A Novel Lipid Prognostic Signature of ADCY2, LIPE, and OLR1 in Head and Neck Squamous Cell Carcinoma

A signature based on circadian rhythm-associated genes for the evaluation of prognosis and the tumour microenvironment in HNSCC

The morbidity and mortality rates of head and neck squamous cell carcinoma (HNSCC) remain high worldwide. Therefore, there is an urgent need to identify a new prognostic biomarker to guide the personalized treatment of HNSCC patients. Increasing evidence suggests that circadian rhythm genes play an important role in the development and progression of cancer. We aimed to explore the value of circadian rhythm genes in predicting prognosis and guiding the treatment of HNSCC. We first obtained a list of circadian rhythm genes from previous research. The sequencing data were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Finally, univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox proportional hazard analysis were performed to develop a prognostic signature (Circadian Rhythm-Related Gene Prognostic Index, CRRGPI) consisting of nine circadian rhythm genes. The signature exhibited good performance in predicting overall survival. Patients with low CRRGPI scores had lower metabolic activities and an active antitumour immunity ability. Additionally, a clinical cohort was used to further evaluate the ability of the CRRGPI to predict the efficacy of immune checkpoint inhibitors. In conclusion, the novel circadian rhythm-related gene signature can provide a precise prognostic evaluation with the potential capacity to guide individualized treatment regimens for HNSCC patients.

Oral squamous cell carcinoma gene patterns connected with RNA methylation for prognostic prediction

OBJECTIVES

To determine whether m6A/m1A/m5C/m7G/m6Am/Ψ-related genes influence the prognosis of a patient with oral squamous cell carcinoma.

MATERIALS AND METHODS

We investigated the changes in regulatory genes using publicly available data from The Cancer Genome Atlas. Consensus clustering by RNA methylation-related regulators was used to describe oral squamous cell carcinomas (OSCCs). Then, we developed the prediction model. The tumor microenvironment was investigated using ESTIMATE. Gene set enrichment analysis was used to determine whether pathways or cell types were enriched in different groups. The association between the model and immune-related risk scores was investigated using correlation analysis.

RESULTS

We found 22 gene signatures in this analysis and then developed a predictive model that reveals the genes that are highly connected to the overall survival of OSCC patients. The survival and death rates were substantially different in the two groups (high and low risk) classified by the risk scores. The validation cohort verified the phenotypic diversity and prognostic effects of these genes.

CONCLUSION

Our data reveal that immune cell infiltration, genetic mutation, and survival potential in OSCC patients are linked to m6A/m1A/m5C/m7G/m6Am/Ψ-related genes, and we constructed a dependable prognostic model for OSCC patients.

LncRNA SOX9-AS1 triggers a transcriptional program involved in lipid metabolic reprogramming, cell migration and invasion in triple-negative breast cancer

At the molecular level, triple-negative breast cancer (TNBC) is frequently categorized as PAM50 basal-like subtype, but despite the advances in molecular analyses, the clinical outcome for these subtypes is uncertain. Long non-coding RNAs (lncRNAs) are master regulators of genes involved in hallmarks of cancer, which makes them suitable biomarkers for breast cancer (BRCA) diagnosis and prognosis. Here, we evaluated the regulatory role of lncRNA SOX9-AS1 in these subtypes. Using the BRCA-TCGA cohort, we observed that SOX9-AS1 was significantly overexpressed in basal-like and TNBC in comparison with other BRCA subtypes. Survival analyzes showed that SOX9-AS1 overexpression was associated with a favorable prognosis in TNBC and basal-like patients. To study the functions of SOX9-AS1, we determined the expression levels in a panel of nine BRCA cell lines finding increased levels in MDA-MB-468 and HCC1187 TNBC. Using subcellular fractionation in these cell lines, we ascertained that SOX9-AS1 was located in the cytoplasmic compartment. In addition, we performed SOX9-AS1 gene silencing using two short-harping constructs, which were transfected in both cell models and performed a genome-wide RNA-seq analysis. Data showed that 351 lncRNAs and 740 mRNAs were differentially expressed in MDA-MB-468 while 56 lncRNAs and 100 mRNAs were modulated in HCC1187 cells (Log2FC < - 1.5 and > 1.5, p.adj value < 0.05). Pathway analysis revealed that the protein-encoding genes potentially regulate lipid metabolic reprogramming, and epithelial-mesenchymal transition (EMT). Expression of lipid metabolic-related genes LIPE, REEP6, GABRE, FBP1, SCD1, UGT2B11, APOC1 was confirmed by RT-qPCR. Functional analysis demonstrated that the knockdown of SOX9-AS1 increases the triglyceride synthesis, cell migration and invasion in both two TNBC cell lines. In conclusion, high SOX9-AS1 expression predicts an improved clinical course in patients, while the loss of SOX9-AS1 expression enhances the aggressiveness of TNBC cells.

