Targeting molecular signal transduction pathways in hepatocellular carcinoma and its implications for cancer therapy

Mir, Ishfaq Hassan; Guha, Shreyoshi; Behera, Jajnasenee; Thirunavukkarasu, Chinnasamy

doi:10.1002/cbin.11670

Cited by 12 publications

(13 citation statements)

References 131 publications

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“…In addition, these ML-signature also show decreasing transduction-related signaling pathways. Signal transduction pathways involve each stage of cancer cells, from stem cells to advanced cancer cells, including proliferation, metabolism, cell cycle, DNA repair, apoptosis, differentiation, tumor extracellular matrix remodeling, angiogenesis and metastasis ( 28 , 29 ). Notably, there was no significance in metabolism, DNA repair, apoptosis or other inflammatory signaling pathways.…”

Section: Discussionmentioning

confidence: 99%

Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms

et al. 2022

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BackgroundDeveloping prediction tools for immunotherapy approaches is a clinically important and rapidly emerging field. The routinely used prediction biomarker is inaccurate and may not adequately utilize large amounts of medical data. Machine learning is a promising way to predict the benefit of immunotherapy from individual data by individuating the most important features from genomic data and clinical characteristics.MethodsMachine learning was applied to identify a list of candidate genes that may predict immunotherapy benefits using data from the published cohort of 853 patients with NSCLC. We used XGBoost to capture nonlinear relations among many mutation genes and ICI benefits. The value of the derived machine learning-based mutation signature (ML-signature) on immunotherapy efficacy was evaluated and compared with the tumor mutational burden (TMB) and other clinical characteristics. The predictive power of ML-signature was also evaluated in independent cohorts of patients with NSCLC treated with ICI.ResultsWe constructed the ML-signature based on 429 (training/validation = 8/2) patients who received immunotherapy and extracted 88 eligible predictive genes. Additionally, we conducted internal and external validation with the utility of the OAK+POPLAR dataset and independent cohorts, respectively. This ML-signature showed the enrichment in immune-related signaling pathways and compared to TMB, ML-signature was equipped with favorable predictive value and stratification.ConclusionPrevious studies proposed no predictive difference between original TMB and modified TMB, and original TMB contains some genes with no predictive value. To demonstrate that fewer genetic tests are sufficient to predict immunotherapy efficacy, we used machine learning to screen out gene panels, which are used to calculate TMB. Therefore, we obtained the 88-gene panel, which showed the favorable prediction performance and stratification effect compared to the original TMB.

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Section: Discussionmentioning

confidence: 99%

Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms

et al. 2022

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“…Research in recent years has mainly focused on uncovering the cellular signal transduction mechanisms involved in the development of HCC. Gene modification plays an important role in tumorigenesis, and anomalies of many related signal transduction pathways, such as MAPK pathway, PI3K/AKT/mTOR pathway, WNT/β-catenin pathway, and JAK/STAT pathway, are also related to the progression of HCC ( Mir et al, 2021 ). A previous study has supported that ECM-related proteins establishes a physical and biochemical niche for CSCs, providing structural and biochemical support for regulating CSCs proliferation, self-renewal, and differentiation ( Nallanthighal et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Stemness analysis in hepatocellular carcinoma identifies an extracellular matrix gene–related signature associated with prognosis and therapy response

