Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses

CTGF, FN1, IL-6, THBS1, and WISP1 genes and PI3K-Akt signaling pathway as prognostic and therapeutic targets in gastric cancer identified by gene network modeling

OBJECTIVE

Gastric cancer (GC) is one of the most common malignancies worldwide and it is considered the fourth most common cause of cancer death. This study aimed to find critical genes/pathways in GC pathogenesis to be used as biomarkers or therapeutic targets.

METHODS

Differentially expressed genes were explored between human gastric cancerous and noncancerous tissues, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses were done. Hub genes were identified based on the protein-protein interaction network constructed in the STRING database with Cytoscape software. The hub genes were selected for further investigation using GEPIA2 and DrugBank databases.

RESULTS

Ten overexpressed hub genes in GC were identified in the current study, including FN1, TP53, IL-6, CXCL5, ELN, ADAMTS2, WISP1, MMP2, CTGF, and THBS1. The study demonstrated the PI3K-Akt pathway's central involvement in GC, with pronounced alterations in essential components. Survival analysis revealed significant correlations between CTGF, FN1, IL-6, THBS1, and WISP1 overexpression and reduced overall survival times in GC patients.

CONCLUSION

A mutual interplay emerged, where PI3K-Akt signaling could upregulate certain genes, forming feedback loops and intensifying cancer phenotypes. The interconnected overexpression of genes and the PI3K-Akt pathway fosters gastric tumorigenesis, suggesting therapeutic potential. DrugBank analysis identified limited FDA-approved drugs, advocating for further exploration while targeting these hub genes could reshape GC treatment. The identified genes could be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC, but further clinical validation is required.

A Novel Methylation-based Model for Prognostic Prediction in Lung Adenocarcinoma

Objectives

Specific methylation sites have shown promise in the early diagnosis of lung adenocarcinoma (LUAD). However, their utility in predicting LUAD prognosis remains unclear. This study aimed to construct a reliable methylation-based predictor for accurately predicting the prognosis of LUAD patients.

Methods

DNA methylation data and survival data from LUAD patients were obtained from the TCGA and a GEO series. A DNA methylation-based signature was developed using univariate least absolute shrinkage and selection operators and multivariate Cox regression models.

Results

Eight CpG sites were identified and validated as optimal prognostic signatures for the overall survival of LUAD patients. Receiver operating characteristic analysis demonstrated the high predictive ability of the eight-site methylation signature combined with clinical factors for overall survival.

Conclusion

This research successfully identified a novel eight-site methylation signature for predicting the overall survival of LUAD patients through bioinformatic integrated analysis of gene methylation markers used in the early diagnosis of lung cancer.

Hypoxia-induced TMTC3 expression in esophageal squamous cell carcinoma potentiates tumor angiogenesis through Rho GTPase/STAT3/VEGFA pathway

BACKGROUND

Hypoxia is one of most typical features in the tumor microenvironment of solid tumor and an inducer of endoplasmic reticulum (ER) stress, and HIF-1α functions as a key transcription factor regulator to promote tumor angiogenesis in the adaptive response to hypoxia. Increasing evidence has suggested that hypoxia plays an important regulatory role of ER homeostasis. We previously identified TMTC3 as an ER stress mediator under nutrient-deficiency condition in esophageal squamous cell carcinoma (ESCC), but the molecular mechanism in hypoxia is still unclear.

METHODS

RNA sequencing data of TMTC3 knockdown cells and TCGA database were analyzed to determine the association of TMTC3 and hypoxia. Moreover, ChIP assay and dual-luciferase reporter assay were performed to detect the interaction of HIF-1α and TMTC3 promoter. In vitro and in vivo assays were used to investigate the function of TMTC3 in tumor angiogenesis. The molecular mechanism was determined using co-immunoprecipitation assays, immunofluorescence assays and western blot. The TMTC3 inhibitor was identified by high-throughput screening of FDA-approved drugs. The combination of TMTC3 inhibitor and cisplatin was conducted to confirm the efficiency in vitro and in vivo.

