Development of a novel prognostic signature derived from essential genes in head and neck squamous cell carcinoma.

BACKGROUND: Substantial heterogeneity in head and neck squamous cell carcinoma (HNSCC) compromise accurate patient stratification and personalized treatment planning. Current molecular classification is largely based on genes with highly variable expression without considering their functional roles. Here, we sought to identify HNSCC essential genes for patient stratification and prognostication. METHODS: Essential genes for HNSCC were screened from genome-wide CRISPR knockout datasets. Candidates were further identified through univariate Cox regression. The least absolute shrinkage and selection operator was utilized to develop the prognostic signature. Candidate essential genes were exploited to classify patients into subgroups by consensus clustering. Survival outcomes, genomic alterations, signaling activities, and therapeutic vulnerabilities were compared between patient subgroups. RESULTS: Sixty-eight genes were identified as candidates and utilized to develop an 8-gene prognostic signature. Patients were segregated into two clusters with distinct survival rates across multiple cohorts based on upregulated essential genes. Cluster 2 exhibited higher TP53, CDKN2A, and NOTCH1 mutations, higher stromal activities, worse prognosis as well as and sensitivities to cell cycle inhibitors. Cluster 1 was characterized by a better prognosis and susceptibility to PI3K/AKT and MAPK inhibitors. CONCLUSION: Our study developed a novel and robust prognostic signature and classification derived from essential genes for HNSCC, which sheds new light on HNSCC precision oncology.

Comprehensive analyses of intraoral spindle cell carcinoma: A rare disease entity revisited.

OBJECTIVE: The present study was aimed to comprehensively characterize the epidemiological, clinicopathological characteristics, treatments, and prognosis of intraoral spindle cell carcinoma (SpCC). MATERIALS AND METHODS: Patients diagnosed with intraoral SpCC at our institution in the past 15 years (2005-2019) were screened from inpatient disease registry. All relevant data concerning patients with intraoral SpCC were retrieved. Previous reports about intraoral SpCC with adequate clinicopathological data in both English literature and Chinese literature were collected. Eligible cases were further reviewed and pooled for statistical analyses. RESULTS: Six patients (5 females and 1 male; average age: 59 years) with intraoral SpCC were histopathologically diagnosed and surgically treated at our institution. The literature review identified another 63 published cases from 34 articles. Most cases were presented in the fifth to seventh decade of life with a male preponderance. Gingiva (23/69, 33.3%) was the most common site followed by the tongue (19/69, 27.5%) and buccal mucosa (8/69, 11.6%). Complete surgical ablation remains the primary treatment option. Tumor size, pathological grades, cervical node metastasis, and distant metastasis were significantly associated with reduced survival. CONCLUSIONS: Intraoral SpCC is an uncommon and aggressive malignancy with dismal prognosis. Much attention and effort are needed to characterize this rare entity and improve its clinical outcomes.

Decoding river pollution trends and their landscape determinants in an ecologically fragile karst basin using a machine learning model.

Karst watersheds accommodate high landscape complexity and are influenced by both human-induced and natural activity, which affects the formation and process of runoff, sediment connectivity and contaminant transport and alters natural hydrological and nutrient cycling. However, physical monitoring stations are costly and labor-intensive, which has confined the assessment of water quality impairments on spatial scale. The geographical characteristics of catchments are potential influencing factors of water quality, often overlooked in previous studies of highly heterogeneous karst landscape. To solve this problem, we developed a machining learning method and applied Extreme Gradient Boosting (XGBoost) to predict the spatial distribution of water quality in the world's most ecologically fragile karst watershed. We used the Shapley Addition interpretation (SHAP) to explain the potential determinants. Before this process, we first used the water quality damage index (WQI-DET) to evaluate the water quality impairment status and determined that CODMn, TN and TP were causing river water quality impairments in the WRB. Second, we selected 46 watershed features based on the three key processes (sources-mobilization-transport) which affect the temporal and spatial variation of river pollutants to predict water quality in unmonitored reaches and decipher the potential determinants of river impairments. The predicting range of CODMn spanned from 1.39 mg/L to 17.40 mg/L. The predictions of TP and TN ranged from 0.02 to 1.31 mg/L and 0.25-5.72 mg/L, respectively. In general, the XGBoost model performs well in predicting the concentration of water quality in the WRB. SHAP explained that pollutant levels may be driven by three factors: anthropogenic sources (agricultural pollution inputs), fragile soils (low organic carbon content and high soil permeability to water flow), and pollutant transport mechanisms (TWI, carbonate rocks). Our study provides key data to support decision-making for water quality restoration projects in the WRB and information to help bridge the science:policy gap.

