Genetic Analysis Identifies the Role of HLF in Renal Cell Carcinoma

Deciphering the role of HLF in idiopathic orbital inflammation: integrative analysis via bioinformatics and machine learning techniques

Idiopathic orbital inflammation, formerly known as NSOI (nonspecific orbital inflammation), is characterized as a spectrum disorder distinguished by the polymorphic infiltration of lymphoid tissue, presenting a complex and poorly understood etiology. Recent advancements have shed light on the HLF (Human lactoferrin), proposing its critical involvement in the regulation of hematopoiesis and the maintenance of innate mucosal immunity. This revelation has generated significant interest in exploring HLF's utility as a biomarker for NSOI, despite the existing gaps in our understanding of its biosynthetic pathways and operational mechanisms. Intersecting multi-omic datasets-specifically, common differentially expressed genes between GSE58331 and GSE105149 from the Gene Expression Omnibus and immune-related gene compendiums from the ImmPort database-we employed sophisticated analytical methodologies, including Lasso regression and support vector machine-recursive feature elimination, to identify HLF. Gene set enrichment analysis and gene set variation analysis disclosed significant immune pathway enrichment within gene sets linked to HLF. The intricate relationship between HLF expression and immunological processes was further dissected through the utilization of CIBERSORT and ESTIMATE algorithms, which assess characteristics of the immune microenvironment, highlighting a noteworthy association between increased HLF expression and enhanced immune cell infiltration. The expression levels of HLF were corroborated using data from the GSE58331 dataset, reinforcing the validity of our findings. Analysis of 218 HLF-related differentially expressed genes revealed statistically significant discrepancies. Fifteen hub genes were distilled using LASSO and SVM-RFE algorithms. Biological functions connected with HLF, such as leukocyte migration, ossification, and the negative regulation of immune processes, were illuminated. Immune cell analysis depicted a positive correlation between HLF and various cells, including resting mast cells, activated NK cells, plasma cells, and CD8 T cells. Conversely, a negative association was observed with gamma delta T cells, naive B cells, M0 and M1 macrophages, and activated mast cells. Diagnostic assessments of HLF in distinguishing NSOI showed promising accuracy. Our investigation delineates HLF as intricately associated with NSOI, casting light on novel biomarkers for diagnosis and progression monitoring of this perplexing condition.

Pan-cancer bioinformatics analysis of hepatic leukemia factor and further validation in colorectal cancer

Background

Hepatic leukemia factor (HLF) is associated with cancer onset, growth, and progression; however, little is known regarding its biological role in pan-cancer. In order to further evaluate the diagnostic and prognostic value of HLF in pan-cancer and colorectal cancer (CRC), we performed comprehensive bioinformatics analyses of the molecular mechanism of HLF in pan-cancer, with subsequent verification in CRC.

Methods

We downloaded data (gene expression, clinical data, follow-up duration, and immune-related data) related to 33 solid tumor types from UCSC Xena (University of California Santa Cruz cancer database, https://xena.ucsc.edu/). HLF expression was analyzed in pan-cancer, and its diagnostic efficacy, prognostic value, and correlation with pathological stage and cancer immunity were determined. We also analyzed gene alterations in HLF and biological processes involved in its regulation in pan-cancer. Using CRC data in The Cancer Genome Atlas (TCGA), we assessed correlations between HLF and CRC diagnosis, prognosis, and drug sensitivity and performed functional enrichment analyses. Moreover, we constructed an HLF-related ceRNA regulatory network. Finally, we externally validated HLF expression and diagnostic and prognostic value in CRC using Gene Expression Omnibus (GEO) database, as well as by performing in vitro experiments.

