Single-cell RNA seq data analysis reveals molecular markers and possible treatment targets for Laryngeal Squamous Cell Carcinoma (LSCC): An in-silico approach

Single-cell RNA seq data analysis reveals molecular markers and possible treatment targets for Laryngeal Squamous Cell Carcinoma (LSCC): An in-silico approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Single-cell RNA seq data analysis reveals molecular markers and possible treatment targets for Laryngeal Squamous Cell Carcinoma (LSCC): An in-silico approach Md. Hasan Jafre Shovon, Partha Biswas, Md. Imtiaz, Shirajut Mobin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6517892/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jun, 2025 Read the published version in In Silico Pharmacology → Version 1 posted 9 You are reading this latest preprint version Abstract Laryngeal squamous cell carcinoma (LSCC), a complex cancer driven by genetic mutations, poses significant challenges for detection and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as a promising tool to uncover the cellular heterogeneity in cancer and identify novel therapeutic targets. In this study, we used scRNA-seq data (GSE252490) to explore molecular biomarkers for LSCC diagnosis and treatment. After processing and standardizing the data, we performed principal component analysis to identify highly variable genes. Cell clustering revealed 12 distinct clusters with unique molecular features. Differential gene expression analysis identified 6434 differentially expressed genes (DEGs), which were further analyzed using gene ontology enrichment to explore biological processes involved in LSCC progression. Protein-protein interaction (PPI) network analysis revealed 20 central genes associated with key cancer pathways. Pathway enrichment analysis through KEGG highlighted the involvement of these genes in various cancer-related pathways. Notably, genes such as CCL3, EPCAM, and IL8, with elevated expression, were linked to survival outcomes in LSCC. This comprehensive analysis provides valuable insights into the molecular landscape of LSCC, identifying potential biomarkers and therapeutic targets for improved diagnosis and treatment. scRNA-seq LSCC Biomarker DEG PPI Gene ontology Seurat Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. Introduction Laryngeal squamous cell carcinoma (LSCC) poses a significant challenge in the field of oncology, representing a prevalent and debilitating form of head and neck cancer with profound implications for patient morbidity and mortality. Despite advancements in diagnostic techniques and therapeutic interventions, the prognosis for LSCC patients remains suboptimal, particularly in cases of advanced disease or treatment resistance [ 1 ]–[ 3 ]. As such, there is a pressing need to deepen our understanding of the molecular mechanisms driving LSCC pathogenesis and to identify novel therapeutic targets that could revolutionize patient care and outcomes. The burden of LSCC extends beyond its clinical manifestations to encompass socioeconomic ramifications and strain on healthcare systems worldwide. With an estimated 177,000 new cases diagnosed annually and over 94,000 deaths attributed to the disease globally, LSCC exerts a significant toll on individuals, families, and societies at large [ 4 ], [ 5 ]. Furthermore, the functional implications of laryngeal involvement, including compromised speech production and airway patency, underscore the urgency of addressing this malignancy comprehensively and effectively. Despite the advent of multimodal treatment approaches, such as surgery, radiotherapy, chemotherapy, and targeted therapies, the prognosis for LSCC patients remains far from satisfactory, necessitating innovative strategies to improve outcomes [ 1 ], [ 6 ]. Histologically, LSCC encompasses a spectrum of tumors with diverse morphological phenotypes, ranging from well-differentiated lesions to poorly differentiated or undifferentiated neoplasms [ 2 ]. This histological heterogeneity is mirrored by molecular diversity, with distinct molecular subtypes harboring unique genetic alterations and dysregulated signaling pathways. Comprehensive molecular profiling studies have identified key genomic alterations, including mutations in tumor suppressor genes (e.g., TP53, CDKN2A) and oncogenes (e.g., EGFR, PIK3CA), as well as aberrant DNA methylation patterns [ 3 ], [ 7 ], [ 8 ]. Understanding these molecular alterations is crucial for delineating the underlying mechanisms driving LSCC tumorigenesis and progression. The tumor microenvironment (TME) plays a pivotal role in shaping LSCC biology, influencing tumor growth, invasion, and response to therapy [ 7 ], [ 9 ]. Comprising cancer cells, stromal components, immune cells, and extracellular matrix molecules, the TME orchestrates complex interactions that promote tumor progression and immune evasion [ 10 ], [ 11 ]. Inflammation, angiogenesis, and immune suppression within the TME create a supportive niche for tumor growth, facilitating metastasis and therapeutic resistance. Understanding the cellular and molecular dynamics within the TME is essential for identifying novel therapeutic targets and developing effective immunomodulatory strategies for LSCC. In this context, single-cell RNA sequencing (scRNA-seq) emerges as a powerful tool for dissecting the molecular landscape of LSCC at unprecedented resolution. By profiling individual cells within tumor tissues, scRNA-seq enables the identification of heterogeneous cell populations, characterization of cell states, and elucidation of intercellular communication networks [ 12 ]–[ 14 ]. In this study, we employed an in-silico approach utilizing scRNA-seq data analysis utilizing R (version 4.3.2) to unveil molecular signatures and potential personalized therapeutic targets in LSCC. Leveraging publicly available datasets, we conducted a comprehensive analysis encompassing data processing, principal component analysis, cell clustering, marker gene analysis, gene ontology enrichment, protein-protein interaction network construction, and hub gene identification. Through integrative bioinformatics analyses, we aimed to uncover novel insights into LSCC pathobiology and identify candidate genes with diagnostic, prognostic, or therapeutic significance. In the subsequent sections, we detailed the methods employed for data acquisition, processing, and analysis, followed by a comprehensive presentation of our results. We elucidate the tumor heterogeneity observed in the scRNA-seq dataset, unveil cellular diversity within LSCC, explore marker genes associated with distinct cell clusters, analyze gene ontology enrichment to delineate biological roles, construct protein-protein interaction networks to identify hub genes, and assess the validity of hub genes through pathway analysis and survival analysis [ 15 ]–[ 17 ]. Finally, we discuss the implications of our findings and highlight the potential of identified molecular signatures as therapeutic targets for LSCC. Through this in-depth analysis, we aim to contribute to the growing body of knowledge surrounding LSCC pathogenesis and pave the way for the development of precision medicine approaches tailored to individual patient needs. 2. Methodology 2.1 Single-Cell RNA-Seq Data Collection For the analysis of Laryngeal squamous cell carcinoma (LSCC), we collected single-cell Rna-seq data (GSE252490) by accessing the gene expression omnibus server ( https://www.ncbi.nlm.nih.gov/geo/ ). The dataset was sequenced on Illumina NovaSeq 6000 (Homo sapiens) which contains three patient samples. We randomly chose GSM8002074 as the target LSCC sample for the goal of finding molecular signatures and potential targets to treat LSCC patients. 2.2 Data Processing Using the Seurat package in R (version 4.3.2), at first, we processed the data as often it’s found that there could be some unwanted cells that may have fewer genes [ 18 ]. There may be cell doublets or multiplets that may aberrantly exhibit high gene counts and there is often the occurrence of high levels of mitochondrial contamination due to low-quality or drying cells. We then normalized the data followed by the scaling of the data after the preprocessing [ 19 ]. Subsequently, we used the FindVariable feature of the Seurat package for the identification of highly variable genes [ 20 ]. 2.3 Exploration of Principle Component The data that had been scaled was subsequently utilized for linear dimension reduction in order to conduct an analysis of the principal components [ 21 ]. In this analysis, only genes that exhibit high variability were taken into consideration. Seurat, in the case of the first principal components, generates a compilation of genes with the most positive and negative loadings. These genes represent groups of genes that demonstrate either correlation or anti-correlation among individual cells within the dataset. 2.4 Grouping Cells by Similarity In order to address the considerable technical noise present in each individual feature of scRNA-seq data, the Seurat package was utilized for the clustering of cells based on their PCA scores [ 22 ]. Each principal component essentially acts as a 'metafeature' by amalgamating information from a correlated set of features. Cell clustering is achieved by utilizing modularity optimization techniques like the Louvain algorithm. This process involves iteratively grouping cells together to enhance the standard modularity function. The FindClusters feature within the Seurat package executes this procedure and includes a resolution parameter that determines the 'granularity' of subsequent clustering [ 23 ]. Higher values of this parameter result in a greater number of clusters being formed. 2.5 Identifying Molecular Signatures Uniform Manifold Approximation and Projection (UMAP) were used for dimension reduction through the use of the RunUMAP feature of the Seurat package for the visualization of different cell clusters [ 24 ], [ 25 ]. Each of the clusters was used for getting possible marker genes. The FindAll-Markers feature of the Seurat package was used for identifying the marker genes with Log2FC cut-off value was considered 2 or more as a threshold [ 26 ]. 2.6 Gene Ontology (GO) Enrichment Analysis The top 10 genes from each cluster were selected for further analysis. The top 10 genes from each cluster were selected based on the p-value and they were subjected to the gene ontology (GO) enrichment analysis to check the biological, cellular, and molecular roles of the genes [ 27 ]. The GO enrichment analysis was done from the SRplot server where the threshold for the functional category was set at an adjusted p-value of 0.05 or less [ 28 ]. 2.7 Construction Of PPI Network And Hub Genes Validation The top genes from each cluster were then subjected to the construction of a PPI network using the STRING database [ 29 ]. The visualization of the PPI network was carried out using the software Cytoscape [ 30 ]. Hub genes within the PPI network were identified based on their high degree of confidence and stringent FDR, in conjunction with data derived from various databases [ 31 ]. Through this method, the top 20 hub genes were pinpointed and further scrutinized to validate their status as molecular markers. The engagement of the hub genes in different pathways was then checked based on the KEGG database from the SRplot server where the p-value was set to 0.05 as the threshold [ 32 ]. The mRNA expression and survivability of the top 20 hub genes were then checked from the UALCAN server [ 33 ]. 