Systems biology approaches to develop innovative strategies for lung cancer therapy

Comprehensive analysis of multiple regulated cell death risk signatures in lung adenocarcinoma

Background

Regulated cell death (RCD) has considerable impact on tumor progress and sensitivity of treatment. Lung adenocarcinoma (LUAD) show a high resistance for conventional radiotherapies and chemotherapies. Currently, regulation of cancer cell death has been emerging as a new promising therapeutic avenue for LUAD patients. However, the crosstalk in each pattern RCD is unclear.

Methods

We integrated collected the hub-genes of 12 RCD subroutines and compressively analyzed these hub-genes synergistic effect in LUAD. The characters of RCD genes expression and prognosis were developed in The Cancer Genome Atlas (TCGA)-LUAD data. We developed and validated an RCD risk model based on TCGA and GSE70294 data set, respectively. Functional annotation and tumor immunotherapy based on the risk model were also investigated.

Results

28 RCD-related genes and two LUAD molecular cluster were identified. Survival analysis revealed that the prognosis in high-risk group was worser than those in low-risk group. Functional enrichment analysis indicated that the RCD risk model correlated with immune responses. Further analysis indicated that the high-risk group in RCD risk model exhibited an immunosuppressive microenvironment and a lowly immunotherapy responder ratio.

Conclusions

We present an RCD risk model which have a promising ability in predicting LUAD prognosis and immunotherapy response.

A Critical Review on the Role of Probiotics in Lung Cancer Biology and Prognosis

Lung cancer remains the leading cause of cancer-related deaths worldwide. According to the American Cancer Society (ACS), it ranks as the second most prevalent type of cancer globally. Recent findings have highlighted bidirectional gut-lung interactions, known as the gut-lung axis, in the pathophysiology of lung cancer. Probiotics are live microorganisms that boost host immunity when consumed adequately. The immunoregulatory mechanisms of probiotics are thought to operate through the generation of various metabolites that impact both the gut and distant organs (e.g., the lungs) through blood. Several randomized controlled trials have highlighted the pivotal role of probiotics in gut health especially for the prevention and treatment of malignancies, with a specific emphasis on lung cancer. Current research indicates that probiotic supplementation positively affects patients, leading to a suppression in cancer symptoms and a shortened disease course. While clinical trials validate the therapeutic benefits of probiotics, their precise mechanism of action remains unclear. This narrative review aims to provide a comprehensive overview of the present landscape of probiotics in the management of lung cancer.

Identification of 5-nitroindazole as a multitargeted inhibitor for CDK and transferase kinase in lung cancer: a multisampling algorithm-based structural study

Lung cancer is the second most common cancer, which is the leading cause of cancer death worldwide. The FDA has approved almost 100 drugs against lung cancer, but it is still not curable as most drugs target a single protein and block a single pathway. In this study, we screened the Drug Bank library against three major proteins- ribosomal protein S6 kinase alpha-6 (6G77), cyclic-dependent protein kinase 2 (1AQ1), and insulin-like growth factor 1 (1K3A) of lung cancer and identified the compound 5-nitroindazole (DB04534) as a multitargeted inhibitor that potentially can treat lung cancer. For the screening, we deployed multisampling algorithms such as HTVS, SP and XP, followed by the MM\GBSA calculation, and the study was extended to molecular fingerprinting analysis, pharmacokinetics prediction, and Molecular Dynamics simulation to understand the complex's stability. The docking scores against the proteins 6G77, 1AQ1, and 1K3A were - 6.884 kcal/mol, - 7.515 kcal/mol, and - 6.754 kcal/mol, respectively. Also, the compound has shown all the values satisfying the ADMET criteria, and the fingerprint analysis has shown wide similarities and the water WaterMap analysis that helped justify the compound's suitability. The molecular dynamics of each complex have shown a cumulative deviation of less than 2 Å, which is considered best for the biomolecules, especially for the protein-ligand complexes. The best feature of the identified drug candidate is that it targets multiple proteins that control cell division and growth hormone mediates simultaneously, reducing the burden of the pharmaceutical industry by reducing the resistance chance.

