GFRA3 promoter methylation may be associated with decreased postoperative survival in gastric cancer

Unveiling the Neural Environment in Cancer: Exploring the Role of Neural Circuit Players and Potential Therapeutic Strategies

The regulation of the immune environment within the tumor microenvironment has provided new opportunities for cancer treatment. However, an important microenvironment surrounding cancer that is often overlooked despite its significance in cancer progression is the neural environment surrounding the tumor. The release of neurotrophic factors from cancer cells is implicated in cancer growth and metastasis by facilitating the infiltration of nerve cells into the tumor microenvironment. This nerve-tumor interplay can elicit cancer cell proliferation, migration, and invasion in response to neurotransmitters. Moreover, it is possible that cancer cells could establish a network resembling that of neurons, allowing them to communicate with one another through neurotransmitters. The expression levels of players in the neural circuits of cancers could serve as potential biomarkers for cancer aggressiveness. Notably, the upregulation of certain players in the neural circuit has been linked to poor prognosis in specific cancer types such as breast cancer, pancreatic cancer, basal cell carcinoma, and stomach cancer. Targeting these players with inhibitors holds great potential for reducing the morbidity and mortality of these carcinomas. However, the efficacy of anti-neurogenic agents in cancer therapy remains underexplored, and further research is necessary to evaluate their effectiveness as a novel approach for cancer treatment. This review summarizes the current knowledge on the role of players in the neural circuits of cancers and the potential of anti-neurogenic agents for cancer therapy.

Reclassification of Hepatocellular Cancer With Neural-Related Genes

Neural infiltration is a critical component of the tumor microenvironment; however, owing to technological limitations, its role in hepatocellular cancer remains obscure. Herein, we obtained the RNA-sequencing data of liver hepatocellular carcinoma (LIHC) from The Cancer Genome Atlas database and performed a series of bioinformatic analyses, including prognosis analysis, pathway enrichment, and immune analysis, using the R software packages, Consensus Cluster Plus and Limma. LIHC could be divided into two subtypes according to the expression of neural-related genes (NRGs); moreover, there are statistic differences in the prognosis, stage, and immune regulation between the two subtypes. The prognostic model showed that high expression of NRGs correlated with a poor survival prognosis (P<0.05). Further, CHRNE, GFRA2, GFRA3, and GRIN2D was significantly correlated with LIHC clinical prognosis, clinical stage, immune infiltration, immune response, and vital signaling pathways. There was nerve-cancer crosstalk in LIHC. A reclassification of LIHC based on NRG expression may prove beneficial to clinical practice. CHRNE, GFRA2, GFRA3, and GRIN2D may serve as potential biomarker for liver cancer prognosis or immune response.

Immune Subtype Profiling and Establishment of Prognostic Immune-Related lncRNA Pairs in Human Ovarian Cancer

This study collected immune-related genes (IRGs) and used gene expression data from TCGA database to construct a molecular subtype of ovarian cancer (OV) based on immune-related lncRNA gene pairs (IRLnc_GPs). The relationships between molecular subtypes and prognosis and clinical characteristics were further explored. IRGs were acquired from the ImmPort database, and round-robin pairing of immune-related lncRNAs was performed. The NMF algorithm was used to identify molecular subtypes, and the immune score of a single sample was calculated through ESTIMATE, TIMER, ssGSEA, MCPcounter, and CIBERSORT. The relationship between molecular subtypes and immune microenvironments was identified. A hypergeometric test was used to test the lncRNA pairs among the OV molecular subtypes (C1 and C2 subtypes). The BH method was used to screen the different lncRNA pairs, and a predictive risk model was constructed and verified. Finally, correlation analysis between the risk model, immune checkpoint genes, and chemotherapy drugs was carried out. Based on IRLnc_GP to classify 373 OV samples of TCGA, the samples were divided into two subtypes, and the prognosis between the subtypes showed significant differences. The C1 subtype with a poor prognosis was more related to the pathways of tumor occurrence and development. We identified 180 differential lncRNA pairs between subtypes and constructed a prognostic risk model based on 8 IRLnc_GPs. In the independent dataset, the distribution of subtypes in functional modules was different and highly repeatable. There were significant differences in the molecular and clinical characteristics of the subtypes and the drug sensitivity of immunotherapy/chemotherapy. In conclusion, the risk model established based on IRLnc_GP can better evaluate the prognosis of OV samples and can also assess the effects of different drug treatments in the high- and low-risk groups, providing new insights and ideas for the treatment of OV.