RESULTS

The expression of TMTC3 was remarkably increased under hypoxia and regulated by HIF-1α. Knockdown of TMTC3 inhibited the capability of tumor angiogenesis and ROS production in ESCC. Mechanistically, TMTC3 promoted the production of GTP through interacting with IMPDH2 Bateman domain. The activity of Rho GTPase/STAT3, regulated by cellular GTP levels, decreased in TMTC3 knockdown cells, whereas reversed by IMPDH2 overexpression. Additionally, TMTC3 regulated the expression of VEGFA through Rho GTPase/STAT3 pathway. Allopurinol inhibited the expression of TMTC3 and further reduced the phosphorylation and activation of STAT3 signaling pathway in a dose-dependent manner in ESCC. Additionally, the combination of allopurinol and cisplatin significantly inhibited the cell viability in vitro and tumor growth in vivo, comparing with single drug treatment, respectively.

CONCLUSIONS

Collectively, our study clarified the molecular mechanism of TMTC3 in regulating tumor angiogenesis and highlighted the potential therapeutic combination of TMTC3 inhibitor and cisplatin, which proposed a promising strategy for the treatment of ESCC.

A systems biology approach to pathogenesis of gastric cancer: gene network modeling and pathway analysis

BACKGROUND

Gastric cancer (GC) ranks among the most common malignancies worldwide. This study aimed to find critical genes/pathways in GC pathogenesis.

METHODS

Gene interactions were analyzed, and the protein-protein interaction network was drawn. Then enrichment analysis of the hub genes was performed and network cluster analysis and promoter analysis of the hub genes were done. Age/sex analysis was done on the identified genes.

RESULTS

Eleven hub genes in GC were identified in the current study (ATP5A1, ATP5B, ATP5D, MT-ATP8, COX7A2, COX6C, ND4, ND6, NDUFS3, RPL8, and RPS16), mostly involved in mitochondrial functions. There was no report on the ATP5D, ND6, NDUFS3, RPL8, and RPS16 in GC. Our results showed that the most affected processes in GC are the metabolic processes, and the oxidative phosphorylation pathway was considerably enriched which showed the significance of mitochondria in GC pathogenesis. Most of the affected pathways in GC were also involved in neurodegenerative diseases. Promoter analysis showed that negative regulation of signal transduction might play an important role in GC pathogenesis. In the analysis of the basal expression pattern of the selected genes whose basal expression presented a change during the age, we found that a change in age may be an indicator of changes in disease insurgence and/or progression at different ages.

CONCLUSIONS

These results might open up new insights into GC pathogenesis. The identified genes might be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC. This work, being based on bioinformatics analysis act as a hypothesis generator that requires further clinical validation.

Xanthine oxidoreductase mediates genotoxic drug-induced autophagy and apoptosis resistance by uric acid accumulation and TGF-β-activated kinase 1 (TAK1) activation

Autophagy is a highly conserved cellular process that profoundly impacts the efficacy of genotoxic chemotherapeutic drugs. TGF-β-activated kinase 1 (TAK1) is a serine/threonine kinase that activates several signaling pathways involved in inducing autophagy and suppressing cell death. Xanthine oxidoreductase (XOR) is a rate-limiting enzyme that converts hypoxanthine to xanthine, and xanthine to uric acid and hydrogen peroxide in the purine catabolism pathway. Recent studies showed that uric acid can bind to TAK1 and prolong its activation. We hypothesized that genotoxic drugs may induce autophagy and apoptosis resistance by activating TAK1 through XOR-generated uric acid. Here, we report that gemcitabine and 5-fluorouracil (5-FU), two genotoxic drugs, induced autophagy in HeLa and HT-29 cells by activating TAK1 and its two downstream kinases, AMP-activated kinase (AMPK) and c-Jun terminal kinase (JNK). XOR knockdown and the XOR inhibitor allopurinol blocked gemcitabine-induced TAK1, JNK, AMPK, and Unc51-like kinase 1 (ULK1)^S555 phosphorylation and gemcitabine-induced autophagy. Inhibition of the ATM-Chk pathway, which inhibits genotoxic drug-induced uric acid production, blocked gemcitabine-induced autophagy by inhibiting TAK1 activation. Exogenous uric acid in its salt form, monosodium urate (MSU), induced autophagy by activating TAK1 and its downstream kinases JNK and AMPK. Gene knockdown or the inhibitors of these kinases blocked gemcitabine- and MSU-induced autophagy. Inhibition of autophagy by allopurinol, chloroquine, and 5Z-7-oxozeaenol (5Z), a TAK1-specific inhibitor, enhanced gemcitabine-induced apoptosis. Our study uncovers a previously unrecognized role of XOR in regulating genotoxic drug-induced autophagy and apoptosis and has implications for designing novel therapeutic strategies for cancer treatment.