Unravelling the Triad of Lung Cancer, Drug Resistance, and Metabolic Pathways

Lung cancer, characterized by its heterogeneity, presents a significant challenge in therapeutic management, primarily due to the development of resistance to conventional drugs. This resistance is often compounded by the tumor's ability to reprogram its metabolic pathways, a survival strategy that enables cancer cells to thrive in adverse conditions. This review article explores the complex link between drug resistance and metabolic reprogramming in lung cancer, offering a detailed analysis of the molecular mechanisms and treatment strategies. It emphasizes the interplay between drug resistance and changes in metabolic pathways, crucial for developing effective lung cancer therapies. This review examines the impact of current treatments on metabolic pathways and the significance of considering metabolic factors to combat drug resistance. It highlights the different challenges and metabolic alterations in non-small-cell lung cancer and small-cell lung cancer, underlining the need for subtype-specific treatments. Key signaling pathways, including PI3K/AKT/mTOR, MAPK, and AMPK, have been discussed for their roles in promoting drug resistance and metabolic changes, alongside the complex regulatory networks involved. This review article evaluates emerging treatments targeting metabolism, such as metabolic inhibitors, dietary management, and combination therapies, assessing their potential and challenges. It concludes with insights into the role of precision medicine and metabolic biomarkers in crafting personalized lung cancer treatments, advocating for metabolic targeting as a promising approach to enhance treatment efficacy and overcome drug resistance. This review underscores ongoing advancements and hurdles in integrating metabolic considerations into lung cancer therapy strategies.

Global research and emerging trends in autophagy in lung cancer: a bibliometric and visualized study from 2013 to 2022

Purpose: To highlight the knowledge structure and evolutionary trends in research on autophagy in lung cancer. Methods: Research publications on autophagy in lung cancer were retrieved from the Web of Science Core Collection database. VOSviewer and CiteSpace data analysis software were used for the bibliometric and visualization analysis of countries, institutions, authors, journals, and keywords related to this field. Results: From 2013 to 2022, research on autophagy in lung cancer developed rapidly, showing rising trends in annual publications and citations. China (1,986 papers; 48,913 citations), Shandong University (77 publications; 1,460 citations), and Wei Zhang (20 publications; 342 citations) were the most productive and influential country, institution, and author, respectively. The journal with the most publications and citations on autophagy in lung cancer was the International Journal of Molecular Sciences (93 publications; 3,948 citations). An analysis of keyword co-occurrence showed that related research topics were divided into five clusters: 1) Mechanisms influencing autophagy in lung cancer and the role of autophagy in lung cancer; 2) Effect of autophagy on the biological behavior of lung cancer; 3) Regulatory mechanisms of 2 cell death processes: autophagy and apoptosis in lung cancer cells; 4) Role of autophagy in lung cancer treatment and drug resistance; and 5) Role of autophagy-related genes in the occurrence and development of lung cancer. Cell proliferation, migration, epithelial-mesenchymal transition, and tumor microenvironment were the latest high-frequency keywords that represented promising future research directions. Conclusion: This is the first comprehensive study describing the knowledge structure and emerging frontiers of research on autophagy in lung cancer from 2013 to 2022 by means of a bibliometric analysis. The study points to promising future research directions focusing on in-depth autophagy mechanisms, clinical applications, and potential therapeutic strategies, providing a valuable reference for researchers in the field. Systematic Review Registration: [https://systematicreview.gov/], identifier [registration number].

Molecular screening reveals Variolin B as a multitargeted inhibitor of lung cancer: a molecular docking-based fingerprinting and molecular dynamics simulation study

Lung Cancer is the topmost death causing cancer and results from smoking, air pollution, cigar, exposure to asbestos or radon-like substances, and genetic factors. The cases of Lung Cancer in south Asian developing nations are being seen most due to heavy pollution and unbalanced lifestyle and putting a considerable burden on healthcare systems. The Food and Drug Administration of the USA has approved almost 100 drugs against SCLC and NSLC and a few drugs that are given to minimise the side effect of anticancer drugs. However, the drugs are shown to be resistant at significantly higher stages and non-affective on cancerous cells and have long-term side effects due to designing the drug by keeping one protein/gene target while designing or repurposing the drugs. In this study, we have taken five main lung cancer protein targets- Nerve growth factor protein (1SG1), Apoptosis inhibitor survivin (1XOX), Heat shock protein (3IUC), Protein tyrosine phosphate (3ZM3), Aldo-keto reductase (4XZL) and screened the complete prepared Drug Bank library of 155888 compounds and identified Variolin B (DB08694) as a multitargeted inhibitor against lung cancer using HTVS, SP and XP sampling algorithms followed by MM\GBSA calculation to sort the best pose. Variolin B is a natural marine antitumor and antiviral compound, so we analysed the ADMET properties and interaction patterns and then simulated all five P-L complexes for 100 ns in water using the NPT ensemble to check its selves against lung cancer. The docking results, ADMET and fingerprints have shown a good performance, and RMSD and RMSF results were with least deviation and fluctuations (<2Å) and produced a huge contact with other residues making the complex stable. The complexes initially fluctuated and deviated due to changes in the solute medium and sudden heat and stabilise after a few ns. However, extensive experimental validation is required before human use.Communicated by Ramaswamy H. Sarma.

