Identification of Potential Biomarkers and Survival Analysis for Head and Neck Squamous Cell Carcinoma Using Bioinformatics Strategy: A Study Based on TCGA and GEO Datasets

Computed tomography-based radiomics predicts the fibroblast-related gene EZH2 expression level and survival of hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. The primary treatment strategies for HCC currently include liver transplantation and surgical resection. However, these methods often yield unsatisfactory outcomes, leading to a poor prognosis for many patients. This underscores the urgent need to identify and evaluate novel therapeutic targets that can improve the prognosis and survival rate of HCC patients.

AIM

To construct a radiomics model that can accurately predict the EZH2 expression in HCC.

METHODS

Gene expression, clinical parameters, HCC-related radiomics, and fibroblast-related genes were acquired from public databases. A gene model was developed, and its clinical efficacy was assessed statistically. Drug sensitivity analysis was conducted with identified hub genes. Radiomics features were extracted and machine learning algorithms were employed to generate a radiomics model related to the hub genes. A nomogram was used to illustrate the prognostic significance of the computed Radscore and the hub genes in the context of HCC patient outcomes.

RESULTS

EZH2 and NRAS were independent predictors for prognosis of HCC and were utilized to construct a predictive gene model. This model demonstrated robust performance in diagnosing HCC and predicted an unfavorable prognosis. A negative correlation was observed between EZH2 expression and drug sensitivity. Elevated EZH2 expression was linked to poorer prognosis, and its diagnostic value in HCC surpassed that of the risk model. A radiomics model, developed using a logistic algorithm, also showed superior efficiency in predicting EZH2 expression. The Radscore was higher in the group with high EZH2 expression. A nomogram was constructed to visually demonstrate the significant roles of the radiomics model and EZH2 expression in predicting the overall survival of HCC patients.

CONCLUSION

EZH2 plays significant roles in diagnosing HCC and therapeutic efficacy. A radiomics model, developed using a logistic algorithm, efficiently predicted EZH2 expression and exhibited strong correlation with HCC prognosis.

Insights into the Gene Expression Profile of Classical Hodgkin Lymphoma: A Study towards Discovery of Novel Therapeutic Targets

Classical Hodgkin lymphoma (cHL) is a common B-cell cancer and a significant health concern, especially in Western and Asian countries. Despite the effectiveness of chemotherapy, many relapse cases are being reported, highlighting the need for improved treatments. This study aimed to address this issue by discovering biomarkers through the analysis of gene expression data specific to cHL. Additionally, potential anticancer inhibitors were explored to target the discovered biomarkers. This study proceeded by retrieving microarray gene expression data from cHL patients, which was then analyzed to identify significant differentially expressed genes (DEGs). Functional and network annotation of the upregulated genes revealed the active involvement of matrix metallopeptidase 12 (MMP12) and C-C motif metallopeptidase ligand 22 (CCL22) genes in the progression of cHL. Additionally, the mentioned genes were found to be actively involved in cancer-related pathways, i.e., oxidative phosphorylation, complement pathway, myc_targets_v1 pathway, TNFA signaling via NFKB, etc., and showed strong associations with other genes known to promote cancer progression. MMP12, topping the list with a logFC value of +6.6378, was selected for inhibition using docking and simulation strategies. The known anticancer compounds were docked into the active site of the MMP12 molecular structure, revealing significant binding scores of -7.7 kcal/mol and -7.6 kcal/mol for BDC_24037121 and BDC_27854277, respectively. Simulation studies of the docked complexes further supported the effective binding of the ligands, yielding MMGBSA and MMPBSA scores of -78.08 kcal/mol and -82.05 kcal/mol for MMP12-BDC_24037121 and -48.79 kcal/mol and -49.67 kcal/mol for MMP12-BDC_27854277, respectively. Our findings highlight the active role of MMP12 in the progression of cHL, with known compounds effectively inhibiting its function and potentially halting the advancement of cHL. Further exploration of downregulated genes is warranted, as associated genes may play a role in cHL. Additionally, CCL22 should be considered for further investigation due to its significant role in the progression of cHL.

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

Unveiling Immune-related feature genes for Alzheimer's disease based on machine learning

The identification of diagnostic and therapeutic biomarkers for Alzheimer's Disease (AD) remains a crucial area of research. In this study, utilizing the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we identified RHBDF2 and TNFRSF10B as feature genes associated with AD pathogenesis. Analyzing data from the GSE33000 dataset, we revealed significant upregulation of RHBDF2 and TNFRSF10B in AD patients, with correlations to age and gender. Interestingly, their expression profile in AD differs notably from that of other neurodegenerative conditions. Functional analysis unveiled their involvement in immune response and various signaling pathways implicated in AD pathogenesis. Furthermore, our study demonstrated the potential of RHBDF2 and TNFRSF10B as diagnostic biomarkers, exhibiting high discrimination power in distinguishing AD from control samples. External validation across multiple datasets confirmed the robustness of the diagnostic model. Moreover, utilizing molecular docking analysis, we identified dinaciclib and tanespimycin as promising small molecule drugs targeting RHBDF2 and TNFRSF10B for potential AD treatment. Our findings highlight the diagnostic and therapeutic potential of RHBDF2 and TNFRSF10B in AD management, shedding light on novel strategies for precision medicine in AD.

Ferroptosis-Related Genes Are Associated with Radioresistance and Immune Suppression in Head and Neck Cancer

Background: Ferroptosis is associated with tumor development; however, its contribution to radioresistant head and neck cancer (HNC) remains unclear. In this study, we used bioinformatics analysis and in vitro testing to explore ferroptosis-related genes associated with HNCs radiosensitivity. Materials and Methods: GSE9714, GSE90761, and The Cancer Genome Atlas (TCGA) datasets were searched to identify ferroptosis-related differentially expressed genes between radioresistant and radiosensitive HNCs or radiation-treated and nonradiation-treated HNCs. A protein-protein interaction analysis on identified hub genes was then performed. Receiver operating characteristic curves and Kaplan-Meier survival analysis were used to assess the diagnostic and prognostic potential of the hub genes. Cell counting kit-8, transwell assay, and flow cytometry were applied to examine the role of hub gene collagen type IV, alpha1 chain (COL4A1) on the proliferation, migration, invasion, and apoptosis of TU686 cells. Results: Hub genes MMP10, MMP1, COL4A1, IFI27, and INHBA showed diagnostic potential for HNC and were negatively correlated with overall survival and disease-free survival in the TCGA dataset. Also, IL-1B, IFI27, INHBA, and COL4A1 mRNA levels were significantly increased in TCGA patients with advanced clinical stages or receiving radiotherapy, whereas COL4A1, MMP10, and INHBA expressions were negatively correlated with immune infiltration. Furthermore, the knockdown of COL4A1 inhibited cell proliferation, migration, and invasion while promoting apoptosis in TU686 cells. Conclusion: Ferroptosis-related hub genes, such as COL4A1, are potential diagnostic and prognostic indicators as well as therapeutic targets for HNC.