3. Results 3.1 Tumor Heterogeneity in Single-Cell RNA-Seq Dataset Processing Single-cell RNA seq dataset (GSE252490) was used for analysis and finding potential molecular signatures for diagnosis and therapeutic targets for LSCC. We first checked the quality of the dataset and filtered out the dataset based on the count number, gene count, and mitochondrial percentage using the Seurat package of R. We kept cells that have at least 200 molecules, genes that have at least expressed in three different cells and filtered out cells that have more than 5% of mitochondrial genes showed in Fig. 1 . After that, we normalized the data to remove unwanted cells from the dataset. We used the LogNormalize feature of the Seurat package for data normalization. The normalized data was then used to find the cell-to-cell variable genes. We retrieved the top 2000 variable genes from the dataset for further downstream analysis. Figure 2 shows the top 10 variable genes from the dataset. Subsequently, a linear transformation (data scaling) was employed, and a standard pre-processing procedure was carried out before employing dimensional reduction methodologies. The Seurat package's ScaleData feature was utilized for data scaling, a necessary step for examining the principal components. 3.2 Unveiling Cellular Diversity Following this, principal component analysis (PCA) was conducted on the standardized data. Seurat provides a list of genes with the highest positive and negative loadings for the first principal components, demonstrating groups of genes that display either correlation or anti-correlation across individual cells within the dataset. An 'Elbow plot' is generated using a heuristic approach in the Seurat package, ranking the principal components based on the percentage of variance explained by each component (Fig. 3 ). Based on variance, the initial nine PCs were selected for further investigation. The variability in the first nine PCs was illustrated through VizDimplot (Fig. 4 ) and DimHeatmaps (Fig. 5 ). To cluster cells based on the chosen nine PCs, modularity optimization techniques like the Louvain algorithm were applied iteratively to group cells together and enhance the standard modularity function. The FindClusters function of Seurat was employed for this process, featuring a resolution parameter that determines the granularity of downstream clustering, where higher values result in more clusters. The UMAP function of the Seurat package was utilized for visualizing the cell clusters. These algorithms aim to identify the underlying structure in the dataset to group similar cells together in a lower-dimensional space. Thus, cells clustered within the graph-based clusters identified earlier should be close together on these dimension reduction plots. A total of 12 distinct cell clusters were identified and further examined to uncover potential molecular signatures (Fig. 6 ). 3.3 Exploring Marker Genes Those 12 cell clusters were used to discover differentially expressed genes (DEG). We used the FindAllMarkers feature of the Seurat package for finding DEGs. The LogFc cut-off value was considered < 2, where we found 6434 DEGs in a total of 12 clusters. The top 5 genes from each cluster were used to identify patterns in gene expression across cell clusters using the Doheatmap feature of the Seurat package (Fig. 7 ). 3.4 Analyzing Gene Ontology Enrichment The Gene ontology (GO) enrichment analysis focused on the top 10 significant DEGs from each cluster, as determined by their p-values. Our investigation produced a total of 269 GO entries, with the majority pertaining to biological processes (BP), followed by molecular function (MF), and cellular component (CC). Importantly, all of these entries met the stringent criterion of an Adjusted P value less than 0.05. Specifically, 230 of these terms were associated with BP, while CC and MF were represented by 18 and 21 terms, respectively. The barplot and dotplot in Fig. 8 A and 8 B, respectively exhibited the top 10 enrichment entries for these genes. 3.5 PPI Network Construction and Hub Genes Identification The aforementioned genes were subsequently employed in the construction of a protein-protein interaction (PPI) network, whereby the nodes and edges were generated by the STRING database in order to identify the potential hub genes. In this study, Cytoscape version 3.10.1, a widely utilized software tool for visualizing and analyzing biological networks, was employed to construct the PPI network of the top 10 selected genes from each cluster (Fig. 9 ). To further ascertain the hub genes within this network, the Cytohubba plugin of Cytoscape was employed, which aids in the identification of important genes within a network. Through this analysis, we were able to identify the top 20 hub genes based on their degree of relationship within the network (Fig. 10 ), as presented in Table 1 . These top 20 hub genes, which possess the highest degree of connection within the network, were found to be NKG7, TPX2, FCGR2A, CXCL8, CCNB1, EGFR, NUF2, CXCR4, HAVCR2, EPCAM, PTGS2, CD4, CCL3, CSF3R, MKI67, CCL5, FCER1G, CCRL2, IL2RB, and CEP55. Table 1 Information about the subnetwork of hub genes. Hub genes Degree EcCentricity Closeness Betweenness Clustering Coefficient NKG7 7 0.5 13 9.59214 0.71429 TPX2 5 0.33333 11.5 6.95325 0.6 FCGR2A 12 0.33333 15.33333 16.01118 0.63636 CXCL8 11 0.5 15 26.53983 0.65455 CCNB1 6 0.33333 12 9.19134 0.6 EGFR 11 0.5 15 39.28066 0.52727 NUF2 5 0.33333 11.16667 2.40476 0.8 CXCR4 11 0.33333 14.83333 10.65801 0.69091 HAVCR2 11 0.33333 14.83333 7.94452 0.72727 EPCAM 6 0.5 12.5 2.45238 0.8 PTGS2 8 0.5 13.5 12.53983 0.71429 CD4 15 0.5 17 45.48268 0.53333 CCL3 11 0.33333 14.66667 7.22937 0.72727 CSF3R 4 0.33333 10.83333 0.33333 0.83333 MKI67 8 0.5 13.5 36.86046 0.42857 CCL5 12 0.33333 15.33333 10.7715 0.69697 FCER1G 7 0.33333 12.33333 2.83333 0.7619 CCRL2 9 0.33333 13.66667 2.1881 0.86111 IL2RB 7 0.33333 12.33333 0.73333 0.90476 CEP55 4 0.33333 10 0 1 3.6 Assessing the Validity of Hub Genes The SRplot server was employed to conduct KEGG pathway analysis on the top 20 hub genes. The findings revealed that these genes exhibited upregulation in 19 major pathways with a p-value of 0.01 or lower (Table 2 ). These pathways encompassed various processes such as viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, human cytomegalovirus infection, chemokine signaling pathway, epithelial cell signaling in Helicobacter pylori infection, rheumatoid arthritis, Chagas disease, toll-like receptor signaling pathway, Yersinia infection, bladder cancer, lipid and atherosclerosis, coronavirus disease - COVID-19, Shigellosis, endocytosis, viral life cycle - HIV-1, PD-L1 expression and PD-1 checkpoint pathway in cancer, Th1 and Th2 cell differentiation, Th17 cell differentiation, and phospholipase D signaling pathway. Figures 11 A and 11 B show the major related pathways in CNET and EMAP plots and Fig. 12 A- 12 F shows the major upregulated pathways of the potential marker genes. Additionally, the mRNA expression and survivability of these genes were evaluated using the TCGA database of LSCC from the UALCAN server, where a log-rank P value below 0.05 indicated statistical significance (Figs. 12 and 13 ). Table 2 Information about 19 major upregulated pathways of the hub genes. ID Description p-value p.adjust q-value geneID Count hsa04061 Viral protein interaction with cytokine and cytokine receptor 1.31E-08 1.3E-06 9.23E-07 CCL3L3/CCL5/CXCR4/IL2RB/CXCL8 5 hsa04060 Cytokine-cytokine receptor interaction 5.34E-08 2.65E-06 1.88E-06 CCL3L3/CCL5/CXCR4/CD4/IL2RB/CXCL8 6 hsa05163 Human cytomegalovirus infection 7.69E-07 2.54E-05 1.81E-05 CCL3L3/CCL5/CXCR4/EGFR/CXCL8 5 hsa04062 Chemokine signaling pathway 1.87E-05 0.000464 0.00033 CCL3L3/CCL5/CXCR4/CXCL8 4 hsa05120 Epithelial cell signaling in Helicobacter pylori infection 3.21E-05 0.000635 0.000453 CCL5/EGFR/CXCL8 3 hsa05323 Rheumatoid arthritis 7.53E-05 0.001242 0.000885 CCL3L3/CCL5/CXCL8 3 hsa05142 Chagas disease 9.92E-05 0.001301 0.000927 CCL3L3/CCL5/CXCL8 3 hsa04620 Toll-like receptor signaling pathway 0.000105 0.001301 0.000927 CCL3L3/CCL5/CXCL8 3 hsa05135 Yersinia infection 0.000238 0.002622 0.001868 CD4/CXCL8/FCGR2A 3 hsa05219 Bladder cancer 0.000666 0.006598 0.0047 EGFR/CXCL8 2 hsa05417 Lipid and atherosclerosis 0.000896 0.008062 0.005743 CCL3L3/CCL5/CXCL8 3 hsa05171 Coronavirus disease - COVID-19 0.001118 0.009222 0.006569 EGFR/CXCL8/FCGR2A 3 hsa05131 Shigellosis 0.001341 0.009932 0.007075 CCL5/EGFR/CXCL8 3 hsa04144 Endocytosis 0.001404 0.009932 0.007075 CXCR4/EGFR/IL2RB 3 hsa03250 Viral life cycle - HIV-1 0.00157 0.010364 0.007383 CXCR4/CD4 2 hsa05235 PD-L1 expression and PD-1 checkpoint pathway in cancer 0.003109 0.019236 0.013704 EGFR/CD4 2 hsa04658 Th1 and Th2 cell differentiation 0.003318 0.019325 0.013767 CD4/IL2RB 2 hsa04659 Th17 cell differentiation 0.004545 0.024997 0.017807 CD4/IL2RB 2 hsa04072 Phospholipase D signaling pathway 0.008391 0.043721 0.031146 EGFR/CXCL8 2 3.7 Identifying Molecular Signatures and Potential Therapeutic Targets for LSCC CCL3, EPCAM, and IL8 exhibit a greater level of mRNA expression in comparison to the normal samples, and it is noteworthy that these genes possess a p-value that falls below the threshold of 0.05, thereby signifying their statistical significance and establishing a robust association with the overall survivability of LSCC. Furthermore, it is worth mentioning that these genes have also demonstrated upregulation in multiple pathways when contrasted with the normal samples, thereby accentuating their potential contributions in the context of LSCC survival. 4. Discussion The analysis of scRNA-seq data presents profound insights into the intricate cellular heterogeneity of the molecular landscape in LSCC [ 34 ]. Through the utilization of R, this investigation has elucidated fundamental aspects of LSCC biology, paving the path for innovative diagnostic and therapeutic approaches. A notable discovery from this study is the recognition of tumor diversity within the microenvironment of LSCC. By implementing rigorous data preprocessing and quality assurance procedures, the reliability of subsequent analyses was ensured, establishing a strong basis for unveiling the inherent cellular diversity in LSCC [ 35 ]. This initial phase is critical for comprehending the complex interactions among distinct cell populations within the tumor, with substantial implications for disease advancement and treatment response. The subsequent examination of cellular diversity utilizing PCA revealed unique gene modules that exhibit correlations across individual cells. This strategy not only emphasizes the molecular heterogeneity in LSCC but also offers valuable insights into the underlying regulatory networks steering tumor growth and advancement [ 36 ], [ 37 ]. The recognition of 12 distinct cell clusters further accentuates the intricacy of LSCC biology, with each cluster likely embodying unique cellular conditions or subpopulations with specific functional roles. By scrutinizing DEGs across these clusters, the study disclosed potential molecular markers linked with particular cellular subtypes, providing crucial hints for understanding the mechanisms steering tumor diversity and progression [ 38 ], [ 39 ]. Additionally, the GO enrichment analysis of genes provided functional context to the identified DEGs, elucidating the enriched biological processes, molecular functions, and cellular components within the LSCC dataset [ 40 ]. This thorough analysis not only aids in interpreting the functional importance of the recognized genes but also offers valuable insights into the biological pathways steering LSCC pathogenesis. The establishment of a PPI network further emphasized crucial hub genes with significant functional importance in LSCC, presenting potential targets for therapeutic interventions. The top 20 hub genes identified, such as NKG7, TPX2, and EGFR, present promising candidates for further exploration and validation as potential biomarkers or therapeutic targets in LSCC. The validation of hub genes through pathway analysis and survival assessment further underscores their clinical relevance in LSCC [ 41 ]. The upregulation of these genes in various cancer-related pathways, alongside their substantial associations with LSCC survival, highlights their potential utility as prognostic indicators and therapeutic targets. Furthermore, the recognition of specific genes like CCL3, EGFR, and EPCAM as potential molecular markers for LSCC may show promise for enhancing cancer prognosis and treatment outcomes in LSCC patients. These findings enhance our comprehension of LSCC biology and offer concrete clinical implications for personalized medicine strategies in LSCC treatment. Furthermore, the integration of multi-omics data and the application of advanced computational algorithms hold immense potential for further unraveling the complexities of LSCC biology and identifying novel therapeutic avenues [ 42 ]. By leveraging cutting-edge technologies and analytical methods, future studies can build upon the findings of this study to develop more precise diagnostic tools, prognostic indicators, and targeted therapies tailored to the individual molecular profiles of LSCC patients [ 43 ]. 