Results

HLF expression was downregulated in most tumors, and HLF showed good predictive potential for pan-cancer diagnosis and prognosis. It was closely related to the clinicopathological stages of pan-cancer. Further, HLF was associated with tumor microenvironment and immune cell infiltration in many tumors. Analyses involving cBioPortal revealed changes in HLF amplifications and mutations in most tumors. HLF was also closely associated with microsatellite instability and tumor mutational burden in pan-cancer and involved in regulating various tumor-related pathways and biological processes. In CRC, HLF expression was similarly downregulated, with implications for CRC diagnosis and prognosis. Functional enrichment analysis indicated the association of HLF with many cancer-related pathways. Further, HLF was associated with drug (e.g., oxaliplatin) sensitivity in CRC. The ceRNA regulatory network showed multigene regulation of HLF in CRC. External validation involving GEO databases and quantitative real-time polymerase chain reaction (qRT-PCR) data substantiated these findings.

Conclusions

HLF expression generally exhibited downregulation in pan-cancer, contributing to tumor occurrence and development by regulating various biological processes and affecting tumor immune characteristics. HLF was also closely related to CRC occurrence and development. We believe HLF can serve as a reliable diagnostic, prognostic, and immune biomarker for pan-cancer.

HLF is a promising prognostic, immunological, and therapeutic biomarker in human tumors

Despite past research linking HLF mutations to cancer development, no pan-cancer analyses of HLF have been published. As a result, we utilized multiple databases to illustrate the potential roles of HLF in diverse types of cancers. Several databases were used to assess HLF expression in the TCGA cancer samples. Additional assessments were undertaken to investigate the relationship between HLF and overall survival, immune cell infiltration, genetic alterations, promoter methylation, and protein-protein interaction. HLF's putative roles and the relationship between HLF expression and drug reactivity were investigated. HLF expression was shown to be lower in tumor tissues from a variety of malignancies when compared to normal tissues. There was a substantial link found between HLF expression and patient survival, genetic mutations, and immunological infiltration. HLF influenced the pathways of apoptosis, cell cycle, EMT, and PI3K/AKT signaling. Abnormal expression of HLF lowered sensitivity to numerous anti-tumor drugs and small compounds. According to our findings, reduced HLF expression drives cancer growth, and it has the potential to be identified as a vital biomarker for use in prognosis, immunotherapy, and targeted treatment of a range of malignancies.

Tensin 1 regulated by hepatic leukemia factor represses the progression of prostate cancer

Hepatic leukemia factor (HLF), a transcription factor, is dysregulated in many cancers. This study investigates the function of HLF in prostate cancer (PCa) and its relation to tensin 1 (TNS1). Clinical tissues were collected from 24 PCa patients. Duke University 145 (DU145) and PC3 cells overexpressing HLF were established. HLF signaling was downregulated in PCa tissues compared to adjacent tissues and in DU145 and PC3 cells compared to prostate epithelial cells RWPE-1 or prostate stromal cells (WPMY-1). PCa cell lines with overexpression of HLF had reduced proliferative, migratory, and invasive activity, increased apoptosis, and cell mitosis mostly in the G0/G1 phase. HLF induced the TNS1 transcription to activate the p53 pathway. Depletion of TNS1 reversed the anti-tumor effects of HLF on PCa cells and tumor growth and metastasis in vivo. In summary, our findings suggest that HLF suppressed PCa progression by upregulating TNS1 expression and inducing the p53 pathway activation, which might provide insights into novel strategies for combating PCa.

CYBA as a Potential Biomarker for Renal Cell Carcinoma: Evidence from an Integrated Genetic Analysis

BACKGROUND/AIM

Oxidative stress plays an important role in various pathogenic processes, and disruption in the coordinated production of NADPH oxidase (NOX)-derived reactive oxygen species has been associated with carcinogenesis. However, little is known about whether genetic variants in NOX can contribute to the development of renal cell carcinoma (RCC).

PATIENTS AND METHODS

This study aimed to bridge this knowledge gap by analysing the association of 10 single-nucleotide polymorphisms in the phagocyte NOX genes, CYBA and CYBB, with RCC risk and tumour characteristics in 630 RCC patients and controls. Differential gene expression and patient prognosis analyses were performed using gene expression data obtained from public databases.