Identification of Plausible Candidates in Prostate Cancer Using Integrated Machine Learning Approaches

Background

Currently, prostate-specific antigen (PSA) is commonly used as a prostate cancer (PCa) biomarker. PSA is linked to some factors that frequently lead to erroneous positive results or even needless biopsies of elderly people.

Objectives

In this pilot study, we undermined the potential genes and mutations from several databases and checked whether or not any putative prognostic biomarkers are central to the annotation. The aim of the study was to develop a risk prediction model that could help in clinical decision-making.

Methods

An extensive literature review was conducted, and clinical parameters for related comorbidities, such as diabetes, obesity, as well as PCa, were collected. Such parameters were chosen with the understanding that variations in their threshold values could hasten the complicated process of carcinogenesis, more particularly PCa. The gathered data was converted to semi-binary data (-1, -0.5, 0, 0.5, and 1), on which machine learning (ML) methods were applied. First, we cross-checked various publicly available datasets, some published RNA-seq datasets, and our whole-exome sequencing data to find common role players in PCa, diabetes, and obesity. To narrow down their common interacting partners, interactome networks were analysed using GeneMANIA and visualised using Cytoscape, and later cBioportal was used (to compare expression level based on Z scored values) wherein various types of mutation w.r.t their expression and mRNA expression (RNA seq FPKM) plots are available. The GEPIA 2 tool was used to compare the expression of resulting similarities between the normal tissue and TCGA databases of PCa. Later, top-ranking genes were chosen to demonstrate striking clustering coefficients using the Cytoscape-cytoHubba module, and GEPIA 2 was applied again to ascertain survival plots.

Results

Comparing various publicly available datasets, it was found that BLM is a frequent player in all three diseases, whereas comparing publicly available datasets, GWAS datasets, and published sequencing findings, SPFTPC and PPIMB were found to be the most common. With the assistance of GeneMANIA, TMPO and FOXP1 were found as common interacting partners, and they were also seen participating with BLM.

Conclusion

A probabilistic machine learning model was achieved to identify key candidates between diabetes, obesity, and PCa. This, we believe, would herald precision scale modeling for easy prognosis.

Advanced stage, high-grade primary tumor ovarian cancer: a multi-omics dissection and biomarker prediction process

Ovarian cancer (OC) incidence and mortality rates continue to escalate globally. Early detection of OC is challenging due to extensive metastases and the ambiguity of biomarkers in advanced High-Grade Primary Tumors (HGPTs). In the present study, we conducted an in-depth in silico analysis in OC cell lines using the Gene Expression Omnibus (GEO) microarray dataset with 53 HGPT and 10 normal samples. Differentially-Expressed Genes (DEGs) were also identified by GEO2r. A variety of analyses, including gene set enrichment analysis (GSEA), ChIP enrichment analysis (ChEA), eXpression2Kinases (X2K) and Human Protein Atlas (HPA), elucidated signaling pathways, transcription factors (TFs), kinases, and proteome, respectively. Protein-Protein Interaction (PPI) networks were generated using STRING and Cytoscape, in which co-expression and hub genes were pinpointed by the cytoHubba plug-in. Validity of DEG analysis was achieved via Gene Expression Profiling Interactive Analysis (GEPIA). Of note, KIAA0101, RAD51AP1, FAM83D, CEP55, PRC1, CKS2, CDCA5, NUSAP1, ECT2, and TRIP13 were found as top 10 hub genes; SIN3A, VDR, TCF7L2, NFYA, and FOXM1 were detected as predominant TFs in HGPTs; CEP55, PRC1, CKS2, CDCA5, and NUSAP1 were identified as potential biomarkers from hub gene clustering. Further analysis indicated hsa-miR-215-5p, hsa-miR-193b-3p, and hsa-miR-192-5p as key miRNAs targeting HGPT genes. Collectively, our findings spotlighted HGPT-associated genes, TFs, miRNAs, and pathways as prospective biomarkers, offering new avenues for OC diagnostic and therapeutic approaches.

UBE2C serves as a prognosis biomarker of uterine corpus endometrial carcinoma via promoting tumor migration and invasion

The biological functions of ubiquitin-conjugating enzymes E2 (UBE2) family members in uterine corpus endometrial carcinoma (UCEC) remains unclear. Our study aimed to systematically analyze the expression patterns, prognostic value, biological functions and molecular regulatory mechanisms of UBE2 family in UCEC. Among nine screened UBE2 family members associated with UCEC, UBE2C was the most significantly overexpressed gene with poor prognosis. High expression levels of UBE2C in UCEC was correlated with stages, histological subtypes, patient's menopause status and TP53 mutation. Three molecules (CDC20, PTTG1 and AURKA), were identified as the key co-expression proteins of UBE2C. The generic alterations (mutation, amplification) and DNA hypomethylation might contribute to UBE2C's high expression in UCEC. Furthermore, in vitro experiments showed that the interference of UBE2C inhibited the migration and invasion of endometrial cancer cells, while partially impact cell proliferation and didn't impact the expression of epithelial-mesenchymal transition (EMT) markers. Using comprehensive bioinformatics analysis and in vitro experiments, our study provided a novel insight into the oncogenic role of UBE2 family, specifically UBE2C in UCEC. UBE2C might serve as an effective biomarker to predict poor prognosis and a potential therapeutic target in clinical practice.

GOLM1 and FAM49B: Potential Biomarkers in HNSCC Based on Bioinformatics and Immunohistochemical Analysis

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide. We aimed to identify potential genetic markers that could predict the prognosis of HNSCC. A total of 44 samples of GSE83519 from Gene Expression Omnibus (GEO) datasets and 546 samples of HNSCC from The Cancer Genome Atlas (TCGA) were adopted. The differently expressed genes (DEGs) of the samples were screened by GEO2R. We integrated the expression information of DEGs with clinical data from GES42743 using the weighted gene co-expression network analysis (WGCNA). A total of 17 hub genes were selected by the module membership (|MM| > 0.8), and the gene significance (|GS| > 0.3) was selected from the turquoise module. GOLM1 and FAM49B genes were chosen based on single-gene analysis results. Survival analysis showed that the higher expression of GOLM1 and FAM49B genes was correlated with a worse prognosis of HNSCC patients. Immunohistochemistry and multiplex immunofluorescence techniques verified that GOLM1 and FAM49B genes were highly expressed in HNSCC cells, and high expressions of GOLM1 were associated with the pathological grades of HNSCC. In conclusion, our study illustrated a new insight that GOLM1 and FAM49B genes might be used as potential biomarkers to determine the development of HNSCC, while GOLM1 and FAM49B have the possibility to be prognostic indicators for HNSCC.

