Circ-ATIC regulates esophageal squamous cell carcinoma growth and metastasis through miR-1294/PBX3 pathway

Esophageal squamous cell carcinoma (ESCC) is a digestive tract malignancy associated with poor clinical outcome. Growing evidence have elucidated that circular RNAs (circRNAs) play important roles in the pathological process of ESCC. However, the detailed mechanisms how circRNAs modulate the development of ESCC remain largely unknown. Our study aimed to decipher the role and mechanism of circ-ATIC (also termed as circRNA_0058063) in regulating the progression of ESCC. We found that circ-ATIC and its host gene ATIC were significantly increased in ESCC tissues and cells compared with the adjacent noncancerous tissues or normal esophagus epithelial cell. Circ-ATIC knockdown substantially reduced proliferation and the number of invaded ESCC cells and retarded EMT process, reflecting by the decreased N-cadherin and elevated E-cadherin. However, the level of host gene ATIC was not changed under circ-ATIC suppression. It was predicted that circ-ATIC could bind to miR-1294 and serve as a sponge RNA. The luciferase reporter assay and RNA immunoprecipitation (RIP) assay confirmed their relations. MiR-1294 was decreased in ESCC tissues and cells, which was reversely correlated with circ-ATIC level. Furthermore, PBX3 was predicted and proved to be a downstream direct target of miR-1294. PBX3 mRNA and protein were obviously upregulated in ESCC tumor tissues and cells. PBX3 overexpression could reverse the suppressive roles of miR-1294 mimics on ESCC proliferation and invasion. In an xenograft nude mice model, stable transfection of sh-circ-ATIC significantly retarded the growth of tumor and suppressed VEGF and Ki67. Collectively, circ-ATIC promoted ESCC proliferation and invasion by regulating miR-1294/PBX3 axis.

Global profiling of protein lysine lactylation and potential target modified protein analysis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC), the most common type of primary liver cancer, often metastasizes to the lungs. The implications of lysine lactylation (Kla), a recently identified histone post-translational modification (PTM), in the pathology of HCC remain unclear. Here, we report the first proteomic survey of this specific modification in HCC (with no metastasis during 3 years of follow-up), normal liver tissues, and lung metastasis samples of HCC. Of the 2045 modification sites detected on 960 proteins, 1438 sites on 772 proteins contained quantitative information. Subsequently, we analyzed the differentially modified proteins among the different groups. Differentially lactylated proteins were found to be involved in several biological processes, including-but not limited to-amino acid metabolism, ribosomal protein synthesis, and fatty acid metabolism. In addition, we identified numerous highly valuable lactate-modified proteins from the literature. Among them, we verified the lactate modification levels of the following two tumor-related proteins and obtained similar results: USP14 and ABCF1. These two modified proteins will be further investigated in our future studies. This paper is the first report on the lactylome of HCC and it provides a reliable foundation for further research on Kla in HCC.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

DM-MOGA: a multi-objective optimization genetic algorithm for identifying disease modules of non-small cell lung cancer

BACKGROUND

Constructing molecular interaction networks from microarray data and then identifying disease module biomarkers can provide insight into the underlying pathogenic mechanisms of non-small cell lung cancer. A promising approach for identifying disease modules in the network is community detection.

RESULTS

In order to identify disease modules from gene co-expression networks, a community detection method is proposed based on multi-objective optimization genetic algorithm with decomposition. The method is named DM-MOGA and possesses two highlights. First, the boundary correction strategy is designed for the modules obtained in the process of local module detection and pre-simplification. Second, during the evolution, we introduce Davies-Bouldin index and clustering coefficient as fitness functions which are improved and migrated to weighted networks. In order to identify modules that are more relevant to diseases, the above strategies are designed to consider the network topology of genes and the strength of connections with other genes at the same time. Experimental results of different gene expression datasets of non-small cell lung cancer demonstrate that the core modules obtained by DM-MOGA are more effective than those obtained by several other advanced module identification methods.

CONCLUSIONS

The proposed method identifies disease-relevant modules by optimizing two novel fitness functions to simultaneously consider the local topology of each gene and its connection strength with other genes. The association of the identified core modules with lung cancer has been confirmed by pathway and gene ontology enrichment analysis.

CRdb: a comprehensive resource for deciphering chromatin regulators in human

Chromatin regulators (CRs) regulate epigenetic patterns on a partial or global scale, playing a critical role in affecting multi-target gene expression. As chromatin immunoprecipitation sequencing (ChIP-seq) data associated with CRs are rapidly accumulating, a comprehensive resource of CRs needs to be built urgently for collecting, integrating, and processing these data, which can provide abundant annotated information on CR upstream and downstream regulatory analyses as well as CR-related analysis functions. This study established an integrative CR resource, named CRdb (http://cr.liclab.net/crdb/), with the aim of curating a large number of available resources for CRs and providing extensive annotations and analyses of CRs to help biological researchers clarify the regulation mechanism and function of CRs. The CRdb database comprised a total of 647 CRs and 2,591 ChIP-seq samples from more than 300 human tissues and cell types. These samples have been manually curated from NCBI GEO/SRA and ENCODE. Importantly, CRdb provided the abundant and detailed genetic annotations in CR-binding regions based on ChIP-seq. Furthermore, CRdb supported various functional annotations and upstream regulatory information on CRs. In particular, it embedded four types of CR regulatory analyses: CR gene set enrichment, CR-binding genomic region annotation, CR-TF co-occupancy analysis, and CR regulatory axis analysis. CRdb is a useful and powerful resource that can help in exploring the potential functions of CRs and their regulatory mechanism in diseases and biological processes.

RNA-seq preprocessing and sample size considerations for gene network inference

Background

Gene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to RNA-seq data is relatively recent. As the characteristics of RNA-seq data are different from microarray data, it is an unanswered question what preprocessing methods for RNA-seq data should be applied prior to GNI to attain optimal performance, or what the required sample size for RNA-seq data is to obtain reliable GNI estimates.

Results

We ran 9144 analysis of 7 different RNA-seq datasets to evaluate 300 different preprocessing combinations that include data transformations, normalizations and association estimators. We found that there was no single best performing preprocessing combination but that there were several good ones. The performance varied widely over various datasets, which emphasized the importance of choosing an appropriate preprocessing configuration before GNI. Two preprocessing combinations appeared promising in general: First, Log-2 TPM (transcript per million) with Variance-stabilizing transformation (VST) and Pearson Correlation Coefficient (PCC) association estimator. Second, raw RNA-seq count data with PCC. Along with these two, we also identified 18 other good preprocessing combinations. Any of these algorithms might perform best in different datasets. Therefore, the GNI performances of these approaches should be measured on any new dataset to select the best performing one for it. In terms of the required biological sample size of RNA-seq data, we found that between 30 to 85 samples were required to generate reliable GNI estimates.

Conclusions

This study provides practical recommendations on default choices for data preprocessing prior to GNI analysis of RNA-seq data to obtain optimal performance results.

The Molecular Characterization of Genetic Abnormalities in Esophageal Squamous Cell Carcinoma May Foster the Development of Targeted Therapies

Esophageal cancer is among the most common tumors in the world and is associated with poor outcomes, with a 5-year survival rate of about 10-20%. Two main histological subtypes are observed: esophageal squamous cell carcinoma (ESCC), more frequent among Asian populations, and esophageal adenocarcinoma (EAC), the predominant type in Western populations. The development of molecular analysis techniques has led to the definition of the molecular alterations observed in ESCC, consistently differing from those observed in EAC. The genetic alterations observed are complex and heterogeneous and involve gene mutations, gene deletions and gene amplifications. However, despite the consistent progress in the definition of the molecular basis of ESCC, precision oncology for these patients is still virtually absent. The recent identification of molecular subtypes of ESCC with clinical relevance may foster the development of new therapeutic strategies. It is estimated that about 40% of the genetic alterations observed in ESCC are actionable. Furthermore, the recent introduction of solid tumor immunotherapy with immune checkpoint inhibitors (ICIs) showed that a minority of ESCC patients are responsive, and the administration of ICIs, in combination with standard chemotherapy, significantly improves overall survival over chemotherapy in ESCC patients with advanced disease.

