Probing Folate-Responsive and Stage-Sensitive Metabolomics and Transcriptional Co-Expression Network Markers to Predict Prognosis of Non-Small Cell Lung Cancer Patients

Lin, Yu-Shun; Chen, Yen-Chu; Chen, Tzu-En; Cheng, Mei‐Ling; Lynn, Ke-Shiuan; Shah, Pramod M.; Chen, Jin‐Shing; Huang, Rachel

doi:10.3390/nu15010003

Cited by 4 publications

(4 citation statements)

References 75 publications

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“… 11 , 213 Recently, a lipid metabolism gene prognostic signature was successfully validated for tumour patient survival, immune infiltration, cell mutation and treatment prognosis 214 , 215 and was also validated via scRNA‐seq‐based construct analysis for patients with hepatocellular carcinoma. 216 , 217 Furthermore, the effects of several metabolic pathways that have received less attention, such as selenium metabolism, 218 polyamine metabolism 219 and folate metabolism, 220 on tumour prognosis have been supported by preliminary validation data. In the context of immunotherapy, the innovative value of several methods, including the T‐cell‐related prognostic index and the multimetric analysis of biomarkers for immunotherapy, such as CAMOIP, for assessing the prognosis of patients treated with ICIs has been identified as an additional direction for the development of new prognostic models made possible by immunometabolomics.…”

Section: Discussionmentioning

confidence: 97%

Unlocking the potential of T‐cell metabolism reprogramming: Advancing single‐cell approaches for precision immunotherapy in tumour immunity

Huang,

Li,

Zhang

et al. 2024

Clinical & Translational Med

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As single‐cell RNA sequencing enables the detailed clustering of T‐cell subpopulations and facilitates the analysis of T‐cell metabolic states and metabolite dynamics, it has gained prominence as the preferred tool for understanding heterogeneous cellular metabolism. Furthermore, the synergistic or inhibitory effects of various metabolic pathways within T cells in the tumour microenvironment are coordinated, and increased activity of specific metabolic pathways generally corresponds to increased functional activity, leading to diverse T‐cell behaviours related to the effects of tumour immune cells, which shows the potential of tumour‐specific T cells to induce persistent immune responses. A holistic understanding of how metabolic heterogeneity governs the immune function of specific T‐cell subsets is key to obtaining field‐level insights into immunometabolism. Therefore, exploring the mechanisms underlying the interplay between T‐cell metabolism and immune functions will pave the way for precise immunotherapy approaches in the future, which will empower us to explore new methods for combating tumours with enhanced efficacy.

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Section: Discussionmentioning

confidence: 97%

Unlocking the potential of T‐cell metabolism reprogramming: Advancing single‐cell approaches for precision immunotherapy in tumour immunity

Huang,

Li,

Zhang

et al. 2024

Clinical & Translational Med

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“…Notably, all cancers present with an adjustable metabolic dimension, wherein a competition to acquire metabolic resources exists between cancer and the surrounding milieu [122]. With the advent of advanced genomics and metabolomics, a remarkable plasticity of tumor metabolic and bioenergetics mechanisms have been uncovered, and several cancer-associated bioenergetics signatures have been identified [123][124][125]. Long-standing metabolic challenges can lead to cancer initiation, with subsequent demands as cancer progresses and metastasize.…”

Section: Crosstalk Between Mitochondrial Bioenergetics and Tme-drivin...mentioning

confidence: 99%

Cancer Bioenergetics and Tumor Microenvironments—Enhancing Chemotherapeutics and Targeting Resistant Niches through Nanosystems