Development and validation of a novel hypoxia-related signature for prognostic and immunogenic evaluation in head and neck squamous cell carcinoma

Hypoxia plays a critical role in head and neck squamous cell carcinoma (HNSCC) prognosis. However, till now, robust and reliable hypoxia-related prognostic signatures have not been established for an accurate prognostic evaluation in HNSCC patients. This article focused on establishing a risk score model to evaluate the prognosis and guide treatment for HNSCC patients. RNA-seq data and clinical information of 502 HNSCC patients and 44 normal samples were downloaded from The Cancer Genome Atlas (TCGA) database. 433 samples from three Gene Expression Omnibus (GEO) datasets were incorporated as an external validation cohort. In the training cohort, prognostic-related genes were screened and LASSO regression analyses were performed for signature establishment. A scoring system based on SRPX, PGK1, STG1, HS3ST1, CDKN1B, and HK1 showed an excellent prediction capacity for an overall prognosis for HNSCC patients. Patients were divided into high- and low-risk groups, and the survival status of the two groups exhibited a statistically significant difference. Subsequently, gene set enrichment analysis (GSEA) was carried out to explore the underlying mechanisms for the prognosis differences between the high- and low-risk groups. The tumor immune microenvironment was evaluated by CIBERSORT, ESTIMATE, TIDE, and xCell algorithm, etc. Then, we explored the relationships between this prognostic model and the levels of immune checkpoint-related genes. Cox regression analysis and nomogram plot indicated the scoring system was an independent predictor for HNSCC. Moreover, a comparison of predictive capability has been made between the present signature and existing prognostic signatures for HNSCC patients. Finally, we detected the expression levels of proteins encoded by six-HRGs via immunohistochemical analysis in tissue microarray. Collectively, a novel integrated signature considering both HRGs and clinicopathological parameters will serve as a prospective candidate for the prognostic evaluation of HNSCC patients.

Dynamic contrast-enhanced MRI and Doppler sonography in patients with squamous cell carcinoma of head and neck treated with induction chemotherapy

In view of the inherent limitations associated with performing dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) in clinical settings, current study was designed to provide a proof of principle that Doppler sonography and DCE-MRI derived perfusion parameters yield similar hemodynamic information from metastatic lymph nodes in squamous cell carcinomas of head and neck (HNSCCs). Strong positive correlations between volume fraction of plasma space in tissues (V_p ) and blood volume (r = 0.72, p = 0.02) and between V_p and %area perfused (r = 0.65, p = 0.04) were observed. Additionally, a moderate positive correlation trending towards significance was obtained between volume transfer constant (K^trans ) and %area perfused (r = 0.49, p = 0.09).

Database Mining Detected a Cuproptosis-Related Prognostic Signature and a Related Regulatory Axis in Breast Cancer

Background

Breast cancer is the frequent cause of disease burden related to cancer among women. It affects one in 20 women globally and up to one in eight women in high-income countries. Cuproptosis is a copper-induced modality of mitochondrial cell death that is involved in tumor proliferation and metastasis.

Methods

To construct a prognostic cuproptosis-related signature, LASSO Cox regression analysis was employed. Additionally, ceRNA was developed with an aim of exploring the possible lncRNA-miRNA-mRNA regulatory axis in breast cancer.

Results

The expression of FDX1, DLD, DLAT, LIAS, LIPT1, GLS MTF1, and PDHA1 was downregulated, while CDKN2A expression level was elevated in breast cancer in contrast with normal tissue. We furthermore reviewed the genetic mutation landscape of genes linked to cuproptosis in breast cancer. Prognosis analysis revealed poor OS and RFS rates in breast cancer patients with elevated levels of CDKN2A and PDHA1 and low levels of MTF1, DLD, LIPT1, and FDX1. We then constructed a cuproptosis-related signature with six genes (DKN2A, MTF1, PDHA1, DLD, LIPT1, and FDX1) for breast cancer, which predicted the OS rate with an accuracy that ranged from medium to high. Further analysis demonstrated a significant correlation between the cuproptosis-related prognostic signature and pTNM stage, MSI score, drug sensitivity, TMB score, and immune cell infiltration. Moreover, we identified the lncRNA XIST/miR-92b-3p/MTF1 regulatory axis for breast cancer.

Conclusion

Multiomics approaches were used to create a cuproptosis-related signature with six genes (DKN2A, MTF1, PDHA1, DLD, LIPT1, and FDX1) for breast cancer. We discovered the lncRNA XIST/miR-92b-3p/MTF1 regulatory axis for breast cancer, which has not yet been investigated previously.

C10orf55, CASC2, and SFTA1P lncRNAs Are Potential Biomarkers to Assess Radiation Therapy Response in Head and Neck Cancers

Long non-coding RNAs have proven to be important molecules in carcinogenesis. Due to little knowledge about them, the molecular mechanisms of tumorigenesis are still being explored. The aim of this work was to study the effect of ionizing radiation on the expression of lncRNAs in head and neck squamous cell carcinoma (HNSCC) in patients responding and non-responding to radiotherapy. The experimental model was created using a group of patients with response (RG, n = 75) and no response (NRG, n = 75) to radiotherapy based on the cancer genome atlas (TCGA) data. Using the in silico model, statistically significant lncRNAs were defined and further validated on six HNSCC cell lines irradiated at three different doses. Based on the TCGA model, C10orf55, C3orf35, C5orf38, CASC2, MEG3, MYCNOS, SFTA1P, SNHG3, and TMEM105, with the altered expression between the RG and NRG were observed. Analysis of pathways and immune profile indicated that these lncRNAs were associated with changes in processes, such as epithelial-to-mesenchymal transition, regulation of spindle division, and the p53 pathway, and differences in immune cells score and lymphocyte infiltration signature score. However, only C10orf55, CASC2, and SFTA1P presented statistically altered expression after irradiation in the in vitro model. In conclusion, the expression of lncRNAs is affected by ionization radiation in HNSCC, and these lncRNAs are associated with pathways, which are important for radiation response and immune response. Potentially presented lncRNAs could be used as biomarkers for personalized radiotherapy in the future. However, these results need to be verified based on an in vitro experimental model to show a direct net of interactions.

A Four-Gene Signature Associated with Radioresistance in Head and Neck Squamous Cell Carcinoma Identified by Text Mining and Data Analysis

Purpose

Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer globally, and radiotherapy plays a crucial part in its treatment. This study was designed to identify potential genes related to radiation resistance in HNSCC.

