Comprehensive analysis of transcriptome data for identifying biomarkers and therapeutic targets in head and neck squamous cell carcinoma

Exosomes regulate doxorubicin resistance in breast cancer via miR-34a-5p/NOTCH1

Breast cancer (BRCA) is the most common cancer among women. Adriamycin (ADR), also known as doxorubicin (Dox), is a commonly used chemotherapeutic agent for BRCA patients, however, the susceptibility of tumor cells to develop resistance to Dox has severely limited its clinical use. One new promising therapeutic target for breast cancer patients is exosomes. The objective of this study was to investigate the role of exosomes in regulating Dox resistance in BRCA. In this study, the exosomes from both types of cells were extracted by differential centrifugation. The effect of exosomes on drug resistance was assessed by laser confocal microscopy, MTT assay, and qRT-PCR. The miRNA was transfected into cells using Lipofectamine 2000, which was then evaluated for downstream genes and changes in drug resistance. Exosomes from MCF-7 cells (MCF-7/exo) and MCF-7/ADR cells (ADR/exo) were effectively extracted in this study. The ADR/exo was able to endocytose MCF-7 cells and make them considerably more resistant to Dox. Moreover, we observed a significant difference in miR-34a-5p expression in MCF-7/ADR and ADR/exo compared to MCF-7 and MCF-7/exo. Among the miR-34a-5p target genes, NOTCH1 displayed a clear change with a negative correlation. In addition, when miR-34a-5p expression was elevated in MCF-7/ADR cells, the expression of miR-34a-5p in ADR/exo was also enhanced alongside NOTCH1, implying that exosomes may carry miRNA into and out of cells and perform their function. In conclusion, exosomes can influence Dox resistance in breast cancer cells by regulating miR-34a-5p/NOTCH1. These findings provide novel insights for research into the causes of tumor resistance and the enhancement of chemotherapy efficacy in breast cancer.

A bioinformatics approach of specificity protein transcription factors in head and neck squamous cell carcinoma

Background and purpose

The seventh most common type of cancer with increasing diagnosis rates around the world is head and neck squamous cell carcinoma (HNSCC). Specificity proteins (SPs) have been known for their role in the regulation of cellular division, growth, and apoptotic pathways in various cancers. In this work, we analyzed the expression levels of SPs in HNSCC to assess their diagnostic and prognostic biomarker potential.

Experimental approach

Differential gene expression and correlation analysis methods were used to determine the top dysregulated genes in HNSCC. Functional enrichment and protein-protein interaction analyses were done with the DAVID database and Cytoscape software to understand their function and biological processes. Receiver operating test, logistic regression, and Cox regression analyses were performed to check SP genes' diagnostic and prognostic potential.

Findings/Results

SP1 (LogFC = -0.27, P = 0.0013) and SP2 (LogFC = -0.20, P = 0.0019) genes were upregulated in HNSCC samples, while SP8 (LogFC = 2.57, P < 0.001) and SP9 (LogFC = 2.57, P < 0.001) genes were downregulated in cancer samples. A moderate positive correlation was observed among the expression levels of SP1, SP2, and SP3 genes. The SP8 and SP9 genes with AUC values of 0.79 and 0.75 demonstrated diagnostic potential which increased to 0.84 when both genes were assessed by logistic regression test. Also, the SP1 gene held a marginally significant prognostic potential.

Conclusion and implications

Our findings clarify the potential of SP transcription factors as candidate diagnostic and prognostic biomarkers for early screening and treatment of HNSCC.