A comprehensive analysis of mRNA expression profiles of Esophageal Squamous Cell Carcinoma reveals downregulation of Desmoglein 1 and crucial genomic targets

AIM

Esophageal Squamous Cell Carcinoma (ESCC) is a histological subtype of esophageal cancer that begins in the squamous cells in the esophagus. In only 19% of the ESCC-diagnosed patients, a five-year survival rate has been seen. This necessitates the identification of high-confidence biomarkers for early diagnosis, prognosis, and potential therapeutic targets for the mitigation of ESCC.

METHOD

We performed a meta-analysis of 10 mRNA datasets and identified consistently perturbed genes across the studies. Then, integrated with ESCC ATLAS to segregate 'core' genes to identify consequences of primary gene perturbation events leading to gene-gene interactions and dysregulated molecular signaling pathways. Further, by integrating with toxicogenomics data, inferences were drawn for gene interaction with environmental exposures, trace elements, chemical carcinogens, and drug chemicals. We also deduce the clinical outcomes of candidate genes based on survival analysis using the ESCC related dataset in The Cancer Genome Atlas.

RESULT

We identified 237 known and 18 novel perturbed candidate genes. Desmoglein 1 (DSG1) is one such gene that we found significantly downregulated (Fold Change =-1.89, p-value = 8.2e-06) in ESCC across six different datasets. Further, we identified 31 'core' genes (that either harbor genetic variants or are regulated by epigenetic modifications) and found regulating key biological pathways via adjoining genes in gene-gene interaction networks. Functional enrichment analysis showed dysregulated biological processes and pathways including "Extracellular matrix", "Collagen trimmer" and "HPV infection" are significantly overrepresented in our candidate genes. Based on the toxicogenomic inferences from Comparative Toxicogenomics Database we report the key genes that interacted with risk factors such as tobacco smoking, zinc, nitroso benzylmethylamine, and drug chemicals such as cisplatin, Fluorouracil, and Mitomycin in relation to ESCC. We also point to the STC2 gene that shows a high risk for mortality in ESCC patients.

CONCLUSION

We identified novel perturbed genes in relation to ESCC and explored their interaction network. DSG1 is one such gene, its association with microbiota and a clinical presentation seen commonly with ESCC hints that it is a good candidate for early diagnostic marker. Besides, in this study we highlight candidate genes and their molecular connections to risk factors, biological pathways, drug chemicals, and the survival probability of ESCC patients.

A novel method to identify and characterize personalized functional driver lncRNAs in cancer samples

Cancer is a highly heterogeneous disease, and different individuals of the same cancer type can display different therapeutic effects and prognosis. Genetic variation of long non-coding RNA is the key factor driving tumor development, and plays an important role in genetic and biological heterogeneity. Therefore, it is of great significance to identify lncRNA as a driving factor in the non-coding region and explain its function in tumors for revealing the pathogenesis of cancer. In this study, we developed an integrated method to identify Personalized Functional Driver lncRNAs (PFD-lncRNAs) by integrating the DNA copy number data, gene expression data, and the biological subpathways information. Then, we applied the method to identify 2695 PFD-lncRNAs in 5334 samples across 19 cancer types. We performed an analysis of the association between PFD-lncRNAs and drug sensitivity, which provides medication guidance in disease therapy and drug discovery in the individual. Our research is of great significance for elucidating the biological roles of lncRNA genetic variation in cancer, revealing the related mechanism of cancer, and providing novel insights for individualized medicine.

Mitigating the BFL1-mediated antiapoptotic pathway in diffuse large B cell lymphoma by inhibiting HDACs

Endogenous BFL1 expression renders diffuse large B-cell lymphoma (DLBCL) cells insensitive to B-cell lymphoma 2 (BCL2) and/or MCL1 inhibitors. Considering the difficulties in developing a direct BFL1 inhibitor, we intended to inhibit histone deacetylase (HDAC) to mitigate the biological role of BFL1 by modulating WT1 and NOXA. Cells expressing high BFL1 exhibited enhanced sensitivity to pan-HDAC inhibitor compared to low BFL1 expressing cells, mainly attributable to the difference in the amount of apoptosis. HDAC inhibitors decreased BFL1 and WT1 expressions while increasing NOXA levels. The BFL1 knockdown experiment demonstrated that HDAC inhibitor's sensitivity depends on the BFL1 expression in DLBCL cells. Furthermore, we found that the specific HDAC class was expected to play a critical role in BFL1 inhibition by comparing the effects of several HDAC inhibitors. Thus, our study provides a rationale for using HDAC inhibitors to induce apoptosis in DLBCL patients using BFL1 as a predictive biomarker.

Potential shared gene signatures and molecular mechanisms between atherosclerosis and depression: Evidence from transcriptome data

BACKGROUND

Atherosclerosis and depression contribute to each other; however, mechanisms linking them at the genetic level remain unexplored. This study aimed to identify shared gene signatures and related pathways between these comorbidities.

METHODS

Atherosclerosis-related datasets were downloaded from the Gene Expression Omnibus database. Differential and weighted gene co-expression network analyses were employed to identify atherosclerosis-related genes. Depression-related genes were downloaded from the DisGeNET database, and the overlaps between atherosclerosis-related genes and depression-related genes were characterized as crosstalk genes. The functional enrichment analysis and protein-protein interaction network were performed in these gene sets. Subsequently, the Boruta algorithm and Recursive Feature Elimination algorithm were performed to identify feature-selection genes. A support vector machine was constructed to measure the accuracy of calculations, and two external validation sets were included to verify the results.

RESULTS

Based on two atherosclerosis-related datasets (GSE28829 and GSE43292), 165 genes were determined as atherosclerosis-related genes. Meanwhile, 1478 depression-related genes were obtained. After intersecting, 24 crosstalk genes were identified, and two pathways, "lipid and atherosclerosis" and "tryptophan metabolism," were revealed as mutual pathways according to the enrichment analysis results. Through the protein-protein interaction network, Molecular Complex Detection plugin, and cytoHubba plugin, PTPRC and MMP9 were identified as the hub gene. Moreover, SLC22A3, CASP1, AMPD3, and PIK3CG were recognized as feature-selection genes. Based on two external validation sets, CASP1 and MMP9 were finally determined as the critical crosstalk genes.

CONCLUSIONS

"Lipid and atherosclerosis" and "tryptophan metabolism" were possibly the pathways of atherosclerosis secondary to depression and depression due to atherosclerosis, respectively. CASP1 and MMP9 were revealed as the most pivotal candidates linking atherosclerosis and depression by mediating these two pathways. Further experimentation is needed to confirm these conclusions.

Pepsinogen/Proton Pump Co-Expression in Barrett's Esophageal Cells Induces Cancer-Associated Changes

EDUCATIONAL OBJECTIVE

At the conclusion of this presentation, participants should better understand the carcinogenic potential of pepsin and proton pump expression in Barrett's esophagus.

OBJECTIVE

Barrett's esophagus (BE) is a well-known risk factor for esophageal adenocarcinoma (EAC). Gastric H⁺ /K⁺ ATPase proton pump and pepsin expression has been demonstrated in some cases of BE; however, the contribution of local pepsin and proton pump expression to carcinogenesis is unknown. In this study, RNA sequencing was used to examine global transcriptomic changes in a BE cell line ectopically expressing pepsinogen and/or gastric H⁺ /K⁺ ATPase proton pumps.

STUDY DESIGN

In vitro translational.

METHODS

BAR-T, a human BE cell line devoid of expression of pepsinogen or proton pumps, was transduced by lentivirus-encoding pepsinogen (PGA5) and/or gastric proton pump subunits (ATP4A, ATP4B). Changes relative to the parental line were assessed by RNA sequencing.