Screening of Genes Related to Fat Deposition of Pekin Ducks Based on Transcriptome Analysis

Subcutaneous fat deposition is an important index with which to evaluate meat-producing ducks, and affects their meat quality and feed conversion rate. Studying the differentially expressed genes in subcutaneous fat will help to comprehensively understand the potential mechanisms regulating fat deposition in ducks. In this study, 72 Nankou 1 Pekin Ducks and 72 Jingdian Pekin Ducks (half male and half female) at 42 days of age were selected for slaughter performance and transcriptome analysis. The results showed that the breast-muscle yield of Nankou 1 ducks was significantly higher than that of Jingdian ducks, but that the abdominal fat yield and subcutaneous fat yield were higher than that of Jingdian ducks. Thousands of DEGs, including many important genes involved in fat metabolism regulation, were detected by transcriptome. KEGG enrichment analysis showed that the DEGs were significantly enriched on pathways such as regulation of lipolysis in adipocytes, primary bile acid biosynthesis, and biosynthesis of unsaturated fatty acids. SCD, FGF7, LTBP1, PNPLA3, ADCY2, and ACOT8 were selected as candidate genes for regulating subcutaneous fat deposition. The results indicated that Nankou 1 had superior fat deposition ability compared to Jingdian ducks, and that the candidate genes regulated fat deposition by regulating fat synthesis and decomposition.

Identification of potential biomarkers and survival analysis for oral squamous cell carcinoma: A transcriptomic study

OBJECTIVE

Oral squamous cell carcinoma (OSCC) is one of the most common neoplasms worldwide. The current study aimed to identify potential biomarkers associated with OSCC survival.

MATERIALS AND METHODS

Differentially expressed genes (DEGs) in atypical OSCC cases were identified using two public datasets: The Cancer Genome Atlas and the Gene Expression Omnibus database. Receiver operating characteristic (ROC) analysis was performed to identify the cutoff, and the candidate DEGs related to survival. Kaplan-Meier and Cox regression analysis using the categorized genes were employed to identify genes that impact the overall survival in OSCC.

RESULTS

A total of 263 OSCC samples and 105 healthy tissues were used to identify 295 upregulated and 131 downregulated genes expressed only in non-smokers. ROC analyses identified 25 candidate genes associated with death. Survival analyses demonstrated that the following DEGs, namely CSTA, FGFR2, MMP19, OLR1, PCSK1, RAMP2, and CGB5, are potential OSCC prognostic factors.

CONCLUSION

We found that CSTA, FGFR2, MMP19, OLR1, PCSK1, RAMP2, and CGB5 are associated with a low survival rate in OSCC. However, further studies are needed to validate our findings and facilitate the development of these factors as potential biomarkers for OSCC survival.

Comparative Analyses Reveal the Genetic Mechanism of Ambergris Production in the Sperm Whale Based on the Chromosome-Level Genome

Sperm whales are a marine mammal famous for the aromatic substance, the ambergris, produced from its colon. Little is known about the biological processes of ambergris production, and this study aims to investigate the genetic mechanism of ambergris production in the sperm whale based on its chromosome-level genome. Comparative genomics analyses found 1207 expanded gene families and 321 positive selected genes (PSGs) in the sperm whale, and functional enrichment analyses suggested revelatory pathways and terms related to the metabolism of steroids, terpenoids, and aldosterone, as well as microbiota interaction and immune network in the intestine. Furthermore, two sperm-whale-specific missense mutations (Tyr393His and Leu567Val) were detected in the PSG LIPE, which has been reported to play vital roles in lipid and cholesterol metabolism. In total, 46 CYP genes and 22 HSD genes were annotated, and then mapped to sperm whale chromosomes. Furthermore, phylogenetic analysis of CYP genes in six mammals found that CYP2E1, CYP51A and CYP8 subfamilies exhibited relative expansion in the sperm whale. Our results could help understand the genetic mechanism of ambergris production, and further reveal the convergent evolution pattern among animals that produce similar odorants.

Effects of immune inflammation in head and neck squamous cell carcinoma: Tumor microenvironment, drug resistance, and clinical outcomes

