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      Diagnostic and predictive significance of the ferroptosis-related gene TXNIP in lung adenocarcinoma stem cells based on multi-omics

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          Highlights

          • Innovative approach: This research proposed a novel method that addresses a long-standing challenge in the field of the chemo/radio-therapy resistence and immune escape of cancer stem cells (CSCs). By in-depth study of CSCs, our approach opens new possibilities for anti-CSC clinical translational therapy.

          • Performance excellence: Experimental results demonstrate the superior performance of our method in the context of mechanisms of ferroptosis and immunosuppressive infiltration in CSCs. This provides valuable insights for future research and applications in diagnosis and prediction of TXNIP in advanced lung cancer.

          • Revealing new perspectives: Uncovered a prominent phenomenon, shedding light on a previously unexplored aspect of the immune microenvironment conditions in CSCs. This discovery offers a fresh perspective for understanding how TXNIP regulates ferroptosis and why tumor stem cells are prone to drug resistance, relapse and immune escape.

          Abstract

          Background

          Lung cancer stands as the foremost cause of cancer-related fatalities globally. The presence of cancer stem cells (CSCs) poses a challenge, rendering current targeted tumor therapies ineffective. This study endeavors to investigate a novel therapeutic approach focusing on ferroptosis and delves into the expression of ferroptosis-related genes within lung CSCs.

          Methods

          We systematically examined RNA-seq datasets derived from lung tumor cells (LTCs) and lung cancer stem cells (LSCs), as previously investigated in our research. Our focus was on analyzing differentially expressed genes (DEGs) related to ferroptosis. Utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO), we conducted functional analysis of these ferroptosis-related DEGs. Additionally, we employed protein‒protein interaction networks to identify hub genes. LC‒MS/MS analysis of LTCs and LSCs was conducted to pinpoint the crucial ferroptosis-related gene–thioredoxin-interacting protein (TXNIP).Further, we delved into the immune cell infiltration landscape of LTCs and LSCs, examining the correlation between TXNIP and lung adenocarcinoma (LUAD) using data from The Cancer Genome Atlas (TCGA) database. To complement these findings, we measured the expression levels of TXNIP, glutathione peroxidase 4(GPX4), nuclear receptor coactivator 4 (NCOA4) in LUAD tissues through immunohistochemistry (IHC) staining.

          Results

          A total of 651 DEGs were identified, with 17 of them being ferroptosis-related DEGs. These seventeen genes were categorized into four groups: driver genes, suppressor genes, unclassified genes, and inducer genes. Enrichment analysis revealed significant associations with oxidative stress, cell differentiation, tissue development, and cell death processes. The RNA-seq analysis demonstrated consistent gene expression patterns with protein expression, as evidenced by mass spectrometry analysis. Among the identified genes, SFN and TXNIP were singled out as hub genes, with TXNIP showing particularly noteworthy expression. The expression of the ferroptosis-related gene TXNIP exhibited correlations with the presence of an immunosuppressive microenvironment, TNM stages, and the degree of histological differentiation.Also, the ferroptosis-markers GPX4 and NCOA4 displayed correlations with LUAD. This comprehensive analysis underscores the significance of TXNIP in the context of ferroptosis-related processes and their potential implications in cancer development and progression.

          Conclusion

          The investigation conducted in this study systematically delved into the role of the ferroptosis-related gene TXNIP in Lung CSCs. The identification of TXNIP as a potentially valuable biomarker in this context could have significant implications for refining prognostic assessments and optimizing therapeutic strategies for advanced lung cancer.

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          Most cited references86

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              Cytoscape: a software environment for integrated models of biomolecular interaction networks.

              Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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                Author and article information

                Contributors
                Journal
                Transl Oncol
                Transl Oncol
                Translational Oncology
                Neoplasia Press
                1936-5233
                13 April 2024
                July 2024
                13 April 2024
                : 45
                : 101926
                Affiliations
                [a ]Department of Oncology, Shenzhen Key Laboratory of Gastrointestinal Cancer Translational Research, Cancer Institute, Peking University Shenzhen Hospital, Shenzhen Peking University-Hong Kong University of Science & Technology Medical Center, Shenzhen 518036, China
                [b ]GeneMind Biosciences Company Limited, Shenzhen 518000, China
                [c ]Department of General Practice, The Second Clinical Medical College (Shenzhen People's Hospital),Jinan University, Shenzhen 518020, China
                [d ]Department of Radiation Oncology, Shenzhen Key Laboratory of Gastrointestinal Cancer Translational Research, Cancer Institute, Peking University Shenzhen Hospital, Shenzhen Peking University-Hong Kong University of Science & Technology Medical Center, Shenzhen 518036, China
                Author notes
                [1]

                Yuanyuan Zheng and Wei Yang contributed equally to this work.

                Article
                S1936-5233(24)00051-2 101926
                10.1016/j.tranon.2024.101926
                11033204
                38615437
                8a3f4196-ed76-4171-8a6b-7061b0416ba2
                © 2024 The Authors. Published by Elsevier Inc.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 6 December 2023
                : 19 February 2024
                : 28 February 2024
                Categories
                Original Research

                lung adenocarcinoma cancer,cancer stem cells,ferroptosis,txnip,multi-omics analysis

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