5. Conclusion Taken together, the study is an important contribution to a more comprehensive portrait of molecular pathways and cell composition in LSCCs. This study clarified the molecular basis and core hub genes of LSCC, providing an impetus for further studies on novel diagnostic markers or therapeutic targets to improve patient outcomes in LSCC. In the future, further studies exploring the sophisticated mechanisms of LSCC development and resistance to therapy are warranted to achieve improved fulfilling precision oncology methods for becoming a game-changer in managing this insidious disease. Abbreviations scRNA-seq, single-cell RNA sequencing; LSCC, Laryngeal Squamous Cell Carcinoma ; DEGs, differentially expressed genes; PPI, Protein-Protein interaction; KEGG, Kyoto Encyclopedia of Genes and Genomes; TME, Tumor microenvironment; PCA, Principle component analysis; GO, Gene Ontology. Declarations Acknowledgments The authors are immensely grateful to each other for their intellectual contributions to this project. Ethics Approval and Consent to Participate Not Applicable Consent for Publication Not Applicable Availability of Data and Materials Not Applicable Competing Interests The authors declare that they have no competing interests. Funding No external or internal funding was received for this project. Authors’ Contributions HJS: Writing-Original draft preparation, resources, data curation, visualization, investigation. PB: Investigation, data curation. MI: Investigation, resources. SM: Investigation, visualization. MNH: Conceptualization, validation, supervision, project administration. All authors have read and agreed to the published version of the manuscript. References Falco M et al (2022) Overview on Molecular Biomarkers for Laryngeal Cancer: Looking for New Answers to an Old Problem. Cancers (Basel) 14(7). 10.3390/cancers14071716 Mannelli G, Gallo O (2012) Cancer stem cells hypothesis and stem cells in head and neck cancers. Cancer Treat Rev 38(5):515–539. 10.1016/j.ctrv.2011.11.007 Biswas P et al (2021) Analysis of SYK gene as a prognostic biomarker and suggested potential bioactive phytochemicals as an alternative therapeutic option for colorectal cancer: An in-silico pharmaco-informatics investigation. J Pers Med 11(9). 10.3390/jpm11090888 Wang D et al (2020) Targeting laryngeal cancer cells with 5-fluorouracil and curcumin using mesoporous silica nanoparticles. Technol Cancer Res Treat 19:1–11. 10.1177/1533033820962114 Gao J, Tao Z (2021) HPV, p16 and p53 As Indicators of Response to Survival in Larynx Squamous Cell Carcinoma HPV, p16 and p53 As Indicators of Response to Survival in Larynx Squamous Cell Carcinoma Fong PY et al (2019) Association of clinical factors with survival outcomes in laryngeal squamous cell carcinoma (LSCC). PLoS ONE 14(11):1–18. 10.1371/journal.pone.0224665 Bhat AA et al (2021) Tumor microenvironment: an evil nexus promoting aggressive head and neck squamous cell carcinoma and avenue for targeted therapy. Signal Transduct Target Ther 6(1). 10.1038/s41392-020-00419-w Niu Z, Jin R, Zhang Y, Li H (2022) Signaling pathways and targeted therapies in lung squamous cell carcinoma: mechanisms and clinical trials. Signal Transduct Target Ther 7(1). 10.1038/s41392-022-01200-x Shovon MHJ, Khan DA, Tareq MMI, Imtiaz M, Zilani MNH, Hasan MN (2023) A comprehensive assessment of VCAN transcriptional expression and evaluation as an effective prognostic biomarker against breast cancer: in silico study. Bull Natl Res Cent 47(1). 10.1186/s42269-023-01062-5 Neophytou CM, Panagi M, Stylianopoulos T, Papageorgis P (2021) The role of tumor microenvironment in cancer metastasis: Molecular mechanisms and therapeutic opportunities. Cancers (Basel) 13(9). 10.3390/cancers13092053 Du W, Xia X, Hu F, Yu J (2023) Extracellular matrix remodeling in the tumor immunity. Front Immunol 14:1–16. 10.3389/fimmu.2023.1340634 Wilk AJ, Shalek AK, Holmes S, Blish CA (2024) Comparative analysis of cell–cell communication at single-cell resolution. no 3 Springer US 42. 10.1038/s41587-023-01782-z Communication CC, Cheng C, Chen W, Jin H, Chen X (2023) A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication, pp. 1–39 Zhou ZQ et al (2023) Dissecting cellular heterogeneity and intercellular communication in cholangiocarcinoma: implications for individualized therapeutic strategies. Front Genet 14:1–10. 10.3389/fgene.2023.1241834 Sun Y, Chen S, Lu Y, Xu Z, Fu W, Yan W (2024) Single-cell transcriptomic analyses of tumor microenvironment and molecular reprograming landscape of metastatic laryngeal squamous cell carcinoma. Commun Biol 7(1):1–15. 10.1038/s42003-024-05765-x Liang WW et al (2023) Integrative multi-omic cancer profiling reveals DNA methylation patterns associated with therapeutic vulnerability and cell-of-origin. Cancer Cell 41(9):1567–1585. 10.1016/j.ccell.2023.07.013 Wang H et al (2024) Identification and preliminary analysis of hub genes associated with bladder cancer progression by comprehensive bioinformatics analysis. Sci Rep 14(1):1–12. 10.1038/s41598-024-53265-z Xu K, Yu D, Zhang S, Chen L, Liu Z, Xie L (2024) Deciphering the Immune Microenvironment at the Forefront of Tumor Aggressiveness by Constructing a Regulatory Network with Single-Cell and Spatial Transcriptomic Data, Genes (Basel). , vol. 15, no. 1. 10.3390/genes15010100 Wang J, Zou Q, Lin C (2022) A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data. Brief Bioinform 23(1):1–19. 10.1093/bib/bbab345 Hanley CJ et al (2023) Single-cell analysis reveals prognostic fibroblast subpopulations linked to molecular and immunological subtypes of lung cancer. Nat Commun 14(1). 10.1038/s41467-023-35832-6 Hsu LL, Culhane AC (2023) Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data. Sci Rep 13(1):1–17. 10.1038/s41598-022-26434-1 Zhang B et al (2023) Single-cell RNA sequencing reveals intratumoral heterogeneity and potential mechanisms of malignant progression in prostate cancer with perineural invasion. Front Genet 13:1–10. 10.3389/fgene.2022.1073232 Sankowski R et al (2024) Multiomic spatial landscape of innate immune cells at human central nervous system borders. Nat Med 30(1):186–198. 10.1038/s41591-023-02673-1 Xia L, Lee C, Li JJ (2024) Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters. Nat Commun 15(1):1–21. 10.1038/s41467-024-45891-y Morabito S, Reese F, Rahimzadeh N, Miyoshi E, Swarup V (2023) hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep Methods 3(6):100498. 10.1016/j.crmeth.2023.100498 Song X et al (2023) Analyzation of the Peripheral Blood Mononuclear Cells Atlas and Cell Communication of Rheumatoid Arthritis Patients Based on Single-Cell RNA-Seq, J. Immunol. Res. , vol. 2023. 10.1155/2023/6300633 Yin M et al (2023) Identification of key genes and pathways in adrenocortical carcinoma: evidence from bioinformatic analysis, Front. Endocrinol. (Lausanne). , vol. 14, no. November, pp. 1–12. 10.3389/fendo.2023.1250033 Wan H, Teh MT, Mastroianni G, Ahmad US (2023) Comparative Transcriptome Analysis Identifies Desmoglein-3 as a Potential Oncogene in Oral Cancer Cells. Cells 12(23):1–19. 10.3390/cells12232710 Hu H et al (2023) Network pharmacology analysis reveals potential targets and mechanisms of proton pump inhibitors in breast cancer with diabetes. Sci Rep 13(1):1–13. 10.1038/s41598-023-34524-x Basar MA, Hosen MF, Kumar Paul B, Hasan MR, Shamim SM, Bhuyian T (2022) Identification of drug and protein-protein interaction network among stress and depression: A bioinformatics approach, Informatics Med. Unlocked , vol. 37, no. December p. 101174, 2023. 10.1016/j.imu.2023.101174 Jiao K, Su P, Feng Y, Li C (2023) Bioinformatics analysis and identification of hub genes associated with female acute myocardial infarction patients by using weighted gene co-expression networks. Med (United States) 102(17):E33634. 10.1097/MD.0000000000033634 Tanim MTH, Nath SD, Khan SF, Khan A, Sajib AA (2024) Transcriptomes of cervical cancer provide novel insights into dysregulated pathways, potential therapeutic targets, and repurposed drugs. Cancer Treat Res Commun 39:100808. 10.1016/j.ctarc.2024.100808 Yu S et al (2023) Pancancer analysis of oncogenic BARX2 identifying its prognostic value and immunological function in liver hepatocellular carcinoma. Sci Rep 13(1):1–14. 10.1038/s41598-023-34519-8 Deng G et al (2023) Single-cell transcriptome sequencing reveals heterogeneity of gastric cancer: progress and prospects. Front Oncol 13:1–11. 10.3389/fonc.2023.1074268 Li Y et al (2023) Proteogenomic data and resources for pan-cancer analysis. Cancer Cell 41(8):1397–1406. 10.1016/j.ccell.2023.06.009 Jiang Xfan, jing Jiang W (2023) The construction and validation of ECM-related prognosis model in laryngeal squamous cell carcinoma. Heliyon 9(9):e19907. 10.1016/j.heliyon.2023.e19907 Munshi M et al (2022) Novel compounds from endophytic fungi of Ceriops decandra inhibit breast cancer cell growth through estrogen receptor alpha in in-silico study. Inf Med Unlocked 32:101046. 10.1016/j.imu.2022.101046 Biswas P et al (2022) A Comprehensive Analysis and Anti-Cancer Activities of Quercetin in ROS-Mediated Cancer and Cancer Stem Cells. Int J Mol Sci 23(19). 10.3390/ijms231911746 Niemira M et al (2020) Molecular signature of subtypes of non-small-cell lung cancer by large-scale transcriptional profiling: Identification of key modules and genes by weighted gene co-expression network analysis (WGCNA), Cancers (Basel). , vol. 12, no. 1. 10.3390/cancers12010037 Ma B, Huang Z, Wang Q, Zhang J, Zhou B, Wu J (2019) Integrative analysis of genetic and epigenetic profiling of lung squamous cell carcinoma (LSCC) patients to identify smoking level relevant biomarkers. BioData Min 12(1):1–18. 10.1186/s13040-019-0207-y Hong F, Wan X, Bai Y (2024) Identification of Down-Expressed CRNN Associated with Cancer Progression and Poor Prognosis in Laryngeal Squamous Cell Carcinoma. Front Biosci (Landmark Ed 29(3):125. 10.31083/j.fbl2903125 Subramanian I, Verma S, Kumar S, Jere A, Anamika K (2020) Multi-omics Data Integration, Interpretation, and Its Application. Bioinform Biol Insights 14:7–9. 10.1177/1177932219899051 Huang J, Zhang Q, Pan GZ, Hu X, Chen D, Zhang K (2023) Editorial: Biomarkers, functional mechanisms, and therapeutic potentials in gastrointestinal cancers, Front. Oncol. , vol. 13, no. October, pp. 1–5. 