Method

We first used text mining to obtain common genes related to radiotherapy resistance and HNSCC in published articles. Functional enrichment analyses were conducted to identify the significantly enriched pathways and genes. Protein and protein interactions were performed, and the most significant gene modules were determined; then, genes in the gene modules were validated at transcriptional levels and overall survival. Gene set variation analysis (GSVA) score was calculated, and the association between GSVA score and survival/pathway was estimated. Immune cell infiltration, methylation, and genetic alteration analysis of these genes was conducted in HNSCC patients. Finally, potential sensitive anticancer drugs related to target genes were obtained.

Result

We identified 583 common genes through text mining. After further validation, a four-gene signature (EPHB2, SPP1, SERPINE1, and VEGFC) was constructed. The patients with higher GSVA scores have a worse prognosis than those with lower GSVA scores. Differences in methylation of these four genes in HNSCC tumor tissue and normal tissue were compared, with higher methylation levels of EBPH2 and SPP1 in normal tissue and higher methylation levels of SERPINE1 in the tumor. Immune cell infiltration revealed that the increased expression of these genes was closely related to the infiltration level of CD4+ T cell, neutrophil, macrophage, and dendritic cell. Thirty drugs, including 22 positively and eight negatively correlated drugs that most correlated with related genes, were available for treating HNSCC.

Conclusion

In this study, we identified four potential genes as well as corresponding drugs that might be related to radioresistance in HNSCC patients. These candidate genes may provide a promising avenue to further elevate radiotherapy efficacy.

Gene Expression and DNA Methylation in Human Papillomavirus Positive and Negative Head and Neck Squamous Cell Carcinomas

High-risk human papillomaviruses (HPV) are important agents, responsible for a large percentage of the 745,000 cases of head and neck squamous cell carcinomas (HNSCC), which were identified worldwide in 2020. In addition to being virally induced, tobacco and heavy alcohol consumption are believed to cause DNA damage contributing to the high number of HNSCC cases. Gene expression and DNA methylation differ between HNSCC based on HPV status. We used publicly available gene expression and DNA methylation profiles from the Cancer Genome Atlas and compared HPV positive and HPV negative HNSCC groups. We used differential gene expression analysis, differential methylation analysis, and a combination of these two analyses to identify the differences. Differential expression analysis identified 1854 differentially expressed genes, including PCNA, TNFRSF14, TRAF1, TRAF2, BCL2, and BIRC3. SYCP2 was identified as one of the top deregulated genes in the differential methylation analysis and in the combined differential expression and methylation analyses. Additionally, pathway and ontology analyses identified the extracellular matrix and receptor interaction pathway as the most altered between HPV negative and HPV positive HNSCC groups. Combining gene expression and DNA methylation can help in elucidating the genes involved in HPV positive HNSCC tumorigenesis, such as SYCP2 and TAF7L.

Identification and Verification of Potential Biomarkers in Gastric Cancer By Integrated Bioinformatic Analysis

Background: Gastric cancer (GC) is a common cancer with high mortality. This study aimed to identify its differentially expressed genes (DEGs) using bioinformatics methods.

Methods: DEGs were screened from four GEO (Gene Expression Omnibus) gene expression profiles. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. A protein–protein interaction (PPI) network was constructed. Expression and prognosis were assessed. Meta-analysis was conducted to further validate prognosis. The receiver operating characteristic curve (ROC) was analyzed to identify diagnostic markers, and a nomogram was developed. Exploration of drugs and immune cell infiltration analysis were conducted.

Results: Nine up-regulated and three down-regulated hub genes were identified, with close relations to gastric functions, extracellular activities, and structures. Overexpressed Collagen Type VIII Alpha 1 Chain (COL8A1), Collagen Type X Alpha 1 Chain (COL10A1), Collagen Triple Helix Repeat Containing 1 (CTHRC1), and Fibroblast Activation Protein (FAP) correlated with poor prognosis. The area under the curve (AUC) of ADAM Metallopeptidase With Thrombospondin Type 1 Motif 2 (ADAMTS2), COL10A1, Collagen Type XI Alpha 1 Chain (COL11A1), and CTHRC1 was >0.9. A nomogram model based on CTHRC1 was developed. Infiltration of macrophages, neutrophils, and dendritic cells positively correlated with COL8A1, COL10A1, CTHRC1, and FAP. Meta-analysis confirmed poor prognosis of overexpressed CTHRC1.

Conclusion: ADAMTS2, COL10A1, COL11A1, and CTHRC1 have diagnostic values in GC. COL8A1, COL10A1, CTHRC1, and FAP correlated with worse prognosis, showing prognostic and therapeutic values. The immune cell infiltration needs further investigations.

Single-Cell RNA Sequencing Analysis for Oncogenic Mechanisms Underlying Oral Squamous Cell Carcinoma Carcinogenesis with Candida albicans Infection

Oral squamous cell carcinoma (OSCC) carcinogenesis involves heterogeneous tumor cells, and the tumor microenvironment (TME) is highly complex with many different cell types. Cancer cell-TME interactions are crucial in OSCC progression. Candida albicans (C. albicans)-frequently pre-sent in the oral potentially malignant disorder (OPMD) lesions and OSCC tissues-promotes malignant transformation. The aim of the study is to verify the mechanisms underlying OSCC car-cinogenesis with C. albicans infection and identify the biomarker for the early detection of OSCC and as the treatment target. The single-cell RNA sequencing analysis (scRNA-seq) was performed to explore the cell subtypes in normal oral mucosa, OPMD, and OSCC tissues. The cell composi-tion changes and oncogenic mechanisms underlying OSCC carcinogenesis with C. albicans infec-tion were investigated. Gene Set Variation Analysis (GSVA) was used to survey the mechanisms underlying OSCC carcinogenesis with and without C. albicans infection. The results revealed spe-cific cell clusters contributing to OSCC carcinogenesis with and without C. albicans infection. The major mechanisms involved in OSCC carcinogenesis without C. albicans infection are the IL2/STAT5, TNFα/NFκB, and TGFβ signaling pathways, whereas those involved in OSCC carcinogenesis with C. albicans infection are the KRAS signaling pathway and E2F target down-stream genes. Finally, stratifin (SFN) was validated to be a specific biomarker of OSCC with C. albicans infection. Thus, the detailed mechanism underlying OSCC carcinogenesis with C. albicans infection was determined and identified the treatment biomarker with potential precision medicine applications.

Diagnostic Value, Prognostic Value, and Immune Infiltration of LOX Family Members in Liver Cancer: Bioinformatic Analysis

Background

Liver cancer (LC) is well known for its prevalence as well as its poor prognosis. The aberrant expression of lysyl oxidase (LOX) family is associated with liver cancer, but their function and prognostic value in LC remain largely unclear. This study aimed to explore the function and prognostic value of LOX family in LC through bioinformatics analysis and meta-analysis.