Farhana

Alsrhani

Khan

et al. 2023

Cancers

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Cancer is an impending bottleneck in the advanced scientific workflow to achieve diagnostic, prognostic, and therapeutic success. Most cancers are refractory to conventional diagnostic and chemotherapeutics due to their limited targetability, specificity, solubility, and side effects. The inherent ability of each cancer to evolve through various genetic and epigenetic transformations and metabolic reprogramming underlies therapeutic limitations. Though tumor microenvironments (TMEs) are quite well understood in some cancers, each microenvironment differs from the other in internal perturbations and metabolic skew thereby impeding the development of appropriate diagnostics, drugs, vaccines, and therapies. Cancer associated bioenergetics modulations regulate TME, angiogenesis, immune evasion, generation of resistant niches and tumor progression, and a thorough understanding is crucial to the development of metabolic therapies. However, this remains a missing element in cancer theranostics, necessitating the development of modalities that can be adapted for targetability, diagnostics and therapeutics. In this challenging scenario, nanomaterials are modular platforms for understanding TME and achieving successful theranostics. Several nanoscale particles have been successfully researched in animal models, quite a few have reached clinical trials, and some have achieved clinical success. Nanoparticles exhibit an intrinsic capability to interact with diverse biomolecules and modulate their functions. Furthermore, nanoparticles can be functionalized with receptors, modulators, and drugs to facilitate specific targeting with reduced toxicity. This review discusses the current understanding of different theranostic nanosystems, their synthesis, functionalization, and targetability for therapeutic modulation of bioenergetics, and metabolic reprogramming of the cancer microenvironment. We highlight the potential of nanosystems for enhanced chemotherapeutic success emphasizing the questions that remain unanswered.

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“…In the present study, we applied supervised ML algorithms to develop the most suitable classification model based on large-scale targeted metabolomic data for NSCLC diagnostics. Additionally, weighted coexpression network analysis 12 , 13 together with classical statistical analysis was applied for a deeper clarification of the nonlinear biochemical interconnections between the metabolic changes and appearance of NSCLC, as well as for identification of new significant ratios of the metabolites.…”

Section: Introductionmentioning

confidence: 99%

Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer

Shestakova,

Moskaleva,

Boldin

et al. 2023

Sci Rep

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Lung cancer is referred to as the second most common cancer worldwide and is mainly associated with complex diagnostics and the absence of personalized therapy. Metabolomics may provide significant insights into the improvement of lung cancer diagnostics through identification of the specific biomarkers or biomarker panels that characterize the pathological state of the patient. We performed targeted metabolomic profiling of plasma samples from individuals with non-small cell lung cancer (NSLC, n = 100) and individuals without any cancer or chronic pathologies (n = 100) to identify the relationship between plasma endogenous metabolites and NSLC by means of modern comprehensive bioinformatics tools, including univariate analysis, multivariate analysis, partial correlation network analysis and machine learning. Through the comparison of metabolomic profiles of patients with NSCLC and noncancer individuals, we identified significant alterations in the concentration levels of metabolites mainly related to tryptophan metabolism, the TCA cycle, the urea cycle and lipid metabolism. Additionally, partial correlation network analysis revealed new ratios of the metabolites that significantly distinguished the considered groups of participants. Using the identified significantly altered metabolites and their ratios, we developed a machine learning classification model with an ROC AUC value equal to 0.96. The developed machine learning lung cancer model may serve as a prototype of the approach for the in-time diagnostics of lung cancer that in the future may be introduced in routine clinical use. Overall, we have demonstrated that the combination of metabolomics and up-to-date bioinformatics can be used as a potential tool for proper diagnostics of patients with NSCLC.

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Probing Folate-Responsive and Stage-Sensitive Metabolomics and Transcriptional Co-Expression Network Markers to Predict Prognosis of Non-Small Cell Lung Cancer Patients

Cited by 4 publications

References 75 publications

Unlocking the potential of T‐cell metabolism reprogramming: Advancing single‐cell approaches for precision immunotherapy in tumour immunity

Unlocking the potential of T‐cell metabolism reprogramming: Advancing single‐cell approaches for precision immunotherapy in tumour immunity

Cancer Bioenergetics and Tumor Microenvironments—Enhancing Chemotherapeutics and Targeting Resistant Niches through Nanosystems

Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer

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