RESULTS

Top canonical pathways associated with protein-coding genes differentially expressed in pepsinogen and/or proton pump expressing BAR-T cells included those involved in the tumor microenvironment and epithelial-mesenchymal transition. Top upstream regulators of coding transcripts included TGFB1 and ERBB2, which are associated with the pathogenesis and prognosis of BE and EAC. Top upstream regulators of noncoding transcripts included p300-CBP, I-BET-151, and CD93, which have previously described associations with EAC or carcinogenesis. The top associated disease of both coding and noncoding transcripts was cancer.

CONCLUSIONS

These data support the carcinogenic potential of pepsin and proton pump expression in BE and reveal molecular pathways affected by their expression. Further study is warranted to investigate the role of these pathways in carcinogenesis associated with BE.

LEVEL OF EVIDENCE

NA Laryngoscope, 133:59-69, 2023.

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.

Flotillin-2 regulates epidermal growth factor receptor activation, degradation by Cbl-mediated ubiquitination, and cancer growth

Epidermal growth factor receptor (EGFR) signaling is frequently dysregulated in various cancers. The ubiquitin ligase Casitas B-lineage lymphoma proto-oncogene (Cbl) regulates degradation of activated EGFR through ubiquitination and acts as an adaptor to recruit proteins required for trafficking. Here, we used stable isotope labeling with amino acids in cell culture mass spectrometry to compare Cbl complexes with or without epidermal growth factor (EGF) stimulation. We identified over a hundred novel Cbl interactors, and a secondary siRNA screen found that knockdown of Flotillin-2 (FLOT2) led to increased phosphorylation and degradation of EGFR upon EGF stimulation in HeLa cells. In PC9 and H441 cells, FLOT2 knockdown increased EGF-stimulated EGFR phosphorylation, ubiquitination, and downstream signaling, reversible by EGFR inhibitor erlotinib. CRISPR knockout (KO) of FLOT2 in HeLa cells confirmed EGFR downregulation, increased signaling, and increased dimerization and endosomal trafficking. Furthermore, we determined that FLOT2 interacted with both Cbl and EGFR. EGFR downregulation upon FLOT2 loss was Cbl dependent, as coknockdown of Cbl and Cbl-b restored EGFR levels. In addition, FLOT2 overexpression decreased EGFR signaling and growth. Overexpression of wildtype (WT) FLOT2, but not the soluble G2A FLOT2 mutant, inhibited EGFR phosphorylation upon EGF stimulation in HEK293T cells. FLOT2 loss induced EGFR-dependent proliferation and anchorage-independent growth. Lastly, FLOT2 KO increased tumor formation and tumor volume in nude mice and NSG mice, respectively. Together, these data demonstrated that FLOT2 negatively regulated EGFR activation and dimerization, as well as its subsequent ubiquitination, endosomal trafficking, and degradation, leading to reduced proliferation in vitro and in vivo.

Peptide-based covalent inhibitors of protein-protein interactions

Protein-protein interactions (PPI) are involved in all cellular processes and many represent attractive therapeutic targets. However, the frequently rather flat and large interaction areas render the identification of small molecular PPI inhibitors very challenging. As an alternative, peptide interaction motifs derived from a PPI interface can serve as starting points for the development of inhibitors. However, certain proteins remain challenging targets when applying inhibitors with a competitive mode of action. For that reason, peptide-based ligands with an irreversible binding mode have gained attention in recent years. This review summarizes examples of covalent inhibitors that employ peptidic binders and have been tested in a biological context.

Network Pharmacology Research Indicates that Wu-Mei-Wan Treats Obesity by Inhibiting Th17 Cell Differentiation and Alleviating Metabolic Inflammation

BACKGROUND

Wu-Mei-Wan (WMW), a traditional Chinese medicine (TCM) formula, has a good effect on the treatment of obesity and has been proven helpful to promote the metabolism of adipose tissue. However, its underlying mechanism remains to be studied. This study aims to explore the potential pharmacological mechanism of WMW in the treatment of obesity.

METHODS

Network pharmacology was used to sort out the relationship between WMW putative targets and obesity-related drug targets or disease targets, which indicated the mechanism of WMW in treating obesity from two aspects of clinical drugs approved by the Food and Drug Administration (FDA) and obesity-related diseases. Databases such as Traditional Chinese Medicine Systems Pharmacology (TCMSP), PubChem, DrugBank, DisGeNET, and Genecards were used to collect information about targets. String platform was used to convert the data into gene symbol of "homo sapiens", and perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. With the Human Protein Reference Database (HPRD) as background data, Cytoscape 3.6.0 software was used to construct a new protein-protein interaction (PPI) network. Mechanism diagrams of key pathways were obtained from the KEGG database. AutoDock Vina software was used to conduct molecular docking verification.

RESULTS

The number of targets in the overlap between WMW putative targets and obesity-related drug targets accounted for more than 50% of the latter, and HTR3A, SLC6A4, and CYP3A4 were core targets. In obesity-related disease targets-WMW putative targets PPI network, the Th17 cell differentiation pathway, and the IL-17 signaling pathway were key pathways, and the 1st module and the 7th module were central function modules that were highly associated with immunity and inflammation. Molecular docking verified that STAT3, TGFB1, MMP9, AHR, IL1B, and CCL2 were core targets in the treatment of WMW on obesity.

CONCLUSION

WMW has similar effects on lipid and drug metabolism as the current obesity-related drugs, and is likely to treat obesity by inhibiting Th17 cell differentiation and alleviating metabolic inflammation.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Role of CD147 in the development and diagnosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, and the third leading cause of cancer-related deaths worldwide. HCC is characterized by insidious onset, and most patients are diagnosed at an advanced stage with a poor prognosis. Identification of biomarkers for HCC onset and progression is imperative to development of effective diagnostic and therapeutic strategies. CD147 is a glycoprotein that is involved in tumor cell invasion, metastasis and angiogenesis through multiple mechanisms. In this review, we describe the molecular structure of CD147 and its role in regulating HCC invasion, metastasis and angiogenesis. We highlight its potential as a diagnostic and therapeutic target for HCC.

Clinical diagnosis and management of pancreatic cancer: Markers, molecular mechanisms, and treatment options

Pancreatic cancer (PC) is the third-leading cause of cancer deaths. The overall 5-year survival rate of PC is 9%, and this rate for metastatic PC is below 3%. However, the PC-induced death cases will increase about 2-fold by 2060. Many factors such as genetic and environmental factors and metabolic diseases can drive PC development and progression. The most common type of PC in the clinic is pancreatic ductal adenocarcinoma, comprising approximately 90% of PC cases. Multiple pathogenic processes including but not limited to inflammation, fibrosis, angiogenesis, epithelial-mesenchymal transition, and proliferation of cancer stem cells are involved in the initiation and progression of PC. Early diagnosis is essential for curable therapy, for which a combined panel of serum markers is very helpful. Although some mono or combined therapies have been approved by the United States Food and Drug Administration for PC treatment, current therapies have not shown promising outcomes. Fortunately, the development of novel immunotherapies, such as oncolytic viruses-mediated treatments and chimeric antigen receptor-T cells, combined with therapies such as neoadjuvant therapy plus surgery, and advanced delivery systems of immunotherapy will improve therapeutic outcomes and combat drug resistance in PC patients. Herein, the pathogenesis, molecular signaling pathways, diagnostic markers, prognosis, and potential treatments in completed, ongoing, and recruiting clinical trials for PC were reviewed.