RESULTS

Multivariate analysis and multiple testing corrections revealed the A allele of rs7195830 in CYBA to be a significant risk allele for RCC, compared to the G allele [odds ratio (OR)=1.70, 95% confidence interval (CI)=1.27-2.26, p<0.001]. A pooled analysis of 17 renal cancer gene expression datasets revealed a higher CYBA expression in RCC than in normal tissues. Moreover, high CYBA expression was associated with advanced tumour characteristics and worse patient prognosis.

CONCLUSION

CYBA might play an oncogenic role in RCC and serve as a predictive indicator of patient prognosis.

Genetic Analysis Implicates Dysregulation of SHANK2 in Renal Cell Carcinoma Progression

SH3 and multiple ankyrin repeat domains (SHANK) is a family of scaffold proteins that were first identified to be involved in balancing synaptic transmission via regulation of intracellular signalling crosstalk and have been linked to various cancers. However, the role of the SHANK genes in renal cell carcinoma (RCC) remains to be elucidated. In this study, we aimed to evaluate whether genetic variants in SHANK family genes affect the risk of RCC and survival of patients. A genetic association study was conducted using logistic regression and Cox regression analyses, followed by the correction for a false discovery rate (FDR), in 630 patients with RCC and controls. A pooled analysis was further performed to summarise the clinical relevance of SHANK gene expression in RCC. After adjustment for known risk factors and the FDR, the SHANK2 rs10792565 T allele was found to be associated with an increased risk of RCC (adjusted odds ratio = 1.79, 95% confidence interval = 1.32-2.44, p = 1.96 × 10^-4, q = 0.030), whereas no significant association was found with RCC survival. A pooled analysis of 19 independent studies, comprising 1509 RCC and 414 adjacent normal tissues, showed that the expression of SHANK2 was significantly lower in RCC than in normal tissues (p < 0.001). Furthermore, low expression of SHANK2 was correlated with an advanced stage and poor prognosis for patients with clear cell and papillary RCC. This study suggests that SHANK2 rs10792565 is associated with an increased risk of RCC and that SHANK2 may play a role in RCC progression.

Effects and Prognostic Values of Circadian Genes CSNK1E/GNA11/KLF9/THRAP3 in Kidney Renal Clear Cell Carcinoma via a Comprehensive Analysis

Kidney renal clear cell carcinoma (KIRC) is one of the most prevalent and deadly types of renal cancer in adults. Recent research has identified circadian genes as being involved in the development and progression of KIRC by altering their expression. This study aimed to identify circadian genes that are differentially expressed in KIRC and assess their role in KIRC progression. In KIRC, there were 553 differentially expressed rhythm genes (DERGs), with 300 up-regulated and 253 down-regulated DERGs. Functional enrichment analyses showed that DERGs were greatly enriched in the circadian rhythm and immune response pathways. Survival analyses indicated that higher expression levels of CSNK1E were related to shorter overall survival of KIRC patients, whereas lower expression levels of GNA11, KLF9, and THRAP3 were associated with shorter overall survival of KIRC patients. Through cell assay verification, the mRNA level of CSNK1E was significantly up-regulated, whereas the mRNA levels of GNA11, KLF9, and THRAP3 were dramatically down-regulated in KIRC cells, which were consistent with the bioinformatics analysis of KIRC patient samples. Age, grade, stage, TM classification, and CSNK1E expression were all shown to be high-risk variables, whereas GNA11, KLF9, and THRAP3 expression were found to be low-risk factors in univariate Cox analyses. Multivariate Cox analyses showed that CSNK1E and KLF9 were also independently related to overall survival. Immune infiltration analysis indicated that the proportion of immune cells varied greatly between KIRC tissues and normal tissue, whereas CSNK1E, GNA11, KLF9, and THRAP3 expression levels were substantially linked with the infiltration abundance of immune cells and immunological biomarkers. Moreover, interaction networks between CSNK1E/GNA11/KLF9/THRAP3 and immune genes were constructed to explore the stream connections. The findings could help us better understand the molecular mechanisms of KIRC progression, and CSNK1E/GNA11/KLF9/THRAP3 might be used as molecular targets for chronotherapy in KIRC patients in the near future.

