Identification of the HNSC88 Molecular Signature for Predicting Subtypes of Head and Neck Cancer.

Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy. In this study, we collected three HNSC microarrays (n = 306) from the Gene Expression Omnibus (GEO), and HNSC RNA-Seq data (n = 566) from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) and validate our results. Two scoring methods, representative score (RS) and perturbative score (PS), were developed for DEGs to summarize their possible activation functions and influence in tumorigenesis. Based on the RS and PS scoring, we selected candidate genes to cluster TCGA samples for the identification of molecular subtypes in HNSC. We have identified 289 up-regulated DEGs and selected 88 genes (called HNSC88) using the RS and PS scoring methods. Based on HNSC88 and TCGA samples, we determined three HNSC subtypes, including one HPV-associated subtype, and two HPV-negative subtypes. One of the HPV-negative subtypes showed a relationship to smoking behavior, while the other exhibited high expression in tumor immune response. The Kaplan-Meier method was used to compare overall survival among the three subtypes. The HPV-associated subtype showed a better prognosis compared to the other two HPV-negative subtypes (log rank, p = 0.0092 and 0.0001; hazard ratio, 1.36 and 1.39). Additionally, within the HPV-negative group, the smoking-related subgroup exhibited worse prognosis compared to the subgroup with high expression in immune response (log rank, p = 0.039; hazard ratio, 1.53). The HNSC88 not only enables the identification of HPV-associated subtypes, but also proposes two potential HPV-negative subtypes with distinct prognoses and molecular signatures. This study provides valuable strategies for summarizing the roles and influences of genes in tumorigenesis for identifying molecular signatures and subtypes of HNSC.

Densest subgraph-based methods for protein-protein interaction hot spot prediction.

BACKGROUND: Hot spots play an important role in protein binding analysis. The residue interaction network is a key point in hot spot prediction, and several graph theory-based methods have been proposed to detect hot spots. Although the existing methods can yield some interesting residues by network analysis, low recall has limited their abilities in finding more potential hot spots. RESULT: In this study, we develop three graph theory-based methods to predict hot spots from only a single residue interaction network. We detect the important residues by finding subgraphs with high densities, i.e., high average degrees. Generally, a high degree implies a high binding possibility between protein chains, and thus a subgraph with high density usually relates to binding sites that have a high rate of hot spots. By evaluating the results on 67 complexes from the SKEMPI database, our methods clearly outperform existing graph theory-based methods on recall and F-score. In particular, our main method, Min-SDS, has an average recall of over 0.665 and an f2-score of over 0.364, while the recall and f2-score of the existing methods are less than 0.400 and 0.224, respectively. CONCLUSION: The Min-SDS method performs best among all tested methods on the hot spot prediction problem, and all three of our methods provide useful approaches for analyzing bionetworks. In addition, the densest subgraph-based methods predict hot spots with only one residue interaction network, which is constructed from spatial atomic coordinate data to mitigate the shortage of data from wet-lab experiments.

CoMI: consensus mutual information for tissue-specific gene signatures.

BACKGROUND: The gene signatures have been considered as a promising early diagnosis and prognostic analysis to identify disease subtypes and to determine subsequent treatments. Tissue-specific gene signatures of a specific disease are an emergency requirement for precision medicine to improve the accuracy and reduce the side effects. Currently, many approaches have been proposed for identifying gene signatures for diagnosis and prognostic. However, they often lack of tissue-specific gene signatures. RESULTS: Here, we propose a new method, consensus mutual information (CoMI) for analyzing omics data and discovering gene signatures. CoMI can identify differentially expressed genes in multiple cancer omics data for reflecting both cancer-related and tissue-specific signatures, such as Cell growth and death in multiple cancers, Xenobiotics biodegradation and metabolism in LIHC, and Nervous system in GBM. Our method identified 50-gene signatures effectively distinguishing the GBM patients into high- and low-risk groups (log-rank p = 0.006) for diagnosis and prognosis. CONCLUSIONS: Our results demonstrate that CoMI can identify significant and consistent gene signatures with tissue-specific properties and can predict clinical outcomes for interested diseases. We believe that CoMI is useful for analyzing omics data and discovering gene signatures of diseases.