Elevated expression of the membrane-anchored serine protease TMPRSS11E in NSCLC progression

TMPRSS11E was found to be upregulated in human nonsmall cell lung cancer samples (NSCLC) and cell lines, and high expression was associated with poor survival of NSCLC patients. The results of in vitro and in vivo experiments showed that overexpressing TMPRSS11E resulted in A549 cell proliferation and migration promotion, while the TMPRSS11E S372A mutant with the mutated catalytic domain lost the promoting function. In addition, in mouse xenograft models, silencing TMPRSS11E expression inhibited the growth of 95D cell-derived tumors. To explore the mechanism of marked upregulation of TMPRSS11E in NSCLC cells, promoter analysis, EMSA, and ChIP assays were performed. STAT3 was identified as the transcription factor responsible for TMPRSS11E transcription. Moreover, the purified recombinant TMPRSS11E catalytic domain exhibited enzymatic activity for the proteolytic cleavage of PAR2. Recombinant TMPRSS11E catalytic domain incubation further activated the PAR2-EGFR-STAT3 pathway. These findings established a mechanism of TMPRSS11E-PAR2-EGFR-STAT3 positive feedback, and the oncogenic role of TMPRSS11E as a PAR2 modulator in NSCLC was revealed.

Recent developments of sequence-based prediction of protein-protein interactions

The identification of protein-protein interactions (PPIs) can lead to a better understanding of cellular functions and biological processes of proteins and contribute to the design of drugs to target disease-causing PPIs. In addition, targeting host-pathogen PPIs is useful for elucidating infection mechanisms. Although several experimental methods have been used to identify PPIs, these methods can yet to draw complete PPI networks. Hence, computational techniques are increasingly required for the prediction of potential PPIs, which have never been seen experimentally. Recent high-performance sequence-based methods have contributed to the construction of PPI networks and the elucidation of pathogenetic mechanisms in specific diseases. However, the usefulness of these methods depends on the quality and quantity of training data of PPIs. In this brief review, we introduce currently available PPI databases and recent sequence-based methods for predicting PPIs. Also, we discuss key issues in this field and present future perspectives of the sequence-based PPI predictions.

konnect2prot: a web application to explore the protein properties in a functional protein-protein interaction network

MOTIVATION

The regulation of proteins governs the biological processes and functions and, therefore, the organisms' phenotype. So there is an unmet need for a systematic tool for identifying the proteins that play a crucial role in information processing in a protein-protein interaction (PPI) network. However, the current protein databases and web servers still lag behind to provide an end-to-end pipeline that can leverage the topological understanding of a context-specific PPI network to identify the influential spreaders. Addressing this, we developed a web application, 'konnect2prot' (k2p), which can generate context-specific directional PPI network from the input proteins and detect their biological and topological importance in the network.

RESULTS

We pooled together a large amount of ontological knowledge, parsed it down into a functional network, and gained insight into the molecular underpinnings of the disease development by creating a one-stop junction for PPI data. k2p contains both local and global information about a protein, such as protein class, disease mutations, ligands and PDB structure, enriched processes and pathways, multi-disease interactome and hubs and bottlenecks in the directional network. It also identifies spreaders in the network and maps them to disease hallmarks to determine whether they can affect the disease state or not.

AVAILABILITY AND IMPLEMENTATION

konnect2prot is freely accessible using the link https://konnect2prot.thsti.in. The code repository is https://github.com/samrat-lab/k2p_bioinfo-2022.

A systematic review verified by bioinformatic analysis based on TCGA reveals week prognosis power of CAIX in renal cancer

BACKGROUND

Carbonic anhydrase IX (CAIX) protein has been correlated with progression and survival in patients with some tumors such as head and neck carcinoma. But renal cell carcinoma is an exception. The prognostic value of CAIX in RCC used to be associated with patients' survival according to published works. This study aimed to rectify the former conclusion.

METHODS

This study was registered in PROSPERO (CRD42020160181). A literature search of the PubMed, Embase, Cochrane library and Web of Science databases was performed to retrieve original studies until April of 2022. Twenty-seven studies, including a total of 5462 patients with renal cell carcinoma, were reviewed. Standard meta-analysis methods were used to evaluate the prognostic impact of CAIX expression on patient prognosis. The hazard ratio and its 95% confidence interval were recorded for the relationship between CAIX expression and survival, and the data were analyzed using Stata 11.0. Then we verify the meta-analysis resort to bioinformatics (TCGA).

RESULTS

Our initial search resulted in 908 articles in total. From PubMed, Embase, Web of Science electronic and Cochrane library databases, 493, 318 and 97 potentially relevant articles were discovered, respectively. We took the analysis between CA9 and disease-specific survival (HR = 1.18, 95% CI: 0.82-1.70, I2 = 79.3%, P<0.05), a subgroup then was performed to enhance the result (HR = 1.63, 95%CI: 1.30-2.03, I2 = 26.3%, P = 0.228); overall survival was also parallel with the former (HR = 1.13, 95%CI: 0.82-1.56, I2 = 79.8%, P<0.05), then a subgroup also be performed (HR = 0.90, 95%CI:0.75-1.07, I2 = 23.1%, P = 0.246) to verify the result; the analysis between CAIX and progression-free survival got the similar result (HR = 1.73, 95%CI:0.97-3.09, I2 = 82.4%, P<0.05), we also verify the result by subgroup analysis (HR = 1.04, 95%CI:0.79-1.36, I2 = 0.0%, P = 0.465); at last the relationship between CAIX and recurrence-free survival got the same result, too (HR = 0.99, 95%CI: 0.95-1.02, I2 = 57.8%, P = 0.050), the subgroup's result was also parallel with the former (HR = 1.01, 95%CI: 0.91-1.03, I2 = 0.00%, P = 0.704). To validate our meta-analysis, we took a bioinformatic analysis based on TCGA database, survival curve between low and high CAIX expression in four endpoints (DSS, OS, PFI, DFI) have corresponding P value (DSS:P = 0.23, OS:P = 0.77, PFI:P = 0.25, DFI:P = 0.78).

CONCLUSIONS

CAIX expression in patients with RCC is an exception to predict tumor survival. Both low CAIX expression and high expression are not associated with survivals in RCC patients.

Establishment and validation of a redox-related long non-coding RNAs prognostic signature in head and neck squamous cell carcinoma

Reduction and oxidation (redox) reactions occur in living organisms as part of normal cellular metabolism. Here, we established a novel redox-related long non-coding RNAs (rrlncRNAs) signature to predict the prognosis and therapeutic response in Head and neck squamous cell carcinoma (HNSCC). The expression profile and clinical information were obtained from the TCGA project. In total, 10 differently expressed rrlncRNAs associated with prognosis were identified and involved in a prognostic risk score signature by the least absolute shrinkage and selection operator penalized Cox analysis. The area under the receiver operating characteristic curves of the survival rates predicted by the rrlncRNAs signature over one, two, and three years were found to be 0.651, 0.670, and 0.679. Following the completion of the Kaplan-Meier survival study, we discovered that the lower-risk cohort exhibited a much longer overall survival period in contrast with the higher-risk cohort. Univariate and multivariable Cox regression analyses demonstrated that the risk score independently served as a significant predictive factor. GO annotation and KEGG pathway analyses illustrated that the rrlncRNAs signature was strongly associated with immune-related functions as well as signaling pathways. The tumor-infiltrating immune cells, tumor microenvironment, immune-related functions, HLA gene family expression, immune checkpoint genes expression, and somatic variants differed substantially between the low- and high-risk cohorts. Moreover, patients in low-risk group were predicted to present a favorable immunotherapy responsiveness, while in contrast, the high-risk group patients might have a stronger sensitivity to "docetaxel". According to our findings, the rrlncRNAs signature showed an excellent prognosis predictive value and might indicate therapeutic response to immunotherapy in HNSCC.

PCSK9 in Liver Cancers at the Crossroads between Lipid Metabolism and Immunity

Metabolic rewiring and defective immune responses are considered to be the main driving forces sustaining cell growth and oncogenesis in many cancers. The atypical enzyme, proprotein convertase subtilisin/kexin type 9 (PCSK9), is produced by the liver in large amounts and plays a major role in lipid metabolism via the control of the low density lipoprotein receptor (LDLR) and other cell surface receptors. In this context, many clinical studies have clearly demonstrated the high efficacy of PCSK9 inhibitors in treating hyperlipidemia and cardiovascular diseases. Recent data implicated PCSK9 in the degradation of major histocompatibility complex I (MHC-I) receptors and the immune system as well as in other physiological activities. This review highlights the complex crosstalk between PCSK9, lipid metabolism and immunosuppression and underlines the latest advances in understanding the involvement of this convertase in other critical functions. We present a comprehensive assessment of the different strategies targeting PCSK9 and show how these approaches could be extended to future therapeutic options to treat cancers with a main focus on the liver.