Development of a novel head and neck squamous cell carcinoma prognostic signature by bulk/single-cell sequencing data integration.

BACKGROUND: Identifying cell subpopulations conferring unfavorable prognosis in cancer holds clinical significance. Here, we sought to identify prognostic cell subsets and develop a novel, prognostic signature for head neck squamous cell carcinoma (HNSCC). METHODS: Highly prognostic cell subpopulations in HNSCC were identified by integrating single-cell and bulk transcriptomic datasets. The prognostic signature and nomogram were developed by least absolute shrinkage and selection operator and multivariate Cox regression analyses based on significantly upregulated genes in this specific cell subpopulation, respectively. The qRT-PCR experiments were utilized for independent validation in our patient cohort. RESULTS: A specific cancer cell subset associated with unfavorable prognoses was identified. Functional dissections revealed that its transcriptional programs were significantly enriched in E2F, epithelial-to-mesenchymal transition, and glycolysis. A novel prognostic signature comprising six genes was developed and further validated. Risk scores based on qRT-PCR data robustly stratified patients into subgroups with distinct prognoses. A nomogram integrated from this signature and clinical stage had superior performance. CONCLUSION: Our model derived from integrative analyses of single-cell and bulk RNA-sequencing is a novel, robust prognostic biomarker for HNSCC.

Integrative multi-omics analyses identify molecular subtypes of head and neck squamous cell carcinoma with distinct therapeutic vulnerabilities.

Substantial heterogeneity in molecular features, patient prognoses, and therapeutic responses in head and neck squamous cell carcinomas (HNSCC) highlights the urgent need to develop molecular classifications that reliably and accurately reflect tumor behavior and inform personalized therapy. Here, we leveraged the similarity network fusion bioinformatics approach to jointly analyze multi-omics datasets spanning copy number variations, somatic mutations, DNA methylation, and transcriptomic profiling and derived a prognostic classification system for HNSCC. The integrative model consistently identified three subgroups (IMC1-3) with specific genomic features, biological characteristics, and clinical outcomes across multiple independent cohorts. The IMC1 subgroup included proliferative, immune-activated tumors and exhibited a more favorable prognosis. The IMC2 subtype harbored activated EGFR signaling and an inflamed tumor microenvironment with cancer-associated fibroblast/vascular infiltrations. Alternatively, the IMC3 group featured highly aberrant metabolic activities and impaired immune infiltration and recruiting. Pharmacogenomics analyses from in silico predictions and from patient-derived xenograft model data unveiled subtype-specific therapeutic vulnerabilities including sensitivity to cisplatin and immunotherapy in IMC1 and EGFR inhibitors (EGFRi) in IMC2, which was experimentally validated in patient-derived organoid models. Two signatures for prognosis and EGFRi sensitivity were developed via machine learning. Together, this integrative multi-omics clustering for HNSCC improves current understanding of tumor heterogeneity and facilitates patient stratification and therapeutic development tailored to molecular vulnerabilities.

Development of a novel prognostic signature derived from enhancer RNA-regulated genes in head neck squamous cell carcinoma.

BACKGROUND: Enhancer RNAs (eRNAs) are increasingly recognized as prognostic biomarkers-across human cancers. Here, we sought to develop a novel eRNA-regulated genes (ERGs)-derived prognostic signature for head neck squamous cell carcinoma (HNSCC). METHODS: Candidate ERGs were identified via co-expression between individual survival-related eRNAs and their putative targets by Spearman's correlation analyses. The ERG signature was developed by univariate Cox regression, Kaplan-Meier survival analysis and maximum AUC in 1000 iterations of LASSO-penalized multivariate Cox regression. An ERG nomogram incorporating ERG signature and selected clinicopathological parameters were constructed by multivariate Cox regression. Biological roles of eRNA of interest were further explored in vitro. RESULTS: The ERG signature successfully stratified patients into subgroups with distinct survival in multiple cohorts. An ERG nomogram was developed with satisfactory performance in prognostication. Inhibition of ENSR00000165816 significantly reduced transcript level of SLC2A9 and impaired cell proliferation and invasion. CONCLUSION: Our results establish ERG signature and nomogram as powerful prognostic predictors for HNSCC.