Methotrexate inhibition of SARS-CoV-2 entry, infection and inflammation revealed by bioinformatics approach and a hamster model.

Background: Drug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease. Methods: Here, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of in vitro (EC50 = 0.4 µM) and in vivo models, we show that methotrexate is able to inhibit COVID-19 via multiple mechanisms. Results: Our in vitro studies illustrate that methotrexate can suppress SARS-CoV-2 entry and replication by targeting furin and DHFR of the host, respectively. Additionally, methotrexate inhibits all four SARS-CoV-2 variants of concern. In a Syrian hamster model for COVID-19, methotrexate reduced virus replication, inflammation in the infected lungs. By analysis of transcriptomic analysis of collected samples from hamster lung, we uncovered that neutrophil infiltration and the pathways of innate immune response, adaptive immune response and thrombosis are modulated in the treated animals. Conclusions: We demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials.

Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data.

Many studies have proven the power of gene expression profile in cancer identification, however, the explosive growth of genomics data increasing needs of tools for cancer diagnosis and prognosis in high accuracy and short times. Here, we collected 6136 human samples from 11 cancer types, and integrated their gene expression profiles and protein-protein interaction (PPI) network to generate 2D images with spectral clustering method. To predict normal samples and 11 cancer tumor types, the images of these 6136 human cancer network were separated into training and validation dataset to develop convolutional neural network (CNN). Our model showed 97.4% and 95.4% accuracies in identification of normal versus tumors and 11 cancer types, respectively. We also provided the results that tumors located in neighboring tissues or in the same cell types, would induce machine make error classification due to the similar gene expression profiles. Furthermore, we observed some patients may exhibit better prognosis if their tumors often misjudged into normal samples. As far as we know, we are the first to generate thousands of cancer networks to predict and classify multiple cancer types with CNN architecture. We believe that our model not only can be applied to cancer diagnosis and prognosis, but also promote the discovery of multiple cancer biomarkers.

Dynamic Ultrasonographic Measurement of Inferior Joint Capsule Thickness in Patients with Unilateral Frozen Shoulder.

The diagnostic value of ultrasonography (US) for frozen shoulder (FS) is not well established. This study aimed to assess the diagnostic value of US measurement of inferior joint capsule (IJC) thickness and evaluate changes in the thickness of the IJC by US depending on arm position. A total of 71 patients with clinically diagnosed unilateral FS who underwent bilateral US measurement of the IJC were enrolled in this study. The US measurement of the IJC was performed with a linear transducer positioned around the anterior axillary line with the shoulder 40° abducted and with neutral rotation of the glenohumeral joint (neutral position). We also measured the IJC thickness in the externally rotated and internally rotated positions with the shoulder 40° abducted. In the neutral position, as well as in the internally rotated and externally rotated positions, the thickness of the IJC on US was significantly higher in the affected shoulder than that in the unaffected shoulder (all p < 0.001). On both the affected and unaffected sides, the US thickness of the IJC in the neutral position was significantly higher than that in the externally rotated position (p < 0.001), but lower than that in the internally rotated position (p < 0.001). Regarding IJC thickness in the neutral position, a 3.2-mm cutoff value yielded the highest diagnostic accuracy for FS, with a sensitivity and specificity of 73.2% and 77.5%, respectively. The area under the curve for IJC thickness was 0.824 (95% confidence interval, 0.76-0.89). US measurement of the IJC in the neutral position yielded good diagnostic accuracy for FS. Because IJC thickness is affected by arm rotation, it is important to measure the IJC thickness in a standardized posture to ensure diagnostic value.

An Integrated Genomic Strategy to Identify CHRNB4 as a Diagnostic/Prognostic Biomarker for Targeted Therapy in Head and Neck Cancer.