Potential Role of Seven Proteomics Tissue Biomarkers for Diagnosis and Prognosis of Prostate Cancer in Urine

As the currently available tests for the clinical management of prostate cancer (PCa) are still far from providing precise diagnosis and risk stratification, the identification of new molecular marker(s) remains a pertinent clinical need. Candidate PCa biomarkers from the published proteomic comparative studies of prostate tissue (2002-2020) were collected and systematically evaluated. AZGP1, MDH2, FABP5, ENO1, GSTP1, GSTM2, and EZR were chosen for further evaluation in the urine of 85 PCa patients and controls using ELISA. Statistically significant differences in protein levels between PCa and BPH showed FABP5 (p = 0.019) and ENO1 (p = 0.015). A biomarker panel based on the combination of FABP5, ENO1, and PSA provided the highest accuracy (AUC = 0.795) for PCa detection. The combination of FABP5, EZR, AZGP1, and MDH2 showed AUC = 0.889 in PCa prognosis, with 85.29% of the samples correctly classified into low and high Gleason score (GS) groups. The addition of PSA to the panel slightly increased the AUC to 0.914. AZGP1, FABP5, and EZR showed significant correlation with GS, stage, and percentage of positive biopsy cores. Although validation using larger patient cohorts will be necessary to establish the credibility of the proposed biomarker panels in a clinical context, this study opens a way for the further testing of more high-quality proteomics biomarkers, which could ultimately add value to the clinical management of PCa.

Association between oxidative balance score and new-onset hypertension in adults: A community-based prospective cohort study

Introduction

Oxidative stress plays a key role in the pathophysiology of hypertension development. The oxidative balance score (OBS) comprises dietary and lifestyle pro- and anti-oxidant components and reflects the overall oxidative stress burden. We aimed to evaluate the association between the OBS and new-onset hypertension (HTN) using large, community-based, prospective Korean cohort data.

Methods

Among 10,030 participants aged 40-69 years included in the Korean Genome and Epidemiology Study, the data of 5,181 participants were analyzed. The hazard ratio (HR) and 95% confidence interval (CI) for new-onset HTN according to sex-specific OBS quartile groups were calculated using univariable and multivariable Cox proportional hazard regression analyses.

Results

During the mean 13.6-year follow-up period, 1,157 men and 1,196 women developed new-onset HTN. Compared to the Q1 group, the adjusted HRs (95%CI) for new-onset HTN in the Q2, Q3, and Q4 groups were 0.96 (0.82-1.16), 0.85 (0.72-0.99), and 0.71 (0.59-0.86) in men and 0.81 (0.69-0.95), 0.81(0.68-0.95), and 0.70 (0.57-0.84) in women, respectively.

Discussion

Individuals with high OBS are at lower risk of developing HTN. This study suggests that a healthy lifestyle and antioxidant rich diet could be a preventive strategy for HTN.

Prognostic implications of microRNA-21 overexpression in pancreatic ductal adenocarcinoma: an international multicenter study of 686 patients

Despite progress in genomic characterization, no single prognostic marker that can be evaluated using an easy-to-perform and relatively inexpensive method is available for pancreatic ductal adenocarcinoma (PDAC). MicroRNAs, which are stable, tumor- and tissue-specific molecules, are potentially ideal biomarkers, and we established an inter-laboratory validated method to investigate miR-21 as a prognostic biomarker in PDAC. The study samples of PDAC patients were recruited from a test cohort of Glasgow (n = 189) and three validation cohorts of Pisa (n = 69), Sydney (n = 249), and International Cancer Genome Consortium (ICGC) (n = 249). Tissue microarrays were used for miR-21 staining by chromogenic in situ hybridization (CISH). The patients were subdivided into no/low and high miR-21 staining groups using a specific histoscore. Furthermore, miR-21 staining was evaluated against clinicopathological variables and follow-up data by Fisher/log-rank test and Cox proportional models. The prognostic variables found to be significant in univariate analysis (P value < 0.10) were included in multivariate analysis in a backward-stepwise fashion. MiR-21 expression was cytoplasmic, with more consistent staining in the malignant ductal epithelium than in the stroma. The expression of miR-21 was significantly associated with tumor size and lymph node metastasis, whereas no association was observed with other clinicopathological variables. High miR-21 staining (histoscore ≥ 45 [median score]) was an independent predictor of survival in the Glasgow test cohort (HR 2.37, 95% CI: 1.42-3.96, P < 0.0001) and three validation cohorts (Pisa, HR 2.03, 95% CI: 1.21-3.39, P = 0.007; Sydney, HR 2.58, 95% CI (1.21-3.39), P < 0.0001; and ICGC, HR 3.34, 95% CI: 2.07-5.84, P = 0.002) when adjusted for clinical variables in a multivariate model. In comparison to the patients with low miR-21, the patients with high miR-21 expression had significant increase in OS as they benefit from gemcitabine-based adjuvant chemotherapy (Glasgow 16.5 months [with chemotherapy] vs 10.5 months [without chemotherapy]); Sydney 25.0 vs 10.6; ICGC 25.2 vs 11.9. These results indicated that miR-21 is a predictor of survival, prompting prospective trials. Evaluation of miR-21 offers new opportunities for the stratification of patients with PDAC and might facilitate the implementation of clinical management and therapeutic interventions for this devastating disease.

HPV Prevalence and Predictive Biomarkers for Oropharyngeal Squamous Cell Carcinoma in Mexican Patients

BACKGROUND

Worldwide prevalence of Oropharyngeal Squamous Cell Carcinoma (OPSCC) has increased, affecting mostly young males. OPSCC associated with Human Papillomavirus (HPV) infection exhibits particular characteristics in terms of response to treatment, hence HPV has been proposed as a prognostic factor. The impact of HPV positivity and associated biomarkers on OPSCC in the Mexican population has not been addressed. Therefore, the analysis of OPSCC prognostic markers in the Mexican population is necessary.

METHODS

Retrolective study in Mexican OPSCC patients, where HPV prevalence, p16 and EGFR levels were assessed using INNO-LiPA and immunohistochemistry.

RESULTS

We found an HPV prevalence of 57.6% in OPSCC cases treated at a reference center in Mexico. HPV and p16 positivity, as well as EGFR, associate with better outcomes in OPSCC patients, and they also promote reduced death risk. Notably, HPV presence and p16 positivity showed a significant association with disease-free survival (DFS), with a HR of 0.15 (p = 0.006) and a HR of 0.17 (p = 0.012), respectively, indicating a possible role as predictive biomarkers in Mexican OPSCC patients.

CONCLUSIONS

Our results reflect the clinical utility of p16 analysis to improve overall survival (OS) and to predict recurrence in oropharyngeal cancer. These results position p16 and HPV as predictive biomarkers for OPSCC.

An oncogene addiction phosphorylation signature and its derived scores inform tumor responsiveness to targeted therapies

PURPOSE

Oncogene addiction provides important therapeutic opportunities for precision oncology treatment strategies. To date the cellular circuitries associated with driving oncoproteins, which eventually establish the phenotypic manifestation of oncogene addiction, remain largely unexplored. Data suggest the DNA damage response (DDR) as a central signaling network that intersects with pathways associated with deregulated addicting oncoproteins with kinase activity in cancer cells.

EXPERIMENTAL

DESIGN: We employed a targeted mass spectrometry approach to systematically explore alterations in 116 phosphosites related to oncogene signaling and its intersection with the DDR following inhibition of the addicting oncogene alone or in combination with irradiation in MET-, EGFR-, ALK- or BRAF (V600)-positive cancer models. An NSCLC tissue pipeline combining patient-derived xenografts (PDXs) and ex vivo patient organotypic cultures has been established for treatment responsiveness assessment.