Integrative Single-Cell and Bulk Transcriptomes Analyses Identify Intrinsic HNSCC Subtypes with Distinct Prognoses and Therapeutic Vulnerabilities.

PURPOSE: Tumor heterogeneity in head and neck squamous cell carcinoma (HNSCC) profoundly compromises patient stratification, personalized treatment planning, and prognostic prediction, which underscores the urgent need for more effective molecular subtyping for this malignancy. Here, we sought to define the intrinsic epithelial subtypes for HNSCC by integrative analyses of single-cell and bulk RNA sequencing datasets from multiple cohorts and assess their molecular features and clinical significance. EXPERIMENTAL DESIGN: Malignant epithelial cells were identified from single-cell RNA sequencing (scRNA-seq) datasets and subtyped on the basis of differentially expressed genes. Subtype-specific genomic/epigenetic abnormalities, molecular signaling, genetic regulatory network, immune landscape, and patient survival were characterized. Therapeutic vulnerabilities were further predicted on the basis of drug sensitivity datasets from cell lines, patient-derived xenograft models, and real-world clinical outcomes. Novel signatures for prognostication and therapeutic prediction were developed by machine learning and independently validated. RESULTS: Three intrinsic consensus molecular subtypes (iCMS1-3) for HNSCC were proposed from scRNA-seq analyses and recapitulated in 1,325 patients from independent cohorts using bulk-sequencing datasets. iCMS1 was characterized by EGFR amplification/activation, stromal-enriched environment, epithelial-to-mesenchymal transition, worst survival, and sensitivities to EGFR inhibitor. iCMS2 was featured by human papillomavirus-positive oropharyngeal predilection, immune-hot, susceptibilities to anti-PD-1, and best prognosis. Moreover, iCMS3 displayed immune-desert and sensitivities to 5-FU and MEK, STAT3 inhibitors. Three novel, robust signatures derived from iCMS subtype-specific transcriptomics features were developed by machine learning for patient prognostication and cetuximab and anti-PD-1 response predictions. CONCLUSIONS: These findings reiterate molecular heterogeneity of HNSCC and advantages of scRNA-seq in pinpointing cellular diversities in complex cancer ecosystems. Our HNSCC iCMS regime might facilitate accurate patient stratification and individualized precise treatment.

Development of a novel signature derived from single cell RNA-sequencing for preoperative prediction of lymph node metastasis in head and neck squamous cell carcinoma.

BACKGROUND: Lymph node metastasis (LNM) is considered as an adverse prognostic indicator for cancer patients. Preoperative knowledge of LNM is valuable for pretreatment decision making. Here, we sought to develop and validate an LNM signature for preoperative prediction of LNM in patients with head and neck squamous cell carcinoma (HNSCC). METHODS: By studying single cell RNA-sequencing data (scRNA-seq), differentially expressed mRNA were selected and analyzed through univariate logistic regression and least absolute shrinkage and selection operator (LASSO) to identify an LNM signature. Multivariate logistic regression was utilized to establish an LNM nomogram incorporating LNM signature and T-classification. RESULTS: The LNM signature was significantly associated with lymph node status and prognosis. The LNM signature and LNM nomogram displayed a robust predictive effect. CONCLUSION: Our study reveals that LNM signature is a powerful biomarker for preoperative prediction of LNM in patients with HNSCC, which may be effective to realize individualized outcome prediction.

Comprehensive characterization of pleomorphic adenoma at intraoral unusual sites.