Combination of Radiosensitivity Gene Signature and PD-L1 Status Predicts Clinical Outcome of Patients With Locally Advanced Head and Neck Squamous Cell Carcinoma: A Study Based on The Cancer Genome Atlas Dataset

Aim: The aim of our study was to investigate the potential predictive value of the combination of radiosensitivity gene signature and PD-L1 expression for the prognosis of locally advanced head and neck squamous cell carcinoma (HNSCC).

Methods: The cohort was selected from The Cancer Genome Atlas (TCGA) and classified into the radiosensitive (RS) group and radioresistant (RR) group by a radiosensitivity-related gene signature. The cohort was also grouped as PD-L1-high or PD-L1-low based on PD-L1 mRNA expression. The least absolute shrinkage and selection operator (lasso)-based Cox model was used to select hub survival genes. An independent validation cohort was obtained from the Gene Expression Omnibus (GEO) database.

Results: We selected 288 locally advanced HNSCC patients from TCGA. The Kaplan–Meier method found that the RR and PD-L1-high group had a worse survival than others (p = 0.033). The differentially expressed gene (DEG) analysis identified 553 upregulated genes and 486 downregulated genes (p < 0.05, fold change >2) between the RR and PD-L1-high group and others. The univariate Cox analysis of each DEG and subsequent lasso-based Cox model revealed five hub survival genes (POU4F1, IL34, HLF, CBS, and RNF165). A further hub survival gene-based risk score model was constructed, which was validated by an external cohort. We observed that a higher risk score predicted a worse prognosis (p = 0.0013). The area under the receiver operating characteristic curve (AUC) plots showed that this risk score model had good prediction value (1-year AUC = 0.684, 2-year AUC = 0.702, and 3-year AUC = 0.688). Five different deconvolution methods all showed that the B cells were lower in the RR and PD-L1-high group (p < 0.05). Finally, connectivity mapping analysis showed that the histone deacetylase (HDAC) inhibitor trichostatin A might have the potential to reverse the phenotype of RR and PD-L1-high in locally advanced HNSCC (p < 0.05, false discovery rate <0.1).

Conclusion: The combination of 31-gene signature and the PD-L1 mRNA expression had a potential predictive value for the prognosis of locally advanced HNSCC who had RT. The B cells were lower in the RR and PD-L1-high group. The identified risk gene signature of locally advanced HNSCC and the potential therapeutic drug trichostatin A for the RR and PD-L1-high group are worth being further studied in a prospective homogenous cohort.

Genetic variants in MAPK10 modify renal cell carcinoma susceptibility and clinical outcomes

AIMS

The mitogen-activated protein kinase (MAPK) cascades integrate various upstream signals to regulate many cellular functions, including proliferation, differentiation, and survival. Dysregulation of these pathways has been implicated in the occurrence and progression of a variety of cancers.

MAIN METHODS

This study aimed to assess the association of 192 single nucleotide polymorphisms in 22 MAPK cascade genes with renal cell carcinoma (RCC) risk and survival in 312 patients and 318 controls.

KEY FINDINGS

After multiple testing correction and multivariate analysis, the minor T allele of MAPK10 rs12648265 remained associated with a lower risk of RCC (adjusted odds ratio 0.64, 95% confidence interval 0.50-0.82, P = 0.000426) and metastasis (adjusted hazard ratio 0.50, 95% confidence interval 0.30-0.82, P = 0.006). Presence of the rs12648265 T allele demonstrated a trend towards being associated with increased MAPK10 expression, and meta-analysis of four RCC datasets indicated that high MAPK10 expression is associated with a favourable prognosis. Furthermore, activation of MAPK10 by the potent agonist anisomycin inhibited RCC cell growth in vitro, suggesting an involvement of MAPK10 in RCC progression.

SIGNIFICANCE

In conclusion, MAPK10 may be a meaningful biomarker and a potential therapeutic target in RCC.