RESULTS

We identified an 'oncogene addiction phosphorylation signature' (OAPS) consisting of 8 protein phosphorylations (ACLY S455, IF4B S422, IF4G1 S1231, LIMA1 S490, MYCN S62, NCBP1 S22, P3C2A S259 and TERF2 S365) that are significantly suppressed upon targeted oncogene inhibition solely in addicted cell line models and patient tissues. We show that the OAPS is present in patient tissues and the OAPS-derived score strongly correlates with the ex vivo responses to targeted treatments.

CONCLUSIONS

We propose a score derived from OAPS as a quantitative measure to evaluate oncogene addiction of cancer cell samples. This work underlines the importance of protein phosphorylation assessment for patient stratification in precision oncology and corresponding identification of tumor subtypes sensitive to inhibition of a particular oncogene.

RRBP1 depletion of bone metastatic cancer cells contributes to enhanced expression of the osteoblastic phenotype

Bone metastatic cancer-secreted extracellular factors are capable of modifying the bone microenvironment through interacting with bone cells, including osteoblasts. Reticulum ribosome-binding protein 1 (RRBP1) is substantially expressed in certain bone metastatic cancer cells. This study was undertaken to determine whether RRBP1 from bone metastatic cancer cells affects the osteoblastic phenotype expression. Breast and prostate cancer cells, MDA-MB-231 and PC3, were cultured, respectively, followed by collecting conditioned mediums (CMs) and identifying the abundance of RRBP1 in CMs using LC-MS/MS. MC3T3-E1 cells were cultured with a mixed medium (including CMs from shRRBP1-transduced two-type cancer cells) with or without endoplasmic reticulum (ER) stress inhibitor 4-PBA, followed by measuring the levels of osteoblastic phenotype expression and biomarkers of ER stress using western blotting, qPCR, and ARS staining, respectively. Similar experiments were performed in shRrbp1-transduced MC3T3-E1 cells cultured with a mixed medium (including CMs from the two-type cancer cells). Bone formation parameters were measured in the tibia of nude mice injected with shRRBP1-transduced two-type cancer cells using micro-CT analysis. These results showed that RRBP1 is the sole shared high-abundance protein in CMs from the two-type cancer cells, involving osteoblast differentiation. CMs from shRRBP1-transduced two-type cells boosted the osteoblastic phenotype expression partially through increasing ER stress. CMs from the two-type cancer cells partially offset the similar alterations induced by shRrbp1 in MC3T3-E1 cells. Injection with shRRBP1-transduced two-type cells ameliorated the bone lesions in nude mice. Therefore, RRBP1 depletion of bone metastatic cancer enhanced the osteoblastic phenotype expression, suggesting a role of RRBP1 in the bone microenvironment.

Current Status of Novel Agents for the Treatment of B Cell Malignancies: What's Coming Next?

Resistance to death is one of the hallmarks of human B cell malignancies and often contributes to the lack of a lasting response to today's commonly used treatments. Drug discovery approaches designed to activate the death machinery have generated a large number of inhibitors of anti-apoptotic proteins from the B-cell lymphoma/leukemia 2 family and the B-cell receptor (BCR) signaling pathway. Orally administered small-molecule inhibitors of Bcl-2 protein and BCR partners (e.g., Bruton's tyrosine kinase and phosphatidylinositol-3 kinase) have already been included (as monotherapies or combination therapies) in the standard of care for selected B cell malignancies. Agonistic monoclonal antibodies and their derivatives (antibody-drug conjugates, antibody-radioisotope conjugates, bispecific T cell engagers, and chimeric antigen receptor-modified T cells) targeting tumor-associated antigens (TAAs, such as CD19, CD20, CD22, and CD38) are indicated for treatment (as monotherapies or combination therapies) of patients with B cell tumors. However, given that some patients are either refractory to current therapies or relapse after treatment, novel therapeutic strategies are needed. Here, we review current strategies for managing B cell malignancies, with a focus on the ongoing clinical development of more effective, selective drugs targeting these molecules, as well as other TAAs and signaling proteins. The observed impact of metabolic reprogramming on B cell pathophysiology highlights the promise of targeting metabolic checkpoints in the treatment of these disorders.

DrugRepo: a novel approach to repurposing drugs based on chemical and genomic features

The drug development process consumes 9-12 years and approximately one billion US dollars in costs. Due to the high finances and time costs required by the traditional drug discovery paradigm, repurposing old drugs to treat cancer and rare diseases is becoming popular. Computational approaches are mainly data-driven and involve a systematic analysis of different data types leading to the formulation of repurposing hypotheses. This study presents a novel scoring algorithm based on chemical and genomic data to repurpose drugs for 669 diseases from 22 groups, including various cancers, musculoskeletal, infections, cardiovascular, and skin diseases. The data types used to design the scoring algorithm are chemical structures, drug-target interactions (DTI), pathways, and disease-gene associations. The repurposed scoring algorithm is strengthened by integrating the most comprehensive manually curated datasets for each data type. At DrugRepo score ≥ 0.4, we repurposed 516 approved drugs across 545 diseases. Moreover, hundreds of novel predicted compounds can be matched with ongoing studies at clinical trials. Our analysis is supported by a web tool available at: http://drugrepo.org/ .

Oesophagectomy following noncurative endoscopic resection for oesophageal carcinoma: does interval matter?

OBJECTIVES

Oesophagectomy was always recommended after noncurative endoscopic resection (ER). And the optimal time interval from ER to oesophagectomy remains unclear. This study was to explore the effect of interval on pathologic stage and prognosis.

METHODS

We included 155 patients who underwent ER for cT1N0M0 oesophageal cancer and then received subsequent oesophagectomy from 2009 to 2019. Overall survival and disease-free survival (DFS) were analysed to find an optimal cut-off of interval from ER to oesophagectomy. In addition, pathologic stage after ER was compared to that of oesophagectomy. Logistic regression model was built to identify risk factors for pathological upstage.

RESULTS

The greatest difference of DFS was found in the groups who underwent oesophagectomy before and after 30 days (P = 0.016). Among total 155 patients, 106 (68.39%) received oesophagectomy within 30 days, while 49 (31.61%) had interval over 30 days. Comparing the pathologic stage between ER and oesophagectomy, 26 patients had upstage and thus had worse DFS (hazard ratio = 3.780, P = 0.042). T1b invasion, lymphovascular invasion and interval >30-day group had a higher upstage rate (P = 0.014, P < 0.001 and P < 0.001, respectively). And they were independent risk factors for pathologic upstage (odds ratio = 3.782, 4.522 and 2.844, respectively).

CONCLUSIONS

It was the first study exploring the relationship between time interval and prognosis in oesophageal cancer. The longer interval between noncurative ER and additional oesophagectomy was associated with a worse DFS, so oesophagectomy was recommended performed within 1 month after ER. Older age, T1b stage, lymphovascular invasion and interval >30 days were significantly associated with pathologic upstage, which is related to the worse outcome too.

Synthetic lethal gene pairs: Experimental approaches and predictive models

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.

Emerging role of chemokines in small cell lung cancer: Road signs for metastasis, heterogeneity, and immune response

Small cell lung cancer (SCLC) is a recalcitrant, relatively immune-cold, and deadly subtype of lung cancer. SCLC has been viewed as a single or homogenous disease that includes deletion or inactivation of the two major tumor suppressor genes (TP53 and RB1) as a key hallmark. However, recent sightings suggest the complexity of SCLC tumors that comprises highly dynamic multiple subtypes contributing to high intratumor heterogeneity. Furthermore, the absence of targeted therapies, the understudied tumor immune microenvironment (TIME), and subtype plasticity are also responsible for therapy resistance. Secretory chemokines play a crucial role in immunomodulation by trafficking immune cells to the tumors. Chemokines and cytokines modulate the anti-tumor immune response and wield a pro-/anti-tumorigenic effect on SCLC cells after binding to cognate receptors. In this review, we summarize and highlight recent findings that establish the role of chemokines in SCLC growth and metastasis, and sophisticated intratumor heterogeneity. We also discuss the chemokine networks that are putative targets or modulators for augmenting the anti-tumor immune responses in targeted or chemo-/immuno-therapeutic strategies, and how these combinations may be utilized to conquer SCLC.