Results

The expression levels of all LOX family members were significantly increased in LC. Area under the receiver operating characteristic curve (AUC) of LOXL2 was 0.946 with positive predictive value (PPV) of 0.994. LOX and LOXL3 were correlated with worse prognosis. Meta-analysis also validated effect of LOX on prognosis. Nomogram of these two genes and other predictors was also plotted. There was insufficient data from original studies to conduct meta-analysis on LOXL3. The functions of LOX family members in LC were mostly involved in extracellular and functions and structures. The expressions of LOX family members strongly correlated with various immune infiltrating cells and immunomodulators in LC.

Conclusions

For LC patients, LOXL2 may be a potential diagnostic biomarker, while LOX and LOXL3 have potential prognostic and therapeutic values. Positive correlation between LOX family and infiltration of various immune cells and immunomodulators suggests the need for exploration of their roles in the tumor microenvironment and for potential immunotherapeutic to target LOX family proteins.

Identification of key regulators associated with colon cancer prognosis and pathogenesis

Colon cancer (CC) is the fourth deadliest cancer in the world. New insights into prognostication might be helpful to define the optimal adjuvant treatments for patients in routine clinical practice. Here, a microarray dataset with 30 primary tumors and 30 normal samples was analyzed using GEO2R to find differentially expressed genes (DEGs). Then, DAVID, KEGG, ChEA and X2K were used to analyze DEGs-related Gene Ontology, pathways, transcription factors (TFs) and kinases, respectively. Protein-protein interaction (PPI) networks were constructed using the STRING database and Cytoscape. The modules and hub genes of DEGs was determined through MCODE and CytoHubba plugins, and the expression of hub genes was verified using GEPIA. To find microRNAs and metabolites associated with DEGs, miRTarBase and HMDB were used, respectively. It was found that 233 and 373 genes were upregulated and downregulated in CC, respectively. GO analysis showed that the upregulated DEGs were mainly involved in mitotic nuclear division and cell division. Top 10 hub genes were identified, including AURKB, CDK1, DLGAP5, AURKA, CCNB2, CCNB1, BUB1B, CCNA2, KIF20A and BUB1. Whereas, FOMX1, E2F7, E2F1, E2F4 and AR were identified as top 5 TFs in CC. Moreover, CDK1, CDC2, MAPK14, ATM and CK2ALPHA was identified as top 5 kinases in CC. miRNAs analysis showed that Hsa-miR-215-5p hsa-miR-193b-3p, hsa-miR-192-5p and hsa-miR-16-5p could target the largest number of CC genes. Taken together, CC-related genes, especially the hub genes, TFs, and metabolites might be used as novel biomarkers for CC, as well as for diagnosis and guiding therapeutic strategies for CC.

Identification and Verification of Immune-Related Genes Prognostic Signature Based on ssGSEA for Adrenocortical Carcinoma (ACC)

Purpose

Adrenocortical carcinoma (ACC) is an endocrine malignant tumor with poor prognosis. The study aimed to construct ACC immune-related gene prognostic signature and verify the efficacy of prognostic signature.

Methods

ACC RNA-seq data and clinical information are downloaded from TCGA databases and GEO databases. We used single sample gene set enrichment analysis (ssGSEA) to assess immune cell infiltration in ACC patients and ACC patients were divided into high- and low-immune cell infiltration clusters. The validity of ssGSEA grouping was verified using the ESTIMATE algorithm. A total of 275 differentially expressed immune-related genes (IRGs) were obtained from the intersection of IRGs and differentially expressed genes (DEGs) in high and low immune cell infiltration clusters. LASSO analysis was used to identify 13 IRGs that regulate the prognosis of ACC patients through immune infiltration. Kaplan–Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that these 13 immune-related gene signatures were innovative and significant prognostic factors, which were independent of clinical features. Finally, ACC prognostic nomogram was constructed, ROC curve and calibration curve were drawn to evaluate the accuracy of the prognostic nomogram.

Results

LASSO regression analysis was used to screen out ACC survival-related genes. Univariate and multivariate Cox proportional risk regression models were used to analyze and construct the ACC prognosis nomogram. The AUC for predicting 1-, 3- and 5-year overall survival rate of ACC patients was 0.799, 0.966 and 0.969, suggesting good prediction accuracy. The calibration curve shows that the predicted results of the prognostic nomogram are in good agreement with the actual situation.

Conclusion

ssGSEA technique plays an important role in the construction of ACC prognostic model. Based on IRGs associated with survival independently predicted ACC prognosis, we identified thirteen immune-related genes as prognostic signature for ACC.

Identification of HCC Subtypes With Different Prognosis and Metabolic Patterns Based on Mitophagy

Background: Mitophagy is correlated with tumor initiation and development of malignancy. However, HCC heterogeneity with reference to mitophagy has yet not been systematically explored.

Materials and Methods: Mitophagy-related, glycolysis-related, and cholesterol biosynthesis-related gene sets were obtained from the Reactome database. Mitophagy-related and metabolism-related subtypes were identified using the ConsensusClusterPlus algorithm. Univariate Cox regression was analysis was performed to identify prognosis-related mitophagy regulators. Principal component analysis (PCA) was used to create composite measures of the prognosis-related mitophagy regulators (mitophagyscore). Individuals with a mitophagyscore higher or lower than the median value were classified in high- or low-risk groups. Kaplan-Meier survival and ROC curve analyses were utilized to evaluate the prognostic value of the mitophagyscore. The nomogram and calibration curves were plotted using the“rms” R package. The package “limma” was used for differential gene expression analysis. Differentially expressed genes (DEGs) between high- and low-risk groups were used as queries in the CMap database. R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to determine the sensitivity of 21 previously reported anti-HCC drugs.

Results: Three distinct HCC subtypes with different mitophagic accumulation (low, high, and intermediate mitophagy subtypes) were identified. High mitophagy subtype had the worst outcome and highest glycolysis level. The lowest degree of hypoxia and highest cholesterol biosynthesis was observed in the low mitophagy subtype; oncogenic dedifferentiation level in the intermediate mitophagy subtype was the lowest. Mitophagyscore could serve as a novel prognostic indicator for HCC.High-risk patients had a poorer prognosis (log-rank test, p < 0.001). The area under the ROC curve for mitophagyscore in 1-year survival was 0.77 in the TCGA cohort and 0.75 in the ICGC cohort. Nine candidate small molecules which were potential drugs for HCC treatment were identified from the CMap database. A decline in the sensitivity towards 21 anti-HCC drugs was observed in low-risk patients by GDSC database. We also identified a novel key gene, SPP1, which was highly associated with different mitophagic subtypes.

Conclusion: Based on bioinformatic analyses, we systematically examined the HCC heterogeneity with reference to mitophagy and observed three distinct HCC subtypes having different prognoses and metabolic patterns.