The Emerging Portrait of Glial Cell Line-derived Neurotrophic Factor Family Receptor Alpha (GFRα) in Cancers

Glial cell line-derived neurotrophic factor family receptor alpha (GFRα) members have been widely connected to the mechanisms contributing to cell growth, differentiation, cell migration and tissue maturation. Here we review GFRα biological functions and discussed the evidence indicating whether GFRα signaling complex present novel opportunities for oncogenic intervention and treatment resistance. Thus, our work systematically reviewed the emerging role of GFRα family members in cancers, and provided novel insights for further researches.

Identification and validation of a novel cuproptosis-related signature as a prognostic model for lung adenocarcinoma

BACKGROUND

Cuproptosis is a novel form of copper-induced cell death that targets lipoylated tricarboxylic acid (TCA) cycle proteins. However, its prognostic role in lung adenocarcinoma (LUAD) remains unclear. This study aimed to establish a cuproptosis-related prognostic signature for patients with LUAD.

METHODS

Transcriptome data of LUAD samples were extracted from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The prognostic value of cuproptosis-related genes (CRGs) was investigated using Cox regression analysis to develop a cuproptosis-related prognostic model. Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology (GO) and gene set variation analysis (GSVA) were conducted to characterize different biological activities or pathways between high- or low-CRG groups. The expression pattern and prognostic values of CRGs were validated in 37 paired tumor-normal samples using quantitative PCR. Furthermore, in vitro experiments were performed to investigate the relationship between cuproptosis and CRG expression and to explore the function of target genes in cuproptosis.

RESULTS

Among the 36 CRGs, 17 genes were upregulated, and 3 genes were downregulated in LUAD. A total of 385 CRGs were identified using Pearson correlation analysis. A cuproptosis-related signature was constructed using least absolute shrinkage and selection operator (LASSO) analysis. The prognostic value of the cuproptosis-related signature was validated in six external validation cohorts and in LUAD specimens from our facility. Patients in the high-risk group based on the CRG signature score had shorter overall survival than those in the low-risk group in both the datasets and clinical specimens. In vitro experiments revealed that the expression of BARX1, GFRA3, and KHDRBS2 was upregulated after cuproptosis was induced by elesclomol-CuCL₂, whereas the upregulation was suppressed on pretreatment with tetrathiomolybdate (TTM), a chelator of copper. Further, the cell proliferation assay revealed that the BARX1 and GFRA3 deficiency facilities the cuproptosis induced by elesclomol-CuCL₂.

CONCLUSION

This study established a new CRG signature that can be used to predict the OS of LUAD patients. Moreover, the knockdown of BARX1 and GFRA3 could increase the sensitivity of LUAD cells to the cuproptosis.

Identification of key genes in cutaneous squamous cell carcinoma: a transcriptome sequencing and bioinformatics profiling study

Background

Long-term exposure to ultraviolet (UV) radiation can cause cutaneous squamous cell carcinoma (cSCC), which is one of the most common malignant cancers worldwide. Actinic keratosis (AK) is generally considered a precancerous lesion of cSCC. However, the pathogenesis and oncogenic processes of AK and cSCC remain elusive, especially in the context of photodamage.

Methods

In this study, transcriptome sequencing was performed on AK, cSCC, normal sun-exposed skin (NES) tissues, and normal non-sun-exposed skin (NNS) from 24 individuals. Bioinformatics analysis to identify the differentially expressed genes (DEGs) of 4 groups, and potential key genes of cSCC were validated by real-time quantitative reverse transcription PCR (qRT-PCR).

Results

A total of 46,930 genes were differentially expressed in the 4 groups, including 127 genes that were differentially expressed between NES and NNS, 420 DEGs in AK compared to NES, 1,658 DEGs in cSCC compared to NES, and 1,389 DEGs in cSCC compared to AK. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that the DEGs are involved in multiple pathways, including extracellular matrix (ECM)-receptor interaction, immune, inflammatory, microbial infection, and other related pathways. Finally, 5 new genes (HEPHL1, FBN2, SULF1, SULF2, and TCN1) were confirmed significantly upregulated in cSCC.