Cornulin as a Prognosticator for Lymph Node Involvement in Cutaneous Squamous Cell Carcinoma

Background Cornulin is an epidermal differentiation marker and a stress-related protein. Its expression patterns are likely to reflect the multi-step tumorigenesis process of cSCC, given its role as a tumor suppressor. The aim of this study is to evaluate the utility of Cornulin as a prognosticator for cutaneous squamous cell carcinoma (cSCC). Specifically, the correlation between Cornulin expression and the clinicopathological parameter of lymph node involvement (nodal status), which plays a major role in determining cSCC prognosis and recurrence. We predicted that Cornulin expression declines as cSCC tumors metastasize to regional lymph nodes. Methodology Tissue samples of cSCC lesions of variable nodal involvement status were stained using immunohistochemistry, and high-resolution images were acquired. Aperio ImageScope software (Leica Biosystems) equipped with a positive-pixel-counting algorithm was used to quantify the staining intensity. Subsequently, Cornulin immunoreactivity was calculated as a Histo-score (H-score) value, which is based on the staining intensity and the percentage of positively stained cells. Mean H-scores were compared between groups using an unpaired t-test. Results A significant inverse correlation was found between Cornulin expression levels and metastasis to the lymph nodes. Specifically, primary tumors with metastasis to regional lymph nodes (N1) exhibited 9.5-fold decrease in Cornulin immunoreactivity compared to the primary tumor samples without lymph node involvement (N0). Conclusion Cornulin was found to be significantly downregulated in primary tumors with lymph node metastases. Detection assays to measure Cornulin expression in cSCC primary tumors might aid in determining the nodal status in these patients and possibly help determine cases of occult lymph node metastasis or micrometastasis. Future clinical studies are needed to help establish Cornulin's role in enhancing the predictive power of histopathological examination and improving survival rates for patients suffering from this type of skin cancer.

Characterization of Cornulin as a Molecular Biomarker for the Progression of Oral Squamous Cell Carcinoma

Introduction It has been shown that the expression of the epidermal differentiation marker, Cornulin, declines with the progression of squamous cell carcinomas of several tissue types. Objectives This study aims to examine Cornulin expression at the cellular level in various cell lines representative of the successive progression steps of oral squamous cell carcinoma (OSCC), a major type of head and neck cancer. This can pave the way for the utilization of this novel biomarker as a diagnostic and prognostic indicator for oral cancer and help guide treatment options. Study design Western blotting was performed to measure Cornulin expression levels using standardized cell lysates from four different cell lines representing the successive steps of OSCC progression. Specifically, primary gingival keratinocytes, dysplastic oral keratinocytes (DOK), squamous cell carcinoma 25 (SCC25) cells, and Detroit 562 cells were used to represent normal oral keratinocytes, DOKs, locally invasive OSCC cells, and metastatic OSCC cells, respectively. Results Cornulin expression was found to be downregulated with the progression from normal to premalignant to malignant cells. Quantitative analysis revealed that Cornulin is significantly downregulated by 3.4 folds in DOK cells, by 23.7 folds in SCC25 cells, and by 5.2 folds in Detroit 562 cells compared to normal gingival keratinocytes. Interestingly, Cornulin was upregulated by 4.5 folds in the metastatic Detroit 562 cell line compared to the locally invasive SCC25 cells. Conclusion Altogether, Cornulin proved to be differentially expressed at the cellular level in cell lines representative of the successive steps of OSCC progression. Specifically, we documented a gradual decrease in Cornulin expression with the progression from normal to premalignant to malignant cells. Notably, there is a significant increase in the expression of Cornulin in the metastatic cell line Detroit 562 compared to the locally invasive cell line SCC25, suggesting a possible correlation with the biological behavior and unique characteristics of these two different phenotypes.

PABPC1--mRNA stability, protein translation and tumorigenesis

Mammalian poly A-binding proteins (PABPs) are highly conserved multifunctional RNA-binding proteins primarily involved in the regulation of mRNA translation and stability, of which PABPC1 is considered a central regulator of cytoplasmic mRNA homing and is involved in a wide range of physiological and pathological processes by regulating almost every aspect of RNA metabolism. Alterations in its expression and function disrupt intra-tissue homeostasis and contribute to the development of various tumors. There is increasing evidence that PABPC1 is aberrantly expressed in a variety of tumor tissues and cancers such as lung, gastric, breast, liver, and esophageal cancers, and PABPC1 might be used as a potential biomarker for tumor diagnosis, treatment, and clinical application in the future. In this paper, we review the abnormal expression, functional role, and molecular mechanism of PABPC1 in tumorigenesis and provide directions for further understanding the regulatory role of PABPC1 in tumor cells.

Potential use of the S-protein-Angiotensin converting enzyme 2 binding pathway in the treatment of coronavirus disease 2019

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), infects humans through a strong interaction between the viral spike protein (S-protein) and angiotensin converting enzyme 2 (ACE2) receptors on the cell surface. The infection of host lung cells by SARS-CoV-2 leads to clinical symptoms in patients. However, ACE2 expression is not restricted to the lungs; altered receptors have been found in the nasal and oral mucosa, vessel, brain, pancreas, gastrointestinal tract, kidney, and heart. The future of COVID-19 is uncertain, however, new viral variants are likely to emerge. The SARS-CoV-2 Omicron variant has a total of 50 gene mutations compared with the original virus; 15 of which occur in the receptor binding domain (RBD). The RBD of the viral S-protein binds to the human ACE2 receptor for viral entry. Mutations of the ACE2-RBD interface enhance tight binding by increasing hydrogen bond interactions and expanding the accessible surface area. Extracorporeal membrane oxygenation, hyperbaric oxygen, and aggressive dialysis for the treatment of COVID-19 have shown various degrees of clinical success. The use of decoy receptors based on the ACE2 receptor as a broadly potent neutralizer of SARS-CoV-2 variants has potential as a therapeutic mechanism. Drugs such as 3E8 could block binding of the S1-subunit to ACE2 and restrict the infection of ACE2-expressing cells by a variety of coronaviruses. Here, we discuss the development of ACE2-targeted strategies for the treatment and prevention of COVID-19.

Emerging roles of alternative RNA splicing in oral squamous cell carcinoma

Alternative RNA splicing (ARS) is an essential and tightly regulated cellular process of post-transcriptional regulation of pre-mRNA. It produces multiple isoforms and may encode proteins with different or even opposite functions. The dysregulated ARS of pre-mRNA contributes to the development of many cancer types, including oral squamous cell carcinoma (OSCC), and may serve as a biomarker for the diagnosis and prognosis of OSCC and an attractive therapeutic target. ARS is mainly regulated by splicing factors, whose expression is also often dysregulated in OSCC and involved in tumorigenesis. This review focuses on the expression and roles of splicing factors in OSCC, the alternative RNA splicing events associated with OSCC, and recent advances in therapeutic approaches that target ARS.

Integrative network analysis interweaves the missing links in cardiomyopathy diseasome

Cardiomyopathies are progressive disease conditions that give rise to an abnormal heart phenotype and are a leading cause of heart failures in the general population. These are complex diseases that show co-morbidity with other diseases. The molecular interaction network in the localised disease neighbourhood is an important step toward deciphering molecular mechanisms underlying these complex conditions. In this pursuit, we employed network medicine techniques to systematically investigate cardiomyopathy's genetic interplay with other diseases and uncover the molecular players underlying these associations. We predicted a set of candidate genes in cardiomyopathy by exploring the DIAMOnD algorithm on the human interactome. We next revealed how these candidate genes form association across different diseases and highlighted the predominant association with brain, cancer and metabolic diseases. Through integrative systems analysis of molecular pathways, heart-specific mouse knockout data and disease tissue-specific transcriptomic data, we screened and ascertained prominent candidates that show abnormal heart phenotype, including NOS3, MMP2 and SIRT1. Our computational analysis broadens the understanding of the genetic associations of cardiomyopathies with other diseases and holds great potential in cardiomyopathy research.