Although many studies have shown the association between smoking and the increased incidence and adverse prognosis of head and neck squamous cell carcinoma (HNSCC), the mechanisms and pharmaceutical targets involved remain unclear. Here, we integrated gene expression signatures, genetic alterations, and survival analyses to identify prognostic indicators and therapeutic targets for smoking HNSCC patients, and we discovered that the FDA-approved drug varenicline inhibits the target for cancer cell migration/invasion. We first identified 18 smoking-related and prognostic genes for HNSCC by using RNA-Seq and clinical follow-up data. One of these genes, CHRNB4 (neuronal acetylcholine receptor subunit beta-4), increased the risk of death by approximately threefold in CHRNB4-high expression smokers compared to CHRNB4-low expression smokers (log rank, p = 0.00042; hazard ratio, 2.82; 95% CI, 1.55-5.14), former smokers, and non-smokers. Furthermore, we examined the functional enrichment of co-regulated genes of CHRNB4 and its 246 frequently occurring copy number alterations (CNAs). We found that these genes were involved in promoting angiogenesis, resisting cell death, and sustaining proliferation, and contributed to much worse outcomes for CHRNB4-high patients. Finally, we performed CHRNB4 gene editing and drug inhibition assays, and the results validate these observations. In summary, our study suggests that CHRNB4 is a prognostic indicator for smoking HNSCC patients and provides a potential new therapeutic drug to prevent recurrence or distant metastasis.

Mica-Based Multilayer Nanocoating as a Highly Effective Flame Retardant and Smoke Suppressant.

Highly flammable polyurethane foam (PUF) remains a key risk factor associated with bedding and upholstered furniture, contributing to the yearly destruction of property and loss of lives. In an attempt to tackle this issue and develop a more benign flame retardant for PUF, a mica-based nanocomposite was deposited using layer-by-layer assembly. Chitosan (CH) and poly(acrylic acid) (PAA) were used to stabilize high-aspect-ratio mica. Foam treated with eight bilayers of CH- and PAA-stabilized mica preserves the porous foam structure, prevents melt dripping, and self-extinguishes during a 10 s torch test, while uncoated foam is completely consumed. When exposed to 35 kW/m2 heat flux during cone calorimetry, the peak heat release rate is reduced by 54% and less-volatile molecules are released during combustion, resulting in a 76% reduction in the total smoke release. This multilayer coating serves as an environmentally benign template for flame-retarding PUF and various other three-dimensional substrates.

Identification of the PCA29 gene signature as a predictor in prostate cancer.

Prostate cancer (PCa) is the second leading cause of cancer death among men worldwide. About 70% of PCa patients were diagnosed at later stage, and metastasis has been observed. Additionally, the cure rate of PCa closely relies on the early diagnosis with biomarkers. The identification of biomarkers for diagnosis and prognosis is an urgent clinical issue for PCa. Here, we developed a novel scoring strategy, including cluster score (CS) and predicting score (PS), to identify 29 PCa genes (called PCa29) for early diagnostic biomarkers from two datasets in Gene Expression Omnibus. The result indicates that PCa29 can discriminate between normal and tumor tissues and are specific for prostate cancer. To validate PCa29, we found that 97% of PCa29 were consistently significant with these gene expressions in The Cancer Genome Atlas; furthermore, ∼ 70% of PCa29 are consensus to the protein expression in The Human Protein Atlas. Finally, we examined 10 genes in PCa29 on three PCa cell lines by real-time quantitative polymerase chain reaction. The experimental results show that the trend of the differential PCa29 expression is consistent with the analyzed results from our novel scoring method. We believe that our method is useful and PCa29 are potential biomarkers that provide the clues to develop targeting therapy for PCa.

Membrane protein-regulated networks across human cancers.

Alterations in membrane proteins (MPs) and their regulated pathways have been established as cancer hallmarks and extensively targeted in clinical applications. However, the analysis of MP-interacting proteins and downstream pathways across human malignancies remains challenging. Here, we present a systematically integrated method to generate a resource of cancer membrane protein-regulated networks (CaMPNets), containing 63,746 high-confidence protein-protein interactions (PPIs) for 1962 MPs, using expression profiles from 5922 tumors with overall survival outcomes across 15 human cancers. Comprehensive analysis of CaMPNets links MP partner communities and regulated pathways to provide MP-based gene sets for identifying prognostic biomarkers and druggable targets. For example, we identify CHRNA9 with 12 PPIs (e.g., ERBB2) can be a therapeutic target and find its anti-metastasis agent, bupropion, for treatment in nicotine-induced breast cancer. This resource is a study to systematically integrate MP interactions, genomics, and clinical outcomes for helping illuminate cancer-wide atlas and prognostic landscapes in tumor homo/heterogeneity.