Xanthine dehydrogenase rewires metabolism and the survival of nutrient deprived lung adenocarcinoma cells by facilitating UPR and autophagic degradation

Xanthine dehydrogenase (XDH) is the rate-limiting enzyme in purine catabolism by converting hypoxanthine to xanthine and xanthine to uric acid. The altered expression and activity of XDH are associated with the development and prognosis of multiple types of cancer, while its role in lung adenocarcinoma (LUAD) remains unknown. Herein, we demonstrated that XDH was highly expressed in LUAD and was significantly correlated with poor prognosis. Though inhibition of XDH displayed moderate effect on the viability of LUAD cells cultured in the complete medium, it significantly attenuated the survival of starved cells. Similar results were obtained in XDH-knockout cells. Nucleosides supplementation rescued the survival of starved LUAD cells upon XDH inhibition, while inhibition of purine nucleoside phosphorylase abrogated the process, indicating that nucleoside degradation is required for the XDH-mediated survival of LUAD cells. Accordingly, metabolic flux revealed that ribose derived from nucleoside fueled key carbon metabolic pathways to sustain the survival of starved LUAD cells. Mechanistically, down-regulation of XDH suppressed unfolded protein response (UPR) and autophagic flux in starved LUAD cells. Inhibition of XDH decreased the level of amino acids produced by autophagic degradation, which was accompanied with down-regulation of mTORC1 signaling. Supplementation of amino acids including glutamine or glutamate rescued the survival of starved LUAD cells upon knockout or inhibition of XDH. Finally, XDH inhibitors potentiated the anti-cancer activity of 2-deoxy-D-glucose that induced UPR and/or autophagy in vitro and in vivo. In summary, XDH plays a crucial role in the survival of starved LUAD cells and targeting XDH may improve the efficacy of drugs that induce UPR and autophagy in the therapy of LUAD.

Distinct mechanisms of innate and adaptive immune regulation underlie poor oncologic outcomes associated with KRAS-TP53 co-alteration in pancreatic cancer

Co-occurrent KRAS and TP53 mutations define a majority of patients with pancreatic ductal adenocarcinoma (PDAC) and define its pro-metastatic proclivity. Here, we demonstrate that KRAS-TP53 co-alteration is associated with worse survival compared with either KRAS-alone or TP53-alone altered PDAC in 245 patients with metastatic disease treated at a tertiary referral cancer center, and validate this observation in two independent molecularly annotated datasets. Compared with non-TP53 mutated KRAS-altered tumors, KRAS-TP53 co-alteration engenders disproportionately innate immune-enriched and CD8+ T-cell-excluded immune signatures. Leveraging in silico, in vitro, and in vivo models of human and murine PDAC, we discover a novel intersection between KRAS-TP53 co-altered transcriptomes, TP63-defined squamous trans-differentiation, and myeloid-cell migration into the tumor microenvironment. Comparison of single-cell transcriptomes between KRAS-TP53 co-altered and KRAS-altered/TP53WT tumors revealed cancer cell-autonomous transcriptional programs that orchestrate innate immune trafficking and function. Moreover, we uncover granulocyte-derived inflammasome activation and TNF signaling as putative paracrine mediators of innate immunoregulatory transcriptional programs in KRAS-TP53 co-altered PDAC. Immune subtyping of KRAS-TP53 co-altered PDAC reveals conflation of intratumor heterogeneity with progenitor-like stemness properties. Coalescing these distinct molecular characteristics into a KRAS-TP53 co-altered "immunoregulatory program" predicts chemoresistance in metastatic PDAC patients enrolled in the COMPASS trial, as well as worse overall survival.