et al. 2022

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Background: Tumor stemness is the stem-like phenotype of cancer cells, as a hallmark for multiple processes in the development of hepatocellular carcinoma (HCC). However, comprehensive functions of the regulators of tumor cell’s stemness in HCC remain unclear.Methods: Gene expression data and clinical information of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) dataset as the training set, and three validation datasets were derived from Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC). Patients were dichotomized according to median mRNA expression–based stemness index (mRNAsi) scores, and differentially expressed genes were further screened out. Functional enrichment analysis of these DEGs was performed to identify candidate extracellular matrix (ECM)–related genes in key pathways. A prognostic signature was constructed by applying least absolute shrinkage and selection operator (LASSO) to the candidate ECM genes. The Kaplan–Meier curve and receiver operating characteristic (ROC) curve were used to evaluate the prognostic value of the signature. Correlations between signatures and genomic profiles, tumor immune microenvironment, and treatment response were also explored using multiple bioinformatic methods.Results: A prognostic prediction signature was established based on 10 ECM genes, including TRAPPC4, RSU1, ILK, LAMA1, LAMB1, FLNC, ITGAV, AGRN, ARHGEF6, and LIMS2, which could effectively distinguish patients with different outcomes in the training and validation sets, showing a good prognostic prediction ability. Across different clinicopathological parameter stratifications, the ECMs signature still retains its robust efficacy in discriminating patient with different outcomes. Based on the risk score, vascular invasion, α-fetoprotein (AFP), T stage, and N stage, we further constructed a nomogram (C-index = 0.70; AUCs at 1-, 3-, and 5-year survival = 0.71, 0.75, and 0.78), which is more practical for clinical prognostic risk stratification. The infiltration abundance of macrophages M0, mast cells, and Treg cells was significantly higher in the high-risk group, which also had upregulated levels of immune checkpoints PD-1 and CTLA-4. More importantly, the ECMs signature was able to distinguish patients with superior responses to immunotherapy, transarterial chemoembolization, and sorafenib.Conclusion: In this study, we constructed an ECM signature, which is an independent prognostic biomarker for HCC patients and has a potential guiding role in treatment selection.

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“…Selective preference of biomarker is based on the stage of cancer and its route. Several signal transduction pathways are misregulated in HCC, resulting in metastasis, vasculogenesis, and epithelial to mesenchymal transition 1 . The molecules such as growth and differentiation factors are aberrantly expressed in such pathways and are used as biomarkers for HCC diagnosis.…”

Section: Biomarkers Demanding Further Validationmentioning

confidence: 99%

“…Hepatocellular carcinoma (HCC) is the most prominent kind of liver cancer, accounting for 85% of primary liver malignancies. It is a very aggressive tumor, having a terrible prognosis and poor survival rate 1 . HCC is ranked the sixth most common type of cancer and the third leading cause of cancer‐related mortalities worldwide 2 .…”

Section: Introductionmentioning

confidence: 99%

“…There are multiple treatment options available such as liver transplantation, ablation, resection, chemotherapy, radiation therapy, and percutaneous ethanol injection for HCC 8–10 . However, sorafenib is used as a prototype drug at the advanced stage that acts against vascular endothelial growth factor (VEGF) and platelet‐derived growth factor (PDGF) receptors, preventing angiogenesis and metastasis 1 . HCC recurrence, liver performance, tumor stage affects the treatment strategies causing more deaths.…”

Section: Introductionmentioning

confidence: 99%

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The prominence of potential biomarkers in the diagnosis and management of hepatocellular carcinoma: Current scenario and future anticipation

Mir

Jyothi

Thirunavukkarasu

2021

J of Cellular Biochemistry

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Hepatocellular carcinoma (HCC) is one of the most aggressive and truculent types of cancer. Early detection of HCC is a massive concern that can boost the overall survival rates of HCC patients. As a result, there is a continual quest for advancements in screening, diagnosis, and treatment strategies to enhance the prognosis at its early stages. However, the confluence of inflammation and cirrhosis hampers the early detection of HCC. The analysis of different types of biomarkers such as tissue biomarkers, serum biomarkers, protein biomarkers, autoantibody markers, and improved imaging techniques has played a vital role in ameliorating HCC monitoring responses. Therefore biomarkers that can identify HCC early with a high degree of sensitivity and specificity might be prodigiously serviceable in the diagnosis and treatment of this notorious disorder. This study offers an overview of the contemporary understanding of several types of biomarkers implicated in hepatocarcinogenesis and their applications in monitoring, diagnosis, and prognosis presage. In additament, we address the role of image techniques associated with HCC diagnosis.

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Targeting molecular signal transduction pathways in hepatocellular carcinoma and its implications for cancer therapy

Cited by 12 publications

References 131 publications

Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms

Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms

Stemness analysis in hepatocellular carcinoma identifies an extracellular matrix gene–related signature associated with prognosis and therapy response

The prominence of potential biomarkers in the diagnosis and management of hepatocellular carcinoma: Current scenario and future anticipation

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