Nicotinic Acetylcholine Receptor Subtype Alpha-9 Mediates Triple-Negative Breast Cancers Based on a Spontaneous Pulmonary Metastasis Mouse Model.

Triple-negative breast cancer (TNBC) subtype is associated with poor prognosis and a high risk of recurrence-related death in women. Despite the aggressiveness of TNBCs, targeted TNBC therapy is not yet available in the clinic. To overcome this challenge, we generated highly metastatic TNBC cells (LM) derived from metastasized lung cells via a serial spontaneous pulmonary metastasis animal model to identify targetable molecules for attenuating the progression of TNBC metastasis. Gene analysis of primary tumor (P), first-round (1LM) and second-round (2LM) metastasized lung cells revealed that mesenchymal-related genes were significantly expressed in LM cells, especially in 2LM cells. Interestingly, α9-nAChR gene expression was also dramatically induced in LM cells, confirming our previous finding that α9-nAChR plays important roles in receptor-mediated carcinogenic signals in human breast cancer development. Using α9-nAChR as a biomarker, we transfected 2LM cells with CRISPR/Cas9 lentivirus targeting the α9-nAChR genomic region (2LM-α9-nAChR-null), showing that mesenchymal markers and the migration and invasion abilities of 2LM cells were significantly attenuated in 2LM-α9-nAChR-null cells both in vitro and in vivo. In addition, the high efficiency of editing the α9-nAChR gene using a CRISPR/Cas9 lentivirus was demonstrated by gene sequencing, genomic indel frequency and protein expression analyses. Collectively, these results confirmed those of our previous study that advanced-stage breast tumors are associated with substantially higher levels of α9-nAChR gene expression, indicating that α9-nAChR expression is essential for mediating TNBC metastasis during cancer development and may potentially act as a biomarker for targeted therapy in clinical investigations.

Clinical and radiological outcomes of type 2 superior labral anterior posterior repairs in elite overhead athletes.

BACKGROUND: Although there are multiple reports on surgical outcomes of type 2 superior labral anterior posterior (SLAP) repairs, a literature review noted a paucity of data on clinical and radiological outcomes in elite overhead athletes. PURPOSE: To determine midterm clinical outcomes of type 2 SLAP repairs in elite overhead athletes and whether labral integrity provides consistent return to play. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Medical records were retrospectively reviewed of 24 elite overhead athletes who underwent arthroscopic type 2 SLAP repairs. There were 18 men and 6 women, and their mean age was 22.7 years (range, 19-30 years); the majority of them (16/24) were baseball players. Four outcome measures were used: visual analog scale (VAS) for pain and satisfaction, American Shoulder and Elbow Surgeons (ASES) score, subjective feeling of recovery, and return to play. Multidetector computed tomographic arthrography was performed to evaluate labral integrity after surgery. RESULTS: At a mean follow-up of 45.8 months (range, 24-68 months), overall pain and function improved significantly. The VAS for pain was 5.7 preoperatively and 2.0 postoperatively (P < .01), and VAS for satisfaction was 8.6. The ASES score was 55.8 preoperatively and 87.1 postoperatively (P < .01). The overall mean value of subjective feeling of recovery was approximately 76%. Twelve of 24 athletes (50%) returned to play after the operation. Although there was a trend toward higher return rate in the other overhead athletes (75%) compared with the baseball players (38%), this trend did not reach statistical significance (P = .097). Labral retear with clinical significance was noted in 2 athletes who failed to return to play. Osteolysis was observed in 2 athletes, 1 of whom had a retear. A statistical relation between the integrity of the repair and return to play was not found (P > .05). CONCLUSION: Arthroscopic SLAP repairs show favorable clinical and radiological outcomes; however, the study findings raise a concern that return to play may still be problematic in elite baseball players. This study also indicates that labral healing does not ensure consistent return to play in elite overhead athletes.