Conclusions

Using transcriptome sequencing and integrated bioinformatical analysis, we have identified key DEGs and pathways in cSCC, which could improve our understanding of the cause and underlying molecular events of AK and cSCC. HEPHL1, FBN2, SULF1, SULF2, and TCN1 may be novel potential biomarkers and therapeutic targets of cSCC.

DNA methylation profiling to predict overall survival risk in gastric cancer: development and validation of a nomogram to optimize clinical management

DNA methylation has been reported to serve an important role in the carcinogenesis and development of gastric cancer. Our aim was to systematically develop an individualized prediction model of the survival risk combing clinical and methylation factors in gastric cancer. A univariate Cox proportional risk regression analysis was used to identify the prognosis-associated methylation sites based on the differentially expressed methylation sites between early and advanced gastric cancer group, then we applied least absolute shrinkage and selection operator (LASSO) Cox regression model to screen candidate methylation sites. Subsequently, multivariate Cox proportional risk regression analysis was conducted to identify predictive signature according to the candidate sites. Relative operating characteristic curve (ROC) analysis manifested that an 11-methylation signature exhibited great predictive efficiency for 1-, 3-, 5-year survival events. Patients in the low-risk group classified according to 11-methylation signature-based risk score yield significantly better survival than that in high-risk group. Moreover, Cox regression analysis combing methylation-based risk score and other clinical factors indicated that 11-methylation signature served as an independent risk factor. The predictive value of risk score was validated in the testing dataset. In addition, a nomogram was constructed and the ROC as well as calibration plots analysis demonstrated the good performance and clinical application of the nomogram. In conclusion, the result suggested the 11-DNA methylation signature may be potentially independent prognostic marker and functioned as a significant tool for guiding the clinical prediction of gastric cancer patients' overall survival.

Prediction of gastric cancer prognosis in the next-generation sequencing era

Gastric cancer (GC) is one of the most commonly diagnosed malignancies worldwide, and is caused by complex interactions of multiple risk factors such as environmental (Helicobacter pylori and Epstein–Barr Virus), hereditary (genetic alterations and epigenetic modifications), as well as dietary and lifestyle factors. GC is usually detected at an advanced stage, with a dismal prognosis. Even for patients with similar clinical or pathologic stage receiving similar treatment, the outcomes are still uneven and unpredictable. To better incorporate genetic and epigenetic profiles into GC prognostic predication, gene expression signatures have been developed to predict GC outcomes. More recently, the advancement of high-throughput sequencing technology, also known as next-generation sequencing (NGS) technology, and analysis has provided the basis for accurate molecular classification of GC tumors. Here, we summarized and updated the literature related to NGS studies of GC, including whole-genome sequencing, whole-exome sequencing, RNA sequencing, and targeted sequencing, and discussed current progresses. NGS has facilitated the identification of genetic/epigenetic targets for screening as well as development of targeted agent therapy, thus enabling individualized patient management and treatment.

Epigenetic biomarkers in gastrointestinal cancers: The current state and clinical perspectives

Each year, almost 4.1 million people are diagnosed with gastrointestinal (GI) cancers. Due to late detection of this disease, the mortality is high, causing approximately 3 million cancer-related deaths annually, worldwide. Although the incidence and survival differs according to organ site, earlier detection and improved prognostication have the potential to reduce overall mortality burden from these cancers. Epigenetic changes, including aberrant promoter DNA methylation, are common events in both cancer initiation and progression. Furthermore, such changes may be identified non-invasively with the use of PCR based methods, in bodily fluids of cancer patients. These features make aberrant DNA methylation a promising substrate for the development of disease biomarkers for early detection, prognosis and for predicting response to therapy. In this article, we will provide an update and current clinical perspectives for DNA methylation alterations in patients with colorectal, gastric, pancreatic, liver and esophageal cancers, and discuss their potential role as cancer biomarkers.