The double faced role of xanthine oxidoreductase in cancer

Xanthine oxidoreductase (XOR) is a critical, rate-limiting enzyme that controls the last two steps of purine catabolism by converting hypoxanthine to xanthine and xanthine to uric acid. It also produces reactive oxygen species (ROS) during the catalytic process. The enzyme is generally recognized as a drug target for the therapy of gout and hyperuricemia. The catalytic products uric acid and ROS act as antioxidants or oxidants, respectively, and are involved in pro/anti-inflammatory actions, which are associated with various disease manifestations, including metabolic syndrome, ischemia reperfusion injury, cardiovascular disorders, and cancer. Recently, extensive efforts have been devoted to understanding the paradoxical roles of XOR in tumor promotion. Here, we summarize the expression of XOR in different types of cancer and decipher the dual roles of XOR in cancer by its enzymatic or nonenzymatic activity to provide an updated understanding of the mechanistic function of XOR in cancer. We also discuss the potential to modulate XOR in cancer therapy.

Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O₂ Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

High dimensionality reduction by matrix factorization for systems pharmacology

The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the matrix factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other MF methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a tyrosine kinase inhibitor treatment response in the Cancer Cell Line Encyclopedia.

Vorinostat enhances the therapeutic potential of Erlotinib via MAPK in lung cancer cells

BACKGROUND

Lung cancer is the second most common cancer in both men and women, with an estimated 235,760 new cases and 131,880 deaths in 2021 in the US. Despite the modern therapies being available such as radiotherapy and chemotherapy, death rates are still increasing. Erlotinib (ERL) is one of the treatment options for lung cancer, although the probability for the patients to develop resistance to ERL constrains its reliability. The aim of the present study is to assess the synergetic effect of combining ERL with vorinostat (SAHA) on the progression of lung cancer cells.

RESULTS

Adenocarcinoma alveolar basal epithelial cells (A549) were treated with either ERL, SAHA as mono drugs or with the combination of them for 24 h. Cytotoxicity assay and cell cycle analysis along with apoptosis detection were investigated. The expression profile of CDH1, TGF1, and MAPK was also assessed. Results showed an elevation in the apoptosis level in all treatments compared to WISH; the normal human amnion-derived cells. Furthermore, the treatments caused the cell cycle to arrest at G2/M, indicating its cytotoxic activity.

CONCLUSION

The combination of SAHA and ERL significantly increased the level of apoptosis in lung cancer cells. Meanwhile, this combination treatment downregulated MAPK compared to the mono drugs and the control cells, suggesting the potential role of MAPK in regulating apoptosis and cell cycle machinery in lung cancer.

C1QBP regulates apoptosis of renal cell carcinoma via modulating xanthine dehydrogenase (XDH) mediated ROS generation

Background: Complement component 1 Q subcomponent binding protein (C1QBP) plays a vital role in the progression and metabolism of cancer. Studies have shown that xanthine dehydrogenase (XDH)-derived reactive oxygen species (ROS) accelerates tumor growth, and also induces mutations or produces cytotoxic effects concurrently. However, the role of C1QBP in metabolism, oxidative stress, and apoptosis of renal cell carcinoma (RCC) cells have not yet been explored. Methods: Metabolomics assay was applied to investigate the role of C1QBP in RCC metabolism. C1QBP knockdown and overexpression cells were established via lentiviral infection and subjected to apoptosis and ROS assay in vitro. RNA stability assay was applied to characterize the mechanism of C1QBP regulating XDH transcription. In vivo, orthotopic tumor xenografts assay was performed to investigate the role of C1QBP in RCC progression. Results: Metabolomics investigation revealed that C1QBP dramatically diminished the hypoxanthine content in RCC cells. C1QBP promoted the mRNA and protein expression of hypoxanthine catabolic enzyme XDH. Meanwhile, C1QBP may affect XDH transcription by regulating the mRNA level of XDH transcriptional stimulators IL-6, TNF-α, and IFN-γ. Moreover, the expression of C1QBP and XDH was lower in RCC tumors compared with the tumor-associated normal tissues, and their down-regulation was associated with higher Fuhrman grade. C1QBP significantly increased ROS level, apoptosis, and the expression of apoptotic proteins such as cleaved caspase-3 and bax/bcl2 via regulating XDH. Conclusion: C1QBP promotes the catabolism of hypoxanthine and elevates the apoptosis of RCC cells by modulating XDH-mediated ROS generation.