Data-driven structural analysis of small cell lung cancer transcription factor network suggests potential subtype regulators and transition pathways

Small cell lung cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

Systems Biology and Cytokines Potential Role in Lung Cancer Immunotherapy Targeting Autophagic Axis

Lung cancer accounts for the highest number of deaths among men and women worldwide. Although extensive therapies, either alone or in conjunction with some specific drugs, continue to be the principal regimen for evolving lung cancer, significant improvements are still needed to understand the inherent biology behind progressive inflammation and its detection. Unfortunately, despite every advancement in its treatment, lung cancer patients display different growth mechanisms and continue to die at significant rates. Autophagy, which is a physiological defense mechanism, serves to meet the energy demands of nutrient-deprived cancer cells and sustain the tumor cells under stressed conditions. In contrast, autophagy is believed to play a dual role during different stages of tumorigenesis. During early stages, it acts as a tumor suppressor, degrading oncogenic proteins; however, during later stages, autophagy supports tumor cell survival by minimizing stress in the tumor microenvironment. The pivotal role of the IL6-IL17-IL23 signaling axis has been observed to trigger autophagic events in lung cancer patients. Since the obvious roles of autophagy are a result of different immune signaling cascades, systems biology can be an effective tool to understand these interconnections and enhance cancer treatment and immunotherapy. In this review, we focus on how systems biology can be exploited to target autophagic processes that resolve inflammatory responses and contribute to better treatment in carcinogenesis.

Clinical Network Systems Biology: Traversing the Cancer Multiverse

In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody-drug conjugates, and immunotherapy. This 'Team Medicine' approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing.

Data-driven structural analysis of Small Cell Lung Cancer transcription factor network suggests potential subtype regulators and transition pathways

Small Cell Lung Cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

Quantitative Framework for Bench-to-Bedside Cancer Research

Bioscience is an interdisciplinary venture. Driven by a quantum shift in the volume of high throughput data and in ready availability of data-intensive technologies, mathematical and quantitative approaches have become increasingly common in bioscience. For instance, a recent shift towards a quantitative description of cells and phenotypes, which is supplanting conventional qualitative descriptions, has generated immense promise and opportunities in the field of bench-to-bedside cancer OMICS, chemical biology and pharmacology. Nevertheless, like any burgeoning field, there remains a lack of shared and standardized framework for quantitative cancer research. Here, in the context of cancer, we present a basic framework and guidelines for bench-to-bedside quantitative research and therapy. We outline some of the basic concepts and their parallel use cases for chemical-protein interactions. Along with several recommendations for assay setup and conditions, we also catalog applications of these quantitative techniques in some of the most widespread discovery pipeline and analytical methods in the field. We believe adherence to these guidelines will improve experimental design, reduce variabilities and standardize quantitative datasets.

Correlation of Tryptophan Metabolic Pathway with Immune Activation and Chemosensitivity in Patients with Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common type of lung cancer with high malignancy and easy metastasis in the early stage. In this study, we aimed to figure out the role of tryptophan metabolic pathway in LUAD prognosis and treatment. Different molecular subtypes were constructed based on tryptophan metabolism-related genes. Significant prognostic genes and clinical prognostic characteristics, immune infiltration level, and pathway activity in different subtypes were determined by algorithms, such as the least absolute shrinkage and selection operator (Lasso), CIBERSORT, Tumor Immune Dysfunction and Exclusion (TIDE), and gene set enrichment analysis (GSEA). The effect of different gene mutation types on the prognosis of patients with LUAD was explored. The clinical prognosis model was constructed and its reliability was verified. Of the 40 genes in the tryptophan metabolism pathway, 13 had significant prognostic significance. Based on these 13 genes, three molecular subtypes (C1, C2, and C3) were established. Among them, C1 had the worst prognosis and the lowest enrichment score of tryptophan metabolism. At the same time, C1 had the most genetic variation, the highest level of immune infiltration, and significantly activated pathways related to tumor development. The high-risk and low-risk groups had significant differences in prognosis, immune infiltration and pathway enrichment, which was consistent with the results of subtype analysis. Mutation in tryptophan metabolism-related genes leads to abnormal tryptophan metabolism, immune deficiency, and activation of cancer-promoting pathways. This results in high malignancy, poor prognosis, and failure of traditional clinical treatments. Through the establishment of risk score (RS) clinical prognosis model, we determined that RS could reliably predict the prognosis of patients with LUAD.

