PCA) and its improved versions have been widely used. Recently, methods based on supervised discriminative sparse PCA have been developed to improve the performance of data dimensionality reduction. However, such methods still have limitations: most of them have not taken into consideration the improvement of robustness to outliers and noise, label information, sparsity, as well as capturing intrinsic geometrical structures in one objective function. To address this drawback, in this study, we propose a novel PCA-based method, known as the robust Laplacian supervised discriminative sparse PCA, termed RLSDSPCA, which enforces the L2,1 norm on the error function and incorporates the graph Laplacian into supervised discriminative sparse PCA. To evaluate the efficacy of the proposed RLSDSPCA, we applied it to the problems of characteristic gene selection and tumor classification problems using gene expression data. The results demonstrate that the proposed RLSDSPCA method, when used in combination with other related methods, can effectively identify new pathogenic genes associated with diseases. In addition, RLSDSPCA has also achieved the best performance compared with the state-of-the-art methods on tumor classification in terms of major performance metrics. The codes and data sets used in the study are freely available at http://csbio.njust.edu.cn/bioinf/rlsdspca/.

A pan-cancer analysis of GINS complex subunit 4 to identify its potential role as a biomarker in multiple human cancers

This study was initiated to explore the expression variation, clinical significance, and biological importance of the GINS complex subunit 4 (GINS4) in different human cancers as a shared biomarker via pan-cancer analysis through different platforms including UALCAN, Kaplan Meier (KM) plotter, TNMplot, GENT2, GEPIA, DriverDBv3, Human Protein Atlas (HPA), MEXPRESS, cBioportal, STRING, DAVID, MuTarge, Enrichr, TIMER, and CTD. Our findings have verified the up-regulation of GINS4 in 24 major subtypes of human cancers, and its overexpression was found to be substantially associated with poor overall survival (OS), relapse-free survival (RFs), and metastasis in ESCA, KIRC, LIHC, LUAD, and UCEC. This suggested that GINS4 plays a significant role in the development and progression of these five cancers. Furthermore, we noticed that GINS4 is also overexpressed in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics. Enrichment analysis revealed the involvement of GINS4 associated genes in a variety of diverse GO and KEGG terms. We also explored few significant correlations between GINS4 expression and promoter methylation, genetic alterations, CNVs, other mutant genes, tumor purity, and immune cells infiltration. In conclusion, our results elucidated that GINS4 can serve as a shared diagnostic, prognostic biomarker, and a potential therapeutic target in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics.

Integrative analysis reveals methylenetetrahydrofolate dehydrogenase 1-like as an independent shared diagnostic and prognostic biomarker in five different human cancers

BACKGROUND

Defects in methylenetetrahydrofolate dehydrogenase 1-like (MTHFD1L) expression have earlier been examined in only a few human cancers.

OBJECTIVES

Multi-omics profiling of MTHFD1L as a shared biomarker in distinct subtypes of human cancers.

METHODS

In the current study, for the multi-omics analysis of MTHFD1L in 24 major subtypes of human cancers, a comprehensive in silico approach was adopted to mine different open access online databases including UALCAN, Kaplan-Meier (KM) plotter, LOGpc, GEPIA, Human Protein Atlas (HPA), Gene Expression across Normal and Tumor tissue (GENT2), MEXPRESS, cBioportal, STRING, DAVID, TIMER, and Comparative Toxicogenomics Database (CTD).

RESULTS

We noticed that the expression of MTHFD1L was significantly higher in all the analyzed 24 subtypes of human cancers as compared with the normal controls. Moreover, MTHDF1L overexpression was also found to be significantly associated with the reduced overall survival (OS) duration of Bladder urothelial cancer (BLCA), Head and neck cancer (HNSC), Kidney renal papillary cell carcinoma (KIRP), Lung adenocarcinoma (LUAD), and Uterine corpus endometrial carcinoma (UCEC). This implies that MTHFD1L plays a significant role in the development and progression of these cancers. We further noticed that MTHFD1L was also overexpressed in BLCA, HNSC, KIRP, LUAD, and UCEC patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of MTHFD1L-associated genes in five diverse pathways. We also explored few interesting correlations between MTHFD1L expression and its promoter methylation, genetic alterations, CNVs, and between CD8+ T immune cells level.