The Pivotal Function of SLC16A1 and SLC16A1-AS1 in Cancer Progress: Molecular Pathogenesis and Prognosis

More than 300 membranes make up the SLC family of transporters, utilizing an ion gradient or electrochemical potential difference to move their substrates across biological membranes. The SLC16 gene family contains fourteen members. Proton-linked transportation of monocarboxylates can be promoted by the transporters MCT1, which the SLC16A1 gene family encodes. Glycolysis is constitutively up-regulated in cancer cells, and the amount of lactate produced as a result is correlated with prognosis. Further speaking, SLC16A1 plays an essential role in controlling the growth and spread of tumors, according to mounting evidence. Additionally, LncRNAs are the collective term for all genes that produce RNA transcripts longer than 200 nucleotides but do not convert into proteins. It has steadily developed into a hub for research, offering an innovative approach to tumor study as technology related to molecular biology advances. The growing study has uncovered SLC16A1-AS1, an RNA that acts as an antisense to SLC16A1, which is erroneously expressed in various types of cancers. Therefore, we compiled the most recent information on the physiological functions and underlying processes of SLC16A1 and the LncRNA SLC16A1-AS1 during tumor development to explore their impact on cancer treatment and prognosis. We compiled the most recent information on the physiological functions and underlying processes of SLC16A1 and the LncRNA SLC16A1-AS1 during tumor development to explore their impact on cancer treatment and prognosis. Relevant studies were retrieved and collected through the PubMed system. After determining SLC16A1 and SLC16A1-AS1 as the research object, we found a close relationship between SLC16A1 and tumorigenesis as well as the influencing factors through the analysis of the research articles. SLC16A1 regulates lactate chemotaxis while uncovering SLC16A1- AS1 as an antisense RNA acting through multiple pathways; they affect the metabolism of tumor cells and have an impact on the prognosis of patients with various cancers.

PLAU and LAMC2 can predict a poor prognosis in patients with HNSCC

Objectives: Head and neck squamous cell carcinoma (HNSCC) is the most common malignancy of the head and neck. However, the molecular mechanisms governing the development of HNSCC have not been fully elucidated. Materials and Methods: Differentially expressed genes (DEGs) were screened out from The Cancer Genome Atlas (TCGA) and GSE23036 datasets. Weighted gene coexpression network analysis (WGCNA) was used to reveal the correlations among genes and to search for significantly correlated gene modules. The expression levels of genes in HNSCC and normal samples according to antibody-based detected methods was assessed by utilizing the Human Protein Atlas (HPA). The impact of the selected hub genes on the prognosis of HNSCC patients was assessed by analysing immunohistochemistry (IHC) and immunofluorescence (IF) expression levels and clinical data. Results: Twenty-four genes positively correlated with tumour status and 15 genes negatively correlated with tumour status were screened out by WGCNA. PLAU and LAMC2 were associated with a poor prognosis in patients with HNSCC and were finally screened out and verified by GEPIA and HPA database analysis. Immunohistochemistry of samples collected from 175 patients with HNSCC and subsequent statistical analysis also showed that PLAU and LAMC2 were associated with a poor prognosis in patients with HNSCC, and the levels of these two factors were positively correlated. The expression and co-localization of PLAU and LAMC2 in HNSCC tissues were confirmed by double immunofluorescence labeling. Conclusions: There was a positive correlation between PLAU and LAMC2 expression in HNSCC samples, and PLAU and LAMC2 might be independent prognostic biomarkers for HNSCC.

Comprehensive analysis of pan-cancer reveals the potential of SLC16A1 as a prognostic and immunological biomarker

SLC16A1 plays an important role in the development of multiple cancer types. Pan-cancer analysis may have significant impacts on the exploration of the relationship between SLC16A1 gene expression, prognosis and the molecular mechanisms of tumorigenesis. In this study, through the analysis of TCGA and GEO datasets, we explored the expression level and survival prognosis of SLC16A1 in pan-cancer, and further explored the differences in SLC16A1 gene mutation, methylation, and phosphorylation between tumor and normal tissues. In addition, we focused on the biological function of this gene and the relationship between the prognosis and immune infiltration by immune infiltration analysis and enrichment analysis, in order to evaluate the diagnostic and prognostic significance of SLC16A1 in carcinomas. The study found that SLC16A1 was highly expressed in 14 kinds of tumors, and there were statistically significant differences in the prognosis of 9 tumors. The phosphorylation level of S467 increased in OV, RCC, and UCEC. There was a statistically negative correlation between the CD8+ T-cell infiltration level and the SLC16A1 expression in HNSC, LUSC, SARC, TGCT, and KIRC. The cancer-related fibroblasts were positively correlated with SLC16A1 expression in BLCA, BRCA, KIRC, KIRP, PAAD, PCPG, and THCA. The enrichment analysis indicated that the tumorigenesis mechanism of this gene was mainly related to "glycolysis and glucose metabolism synthesis." SLC16A1 was a promising prognostic and immunological biomarker in pan-cancer.