Q-omics: Smart Software for Assisting Oncology and Cancer Research

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

Increased UBE2L6 regulated by type 1 interferon as potential marker in TB

The aim of this study is to identify potential biomarker of tuberculosis (TB) and determine its function. Differentially expressed mRNAs(DEGs) were selected from a blood database GSE101805, and then, 30 key genes were screened using STING, Cytoscape and further functionally enriched. We then found that only 6 of 13 genes related to ubiquitination (the first in the functional enrichment) were increased significantly. ROC analysis showed that UBE2L6, among 6 genes, had the highest diagnostic value, and then, we found that it also had mild value in differential diagnosis. Moreover, our analysis showed that UBE2L6 may be upregulated by type I interferon, which was further confirmed by us. In addition, we also found that UBE2L6 inhibits the apoptosis of Mycobacterium tuberculosis(Mtb)infected macrophages. Subsequently, we discovered that miR-146a-5p, which may target UBE2L6, is reduced in peripheral blood mononuclear cells (PBMC) and plasma of TB, and it also had certain diagnostic efficiency(AUC=0.791). In brief, we demonstrated that UBE2L6 as well as miR-146a-5p is a potential biomarker for TB and UBE2L6,which may also plays important role in TB by, at least, modulating Mtb-infected macrophage apoptosis.

High STK40 Expression as an Independent Prognostic Biomarker and Correlated with Immune Infiltrates in Low-Grade Gliomas

Background

Expression of STK40 is observed in some cancer types, while its role in low-grade gliomas (LGG) is unclear. The present study aimed to demonstrate the relationship between STK40 and LGG based on The Cancer Genome Atlas (TCGA) database and bioinformatics analysis.

Methods

Kruskal-Wallis test, Wilcoxon sign-rank test, and logistic regression were used to evaluate the relationship between clinicopathological features and STK40 expression. Kaplan-Meier method and Cox regression analysis were used to evaluate prognostic factors. Gene set enrichment analysis (GSEA) and immuno-infiltration analysis were used to determine the significant involvement of STK40 in function.

Results

High STK40 expression in LGG was associated with WHO grade (P<0.001), IDH status (P<0.001), primary therapy outcome (P=0.027), 1p/19q codeletion (P<0.001) and histological type (P<0.001). High STK40 expression predicted a poorer overall survival (OS) (HR: 3.07; 95% CI: 2.09-4.51; P<0.001), progression-free survival (PFS) (HR:2.11; 95% CI: 1.59-2.81; P<0.001) and disease specific survival (DSS) (HR: 3.27; 95% CI: 2.17-4.92; P<0.001). STK40 expression (HR: 2.284; 95% CI: 1.125-4.637; P=0.022) was independently correlated with OS in LGG patients. GSEA demonstrated that pathways including cell cycle mitotic, neutrophil degranulation, signaling by Rho GTPases, signaling by interleukins, M phase, PI3K-Akt signaling pathway and naba secreted factors were differentially enriched in STK40 high expression phenotype. Immune infiltration analysis showed that STK40 expression was correlated with some types of immune infiltrating cells.

Conclusion

STK40 expression was significantly correlated with poor survival and immune infiltration in LGG, and it may be a promising prognostic biomarker in LGG.

Prognostic Gene Signature for Squamous Cell Carcinoma with a Higher Risk for Treatment Failure and Accelerated MEK-ERK Pathway Activity

Simple Summary

Squamous cell carcinoma (SCC) is the most prevalent type of human cancer worldwide and represents the majority of head and neck tumors. As SCC from aerodigestive or genitourinary tracts share not only common etiology and histological features but also molecular patterns, the major objectives of this study were the establishment of a pan-SCC-related prognostic gene signature by an integrative analysis of multi-omics data and the elucidation of underlying oncogenic pathway activities as potential vulnerabilities for a more efficient and less toxic therapy. Our approach delivers a reliable molecular classifier to identify HNSCC and other SCC patients at higher risk for treatment failure with tumors characterized by a more prominent MAPK activity, who might benefit from a targeted treatment with MEK inhibitors.

Abstract

Squamous cell carcinoma (SCC) is the most prevalent histological type of human cancer, including head and neck squamous cell carcinoma (HNSCC). However, reliable prognostic gene signatures for SCC and underlying genetic and/or epigenetic principles are still unclear. We identified 37 prognostic candidate genes by best cutoff computation based on survival in a pan-SCC cohort (n = 1334) of The Cancer Genome Atlas (TCGA), whose expression stratified not only the pan-SCC cohort but also independent HNSCC validation cohorts into three distinct prognostic subgroups. The most relevant prognostic genes were prioritized by a Least Absolute Shrinkage and Selection Operator Cox regression model and were used to identify subgroups with high or low risks for unfavorable survival. An integrative analysis of multi-omics data identified FN1, SEMA3A, CDH2, FBN1, COL5A1, and ADAM12 as key nodes in a regulatory network related to the prognostic phenotype. An in-silico drug screen predicted two MEK inhibitors (Trametinib and Selumetinib) as effective compounds for high-risk SCC based on the Cancer Cell Line Encyclopedia, which is supported by a higher p-MEK1/2 immunohistochemical staining of high-risk HNSCC. In conclusion, our data identified a molecular classifier for high-risk HNSCC as well as other SCC patients, who might benefit from treatment with MEK inhibitors.

Prognostic association of starvation-induced gene expression in head and neck cancer

Autophagy-related genes (ARGs) have been implicated in the initiation and progression of malignant tumor promotion. To investigate the dynamics of expression of genes, including ARGs, head and neck squamous cell carcinoma (HNSCC) cells were placed under serum-free conditions to induce growth retardation and autophagy, and these starved cells were subjected to transcriptome analysis. Among the 21 starvation-induced genes (SIGs) located in the autophagy, cell proliferation, and survival signaling pathways, we identified SIGs that showed prominent up-regulation or down-regulation in vitro. These included AGR2, BST2, CALR, CD22, DDIT3, FOXA2, HSPA5, PIWIL4, PYCR1, SGK3, and TRIB3. The Cancer Genome Atlas (TCGA) database of HNSCC patients was used to examine the expression of up-regulated genes, and CALR, HSPA5, and TRIB3 were found to be highly expressed relative to solid normal tissue in cancer and the survival rate was reduced in patients with high expression. Protein-protein interaction analysis demonstrated the formation of a dense network of these genes. Cox regression analysis revealed that high expression of CALR, HSPA5, and TRIB3 was associated with poor prognosis in patients with TCGA-HNSCC. Therefore, these SIGs up-regulated under serum starvation may be molecular prognostic markers in HNSCC patients.