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.

Towards future directions in data-integrative supervised prediction of human aging-related genes

Motivation

Identification of human genes involved in the aging process is critical due to the incidence of many diseases with age. A state-of-the-art approach for this purpose infers a weighted dynamic aging-specific subnetwork by mapping gene expression (GE) levels at different ages onto the protein-protein interaction network (PPIN). Then, it analyzes this subnetwork in a supervised manner by training a predictive model to learn how network topologies of known aging- versus non-aging-related genes change across ages. Finally, it uses the trained model to predict novel aging-related gene candidates. However, the best current subnetwork resulting from this approach still yields suboptimal prediction accuracy. This could be because it was inferred using outdated GE and PPIN data. Here, we evaluate whether analyzing a weighted dynamic aging-specific subnetwork inferred from newer GE and PPIN data improves prediction accuracy upon analyzing the best current subnetwork inferred from outdated data.

Results

Unexpectedly, we find that not to be the case. To understand this, we perform aging-related pathway and Gene Ontology term enrichment analyses. We find that the suboptimal prediction accuracy, regardless of which GE or PPIN data is used, may be caused by the current knowledge about which genes are aging-related being incomplete, or by the current methods for inferring or analyzing an aging-specific subnetwork being unable to capture all of the aging-related knowledge. These findings can potentially guide future directions towards improving supervised prediction of aging-related genes via -omics data integration.

Availability and implementation

All data and code are available at zenodo, DOI: 10.5281/zenodo.6995045.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Implications and Emerging Therapeutic Avenues of Inflammatory Response in HPV+ Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinomas (HNSCC) are a heterogeneous group of malignancies which have shown exponential incidence in the last two decades especially due to human papillomavirus (HPV) infection. The HPV family comprises more than 100 types of viruses with HPV16 and HPV18 being the most prevalent strains in HNSCC. Literature data reveal that the mutation profile as well as the response to chemotherapy and radiotherapy are distinct among HPV+ versus HPV-negative tumors. Furthermore, the presence of the virus induces activation of an immune response, in particular the recruitment of specific antiviral T lymphocytes to tumor sites. These T cells when activated produce soluble factors including cytokines and chemokines capable of modifying the local immune tumor microenvironment and impact on tumor response to the treatment. In this comprehensive review we investigated current knowledge on how the presence of an HPV can modify the inflammatory response systemically and within the tumor microenvironment's immunological responses, thereby impacting on disease prognosis and survival. We highlighted the research gaps and emerging approaches necessary to discover novel immunotherapeutic targets for HPV-associated HNSCC.

Cornulin as a Potential Novel Biomarker for Cutaneous Squamous Cell Carcinoma

Background This study aimed to evaluate the expression of an epidermal differentiation marker, cornulin, in cutaneous squamous cell carcinoma (cSCC). Cornulin has been found to be downregulated in various squamous cell carcinomas of other tissues; however, its expression in cSCC has never been studied. We predicted that cornulin expression in cSCC is reduced compared to the normal epidermis. Moreover, we hypothesized that an inverse relationship exists between cornulin expression and the loss of differentiation, as defined by histopathological grading of cSCC lesions. Methodology Samples of normal skin and cSCC lesions of variable histopathological grades were stained using immunohistochemistry. High-resolution tissue images were analyzed with Aperio ImageScope (Leica Biosystems) utilizing a positive-pixel-counting algorithm to quantify the staining intensity. Histo-score (H-score) was calculated based on staining intensity and percentage of positive cell staining. Mean H-scores were compared using an unpaired t-test. Results We documented cornulin expression in cSCC for the first time. Cornulin levels were downregulated by more than two-fold in cSCC compared to the normal epidermis. Additionally, we observed a 4.5-fold downregulation in cornulin expression in tumors with high histopathological grades when compared to low histopathological grade tumors. Conclusions Cornulin expression levels measured through immunohistochemistry staining can help distinguish among the different histopathological grades of cSCC. Therefore, we propose that cornulin detection can be an adjunct to pathological examination to evaluate the differentiation status of cSCC specimens. Longitudinal studies are needed to establish the utility of cornulin as a diagnostic and prognostic biomarker for cSCC.

Analysis of exosomal competing endogenous RNA network response to paclitaxel treatment reveals key genes in advanced gastric cancer

Background

Exosome is an important component of the tumor immune microenvironment and plays critical role in cancer pathogenesis. The exosome transcriptome of gastric cancer (GC) response to paclitaxel chemotherapy has not been investigated in the past.

Methods

ceRNA microarrays were performed in exosomes from six advanced GC patients before and after paclitaxel treatment. Bioinformatics tools were used to identify differential expressing genes and construct competing endogenous RNA (ceRNA) networks. The importance of hub genes in the ceRNA network was confirmed by survival analysis and functional analysis.

Results

A total of 213 differential mRNAs, 370 lncRNAs, and 376 circRNAs were identified, and hub genes in ceRNA networks were screened. The differential genes were associated with GO terms SNAP complex, gap junction, protein transporter activity, cytokine receptor, and KEGG pathways synaptic vesicle cycle, propanoate metabolism, Epstein–Barr virus infection, heparin, and steroid biosynthesis, and beta-alanine metabolism. ULK2, CYP2R1, BTLA, and miR-105-5p are prognostic genes for overall survival. Paclitaxel may target ULK2 which is involved in mitosis and cell cycle. miR-105-5p may target ULK2 3’UTR.

Conclusion

The work for the first time identified exosomal RNA biomarkers and constructed a ceRNA network in GC response to paclitaxel, revealed novel molecular mechanisms of GC, and provided new candidates for GC diagnosis and treatment.

Neoplastic cell enrichment of tumor tissues using coring and laser microdissection for proteomic and genomic analyses of pancreatic ductal adenocarcinoma

BACKGROUND

The identification of differentially expressed tumor-associated proteins and genomic alterations driving neoplasia is critical in the development of clinical assays to detect cancers and forms the foundation for understanding cancer biology. One of the challenges in the analysis of pancreatic ductal adenocarcinoma (PDAC) is the low neoplastic cellularity and heterogeneous composition of bulk tumors. To enrich neoplastic cells from bulk tumor tissue, coring, and laser microdissection (LMD) sampling techniques have been employed. In this study, we assessed the protein and KRAS mutation changes associated with samples obtained by these enrichment techniques and evaluated the fraction of neoplastic cells in PDAC for proteomic and genomic analyses.

METHODS

Three fresh frozen PDAC tumors and their tumor-matched normal adjacent tissues (NATs) were obtained from three sampling techniques using bulk, coring, and LMD; and analyzed by TMT-based quantitative proteomics. The protein profiles and characterizations of differentially expressed proteins in three sampling groups were determined. These three PDACs and samples of five additional PDACs obtained by the same three sampling techniques were also subjected to genomic analysis to characterize KRAS mutations.

RESULTS

The neoplastic cellularity of eight PDACs ranged from less than 10% to over 80% based on morphological review. Distinctive proteomic patterns and abundances of certain tumor-associated proteins were revealed when comparing the tumors and NATs by different sampling techniques. Coring and bulk tissues had comparable proteome profiles, while LMD samples had the most distinct proteome composition compared to bulk tissues. Further genomic analysis of bulk, cored, or LMD samples demonstrated that KRAS mutations were significantly enriched in LMD samples while coring was less effective in enriching for KRAS mutations when bulk tissues contained a relatively low neoplastic cellularity.

CONCLUSIONS

In addition to bulk tissues, samples from LMD and coring techniques can be used for proteogenomic studies. The greatest enrichment of neoplastic cellularity is obtained with the LMD technique.