Systems Biology Approach Identifies Prognostic Signatures of Poor Overall Survival and Guides the Prioritization of Novel BET-CHK1 Combination Therapy for Osteosarcoma

Osteosarcoma (OS) patients exhibit poor overall survival, partly due to copy number variations (CNVs) resulting in dysregulated gene expression and therapeutic resistance. To identify actionable prognostic signatures of poor overall survival, we employed a systems biology approach using public databases to integrate CNVs, gene expression, and survival outcomes in pediatric, adolescent, and young adult OS patients. Chromosome 8 was a hotspot for poor prognostic signatures. The MYC-RAD21 copy number gain (8q24) correlated with increased gene expression and poor overall survival in 90% of the patients (n = 85). MYC and RAD21 play a role in replication-stress, which is a therapeutically actionable network. We prioritized replication-stress regulators, bromodomain and extra-terminal proteins (BETs), and CHK1, in order to test the hypothesis that the inhibition of BET + CHK1 in MYC-RAD21+ pediatric OS models would be efficacious and safe. We demonstrate that MYC-RAD21+ pediatric OS cell lines were sensitive to the inhibition of BET (BETi) and CHK1 (CHK1i) at clinically achievable concentrations. While the potentiation of CHK1i-mediated effects by BETi was BET-BRD4-dependent, MYC expression was BET-BRD4-independent. In MYC-RAD21+ pediatric OS xenografts, BETi + CHK1i significantly decreased tumor growth, increased survival, and was well tolerated. Therefore, targeting replication stress is a promising strategy to pursue as a therapeutic option for this devastating disease.

Novel Frog Skin-Derived Peptide Dermaseptin-PP for Lung Cancer Treatment: In vitro/vivo Evaluation and Anti-tumor Mechanisms Study

Lung cancer is the major cause of cancer deaths worldwide, and it has the highest incidence and mortality rate of any cancer among men and women in China. The first-line therapy for lung cancer treatment is platinum-based chemotherapy drugs such as cisplatin. However, the application of present chemotherapies is limited by severe side effects, which stimulates the discovery of new drugs with new anti-tumor mechanisms and fewer side effects. Beneficially, many antimicrobial peptides (AMPs) from frog skin have been reported to exhibit potent anti-cancer activities with low toxicity, high selectivity and a low propensity to induce resistance. In this study, we first reported an AMP named Dermaseptin-PP, from a rarely studied frog species, Phyllomedusa palliata. Dermaseptin-PP exhibited selective cytotoxicity on H157, MCF-7, PC-3, and U251 MG cancer cells instead of normal HMEC-1 cells with low hemolytic effect. Furthermore, on subcutaneous H157 tumor model of nude mice, Dermaseptin-PP was found to display potent in vivo anti-tumor activity in a dose-related manner without obvious hepatopulmonary side effects. It is widely accepted that AMPs usually work through a membrane disruptive mode, and the confocal laser microscope observation confirmed that Dermaseptin-PP could destroy H157 cell membranes. Further investigation of mechanisms by flow cytometry assay and immunohistochemical analysis unraveled that Dermaseptin-PP also exerted its anti-tumor activity by inducing H157 cell apoptosis via both endogenous mitochondrial apoptosis pathway and exogenous death receptor apoptosis pathway. Herein, we emphasize that the membrane disrupting and the apoptosis activation effects of Dermaseptin-PP both depend on its concentration. Overall, a novel frog skin-derived AMP, named Dermaseptin-PP, was identified for the first time. It possesses strong antimicrobial activity and effective anti-tumor activity by distinct mechanisms. This study revealed the possibility of Dermaseptin-PP for lung cancer treatment and provided a new perspective for designing novel AMP-based anti-tumor candidates with low risk of cytotoxicity.