Integration of transcriptomics and metabolomics reveals a novel gene signature guided by FN1 associated with immune response in oral squamous cell carcinoma tumorigenesis

PURPOSE

Oral squamous cell carcinomas (OSCCs) are primary head and neck malignant tumours with a high incidence and mortality. However, the molecular mechanisms involved in OSCC tumorigenesis are not fully understood.

METHODS

OSCC and paired para-carcinoma samples were collected and used to perform multi-omics study. Transcriptomic analysis was used to reveal significant alterations in inflammatory and immune processes in OSCC. Ingenuity Pathway Analysis (IPA) combined with the LASSO Cox algorithm was used to identify and optimize a crucial gene signature. Metabolomics analysis was performed to identify the important metabolites which linked to the crucial gene signature. The public data TCGA-HNSCC cohort was used to perform the multiple bioinformatic analysis.

RESULTS

These findings identified a FN1-mediated crucial network that was composed of immune-relevant genes (FN1, ACP5, CCL5, COL1A1, THBS1, BCAT1, PLAU, IGF2BP3, TNF, CSF2, CXCL1 and CXCL5) associated with immune infiltration and influences the tumour microenvironment, which may contribute to OSCC tumorigenesis and progression. Moreover, we integrated the relevant genes with altered metabolites identified by metabolic profiling and identified 7 crucial metabolites (Glu-Glu-Lys, Ser-Ala, Ser-Ala, N-(octadecanoyl) sphing-4-enine-1-phosphocholine, N-methylnicotinamide, pyrrhoxanthinol and xanthine) as potential downstream targets of the FN1-associated gene signature in OSCC. Importantly, FN1 expression is positively correlated with immune infiltration levels in HNSCC, which was confirmed at the single-cell level.

CONCLUSIONS

Overall, these results revealed the differential genetic and metabolic patterns associated with OSCC tumorigenesis and identified an essential molecular network that plays an oncogenic role in OSCC by affecting amino acid and purine metabolism. These genes and metabolites might, therefore, serve as predictive biomarkers of survival outcomes and potential targets for therapeutic intervention in OSCC.

Current and Future Biomarkers for Immune Checkpoint Inhibitors in Head and Neck Squamous Cell Carcinoma

With the introduction of immunotherapy, significant improvement has been made in the treatment of head and neck squamous cell carcinoma (HNSCC). However, only a small subset of patients with HNSCC benefit from immunotherapy. The current biomarker, a programmed cell death protein ligand 1 (PD-L1) expression that is widely used in treatment decision making for advanced HNSCC, has only a moderate predictive value. Additionally, PD-L1-based assay has critical inherent limitations due to its highly dynamic nature and lack of standardization. With the advance in molecular techniques and our understanding of biology, more reliable, reproducible, and practical novel biomarkers are being developed. These include but are not limited to neoantigen/mutation characteristics, immune transcriptomes, tumor-infiltrating immune cell composition, cancer epigenomic, proteomics and metabolic characteristics, and plasma-based and organoid assays.

Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins

Relapse after surgery for oral squamous cell carcinoma (OSCC) contributes significantly to morbidity, mortality and poor outcomes. The current histopathological diagnostic techniques are insufficiently sensitive for the detection of oral cancer and minimal residual disease in surgical margins. We used whole-transcriptome gene expression and small noncoding RNA profiles from tumour, close margin and distant margin biopsies from 18 patients undergoing surgical resection for OSCC. By applying multivariate regression algorithms (sPLS-DA) suitable for higher dimension data, we objectively identified biomarker signatures for tumour and marginal tissue zones. We were able to define molecular signatures that discriminated tumours from the marginal zones and between the close and distant margins. These signatures included genes not previously associated with OSCC, such as MAMDC2, SYNPO2 and ARMH4. For discrimination of the normal and tumour sampling zones, we were able to derive an effective gene-based classifying model for molecular abnormality based on a panel of eight genes (MMP1, MMP12, MYO1B, TNFRSF12A, WDR66, LAMC2, SLC16A1 and PLAU). We demonstrated the classification performance of these gene signatures in an independent validation dataset of OSCC tumour and marginal gene expression profiles. These biomarker signatures may contribute to the earlier detection of tumour cells and complement existing surgical and histopathological techniques used to determine clear surgical margins.