Global mapping of cancers: The Cancer Genome Atlas and beyond

Cancer genomes have been explored from the early 2000s through massive exome sequencing efforts, leading to the publication of The Cancer Genome Atlas in 2013. Sequencing techniques have been developed alongside this project and have allowed scientists to bypass the limitation of costs for whole-genome sequencing (WGS) of single specimens by developing more accurate and extensive cancer sequencing projects, such as deep sequencing of whole genomes and transcriptomic analysis. The Pan-Cancer Analysis of Whole Genomes recently published WGS data from more than 2600 human cancers together with almost 1200 related transcriptomes. The application of WGS on a large database allowed, for the first time in history, a global analysis of features such as molecular signatures, large structural variations and noncoding regions of the genome, as well as the evaluation of RNA alterations in the absence of underlying DNA mutations. The vast amount of data generated still needs to be thoroughly deciphered, and the advent of machine-learning approaches will be the next step towards the generation of personalized approaches for cancer medicine. The present manuscript wants to give a broad perspective on some of the biological evidence derived from the largest sequencing attempts on human cancers so far, discussing advantages and limitations of this approach and its power in the era of machine learning.

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O 2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person's variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.

A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response

Cancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalized regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumorigenesis and DNA damage response.

Scutellaria baicalensis Flavones as Potent Drugs against Acute Respiratory Injury during SARS-CoV-2 Infection: Structural Biology Approaches

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can result in severe damage to the respiratory system. With no specific treatment to date, it is crucial to identify potent inhibitors of SARS-CoV-2 Chymotrypsin-like protease (3CLpro) that could also modulate the enzymes involved in the respiratory damage that accompanies SARS-CoV-2 infection. Here, flavones isolated from Scutellaria baicalensis (baicalein, baicalin, wogonin, norwogonin, and oroxylin A) were studied as possible compounds in the treatment of SARS-CoV-2 and SARS-CoV-2-induced acute lung injuries. We used structural bioinformatics and cheminformatics to (i) identify the critical molecular features of flavones for their binding activity at human and SARS-CoV-2 enzymes; (ii) predict their drug-likeness and lead-likeness features; (iii) calculate their pharmacokinetic profile, with an emphasis on toxicology; (iv) predict their pharmacodynamic profiles, with the identification of their human body targets involved in the respiratory system injuries; and (v) dock the ligands to SARS-CoV-2 3CLpro. All flavones presented appropriate drug-like and kinetics features, except for baicalin. Flavones could bind to SARS-CoV-2 3CLpro at a similar site, but interact slightly differently with the protease. Flavones’ pharmacodynamic profiles predict that (i) wogonin strongly binds at the cyclooxygenase2 and nitric oxide synthase; (ii) baicalein and norwogonin could modulate lysine-specific demethylase 4D-like and arachidonate 15-lipoxygenase; and (iii) baicalein, wogonin, norwogonin, and oroxylin A bind to SARS-CoV-2 3CLpro. Our results propose these flavones as possible potent drugs against respiratory damage that occurs during SARS-CoV-2 infections, with a strong recommendation for baicalein.