Identification and clinical validation of gene signatures with grade and survival in head and neck carcinomas

This study aimed to explore gene expression profiles that drive malignancy from low- to high-grade head and neck carcinomas (HNC), as well as to analyze their correlations with survival. Gene expressions and clinical data of HNC were downloaded from the Gene Expression Omnibus (GEO) repository. The significantly differential genes (SDGs) between low- and high-grade HNC were screened. Cox regressions were performed to identify prognostic SDGs of progression-free survival (PFS) and disease-specific survival (DSS). The genes were experimentally validated by RT-PCR in clinical tissue specimens. Thirty-five SDGs were identified in 47 low-grade and 30 high-grade HNC samples. Cox regression analyses showed that CXCL14, SLC44A1, and UBD were significantly associated with DSS, and PPP2R2C and SLC44A1 were associated with PFS. Patients were grouped into high-risk or low-risk groups for prognosis based on gene signatures. High-risk patients had significantly shorter DSS and PFS than low-risk patients (P=0.033 and P=0.010, respectively). Multivariate Cox regression showed HPV (P=0.033), lymph node status (P=0.032), and residual status (P<0.044) were independent risk factors for PFS. ROC curves showed the risk score had better efficacy to predict survival both for DSS and PFS (AUC=0.858 and AUC=0.901, respectively). The results showed CXCL14 and SLC44A1 were significantly overexpressed in the low-grade HNC tissues and the UBD were overexpressed in the high-grade HNC tissues. Our results suggested that SDGs had different expression profiles between the low-grade and high-grade HNC, and these genes may serve as prognostic biomarkers to predict survival.

A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications

Survival analysis of the Cancer Genome Atlas (TCGA) dataset is a well-known method for discovering gene expression-based prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). A cutoff point is usually used in survival analysis for patient dichotomization when using continuous gene expression values. There is some optimization software for cutoff determination. However, the software's predetermined cutoffs are usually set at the medians or quantiles of gene expression values. There are also few clinicopathological features available in pre-processed datasets. We applied an in-house workflow, including data retrieving and pre-processing, feature selection, sliding-window cutoff selection, Kaplan-Meier survival analysis, and Cox proportional hazard modeling for biomarker discovery. In our approach for the TCGA HNSCC cohort, we scanned human protein-coding genes to find optimal cutoff values. After adjustments with confounders, clinical tumor stage and surgical margin involvement were found to be independent risk factors for prognosis. According to the results tables that show hazard ratios with Bonferroni-adjusted p values under the optimal cutoff, three biomarker candidates, CAMK2N1, CALML5, and FCGBP, are significantly associated with overall survival. We validated this discovery by using the another independent HNSCC dataset (GSE65858). Thus, we suggest that transcriptomic analysis could help with biomarker discovery. Moreover, the robustness of the biomarkers we identified should be ensured through several additional tests with independent datasets.

Establishment and Validation of a Comprehensive Prognostic Model for Patients With HNSCC Metastasis

Objective

To identify biomarkers related to head and neck squamous cell carcinoma (HNSCC) metastasis and establish a prognostic model for patients with HNSCC.

Methods

HNSCC mRNA expression data of metastasis and non-metastatic samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. After screening the differentially expressed genes (DEGs) in the two datasets, a prognostic model, including clinical factors and biomarkers, was established, and verified in 36 samples of HNSCC by quantitative real-time transcription (qRT)-PCR. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene sets enrichment analysis (GSEA) were consulted to explore the functions of the DEGs.

Results

In total, 108 DEGs were identified. GSEA, GO, and KEGG analyses showed that these DEGs were mainly involved in the proliferation and metastasis of HNSCC. Six genes that were significantly related to metastasis, immune cell infiltration and prognosis were further identified to construct a prognostic gene signature. The reliability of the gene signature was verified in 36 samples of HNSCC. A prognostic model, including tumor stage, risk level, and a nomogram for prediction were further established. Receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), C-index, and calibration plots showed that the model and nomogram perform well.

Conclusion

We constructed a six-gene signature and a nomogram with high performance in predicting the prognosis of patients with HNSCC metastasis.

Identification of mast cells as a candidate significant target of immunotherapy for acute myeloid leukemia

OBJECTION

Immunotherapy based on T cells is a new therapy for Acute myeloid leukemia (AML). However, there has not been considerable improvement compared with traditional chemotherapeutics. This study aimed to identify important immune cells, genes, and drugs associated with the immunotherapy of AML.

METHODS

The gene expression profile and clinical data of patients with AML were downloaded from TCGA database, and the abundance ratio of immune cells was obtained via CIBERSORT. Kaplan-Meier (KM) survival analysis was used to assess the relationship between immune cells and survival time of patients with AML. Differentially expressed genes (DEGs) analysis was conducted to obtained DEGs related to mast cells. Then, protein-protein interaction (PPI) analysis and enrichment analysis were performed to explore the hub genes. Finally, Connectivity Map (CMap) database was utilized to predicts potential drugs that may reverse or induce the mast cell-related gene expression.

RESULTS

Our study showed that mast cell was correlated with survival time of patients with AML, and 135 genes were screened to be related with mast cells. 6 hub genes were identified via PPI network, and 3 potential small molecule drugs were screened to be related to regulating the mast cell-related gene expression via CMap database.

CONCLUSION

The hub genes and drugs have high research value and clinical application in AML therapy. Our study not only provides gene targets and small molecule drugs for AML immunotherapy concerning mast cells but also provides new ideas for researchers to explore immunotherapy targets of other tumors.

Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT

The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis.

Soluble Sema4D in Plasma of Head and Neck Squamous Cell Carcinoma Patients Is Associated With Underlying Non-Inflamed Tumor Profile

Semaphorin 4D (Sema4D) is a glycoprotein that is expressed by several tumors and immune cells. It can function as a membrane bound protein or as a cleaved soluble protein (sSema4D). We sought to investigate the translational potential of plasma sSema4D as an immune marker in plasma of patients with head and neck squamous cell carcinoma (HNSCC). Paired peripheral blood and tumor tissue samples of 104 patients with HNSCC were collected at the same time point to allow for real time analysis. Scoring of the histological inflammatory subtype (HIS) was carried out using Sema4D immunohistochemistry on the tumor tissue. sSema4D was detected in plasma using direct ELISA assay. Defining elevated sSema4D as values above the 95^th percentile in healthy controls, our data showed that sSema4D levels in plasma were elevated in 25.0% (95% CI, 16.7–34.9%) of the patients with HNSCC and showed significant association with HIS immune excluded (HIS-IE) (p = 0.007), Sema4D^+ve tumor cells (TCs) (p = 0.018) and PD-L1^+ve immune cells (ICs) (p = 0.038). A multi-variable logistic regression analysis showed that HIS was significantly (P = 0.004) associated with elevated sSema4D, an association not explained by available patient-level factors. Using the IO-360 nanoString platform, differential gene expression (DGE) analysis of 10 HNSCC tumor tissues showed that patients with high sSema4D in plasma (HsS4D) clustered as IFN-γ negative tumor immune signature and were mostly HIS-IE. The IC type in the HsS4D paired tumor tissue was predominantly myeloid, while the lymphoid compartment was higher in the low sSema4D (LsS4D). The Wnt signaling pathway was upregulated in the HsS4D group. Further analysis using the IO-360, 770 gene set, showed significant non-inflamed profile of the HsS4D tumors compared to the LsS4D. In conclusion, our data reveals an association between sSema4D and the histological inflammatory subtype.