10.3389/fonc.2023.1276414 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Jun, 2025 Read the published version in In Silico Pharmacology → Version 1 posted Editorial decision: Revision requested 19 May, 2025 Reviews received at journal 18 May, 2025 Reviewers agreed at journal 18 May, 2025 Reviews received at journal 17 May, 2025 Reviewers agreed at journal 04 May, 2025 Reviewers invited by journal 01 May, 2025 Editor assigned by journal 24 Apr, 2025 Submission checks completed at journal 24 Apr, 2025 First submitted to journal 24 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6517892","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451636674,"identity":"fff5c80c-0aaf-4944-a8de-64d0b11290b8","order_by":0,"name":"Md. Hasan Jafre Shovon","email":"data:image/png;base64,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","orcid":"","institution":"Jashore University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Hasan Jafre","lastName":"Shovon","suffix":""},{"id":451636675,"identity":"e9422072-b87a-43bd-8631-43242cb232b5","order_by":1,"name":"Partha Biswas","email":"","orcid":"","institution":"Jashore University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Partha","middleName":"","lastName":"Biswas","suffix":""},{"id":451636676,"identity":"253457e6-e36b-4ece-acfd-bdc55c8dc570","order_by":2,"name":"Md. Imtiaz","email":"","orcid":"","institution":"Jashore University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"","lastName":"Imtiaz","suffix":""},{"id":451636677,"identity":"6ca0760a-6a1b-4457-982d-405e2021236f","order_by":3,"name":"Shirajut Mobin","email":"","orcid":"","institution":"Jashore University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shirajut","middleName":"","lastName":"Mobin","suffix":""},{"id":451636678,"identity":"1caf8ed6-4053-4206-a33f-7556ed87cd81","order_by":4,"name":"Md. Nazmul Hasan","email":"","orcid":"","institution":"Jashore University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Nazmul","lastName":"Hasan","suffix":""}],"badges":[],"createdAt":"2025-04-24 06:53:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6517892/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6517892/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40203-025-00382-w","type":"published","date":"2025-06-17T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82207651,"identity":"4bbf874c-6c6a-4daf-a2cd-2261caee3fa5","added_by":"auto","created_at":"2025-05-07 17:57:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":362934,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots of gene number, count number, and mitochondrial percentage in LSCC\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/61adf4a6193d7a54aedc5655.jpeg"},{"id":82207049,"identity":"11843cd2-b4c4-46b4-aaa0-b0951564b4ef","added_by":"auto","created_at":"2025-05-07 17:49:57","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177549,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of top 10 variable genes in LSCC\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/139340f14425630b8d992a54.jpeg"},{"id":82206819,"identity":"02e14fe7-323c-4abd-a2fc-823f94067168","added_by":"auto","created_at":"2025-05-07 17:41:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44307,"visible":true,"origin":"","legend":"\u003cp\u003eElbow plot for selecting the best principal components in LSCC\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/1b742a2f2492cbce41334c9a.jpeg"},{"id":82206822,"identity":"93ec52da-0def-417a-91ef-0ab22162fd97","added_by":"auto","created_at":"2025-05-07 17:41:57","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":164617,"visible":true,"origin":"","legend":"\u003cp\u003eVizDimplot showing genes that exhibit either correlation (or anti-correlation) across 9 selected PC in LSCC.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/fe1fd04bedebed15d3d0d25a.jpeg"},{"id":82206824,"identity":"08d58640-9f0f-4ba0-9c91-38419bc61ad7","added_by":"auto","created_at":"2025-05-07 17:41:57","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":662061,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmaps showing the top 15 marker genes in each selected PC in LSCC.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/b30790fa7e485c24c8f90ebf.jpeg"},{"id":82206834,"identity":"c3e071bd-2407-4498-84c3-b429d70adab7","added_by":"auto","created_at":"2025-05-07 17:41:57","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":223084,"visible":true,"origin":"","legend":"\u003cp\u003eClustering based on UMAP dimension reduction in LSCC.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/6a32d9cadecb2a178ed301dd.jpeg"},{"id":82207054,"identity":"cac45f62-69a9-487e-a042-937c94da5c78","added_by":"auto","created_at":"2025-05-07 17:49:57","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":566489,"visible":true,"origin":"","legend":"\u003cp\u003eheatmap of top 5 unique marker genes from each cluster in LSCC.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/6e9e1a752ef27733a5c09bbc.jpeg"},{"id":82207052,"identity":"94d58fd1-e976-441d-a966-9a113f817069","added_by":"auto","created_at":"2025-05-07 17:49:57","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1061684,"visible":true,"origin":"","legend":"\u003cp\u003eThe plots related to the analysis of gene ontology (GO) enrichment. A) The top 10 enriched functions are displayed as a bar plot, and B) The top 10 enriched functions are shown as a dot plot.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/c6b79cb86796f0860d8c0746.jpeg"},{"id":82208172,"identity":"0768e853-e661-4806-aa67-28774c32de3e","added_by":"auto","created_at":"2025-05-07 18:05:57","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":234879,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network of top 10 genes from each cluster.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/2f9d7c6bea30c41faf1ae904.jpeg"},{"id":82208171,"identity":"f32b9786-a2a0-4c0d-9a3a-de4c45a3b8cf","added_by":"auto","created_at":"2025-05-07 18:05:57","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":130859,"visible":true,"origin":"","legend":"\u003cp\u003eSubnetwork of hub genes.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/6b12d6ad33edf4d600a2d5aa.png"},{"id":82206849,"identity":"f7850d10-954d-4cbf-b21c-dacc432a3ad7","added_by":"auto","created_at":"2025-05-07 17:41:58","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":790251,"visible":true,"origin":"","legend":"\u003cp\u003eMajor upregulated KEGG pathway of the hub genes. A) CNET plot and, B) EMAP plot.\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/c62afcabd00f25b73b2c9227.jpeg"},{"id":82207656,"identity":"0706150e-c3c3-4589-8681-64f795ec2d54","added_by":"auto","created_at":"2025-05-07 17:57:57","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":632103,"visible":true,"origin":"","legend":"\u003cp\u003eMajor elevated pathways involving the marker genes\u003c/p\u003e","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/a22e467a84231639f6d3afac.jpeg"},{"id":82207652,"identity":"d24e0ed1-6e0f-47ba-8f26-e7f95cdedffb","added_by":"auto","created_at":"2025-05-07 17:57:57","extension":"jpeg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":96862,"visible":true,"origin":"","legend":"\u003cp\u003eBased on the TCGA box plots showing the gene expression of hub genes in LSCC; \u0026nbsp;CCL3, IL8, and EPCAM expression is displayed in LSCC and normal samples respectively as red and blue boxes.\u003c/p\u003e","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/34fabc68e722aafc74ce4d40.jpeg"},{"id":82206844,"identity":"fb29b448-5eb9-41eb-b96c-1c4d44c8795d","added_by":"auto","created_at":"2025-05-07 17:41:57","extension":"jpeg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":109630,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall survival of LSCC patients is related to the Kaplan-Meier survival curves of CCL3, IL8, and EPCAM.\u003c/p\u003e","description":"","filename":"floatimage14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/720c341df4ddb0eb359c98ac.jpeg"},{"id":85231446,"identity":"835a80a3-08ca-4650-8b1e-e415b8e4fe18","added_by":"auto","created_at":"2025-06-23 16:08:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6233027,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6517892/v1/91992b78-49ae-479c-9d35-162cd8b3f232.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell RNA seq data analysis reveals molecular markers and possible treatment targets for Laryngeal Squamous Cell Carcinoma (LSCC): An in-silico approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLaryngeal squamous cell carcinoma (LSCC) poses a significant challenge in the field of oncology, representing a prevalent and debilitating form of head and neck cancer with profound implications for patient morbidity and mortality. Despite advancements in diagnostic techniques and therapeutic interventions, the prognosis for LSCC patients remains suboptimal, particularly in cases of advanced disease or treatment resistance [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As such, there is a pressing need to deepen our understanding of the molecular mechanisms driving LSCC pathogenesis and to identify novel therapeutic targets that could revolutionize patient care and outcomes.\u003c/p\u003e \u003cp\u003eThe burden of LSCC extends beyond its clinical manifestations to encompass socioeconomic ramifications and strain on healthcare systems worldwide. With an estimated 177,000 new cases diagnosed annually and over 94,000 deaths attributed to the disease globally, LSCC exerts a significant toll on individuals, families, and societies at large [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, the functional implications of laryngeal involvement, including compromised speech production and airway patency, underscore the urgency of addressing this malignancy comprehensively and effectively. Despite the advent of multimodal treatment approaches, such as surgery, radiotherapy, chemotherapy, and targeted therapies, the prognosis for LSCC patients remains far from satisfactory, necessitating innovative strategies to improve outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHistologically, LSCC encompasses a spectrum of tumors with diverse morphological phenotypes, ranging from well-differentiated lesions to poorly differentiated or undifferentiated neoplasms [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This histological heterogeneity is mirrored by molecular diversity, with distinct molecular subtypes harboring unique genetic alterations and dysregulated signaling pathways. Comprehensive molecular profiling studies have identified key genomic alterations, including mutations in tumor suppressor genes (e.g., TP53, CDKN2A) and oncogenes (e.g., EGFR, PIK3CA), as well as aberrant DNA methylation patterns [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Understanding these molecular alterations is crucial for delineating the underlying mechanisms driving LSCC tumorigenesis and progression.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment (TME) plays a pivotal role in shaping LSCC biology, influencing tumor growth, invasion, and response to therapy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Comprising cancer cells, stromal components, immune cells, and extracellular matrix molecules, the TME orchestrates complex interactions that promote tumor progression and immune evasion [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Inflammation, angiogenesis, and immune suppression within the TME create a supportive niche for tumor growth, facilitating metastasis and therapeutic resistance. Understanding the cellular and molecular dynamics within the TME is essential for identifying novel therapeutic targets and developing effective immunomodulatory strategies for LSCC.