Integrated Analysis of an lncRNA-Associated ceRNA Network Reveals Potential Biomarkers for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide. In this study, we aimed to explore the potential biomarkers and key regulatory pathways related to HCC using integrated bioinformatic analysis and validation. The microarray data of GSE12717 and GSE54238 were downloaded from the Gene Expression Omnibus database. A competing endogenous RNA (ceRNA) network was constructed based on potential long-noncoding RNA (lncRNA)-microRNA (miRNA)-mRNA interactions. A total of 191 mRNAs, 8 miRNAs, and 5 lncRNAs were selected to construct the ceRNA network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to predict their biological functions. The PI3K-Akt signaling pathway was significantly enriched. Kaplan-Meier survival analysis based on the Gene Expression Profiling Interactive Analysis (GEPIA) database was conducted for the weighted mRNAs and lncRNAs. The results showed that SRC, GMPS, CDK2, FEN1, EZH2, ZWINT, MTHFD1L, GINS2, and MAPKAPK5-AS1 were significantly upregulated in tumor tissues. The relative expression levels of these genes were significantly upregulated in HCC patients based on the StarBase database. For further validation, the expression levels of these genes were detected by real-time quantitative reverse transcription-polymerase chain reaction in 20 HCC tumor tissues and paired paracancerous tissues. Receiver operating characteristic analysis revealed that CDK2, MTHFD1L, SRC, ZWINT, and MAPKAPK5-AS1 had significant diagnostic value in HCC, but further studies are needed to explore their mechanisms in HCC.

Combined Methylome and Transcriptome Analyses Reveals Potential Therapeutic Targets for EGFR Wild Type Lung Cancers with Low PD-L1 Expression

Simple Summary

Low expression of programmed death-ligand 1 (PD-L1), epidermal growth factor receptor (EGFR) wild-type non-small cell lung cancer (NSCLCs) are refractory, and only few therapeutic options exist. This study aims to clarify the molecular basis of this special subtype of NSCLC and identify potential therapeutic targets. We performed integrating data from multiple sources including transcriptome, methylome, and clinical outcome to uncover the effect of epigenetic changes acting this special subtype lung cancer. We elucidated both aberrant methylation and associated aberrant gene expression and the emerging methylation-transcription patterns were classified as HypoUp, HypoDown, HyperUp, or HyperDown. We found that the aberrant methylation-transcription patterns significantly affect the overall survival time of the patients. We used protein–drug interaction data and molecular docking analysis to identify potential therapeutic candidates. This study uncovered the distinct methylation-transcription characteristics of this special subtype lung cancer, and provided an adaptable way to identify potential therapeutic targets.

Abstract

Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 have demonstrated remarkable treatment efficacy in advanced non-small cell lung cancer (NSCLC). However, low expression of programmed death-ligand 1 (PD-L1), epidermal growth factor receptor (EGFR) wild-type NSCLCs are refractory, and only few therapeutic options exist. Currently, combination therapy with ICIs is frequently used in order to enhance the treatment response rates. Yet, this regimen is still associated with poor treatment outcome. Therefore, identification of potential therapeutic targets for this subgroup of NSCLC is strongly desired. Here, we report the distinct methylation signatures of this special subgroup. Moreover, several druggable targets and relevant drugs for targeted therapy were incidentally identified. We found hypermethylated differentially methylated regions (DMRs) in three regions (TSS200, TSS1500, and gene body) are significantly higher than hypomethylated ones. Downregulated methylated genes were found to be involved in negative regulation of immune response and T cell-mediated immunity. Moreover, expression of four methylated genes (PLCXD3 (Phosphatidylinositol-Specific Phospholipase C, X Domain Containing 3), BAIAP2L2 (BAR/IMD Domain Containing Adaptor Protein 2 Like 2), NPR3 (Natriuretic Peptide Receptor 3), SNX10 (Sorting Nexin 10)) can influence patients’ prognosis. Subsequently, based on DrugBank data, NetworkAnalyst 3.0 was used for protein–drug interaction analysis of up-regulated differentially methylated genes. Protein products of nine genes were identified as potential druggable targets, of which the tumorigenic potential of XDH (Xanthine Dehydrogenase), ATIC (5-Aminoimidazole-4-Carboxamide Ribonucleotide Formyltransferase/IMP Cyclohydrolase), CA9 (Carbonic Anhydrase 9), SLC7A11 (Solute Carrier Family 7 Member 11), and GAPDH (Glyceraldehyde-3-Phosphate Dehydrogenase) have been demonstrated in previous studies. Next, molecular docking and molecular dynamics simulation were performed to verify the structural basis of the therapeutic targets. It is noteworthy that the identified pemetrexed targeting ATIC has been recently approved for first-line use in combination with anti-PD1 inhibitors against lung cancer, irrespective of PD-L1 expression. In future work, a pivotal clinical study will be initiated to further validate our findings.