Background: Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor with a very high mortality rate, and a large number of studies have confirmed the correlation between inflammation and malignant tumors and the involvement of inflammation-related regulators in the progression of HNSCC. However, a prognostic model for HNSCC based on genes involved in inflammatory factors has not been established. Methods: First, we downloaded transcriptome data and clinical information from patients with head and neck squamous cell carcinoma from TCGA and GEO (GSE41613) for data analysis, model construction, and differential gene expression analysis, respectively. Genes associated with inflammatory factors were screened from published papers and intersected with differentially expressed genes to identify differentially expressed inflammatory factor-related genes. Subgroups were then typed according to differentially expressed inflammatory factor-related genes. Univariate, LASSO and multivariate Cox regression algorithms were subsequently applied to identify prognostic genes associated with inflammatory factors and to construct prognostic prediction models. The predictive performance of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic curve (ROC). Subsequently, we analyzed differences in immune composition between patients in the high and low risk groups by immune infiltration. The correlation between model genes and drug sensitivity (GSDC and CTRP) was also analyzed based on the GSCALite database. Finally, we examined the expression of prognostic genes in pathological tissues, verifying that these genes can be used to predict prognosis. Results: Using univariate, LASSO, and multivariate cox regression analyses, we developed a prognostic risk model for HNSCC based on 13 genes associated with inflammatory factors (ITGA5, OLR1, CCL5, CXCL8, IL1A, SLC7A2, SCN1B, RGS16, TNFRSF9, PDE4B, NPFFR2, OSM, ROS1). Overall survival (OS) of HNSCC patients in the low-risk group was significantly better than that in the high-risk group in both the training and validation sets. By clustering, we identified three molecular subtypes of HNSCC carcinoma (C1, C2, and C3), with C1 subtype having significantly better OS than C2 and C3 subtypes. ROC analysis suggests that our model has precise predictive power for patients with HNSCC. Enrichment analysis showed that the high-risk and low-risk groups showed strong immune function differences. CIBERSORT immune infiltration score showed that 25 related and differentially expressed inflammatory factor genes were all associated with immune function. As the risk score increases, specific immune function activation decreases in tumor tissue, which is associated with poor prognosis. We also screened for susceptibility between the high-risk and low-risk groups and showed that patients in the high-risk group were more sensitive to talazoparib-1259, camptothecin-1003, vincristine-1818, Azd5991-1720, Teniposide-1809, and Nutlin-3a (-) -1047.Finally, we examined the expression of OLR1, SCN1B, and PDE4B genes in HNSCC pathological tissues and validated that these genes could be used to predict the prognosis of HNSCC. Conclusion: In this experiment, we propose a prognostic model for HNSCC based on inflammation-related factors. It is a non-invasive genomic characterization prediction method that has shown satisfactory and effective performance in predicting patient survival outcomes and treatment response. More interdisciplinary areas combining medicine and electronics will be explored in the future.

Identification of adenylate cyclase 2 methylation in bladder cancer with implications for prognosis and immunosuppressive microenvironment

Background

The incidence and mortality of bladder cancer (BCa) are increasing, while the existing diagnostic methods have limitations. Therefore, for early detection and response prediction, it is crucial to improve the prognosis and treatment strategies. However, with existing diagnostic methods, detecting BCa in the early stage is challenging. Hence, novel biomarkers are urgently needed to improve early diagnosis and treatment efficiency.

Methods

The gene expression profile and gene methylation profile dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), differentially methylated genes (DMGs), and methylation-regulated differentially expressed genes (MeDEGs) were gradually identified. A cancer genome map was obtained using online gene expression profile interaction analysis, and survival implications were produced using Kaplan-Meier survival analysis. GSEA was employed to predict the marker pathways where DEGs were significantly involved. The study used bisulfite PCR amplification combined with bisulfite amplicon sequencing (BSAS) to screen for methylation analysis of multiple candidate regions of the adenylate cyclase 2 (ADCY2) based on the sequence design of specific gene regions and CpG islands.

Results

In this study, DEGs and DMGs with significantly up- or down-regulated expression were selected. The intersection method was used to screen the MeDEGs. The interaction network group in STRING was then visualized using Cytoscape, and the PPI network was constructed to identify the key genes. The key genes were then analyzed using functional enrichment. To compare the relationship between key genes and the prognosis of BCa patients, we further investigated ADCY2 and found that ADCY2 can be a potential clinical biomarker in BCa prognosis and immunotherapy response prediction. In human BCa 5637 and MGH1 cells, we developed and verified the effectiveness of ADCY2 primers using BSAS technology. The findings revealed that the expression of ADCY2 is highly regulated by the methylation of the promoter regions.

Conclusion

This study revealed that increased expression of ADCY2 was significantly correlated with increased tumor heterogeneity, predicting worse survival and immunotherapy response in BCa patients.

A lipid metabolism-related risk signature for patients with gliomas constructed with TCGA and CGGA data

Lipid metabolism affects cell proliferation, differentiation, membrane homeostasis and drug resistance. An in-depth exploration of lipid metabolism in gliomas might provide a novel direction for gliomas treatment. A lipid metabolism-related risk signature was constructed in our study to assess the prognosis of patients with gliomas. Lipid metabolism-related genes were extracted. Differentially expressed genes (DEGs) were screened, and a risk signature was built. The ability of the risk signature to predict the outcomes of patients with gliomas was assessed using the log-rank test and Cox regression analysis. The relationships between immunological characteristics, drug sensitivity and the risk score were evaluated, and the risk-related mechanisms were also estimated. Twenty lipid metabolism-related DEGs associated with the patient prognosis were included in the risk signature. The survival rate of high-risk patients was worse than that of low-risk patients. The risk score independently predicted the outcomes of patients. Immunological parameters, drug sensitivity, immunotherapy benefits, and numerous molecular mechanisms were significantly associated with the risk score. A lipid metabolism-related risk signature might effectively assess the prognosis of patients with gliomas. The risk score might guide individualized treatment and further clinical decision-making for patients with gliomas.