Bioinformatics Analysis of mRNAs and miRNAs for Identifying Potential Biomarkers in Lung Adenosquamous Carcinoma

Background

Lung adenosquamous carcinoma (LASC) is a special type of lung cancer. LASC is a malignant tumor with strong aggressiveness and a poor prognosis. Previous studies have revealed that microRNAs (miRNAs) are widely involved in the development of tumors by targeting mRNA. This study is aimed at identifying the key mRNAs and miRNAs of LASC and constructing miRNA-mRNA networks for deeply comprehending the latent molecular mechanisms.

Methods

mRNA dataset (GSE51852) and miRNA dataset (GSE51853) were extracted and downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were picked out by the GEO2R web tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted in the DAVID database. The protein-protein interaction (PPI) network was performed and analyzed by using the STRING database and Cytoscape software, respectively. TransmiR v2.0 was applied to predict potential transcription factors of miRNAs. The target genes of DEMs were predicted in the miRWalk database.

Results

In comparison to normal tissues, a total of 1458 DEGs (511 upregulated and 947 downregulated) and 13 DEMs (5 upregulated and 8 downregulated) were screened out in LASC tissues. The PPI network of the DEGs displayed five key modules and seventeen hub genes. Six target genes of the DEMs were predicted, and five essential miRNA-mRNA regulatory pairs were established. Ensuingly, CENPF, one of the target genes, was also the hub genes of GSE51852, which was obtained from MCODE and cytoHubba and regulated by hsa-miR-205.

Conclusions

We constructed the miRNA-mRNA regulatory pairs, which are helpful to study the potential regulatory mechanisms and find out promising diagnosis biomarkers and therapeutic targets for LASC.

Exploration and validation of related hub gene expression during SARS-CoV-2 infection of human bronchial organoids

Background

In the novel coronavirus pandemic, the high infection rate and high mortality have seriously affected people’s health and social order. To better explore the infection mechanism and treatment, the three-dimensional structure of human bronchus has been employed in a better in-depth study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Methods

We downloaded a separate microarray from the Integrated Gene Expression System (GEO) on a human bronchial organoids sample to identify differentially expressed genes (DEGS) and analyzed it with R software. After processing with R software, Gene Ontology (GO) and Kyoto PBMCs of Genes and Genomes (KEGG) were analyzed, while a protein–protein interaction (PPI) network was constructed to show the interactions and influence relationships between these differential genes. Finally, the selected highly connected genes, which are called hub genes, were verified in CytoHubba plug-in.

Results

In this study, a total of 966 differentially expressed genes, including 490 upregulated genes and 476 downregulated genes were used. Analysis of GO and KEGG revealed that these differentially expressed genes were significantly enriched in pathways related to immune response and cytokines. We construct protein-protein interaction network and identify 10 hub genes, including IL6, MMP9, IL1B, CXCL8, ICAM1, FGF2, EGF, CXCL10, CCL2, CCL5, CXCL1, and FN1. Finally, with the help of GSE150728, we verified that CXCl1, CXCL8, CXCL10, CCL5, EGF differently expressed before and after SARS-CoV-2 infection in clinical patients.

Conclusions

In this study, we used mRNA expression data from GSE150819 to preliminarily confirm the feasibility of hBO as an in vitro model to further study the pathogenesis and potential treatment of COVID-19. Moreover, based on the mRNA differentiated expression of this model, we found that CXCL8, CXCL10, and EGF are hub genes in the process of SARS-COV-2 infection, and we emphasized their key roles in SARS-CoV-2 infection. And we also suggested that further study of these hub genes may be beneficial to treatment, prognostic prediction of COVID-19.