CONCLUSION

In conclusion, our results elucidated that MTHFD1L can serve as a shared diagnostic and prognostic biomarker in BLCA, HNSC, KIRP, LUAD, and UCEC patients of different clinicopathological features.

Fibroblast growth factor (FGF), FGF receptor (FGFR), and cyclin D1 (CCND1) DNA methylation in head and neck squamous cell carcinomas is associated with transcriptional activity, gene amplification, human papillomavirus (HPV) status, and sensitivity to tyrosine kinase inhibitors

Background

Dysregulation of fibroblast growth factor receptor (FGFR) signaling pathway has been observed in head and neck squamous cell carcinoma (HNSCC) and is a promising therapeutic target for selective tyrosine kinase inhibitors (TKIs). Potential predictive biomarkers for response to FGFR-targeted therapies are urgently needed. Understanding the epigenetic regulation of FGF pathway related genes, i.e. FGFRs, FGFs, and CCND1, could enlighten the way towards biomarker-selected FGFR-targeted therapies.

Methods

We performed DNA methylation analysis of the encoding genes FGFR1, FGFR2, FGFR3, FGFR4, FGF1-14, FGF16-23, and CCND1 at single CpG site resolution (840 CpG sites) employing The Cancer Genome Research Atlas (TCGA) HNSCC cohort comprising N = 530 tumor tissue and N = 50 normal adjacent tissue samples. We correlated DNA methylation to mRNA expression with regard to human papilloma virus (HPV) and gene amplification status. Moreover, we investigated the correlation of methylation with sensitivity to the selective FGFR inhibitors PD 173074 and AZD4547 in N = 40 HPV(−) HNSCC cell lines.

Results

We found sequence-contextually nuanced CpG methylation patterns in concordance with epigenetically regulated genes. High methylation levels were predominantly found in the promoter flank and gene body region, while low methylation levels were present in the central promoter region for most of the analyzed CpG sites. FGFRs, FGFs, and CCND1 methylation differed significantly between tumor and normal adjacent tissue and was associated with HPV and gene amplification status. CCND1 promoter methylation correlated with CCND1 amplification. For most of the analyzed CpG sites, methylation levels correlated to mRNA expression in tumor tissue. Furthermore, we found significant correlations of DNA methylation of specific CpG sites with response to the FGFR1/3–selective inhibitors PD 173074 and AZD4547, predominantly within the transcription start site of CCND1.

Conclusions

Our results suggest an epigenetic regulation of CCND1, FGFRs, and FGFs via DNA methylation in HNSCC and warrants further investigation of DNA methylation as a potential predictive biomarker for response to selective FGFR inhibitors in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01212-4.

Multi-Omics Analysis Identified TMED2 as a Shared Potential Biomarker in Six Subtypes of Human Cancer

Introduction

Cancer is one of the most common malignancies and the leading cause of death worldwide. As a member of the transmembrane emp24 domain (Tmed)/p24 family of proteins, TMED2 expression variations have been documented earlier in only a few subtypes of human cancers, and the multi-omics profiling of TMED2 as a shared biomarker in different other subtypes of human cancers remains to be uncovered.

Methods

In the current study, TMED2 multi-omics analysis in 24 major subtypes of human cancer was performed using different authentic online databases and bioinformatics analysis including UALCAN, Kaplan–Meier (KM) plotter, Human Protein Atlas (HPA), GENT2, MEXPRESS, cBioportal, STRING, DAVID, TIMER, and CTD.

Results

In general, the TMED2 expression in 24 major subtypes of human cancers was higher relative to normal controls and was also strongly associated with the lower overall survival (OS) and relapse-free survival (RFS) duration of CESC, ESCA, HNSC, KIRC, LIHC, and LUAD patients. This implies that TMED2 plays a significant role in the development and progression of these cancers. Furthermore, the TMED2 overexpression was also correlated with different clinicopathological features of CESC, ESCA, HNSC, KIRC, LIHC, and LUAD patients. TMED2-associated genes network was involved in 3 diverse pathways, and finally, few stronger correlations were also explored between TMED2 expression and its promoter methylation level, genetic alterations, and CD8+ T immune cells level.

Conclusion

In conclusion, via this in silico study, we have elucidated that TMED2 can serve as a shared diagnostic and prognostic biomarker in CESC, ESCA, HNSC, KIRC, LIHC, and LUAD patients of different clinicopathological features but, further in vitro and in vivo research should be carried out to confirm these findings.