PSMC2, ORC5 and KRTDAP are specific biomarkers for HPV-negative head and neck squamous cell carcinoma

The prognosis of patients with human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) is poorer than those with HPV-positive HNSCC. The present study aimed to identify novel and specific biomarkers of HPV-negative HNSCC using bioinformatics analysis and associated experiments. The gene expression profiles of HPV-negative HNSCC tissues and corresponding clinical data were downloaded from The Cancer Genome Atlas database and used in a weighted gene co-expression network analysis. Genes in clinically significant co-expression modules were used to construct a protein-protein interaction (PPI) network. The genes demonstrating a high degree score in the PPI network and a high correlation with tumor grade were considered hub genes. The diagnostic value of the hub genes associated with HPV-negative and HPV-positive HNSCC was analyzed using differential expression gene (DEG) analysis, immunohistochemical (IHC) staining and a receiver operating characteristic (ROC) curve analysis. Seven genes [Serrate RNA effector molecule (SRRT), checkpoint kinase 2 (CHEK2), small nuclear ribonucleoprotein polypeptide E (SNRPE), proteasome 26S subunit ATPase 2 (PSMC2), origin recognition complex subunit 5 (ORC5), S100 calcium binding protein A7 and keratinocyte differentiation associated protein (KRTDAP)] were demonstrated to be hub genes in clinically significant co-expression modules. DEG, IHC and ROC curve analyses revealed that SRRT, CHEK2 and SNRPE were significantly upregulated in HPV-negative and HPV-positive HNSCC tissues compared with in adjacent tissues, and these genes demonstrated a high diagnostic value for distinguishing HNSCC tissues. However, PSMC2, ORC5 and KRTDAP were the only differentially expressed genes identified in HPV-negative HNSCC tissues, and these genes demonstrated a high diagnostic value for HPV-negative HNSCC. PSMC2, ORC5 and KRTDAP may therefore serve as novel and specific biomarkers for HPV-negative HNSCC, potentially improving the diagnosis and treatment of patients with HPV-negative HNSCC.

The Molecular Landscape and Biological Alterations Induced by PRAS40-Knockout in Head and Neck Squamous Cell Carcinoma

PRAS40 (Prolin-rich Akt substrate of 40 kDa) is a critical protein, which directly connects PI3K/Akt and mTORC1 pathway. It plays an indispensable role in the development of various diseases. However, the relationship between PRAS40 and head and neck squamous cell carcinoma (HNSCC) remains unclear. Here, our study indicated that high expression of PRAS40 mRNA is a favorable prognostic factor in HNSCC patients by analyzing 498 clinical and mRNA data. Moreover, we confirmed that CRISPR/Cas9 induced PRAS40-knockout would promote colony formation, cell migration, and invasion in several HNSCC cell lines. RNA-seq was employed to investigate the further possible mechanisms involving the above regulations by PRAS40 in HNSCC cells. The molecular landscape contributed by 253 differentially expressed mRNA after PRAS40-knockout was enriched in TGF-beta, PI3K-Akt, P53, mTOR, NF-κB signaling pathway. Partial molecular alternations within these pathways were validated by qPCR or Western blotting. Besides, we found that high expression of PRAS40 in HNSC patients would present more CD8⁺ T and T follicular helper cells, but less Th17 cells than the patients with low expression of PRAS40. The altered molecular pathways and tumor-infiltrating immune cells might associate with the mechanism of PRAS40 being a suppressor in HNSCC cells, which would provide a potential prognostic predictor and therapeutic target in HNSCC patients.

Single-Cell Analysis of Different Stages of Oral Cancer Carcinogenesis in a Mouse Model

Oral carcinogenesis involves the progression of the normal mucosa into potentially malignant disorders and finally into cancer. Tumors are heterogeneous, with different clusters of cells expressing different genes and exhibiting different behaviors. 4-nitroquinoline 1-oxide (4-NQO) and arecoline were used to induce oral cancer in mice, and the main factors for gene expression influencing carcinogenesis were identified through single-cell RNA sequencing analysis. Male C57BL/6J mice were divided into two groups: a control group (receiving normal drinking water) and treatment group (receiving drinking water containing 4-NQO (200 mg/L) and arecoline (500 mg/L)) to induce the malignant development of oral cancer. Mice were sacrificed at 8, 16, 20, and 29 weeks. Except for mice sacrificed at 8 weeks, all mice were treated for 16 weeks and then either sacrificed or given normal drinking water for the remaining weeks. Tongue lesions were excised, and all cells obtained from mice in the 29- and 16-week treatment groups were clustered into 17 groups by using the Louvain algorithm. Cells in subtypes 7 (stem cells) and 9 (keratinocytes) were analyzed through gene set enrichment analysis. Results indicated that their genes were associated with the MYC_targets_v1 pathway, and this finding was confirmed by the presence of cisplatin-resistant nasopharyngeal carcinoma cell lines. These cell subtype biomarkers can be applied for the detection of patients with precancerous lesions, the identification of high-risk populations, and as a treatment target.

Determining the Clinical Value and Critical Pathway of GTPBP4 in Lung Adenocarcinoma Using a Bioinformatics Strategy: A Study Based on Datasets from The Cancer Genome Atlas

Lung cancer is the leading cause of cancer-related death worldwide, and the most common histologic subtype is lung adenocarcinoma (LUAD). Due to the significant mortality and morbidity rates among patients with LUAD, the identification of novel biomarkers to guide diagnosis, prognosis, and therapy is urgent. Guanosine triphosphate-binding protein 4 (GTPBP4) has been found to be associated with tumorigenesis in recent years, but the underlying molecular mechanism remains to be elucidated. In the present study, we demonstrate that GTPBP4 is significantly overexpressed in LUAD primary tumors. A total of 55 genes were identified as potential targets of GTPBP4. GO enrichment analysis identified the top 25 pathways among these target genes, among which, ribosome biogenesis was shown to be the most central. Each target gene demonstrated strong and complex interactions with other genes. Of the potential target genes, 12 abnormally expressed candidates were associated with survival probability and correlated with GTPBP4 expression. These findings suggest that GTPBP4 is associated with LUAD progression. Finally, we highlight the importance of the role of GTPBP4 in LUAD in vitro. GTPBP4 knockdown in LUAD cells inhibited proliferation and metastasis, promoted apoptosis, and enhanced sensitivity to TP. Overall, we conclude that GTPBP4 may be considered as a potential biomarker of LUAD.