\u003c/p\u003e \u003cp\u003eIn this context, single-cell RNA sequencing (scRNA-seq) emerges as a powerful tool for dissecting the molecular landscape of LSCC at unprecedented resolution. By profiling individual cells within tumor tissues, scRNA-seq enables the identification of heterogeneous cell populations, characterization of cell states, and elucidation of intercellular communication networks [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this study, we employed an in-silico approach utilizing scRNA-seq data analysis utilizing R (version 4.3.2) to unveil molecular signatures and potential personalized therapeutic targets in LSCC. Leveraging publicly available datasets, we conducted a comprehensive analysis encompassing data processing, principal component analysis, cell clustering, marker gene analysis, gene ontology enrichment, protein-protein interaction network construction, and hub gene identification. Through integrative bioinformatics analyses, we aimed to uncover novel insights into LSCC pathobiology and identify candidate genes with diagnostic, prognostic, or therapeutic significance.\u003c/p\u003e \u003cp\u003eIn the subsequent sections, we detailed the methods employed for data acquisition, processing, and analysis, followed by a comprehensive presentation of our results. We elucidate the tumor heterogeneity observed in the scRNA-seq dataset, unveil cellular diversity within LSCC, explore marker genes associated with distinct cell clusters, analyze gene ontology enrichment to delineate biological roles, construct protein-protein interaction networks to identify hub genes, and assess the validity of hub genes through pathway analysis and survival analysis [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Finally, we discuss the implications of our findings and highlight the potential of identified molecular signatures as therapeutic targets for LSCC. Through this in-depth analysis, we aim to contribute to the growing body of knowledge surrounding LSCC pathogenesis and pave the way for the development of precision medicine approaches tailored to individual patient needs.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Single-Cell RNA-Seq Data Collection\u003c/h2\u003e \u003cp\u003eFor the analysis of Laryngeal squamous cell carcinoma (LSCC), we collected single-cell Rna-seq data (GSE252490) by accessing the gene expression omnibus server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The dataset was sequenced on Illumina NovaSeq 6000 (Homo sapiens) which contains three patient samples. We randomly chose GSM8002074 as the target LSCC sample for the goal of finding molecular signatures and potential targets to treat LSCC patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Processing\u003c/h2\u003e \u003cp\u003eUsing the Seurat package in R (version 4.3.2), at first, we processed the data as often it\u0026rsquo;s found that there could be some unwanted cells that may have fewer genes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. There may be cell doublets or multiplets that may aberrantly exhibit high gene counts and there is often the occurrence of high levels of mitochondrial contamination due to low-quality or drying cells. We then normalized the data followed by the scaling of the data after the preprocessing [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Subsequently, we used the FindVariable feature of the Seurat package for the identification of highly variable genes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Exploration of Principle Component\u003c/h2\u003e \u003cp\u003eThe data that had been scaled was subsequently utilized for linear dimension reduction in order to conduct an analysis of the principal components [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this analysis, only genes that exhibit high variability were taken into consideration. Seurat, in the case of the first principal components, generates a compilation of genes with the most positive and negative loadings. These genes represent groups of genes that demonstrate either correlation or anti-correlation among individual cells within the dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Grouping Cells by Similarity\u003c/h2\u003e \u003cp\u003eIn order to address the considerable technical noise present in each individual feature of scRNA-seq data, the Seurat package was utilized for the clustering of cells based on their PCA scores [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Each principal component essentially acts as a 'metafeature' by amalgamating information from a correlated set of features. Cell clustering is achieved by utilizing modularity optimization techniques like the Louvain algorithm. This process involves iteratively grouping cells together to enhance the standard modularity function. The FindClusters feature within the Seurat package executes this procedure and includes a resolution parameter that determines the 'granularity' of subsequent clustering [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Higher values of this parameter result in a greater number of clusters being formed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Identifying Molecular Signatures\u003c/h2\u003e \u003cp\u003eUniform Manifold Approximation and Projection (UMAP) were used for dimension reduction through the use of the RunUMAP feature of the Seurat package for the visualization of different cell clusters [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Each of the clusters was used for getting possible marker genes. The FindAll-Markers feature of the Seurat package was used for identifying the marker genes with Log2FC cut-off value was considered 2 or more as a threshold [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Gene Ontology (GO) Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe top 10 genes from each cluster were selected for further analysis. The top 10 genes from each cluster were selected based on the p-value and they were subjected to the gene ontology (GO) enrichment analysis to check the biological, cellular, and molecular roles of the genes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The GO enrichment analysis was done from the SRplot server where the threshold for the functional category was set at an adjusted p-value of 0.05 or less [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Construction Of PPI Network And Hub Genes Validation\u003c/h2\u003e \u003cp\u003eThe top genes from each cluster were then subjected to the construction of a PPI network using the STRING database [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The visualization of the PPI network was carried out using the software Cytoscape [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Hub genes within the PPI network were identified based on their high degree of confidence and stringent FDR, in conjunction with data derived from various databases [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Through this method, the top 20 hub genes were pinpointed and further scrutinized to validate their status as molecular markers. The engagement of the hub genes in different pathways was then checked based on the KEGG database from the SRplot server where the p-value was set to 0.05 as the threshold [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The mRNA expression and survivability of the top 20 hub genes were then checked from the UALCAN server [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Tumor Heterogeneity in Single-Cell RNA-Seq Dataset Processing\u003c/h2\u003e \u003cp\u003eSingle-cell RNA seq dataset (GSE252490) was used for analysis and finding potential molecular signatures for diagnosis and therapeutic targets for LSCC. We first checked the quality of the dataset and filtered out the dataset based on the count number, gene count, and mitochondrial percentage using the Seurat package of R. We kept cells that have at least 200 molecules, genes that have at least expressed in three different cells and filtered out cells that have more than 5% of mitochondrial genes showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. After that, we normalized the data to remove unwanted cells from the dataset. We used the LogNormalize feature of the Seurat package for data normalization. The normalized data was then used to find the cell-to-cell variable genes. We retrieved the top 2000 variable genes from the dataset for further downstream analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the top 10 variable genes from the dataset. Subsequently, a linear transformation (data scaling) was employed, and a standard pre-processing procedure was carried out before employing dimensional reduction methodologies. The Seurat package's ScaleData feature was utilized for data scaling, a necessary step for examining the principal components.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.2 Unveiling Cellular Diversity\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eFollowing this, principal component analysis (PCA) was conducted on the standardized data. Seurat provides a list of genes with the highest positive and negative loadings for the first principal components, demonstrating groups of genes that display either correlation or anti-correlation across individual cells within the dataset. An 'Elbow plot' is generated using a heuristic approach in the Seurat package, ranking the principal components based on the percentage of variance explained by each component (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on variance, the initial nine PCs were selected for further investigation. The variability in the first nine PCs was illustrated through VizDimplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and DimHeatmaps (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). To cluster cells based on the chosen nine PCs, modularity optimization techniques like the Louvain algorithm were applied iteratively to group cells together and enhance the standard modularity function. The FindClusters function of Seurat was employed for this process, featuring a resolution parameter that determines the granularity of downstream clustering, where higher values result in more clusters. The UMAP function of the Seurat package was utilized for visualizing the cell clusters. These algorithms aim to identify the underlying structure in the dataset to group similar cells together in a lower-dimensional space. Thus, cells clustered within the graph-based clusters identified earlier should be close together on these dimension reduction plots. A total of 12 distinct cell clusters were identified and further examined to uncover potential molecular signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Exploring Marker Genes\u003c/h2\u003e \u003cp\u003eThose 12 cell clusters were used to discover differentially expressed genes (DEG). We used the FindAllMarkers feature of the Seurat package for finding DEGs. The LogFc cut-off value was considered\u0026thinsp;\u0026lt;\u0026thinsp;2, where we found 6434 DEGs in a total of 12 clusters. The top 5 genes from each cluster were used to identify patterns in gene expression across cell clusters using the Doheatmap feature of the Seurat package (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analyzing Gene Ontology Enrichment\u003c/h2\u003e \u003cp\u003eThe Gene ontology (GO) enrichment analysis focused on the top 10 significant DEGs from each cluster, as determined by their p-values. Our investigation produced a total of 269 GO entries, with the majority pertaining to biological processes (BP), followed by molecular function (MF), and cellular component (CC). Importantly, all of these entries met the stringent criterion of an Adjusted P value less than 0.05. Specifically, 230 of these terms were associated with BP, while CC and MF were represented by 18 and 21 terms, respectively. The barplot and dotplot in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB, respectively exhibited the top 10 enrichment entries for these genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 PPI Network Construction and Hub Genes Identification\u003c/h2\u003e \u003cp\u003eThe aforementioned genes were subsequently employed in the construction of a protein-protein interaction (PPI) network, whereby the nodes and edges were generated by the STRING database in order to identify the potential hub genes. In this study, Cytoscape version 3.10.1, a widely utilized software tool for visualizing and analyzing biological networks, was employed to construct the PPI network of the top 10 selected genes from each cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). To further ascertain the hub genes within this network, the Cytohubba plugin of Cytoscape was employed, which aids in the identification of important genes within a network. Through this analysis, we were able to identify the top 20 hub genes based on their degree of relationship within the network (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e), as presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These top 20 hub genes, which possess the highest degree of connection within the network, were found to be NKG7, TPX2, FCGR2A, CXCL8, CCNB1, EGFR, NUF2, CXCR4, HAVCR2, EPCAM, PTGS2, CD4, CCL3, CSF3R, MKI67, CCL5, FCER1G, CCRL2, IL2RB, and CEP55.