TIPE2 suppressed cisplatin resistance by inducing autophagy via mTOR signalling pathway

Tumour necrosis factor-α-induced protein-8-like-2 (TIPE2) has been associated with the progression of numerous cancers. Cisplatin, as a classical chemotherapy strategy for cancers, has been applied in non-small-cell lung cancer (NSCLC) clinical therapy but bears the disadvantage of chemoresistance. The aim of this study was to investigate the role of TIPE2 in cisplatin resistance and illustrate the detailed molecular mechanism. In this study, we proved that TIPE2 was down-regulated in cisplatin (DDP)-resistant NSCLC tissues and DDP-resistant NSCLC cells compared with the sensitive control. The inhibition of TIPE2 contributed to cell cisplatin-resistance, and the overexpression of TIPE2 enhanced cisplatin sensitivity and autophagy. Furthermore, increased TIPE2 elevated apoptosis in DDP-resistant NSCLC cells. In addition, TIPE2 restored the activity of mTOR signalling. Preconditioning with the mTOR activator 3BDO abrogated TIPE2-mediated depression in cisplatin-evoked autophagy. In conclusion, aberrant TIPE2 expression may contribute to the occurrence of chemoresistance by interfering with autophagy in NSCLC in an mTOR-dependent manner. TIPE2 could be used as a novel therapeutic target to overcome cisplatin-resistant NSCLC.

Cell death-based treatment of lung adenocarcinoma

The most common type of lung cancer is adenocarcinoma (ADC), comprising around 40% of all lung cancer cases. In spite of achievements in understanding the pathogenesis of this disease and the development of new approaches in its treatment, unfortunately, lung ADC is still one of the most aggressive and rapidly fatal tumor types with overall survival less than 5 years. Lung ADC is often diagnosed at advanced stages involving disseminated metastatic tumors. This is particularly important for the successful development of new approaches in cancer therapy. The high resistance of lung ADC to conventional radiotherapies and chemotherapies represents a major challenge for treatment effectiveness. Here we discuss recent advances in understanding the molecular pathways driving tumor progression and related targeted therapies in lung ADCs. In addition, the cell death mechanisms induced by different treatment strategies and their contribution to therapy resistance are analyzed. The focus is on approaches to overcoming drug resistance in order to improve future treatment decisions.

Sensitization strategies in lung cancer

The commonly used treatment avenues employed by cancer physicians include surgery, radiotherapy (RT) and chemotherapy in addition to rapid developmental and confirmatory studies on the efficacy of targeted therapies. However, the success rate in these commonly used treatments remains relatively low due to associated side effects, such as normal cell targeting/toxicity and resistance. In addition, investigators are continuing their efforts to enhance the efficacy of RT and chemotherapy to prevent associated side effects and improve the survival rate of the affected patient in order to increase patient survival. In the present study, we have reviewed the sensitization approaches used to improve chemotherapy and RT sensitivity in tumors.

INO80 is required for oncogenic transcription and tumor growth in non-small cell lung cancer

Epigenetic regulators are attractive targets for the development of new cancer therapies. Among them, the ATP-dependent chromatin remodeling complexes control the chromatin architecture and have important roles in gene regulation. They are often found to be mutated and de-regulated in cancers, but how they influence the cancer gene expression program during cancer initiation and progression is not fully understood. Here we show that the INO80 chromatin remodeling complex is required for oncogenic transcription and tumor growth in non-small-cell lung cancer (NSCLC). Ino80, the SWI/SNF ATPase in the complex, is highly expressed in NSCLC cells compared with normal lung epithelia cells. Further, its expression, as well as that of another subunit Ino80B, negatively correlates with disease prognosis in lung cancer patients. Functionally, INO80 silencing inhibits NSCLC cell proliferation and anchorage-independent growth in vitro and tumor formation in mouse xenografts. It occupies enhancer regions near lung cancer-associated genes, and its occupancy correlates with increased genome accessibility and enhanced expression of downstream genes. Together, our study defines a critical role of INO80 in promoting oncogenic transcription and NSCLC tumorigenesis, and reveals a potential treatment strategy for inhibiting the cancer transcription network by targeting the INO80 chromatin remodeling complex.