Review of recent efforts to discover biomarkers for early detection, monitoring, prognosis, and prediction of treatment responses of patients with gastric cancer

Gastric cancer (GC) is the leading cause of cancer-related death worldwide. Despite recent advances in diagnosis and therapy, the prognosis of patients with GC is poor. Many patients have inoperable disease upon diagnosis or experience recurrent disease after curative gastrectomy. Unfortunately, tumor markers for GC, such as serum carcinoembryonic antigen and carbohydrate antigen 19-9, lack sufficient sensitivity and specificity. Therefore, effective biomarkers are required to detect early GC and to predict tumor recurrence and chemosensitivity. Areas covered: Here we aimed to review recent developments in techniques that improve the detection of aberrant expression of GC-associated molecules, including protein coding genes, microRNAs, long noncoding RNAs, and methylated promoter DNAs. Expert commentary: Detection of genetic and epigenetic alterations in gastric tissue or in the circulation will likely improve the diagnosis and management of GC to achieve significantly improved outcomes.

The GDNF Family: A Role in Cancer?

The glial cell line-derived neurotrophic factor (GDNF) family of ligands (GFLs) comprising of GDNF, neurturin, artemin, and persephin plays an important role in the development and maintenance of the central and peripheral nervous system, renal morphogenesis, and spermatogenesis. Here we review our current understanding of GFL biology, and supported by recent progress in the area, we examine their emerging role in endocrine-related and other non-hormone-dependent solid neoplasms. The ability of GFLs to elicit actions that resemble those perturbed in an oncogenic phenotype, alongside mounting evidence of GFL involvement in tumor progression, presents novel opportunities for therapeutic intervention.

Transcriptome profiling identified differentially expressed genes and pathways associated with tamoxifen resistance in human breast cancer

Tamoxifen (TAM) resistance is an important clinical problem in the treatment of breast cancer. In order to identify the mechanism of TAM resistance for estrogen receptor (ER)-positive breast cancer, we screened the transcriptome using RNA-seq and compared the gene expression profiles between the MCF-7 mamma carcinoma cell line and the TAM-resistant cell line TAMR/MCF-7, 52 significant differential expression genes (DEGs) were identified including SLIT2, ROBO, LHX, KLF, VEGFC, BAMBI, LAMA1, FLT4, PNMT, DHRS2, MAOA and ALDH. The DEGs were annotated in the GO, COG and KEGG databases. Annotation of the function of the DEGs in the KEGG database revealed the top three pathways enriched with the most DEGs, including pathways in cancer, the PI3K-AKT pathway, and focal adhesion. Then we compared the gene expression profiles between the Clinical progressive disease (PD) and the complete response (CR) from the cancer genome altas (TCGA). 10 common DEGs were identified through combining the clinical and cellular analysis results. Protein-protein interaction network was applied to analyze the association of ER signal pathway with the 10 DEGs. 3 significant genes (GFRA3, NPY1R and PTPRN2) were closely related to ER related pathway. These significant DEGs regulated many biological activities such as cell proliferation and survival, motility and migration, and tumor cell invasion. The interactions between these DEGs and drug resistance phenomenon need to be further elucidated at a functional level in further studies. Based on our findings, we believed that these DEGs could be therapeutic targets, which can be explored to develop new treatment options.

Epigenetic silencing of downstream genes mediated by tandem orientation in lung cancer

Epigenetic deregulation is of importance in tumorigenesis. In particular CpG islands (CGI), are frequently hypermethylated. Here, genome-wide DNA-methylation profiles of 480,000 CpGs in lung cancer cells were generated. It was observed that intra- and intergenic CGI exhibited higher methylation compared to normal cells. The functional annotation of hypermethylated CGI revealed that the hypermethylation was associated with homeobox domain genes and targets marked by repressive histone modifications. The strongest methylation variation was observed in transitional areas of CGI, termed shores. 5'-shores of promoter-associated CGI in lung cancer cell lines were higher methylated than 3'-shores. Within two tandem-oriented genes, a significant hypermethylation of the downstream-located CGI promoters was revealed. Hypermethylation correlates with the length of the intergenic region between such tandem genes. As the RASSF1A tumor suppressor gene represents such a downstream tandem gene, its silencing was analyzed using an inducible system. It was determined that the induction of an upstream gene led to a repression of RASSF1A through a process involving histone deacetylases and CPSF1. A tumor-specific increase in expression of histone deacetylases and CPSF1 was detected in lung cancer. Our results suggest that the downstream gene could be susceptible to epigenetic silencing when organized in a tandem orientation.