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation about the subnetwork of hub genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHub genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEcCentricity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCloseness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBetweenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClustering Coefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNKG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.59214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.95325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCGR2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.01118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.53983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCNB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.19134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.28066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNUF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.16667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.40476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.83333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.65801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAVCR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.83333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.94452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEPCAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.45238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTGS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.53983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.48268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.66667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.22937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.83333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMKI67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.86046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.7715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCER1G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.83333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCRL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.66667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL2RB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEP55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Assessing the Validity of Hub Genes\u003c/h2\u003e \u003cp\u003eThe SRplot server was employed to conduct KEGG pathway analysis on the top 20 hub genes. The findings revealed that these genes exhibited upregulation in 19 major pathways with a p-value of 0.01 or lower (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These pathways encompassed various processes such as viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, human cytomegalovirus infection, chemokine signaling pathway, epithelial cell signaling in Helicobacter pylori infection, rheumatoid arthritis, Chagas disease, toll-like receptor signaling pathway, Yersinia infection, bladder cancer, lipid and atherosclerosis, coronavirus disease - COVID-19, Shigellosis, endocytosis, viral life cycle - HIV-1, PD-L1 expression and PD-1 checkpoint pathway in cancer, Th1 and Th2 cell differentiation, Th17 cell differentiation, and phospholipase D signaling pathway. Figures\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB show the major related pathways in CNET and EMAP plots and Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA-\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eF shows the major upregulated pathways of the potential marker genes. Additionally, the mRNA expression and survivability of these genes were evaluated using the TCGA database of LSCC from the UALCAN server, where a log-rank P value below 0.05 indicated statistical significance (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation about 19 major upregulated pathways of the hub genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.adjust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eq-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003egeneID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViral protein interaction with cytokine and cytokine receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.23E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCCL3L3/CCL5/CXCR4/IL2RB/CXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCytokine-cytokine receptor interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.34E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.65E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.88E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCCL3L3/CCL5/CXCR4/CD4/IL2RB/CXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman cytomegalovirus infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.69E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.54E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.81E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCCL3L3/CCL5/CXCR4/EGFR/CXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChemokine signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCCL3L3/CCL5/CXCR4/CXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEpithelial cell signaling in Helicobacter pylori infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.21E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCCL5/EGFR/CXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRheumatoid arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.53E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCCL3L3/CCL5/CXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChagas disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.92E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCCL3L3/CCL5/CXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eToll-like receptor signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCCL3L3/CCL5/CXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYersinia infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD4/CXCL8/FCGR2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBladder cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEGFR/CXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid and atherosclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCCL3L3/CCL5/CXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoronavirus disease - COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEGFR/CXCL8/FCGR2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShigellosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCCL5/EGFR/CXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndocytosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCXCR4/EGFR/IL2RB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa03250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViral life cycle - HIV-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCXCR4/CD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD-L1 expression and PD-1 checkpoint pathway in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEGFR/CD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTh1 and Th2 cell differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD4/IL2RB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTh17 cell differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD4/IL2RB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhospholipase D signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEGFR/CXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.7 Identifying Molecular Signatures and Potential Therapeutic Targets for LSCC\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eCCL3, EPCAM, and IL8 exhibit a greater level of mRNA expression in comparison to the normal samples, and it is noteworthy that these genes possess a p-value that falls below the threshold of 0.05, thereby signifying their statistical significance and establishing a robust association with the overall survivability of LSCC. Furthermore, it is worth mentioning that these genes have also demonstrated upregulation in multiple pathways when contrasted with the normal samples, thereby accentuating their potential contributions in the context of LSCC survival.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe analysis of scRNA-seq data presents profound insights into the intricate cellular heterogeneity of the molecular landscape in LSCC [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Through the utilization of R, this investigation has elucidated fundamental aspects of LSCC biology, paving the path for innovative diagnostic and therapeutic approaches. A notable discovery from this study is the recognition of tumor diversity within the microenvironment of LSCC. By implementing rigorous data preprocessing and quality assurance procedures, the reliability of subsequent analyses was ensured, establishing a strong basis for unveiling the inherent cellular diversity in LSCC [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This initial phase is critical for comprehending the complex interactions among distinct cell populations within the tumor, with substantial implications for disease advancement and treatment response.\u003c/p\u003e \u003cp\u003eThe subsequent examination of cellular diversity utilizing PCA revealed unique gene modules that exhibit correlations across individual cells. This strategy not only emphasizes the molecular heterogeneity in LSCC but also offers valuable insights into the underlying regulatory networks steering tumor growth and advancement [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The recognition of 12 distinct cell clusters further accentuates the intricacy of LSCC biology, with each cluster likely embodying unique cellular conditions or subpopulations with specific functional roles. By scrutinizing DEGs across these clusters, the study disclosed potential molecular markers linked with particular cellular subtypes, providing crucial hints for understanding the mechanisms steering tumor diversity and progression [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, the GO enrichment analysis of genes provided functional context to the identified DEGs, elucidating the enriched biological processes, molecular functions, and cellular components within the LSCC dataset [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This thorough analysis not only aids in interpreting the functional importance of the recognized genes but also offers valuable insights into the biological pathways steering LSCC pathogenesis. The establishment of a PPI network further emphasized crucial hub genes with significant functional importance in LSCC, presenting potential targets for therapeutic interventions. The top 20 hub genes identified, such as NKG7, TPX2, and EGFR, present promising candidates for further exploration and validation as potential biomarkers or therapeutic targets in LSCC.