OBJECTIVE: The present study aimed to comprehensively characterize the epidemiologic characteristics, clinicopathologic characteristics, clinical treatments, and prognoses of pleomorphic adenoma (PA) identified at unusual intraoral sites. STUDY DESIGN: Patients diagnosed with PA in oral and maxillofacial regions at our institution in the past 16 years (2005-2020) were screened from the inpatient disease registry. All data concerning patients with PA found at unusual intraoral sites (defined as intraoral locations except sublingual gland and palate) were retrieved. Previously published cases with adequate clinicopathologic data were collected from PubMed and Embase. Eligible cases were further reviewed and included for statistical analyses. RESULTS: Among 1039 cases of PA diagnosed at our institution, 52 lesions were found at unusual intraoral sites. A literature review identified another 63 eligible cases from 32 articles. The upper lip was the most common sites for these lesions (n = 57), followed by buccal mucosa (n = 34), tongue (n = 8), lower lip (n = 8), and retromolar area (n = 2). Recurrence and malignant transformation after surgical resection were extremely rare for these lesions. CONCLUSIONS: PA might rarely develop at uncommon intraoral sites with atypical presentations, thus complicating its early diagnosis. Surgical resection is the major therapeutic strategy for this rare entity and has a favorable prognosis.

The deubiquitinase USP7 promotes HNSCC progression via deubiquitinating and stabilizing TAZ.

Dysregulated abundance, location and transcriptional output of Hippo signaling effector TAZ have been increasingly linked to human cancers including head neck squamous cell carcinoma (HNSCC). TAZ is subjected to ubiquitination and degradation mediated by E3 ligase ß-TRCP. However, the deubiquitinating enzymes and mechanisms responsible for its protein stability remain underexplored. Here, we exploited customized deubiquitinases siRNA and cDNA library screen strategies and identified USP7 as a bona fide TAZ deubiquitinase in HNSCC. USP7 promoted cell proliferation, migration, invasion in vitro and tumor growth by stabilizing TAZ. Mechanistically, USP7 interacted with, deubiquitinated and stabilized TAZ by selectively removing its K48-linked ubiquitination chain independent of canonical Hippo kinase cascade. USP7 potently antagonized ß-TRCP-mediated ubiquitin-proteasomal degradation of TAZ and enhanced its nuclear retention and transcriptional output. Importantly, overexpression of USP7 correlated with TAZ upregulation, tumor aggressiveness and unfavorable prognosis in HNSCC patients. Pharmacological inhibition of USP7 significantly suppressed tumor growth in both xenograft and PDX models. Collectively, these findings identify USP7 as an essential regulator of TAZ and define USP7-TAZ signaling axis as a novel biomarker and potential therapeutic target for HNSCC.

Identification of diagnostic and prognostic signatures derived from preoperative blood parameters for oral squamous cell carcinoma.

BACKGROUND: We aimed to develop novel diagnostic and prognostic signatures based on preoperative inflammatory, immunological, and nutritional parameters in blood (PIINPBs) by machine learning algorithms for patients with oral squamous cell carcinoma (OSCC). METHODS: A total of 486 OSCC patients and 200 age and gender-matched non-OSCC patients who were diagnosed and treated at our institution for noninfectious, nontumor diseases were retrospectively enrolled and divided into training and validation cohorts. Based on PIINPB, 6 machine learning classifiers including random forest, support vector machine, extreme gradient boosting, naive Bayes, neural network, and logistic regression were used to derive diagnostic models, while least absolute shrinkage and selection operator (LASSO) analyses were employed to construct prognostic signatures. A novel prognostic nomogram integrating a PIINPB-derived prognostic signature and selected clinicopathological parameters was further developed. Performances of these signatures were assessed by receiver operating characteristic (ROC) curves, calibrating curves, and decision tree. RESULTS: Diagnostic models developed by machine learning algorithms from 13 PIINPBs, which included counts of white blood cells (WBC), neutrophils (N), monocytes (M), lymphocytes (L), platelets (P), albumin (ALB), and hemoglobin (Hb), along with albumin-globulin ratio (A/G), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI), displayed satisfactory discriminating capabilities in patients with or without OSCC, and among OSCC patients with diverse pathological grades and clinical stages. A prognostic signature based on 6 survival-associated PIINPBs (L, P, PNI, LMR, SII, A/G) served as an independent factor to predict patient survival. Moreover, a novel nomogram integrating prognostic signature and tumor size, pathological grade, cervical node metastasis, and clinical stage significantly enhanced prognostic power [3-year area under the curve (AUC) =0.825; 5-year AUC =0.845]. CONCLUSIONS: Our results generated novel and robust diagnostic and prognostic signatures derived from PIINPBs by machine learning for OSCC. Performance of these signatures suggest the potential for PIINPBs to supplement current regimens and provide better patient stratification and prognostic prediction.