A Systems Biology Roadmap to Decode mTOR Control System in Cancer

Mechanistic target of rapamycin (mTOR) is a critical protein in the regulation of cell fate decision making, especially in cancer cells. mTOR acts as a signal integrator and is one of the main elements of interactions among the pivotal cellular processes such as cell death, autophagy, metabolic reprogramming, cell growth, and cell cycle. The temporal control of these processes is essential for the cellular homeostasis and dysregulation of mTOR signaling pathway results in different phenotypes, including aging, oncogenesis, cell survival, cell growth, senescence, quiescence, and cell death. In this paper, we have proposed a systems biology roadmap to study mTOR control system, which introduces the theoretical and experimental modalities to decode temporal and dynamical characteristics of mTOR signaling in cancer.

Dynamic modeling of signal transduction by mTOR complexes in cancer

Signal integration has a crucial role in the cell fate decision and dysregulation of the cellular signaling pathways is a primary characteristic of cancer. As a signal integrator, mTOR shows a complex dynamical behavior which determines the cell fate at different cellular processes levels, including cell cycle progression, cell survival, cell death, metabolic reprogramming, and aging. The dynamics of the complex responses to rapamycin in cancer cells have been attributed to its differential time-dependent inhibitory effects on mTORC1 and mTORC2, the two main complexes of mTOR. Two explanations were previously provided for this phenomenon: 1-Rapamycin does not inhibit mTORC2 directly, whereas it prevents mTORC2 formation by sequestering free mTOR protein (Le Chatelier's principle). 2-Components like Phosphatidic Acid (PA) further stabilize mTORC2 compared with mTORC1. To understand the mechanism by which rapamycin differentially inhibits the mTOR complexes in the cancer cells, we present a mathematical model of rapamycin mode of action based on the first explanation, i.e., Le Chatelier's principle. Translating the interactions among components of mTORC1 and mTORC2 into a mathematical model revealed the dynamics of rapamycin action in different doses and time-intervals of rapamycin treatment. This model shows that rapamycin has stronger effects on mTORC1 compared with mTORC2, simply due to its direct interaction with free mTOR and mTORC1, but not mTORC2, without the need to consider other components that might further stabilize mTORC2. Based on our results, even when mTORC2 is less stable compared with mTORC1, it can be less inhibited by rapamycin.

Screening common signaling pathways associated with drug resistance in non-small cell lung cancer via gene expression profile analysis

Lung cancer is the leading cause of cancer‐related deaths worldwide. Although several therapeutic strategies have been employed to curb lung cancer, the survival rate is still poor owing to the development of drug resistance. The mechanisms underlying drug resistance development are incompletely understood. Here, we aimed to identify the common signaling pathways involved in drug resistance in non‐small cell lung cancer (NSCLC). Three published transcriptome microarray data were downloaded from the Gene Expression Omnibus (GEO) database comprising different drug‐resistant cell lines and their parental cell lines. Differentially expressed genes (DEGs) were identified and used to perform Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. An overlapping analysis was performed for KEGG pathways enriched from all the three datasets to identify the common signaling pathways. As a result, we found that metabolic pathways, ubiquitin‐mediated proteolysis, and mitogen‐activated protein kinase (MAPK) signaling were the most aberrantly expressed signaling pathways. The knockdown of nicotinamide phosphoribosyltransferase (NAMPT), the gene involved in metabolic pathways and known to be upregulated in drug‐resistant tumor cells, was shown to increase the apoptosis of cisplatin‐resistant A549 cells following cisplatin treatment. Thus, our results provide an in‐depth analysis of the signaling pathways that are commonly altered in drug‐resistant NSCLC cell lines and highlight the potential strategy that facilitates the development of interventions to interfere with upregulated signaling pathways as well as to boost downregulated signaling pathways in drug‐resistant tumors for the elimination of multiple resistance of NSCLC.