\u003c/p\u003e \u003cp\u003eThe validation of hub genes through pathway analysis and survival assessment further underscores their clinical relevance in LSCC [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The upregulation of these genes in various cancer-related pathways, alongside their substantial associations with LSCC survival, highlights their potential utility as prognostic indicators and therapeutic targets. Furthermore, the recognition of specific genes like CCL3, EGFR, and EPCAM as potential molecular markers for LSCC may show promise for enhancing cancer prognosis and treatment outcomes in LSCC patients. These findings enhance our comprehension of LSCC biology and offer concrete clinical implications for personalized medicine strategies in LSCC treatment.\u003c/p\u003e \u003cp\u003eFurthermore, the integration of multi-omics data and the application of advanced computational algorithms hold immense potential for further unraveling the complexities of LSCC biology and identifying novel therapeutic avenues [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. By leveraging cutting-edge technologies and analytical methods, future studies can build upon the findings of this study to develop more precise diagnostic tools, prognostic indicators, and targeted therapies tailored to the individual molecular profiles of LSCC patients [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eTaken together, the study is an important contribution to a more comprehensive portrait of molecular pathways and cell composition in LSCCs. This study clarified the molecular basis and core hub genes of LSCC, providing an impetus for further studies on novel diagnostic markers or therapeutic targets to improve patient outcomes in LSCC. In the future, further studies exploring the sophisticated mechanisms of LSCC development and resistance to therapy are warranted to achieve improved fulfilling precision oncology methods for becoming a game-changer in managing this insidious disease.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cem\u003escRNA-seq, single-cell RNA sequencing; LSCC,\u0026nbsp;\u003c/em\u003eLaryngeal Squamous Cell Carcinoma\u003cem\u003e; DEGs,\u0026nbsp;\u003c/em\u003edifferentially expressed genes; PPI, Protein-Protein interaction; KEGG, Kyoto Encyclopedia of Genes and Genomes; TME, Tumor microenvironment; PCA, Principle component analysis; GO, Gene Ontology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are immensely grateful to each other for their intellectual contributions to this project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics Approval and Consent to Participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for Publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of Data and Materials\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external or internal funding was received for this project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors’ Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHJS: Writing-Original draft preparation, resources, data curation, visualization, investigation.\u003c/p\u003e\n\u003cp\u003ePB: Investigation, data curation.\u003c/p\u003e\n\u003cp\u003eMI: Investigation, resources.\u003c/p\u003e\n\u003cp\u003eSM: Investigation, visualization.\u003c/p\u003e\n\u003cp\u003eMNH: Conceptualization, validation, supervision, project administration.\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFalco M et al (2022) Overview on Molecular Biomarkers for Laryngeal Cancer: Looking for New Answers to an Old Problem. Cancers (Basel) 14(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers14071716\u003c/span\u003e\u003cspan address=\"10.3390/cancers14071716\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMannelli G, Gallo O (2012) Cancer stem cells hypothesis and stem cells in head and neck cancers. Cancer Treat Rev 38(5):515\u0026ndash;539. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ctrv.2011.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.ctrv.2011.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiswas P et al (2021) Analysis of SYK gene as a prognostic biomarker and suggested potential bioactive phytochemicals as an alternative therapeutic option for colorectal cancer: An in-silico pharmaco-informatics investigation. J Pers Med 11(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jpm11090888\u003c/span\u003e\u003cspan address=\"10.3390/jpm11090888\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang D et al (2020) Targeting laryngeal cancer cells with 5-fluorouracil and curcumin using mesoporous silica nanoparticles. Technol Cancer Res Treat 19:1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1533033820962114\u003c/span\u003e\u003cspan address=\"10.1177/1533033820962114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao J, Tao Z (2021) HPV, p16 and p53 As Indicators of Response to Survival in Larynx Squamous Cell Carcinoma HPV, p16 and p53 As Indicators of Response to Survival in Larynx Squamous Cell Carcinoma\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFong PY et al (2019) Association of clinical factors with survival outcomes in laryngeal squamous cell carcinoma (LSCC). PLoS ONE 14(11):1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0224665\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0224665\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhat AA et al (2021) Tumor microenvironment: an evil nexus promoting aggressive head and neck squamous cell carcinoma and avenue for targeted therapy. Signal Transduct Target Ther 6(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41392-020-00419-w\u003c/span\u003e\u003cspan address=\"10.1038/s41392-020-00419-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu Z, Jin R, Zhang Y, Li H (2022) Signaling pathways and targeted therapies in lung squamous cell carcinoma: mechanisms and clinical trials. Signal Transduct Target Ther 7(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41392-022-01200-x\u003c/span\u003e\u003cspan address=\"10.1038/s41392-022-01200-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShovon MHJ, Khan DA, Tareq MMI, Imtiaz M, Zilani MNH, Hasan MN (2023) A comprehensive assessment of VCAN transcriptional expression and evaluation as an effective prognostic biomarker against breast cancer: in silico study. Bull Natl Res Cent 47(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s42269-023-01062-5\u003c/span\u003e\u003cspan address=\"10.1186/s42269-023-01062-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeophytou CM, Panagi M, Stylianopoulos T, Papageorgis P (2021) The role of tumor microenvironment in cancer metastasis: Molecular mechanisms and therapeutic opportunities. Cancers (Basel) 13(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers13092053\u003c/span\u003e\u003cspan address=\"10.3390/cancers13092053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu W, Xia X, Hu F, Yu J (2023) Extracellular matrix remodeling in the tumor immunity. Front Immunol 14:1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2023.1340634\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1340634\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilk AJ, Shalek AK, Holmes S, Blish CA (2024) Comparative analysis of cell\u0026ndash;cell communication at single-cell resolution. no 3 Springer US 42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41587-023-01782-z\u003c/span\u003e\u003cspan address=\"10.1038/s41587-023-01782-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCommunication CC, Cheng C, Chen W, Jin H, Chen X (2023) A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell\u0026ndash;Cell Communication, pp. 1\u0026ndash;39\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou ZQ et al (2023) Dissecting cellular heterogeneity and intercellular communication in cholangiocarcinoma: implications for individualized therapeutic strategies. Front Genet 14:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fgene.2023.1241834\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2023.1241834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y, Chen S, Lu Y, Xu Z, Fu W, Yan W (2024) Single-cell transcriptomic analyses of tumor microenvironment and molecular reprograming landscape of metastatic laryngeal squamous cell carcinoma. Commun Biol 7(1):1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42003-024-05765-x\u003c/span\u003e\u003cspan address=\"10.1038/s42003-024-05765-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang WW et al (2023) Integrative multi-omic cancer profiling reveals DNA methylation patterns associated with therapeutic vulnerability and cell-of-origin. Cancer Cell 41(9):1567\u0026ndash;1585. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ccell.2023.07.013\u003c/span\u003e\u003cspan address=\"10.1016/j.ccell.2023.07.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H et al (2024) Identification and preliminary analysis of hub genes associated with bladder cancer progression by comprehensive bioinformatics analysis. Sci Rep 14(1):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-53265-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-53265-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu K, Yu D, Zhang S, Chen L, Liu Z, Xie L (2024) Deciphering the Immune Microenvironment at the Forefront of Tumor Aggressiveness by Constructing a Regulatory Network with Single-Cell and Spatial Transcriptomic Data, \u003cem\u003eGenes (Basel).\u003c/em\u003e, vol. 15, no. 1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/genes15010100\u003c/span\u003e\u003cspan address=\"10.3390/genes15010100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Zou Q, Lin C (2022) A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data. Brief Bioinform 23(1):1\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bib/bbab345\u003c/span\u003e\u003cspan address=\"10.1093/bib/bbab345\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanley CJ et al (2023) Single-cell analysis reveals prognostic fibroblast subpopulations linked to molecular and immunological subtypes of lung cancer. Nat Commun 14(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-023-35832-6\u003c/span\u003e\u003cspan address=\"10.1038/s41467-023-35832-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu LL, Culhane AC (2023) Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data. Sci Rep 13(1):1\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-022-26434-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-26434-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang B et al (2023) Single-cell RNA sequencing reveals intratumoral heterogeneity and potential mechanisms of malignant progression in prostate cancer with perineural invasion. Front Genet 13:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fgene.2022.1073232\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2022.1073232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSankowski R et al (2024) Multiomic spatial landscape of innate immune cells at human central nervous system borders. Nat Med 30(1):186\u0026ndash;198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-023-02673-1\u003c/span\u003e\u003cspan address=\"10.1038/s41591-023-02673-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia L, Lee C, Li JJ (2024) Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters. Nat Commun 15(1):1\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-024-45891-y\u003c/span\u003e\u003cspan address=\"10.1038/s41467-024-45891-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorabito S, Reese F, Rahimzadeh N, Miyoshi E, Swarup V (2023) hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep Methods 3(6):100498. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.crmeth.2023.100498\u003c/span\u003e\u003cspan address=\"10.1016/j.crmeth.2023.100498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong X et al (2023) Analyzation of the Peripheral Blood Mononuclear Cells Atlas and Cell Communication of Rheumatoid Arthritis Patients Based on Single-Cell RNA-Seq, \u003cem\u003eJ. Immunol. Res.\u003c/em\u003e, vol. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2023/6300633\u003c/span\u003e\u003cspan address=\"10.1155/2023/6300633\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin M et al (2023) Identification of key genes and pathways in adrenocortical carcinoma: evidence from bioinformatic analysis, \u003cem\u003eFront. Endocrinol. (Lausanne).\u003c/em\u003e, vol. 14, no. November, pp. 1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2023.1250033\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2023.1250033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan H, Teh MT, Mastroianni G, Ahmad US (2023) Comparative Transcriptome Analysis Identifies Desmoglein-3 as a Potential Oncogene in Oral Cancer Cells. Cells 12(23):1\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells12232710\u003c/span\u003e\u003cspan address=\"10.3390/cells12232710\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu H et al (2023) Network pharmacology analysis reveals potential targets and mechanisms of proton pump inhibitors in breast cancer with diabetes. Sci Rep 13(1):1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-34524-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-34524-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasar MA, Hosen MF, Kumar Paul B, Hasan MR, Shamim SM, Bhuyian T (2022) Identification of drug and protein-protein interaction network among stress and depression: A bioinformatics approach, \u003cem\u003eInformatics Med. Unlocked\u003c/em\u003e, vol. 37, no. December p. 101174, 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.imu.2023.101174\u003c/span\u003e\u003cspan address=\"10.1016/j.imu.2023.101174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiao K, Su P, Feng Y, Li C (2023) Bioinformatics analysis and identification of hub genes associated with female acute myocardial infarction patients by using weighted gene co-expression networks. Med (United States) 102(17):E33634. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MD.0000000000033634\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000033634\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanim MTH, Nath SD, Khan SF, Khan A, Sajib AA (2024) Transcriptomes of cervical cancer provide novel insights into dysregulated pathways, potential therapeutic targets, and repurposed drugs. Cancer Treat Res Commun 39:100808. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ctarc.2024.100808\u003c/span\u003e\u003cspan address=\"10.1016/j.ctarc.2024.100808\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu S et al (2023) Pancancer analysis of oncogenic BARX2 identifying its prognostic value and immunological function in liver hepatocellular carcinoma. Sci Rep 13(1):1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-34519-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-34519-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng G et al (2023) Single-cell transcriptome sequencing reveals heterogeneity of gastric cancer: progress and prospects. Front Oncol 13:1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2023.1074268\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2023.1074268\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y et al (2023) Proteogenomic data and resources for pan-cancer analysis. Cancer Cell 41(8):1397\u0026ndash;1406. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ccell.2023.06.009\u003c/span\u003e\u003cspan address=\"10.1016/j.ccell.2023.06.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Xfan, jing Jiang W (2023) The construction and validation of ECM-related prognosis model in laryngeal squamous cell carcinoma. Heliyon 9(9):e19907. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2023.e19907\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e19907\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunshi M et al (2022) Novel compounds from endophytic fungi of Ceriops decandra inhibit breast cancer cell growth through estrogen receptor alpha in in-silico study. Inf Med Unlocked 32:101046. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.imu.2022.101046\u003c/span\u003e\u003cspan address=\"10.1016/j.imu.2022.101046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiswas P et al (2022) A Comprehensive Analysis and Anti-Cancer Activities of Quercetin in ROS-Mediated Cancer and Cancer Stem Cells. Int J Mol Sci 23(19). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms231911746\u003c/span\u003e\u003cspan address=\"10.3390/ijms231911746\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiemira M et al (2020) Molecular signature of subtypes of non-small-cell lung cancer by large-scale transcriptional profiling: Identification of key modules and genes by weighted gene co-expression network analysis (WGCNA), \u003cem\u003eCancers (Basel).\u003c/em\u003e, vol. 12, no. 1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers12010037\u003c/span\u003e\u003cspan address=\"10.3390/cancers12010037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa B, Huang Z, Wang Q, Zhang J, Zhou B, Wu J (2019) Integrative analysis of genetic and epigenetic profiling of lung squamous cell carcinoma (LSCC) patients to identify smoking level relevant biomarkers. BioData Min 12(1):1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13040-019-0207-y\u003c/span\u003e\u003cspan address=\"10.1186/s13040-019-0207-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong F, Wan X, Bai Y (2024) Identification of Down-Expressed CRNN Associated with Cancer Progression and Poor Prognosis in Laryngeal Squamous Cell Carcinoma. Front Biosci (Landmark Ed 29(3):125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31083/j.fbl2903125\u003c/span\u003e\u003cspan address=\"10.31083/j.fbl2903125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian I, Verma S, Kumar S, Jere A, Anamika K (2020) Multi-omics Data Integration, Interpretation, and Its Application. Bioinform Biol Insights 14:7\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1177932219899051\u003c/span\u003e\u003cspan address=\"10.1177/1177932219899051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang J, Zhang Q, Pan GZ, Hu X, Chen D, Zhang K (2023) Editorial: Biomarkers, functional mechanisms, and therapeutic potentials in gastrointestinal cancers, \u003cem\u003eFront. Oncol.\u003c/em\u003e, vol. 13, no. October, pp. 1\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2023.1276414\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2023.1276414\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"in-silico-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"insp","sideBox":"Learn more about [In Silico Pharmacology](https://link.springer.com/journal/40203)","snPcode":"40203","submissionUrl":"https://submission.nature.com/new-submission/40203/3","title":"In Silico Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"scRNA-seq, LSCC, Biomarker, DEG, PPI, Gene ontology, Seurat","lastPublishedDoi":"10.21203/rs.3.rs-6517892/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6517892/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLaryngeal squamous cell carcinoma (LSCC), a complex cancer driven by genetic mutations, poses significant challenges for detection and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as a promising tool to uncover the cellular heterogeneity in cancer and identify novel therapeutic targets. In this study, we used scRNA-seq data (GSE252490) to explore molecular biomarkers for LSCC diagnosis and treatment. After processing and standardizing the data, we performed principal component analysis to identify highly variable genes. Cell clustering revealed 12 distinct clusters with unique molecular features. Differential gene expression analysis identified 6434 differentially expressed genes (DEGs), which were further analyzed using gene ontology enrichment to explore biological processes involved in LSCC progression. Protein-protein interaction (PPI) network analysis revealed 20 central genes associated with key cancer pathways. Pathway enrichment analysis through KEGG highlighted the involvement of these genes in various cancer-related pathways. Notably, genes such as CCL3, EPCAM, and IL8, with elevated expression, were linked to survival outcomes in LSCC. This comprehensive analysis provides valuable insights into the molecular landscape of LSCC, identifying potential biomarkers and therapeutic targets for improved diagnosis and treatment.\u003c/p\u003e","manuscriptTitle":"Single-cell RNA seq data analysis reveals molecular markers and possible treatment targets for Laryngeal Squamous Cell Carcinoma (LSCC): An in-silico approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 17:41:52","doi":"10.21203/rs.3.rs-6517892/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-19T21:40:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-19T01:06:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232613047799515061306163541267706321824","date":"2025-05-19T00:10:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-17T12:01:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206671068263883165700391984971459876119","date":"2025-05-04T10:53:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-02T00:47:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-24T06:50:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-24T06:48:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"In Silico Pharmacology","date":"2025-04-24T06:38:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"in-silico-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"insp","sideBox":"Learn more about [In Silico Pharmacology](https://link.springer.com/journal/40203)","snPcode":"40203","submissionUrl":"https://submission.nature.com/new-submission/40203/3","title":"In Silico Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"646ae50b-3b52-4068-955c-551a392bd96c","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T16:05:12+00:00","versionOfRecord":{"articleIdentity":"rs-6517892","link":"https://doi.org/10.1007/s40203-025-00382-w","journal":{"identity":"in-silico-pharmacology","isVorOnly":false,"title":"In Silico Pharmacology"},"publishedOn":"2025-06-17 15:57:50","publishedOnDateReadable":"June 17th, 2025"},"versionCreatedAt":"2025-05-07 17:41:52","video":"","vorDoi":"10.1007/s40203-025-00382-w","vorDoiUrl":"https://doi.org/10.1007/s40203-025-00382-w","workflowStages":[]},"version":"v1","identity":"rs-6517892","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6517892","identity":"rs-6517892","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}