{"data":{"id":"10.5061/dryad.ghx3ffc05","type":"dois","attributes":{"doi":"10.5061/dryad.ghx3ffc05","prefix":"10.5061","suffix":"dryad.ghx3ffc05","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Zhao, Peiqing","nameType":"Personal","givenName":"Peiqing","familyName":"Zhao","affiliation":["Central Hospital of Zibo"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9311-1927","nameIdentifierScheme":"ORCID"}]},{"name":"Tian, Maojin","nameType":"Personal","givenName":"Maojin","familyName":"Tian","affiliation":["Central Hospital of Zibo"],"nameIdentifiers":[]},{"name":"Du, Jianxin","nameType":"Personal","givenName":"Jianxin","familyName":"Du","affiliation":["Central Hospital of Zibo"],"nameIdentifiers":[]}],"titles":[{"title":"Data from: TIPE drives a cancer stem-like phenotype by promoting glycolysis via PKM2/HIF-1α axis in melanoma"}],"publisher":"Dryad","container":{},"publicationYear":2024,"subjects":[{"subject":"FOS: Basic medicine","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Basic medicine","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"transcriptomics"},{"subject":"Metabolomics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Gene interference and overexpression"}],"contributors":[],"dates":[{"date":"2024-11-04T03:10:05Z","dateType":"Created"},{"date":"2024-11-04T03:10:38Z","dateType":"Submitted"},{"date":"2024-12-13T00:00:00Z","dateType":"Issued"},{"date":"2024-12-13T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.7554/elife.92741.2","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["15027808 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"TIPE (TNFAIP8) has been identified as an oncogene and participates in\n tumor biology. However, how its role in the metabolism of tumor cells\n during melanoma development remains unclear. Here, we demonstrated that\n TIPE promoted glycolysis by interacting with pyruvate kinase M2 (PKM2) in\n melanoma. We found that TIPE induced PKM2 dimerization, thereby\n facilitating its translocation from the cytoplasm to the nucleus.\n TIPE-mediated PKM2 dimerization consequently promoted HIF-1α activation\n and glycolysis, which contributed to melanoma progression and increased\n its stemness features. Notably, TIPE specifically phosphorylated PKM2 at\n Ser 37 in an ERK-dependent manner. Consistently, the expression of TIPE\n was positively correlated with the levels of PKM2 Ser37 phosphorylation\n and cancer stem cell markers in melanoma tissues from clinical samples and\n tumor bearing mice. In summary, our findings indicate that the\n TIPE/PKM2/HIF-1α signaling pathway plays a pivotal role in promoting\n cancer stem cell properties by facilitating the glycolysis, which would\n provide a promising therapeutic target for melanoma intervention.","descriptionType":"Abstract"},{"description":"Transcriptomics  Sample collection and\n preparation RNA quantification and\n qualification ①     RNA degradation and contamination\n was monitored on 1% agarose gels. ②     RNA purity was\n checked using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA)\n . ③     RNA integrity was assessed using the RNA Nano\n 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA,\n USA). \u003cstrong\u003eLibrary preparation for\n Transcriptome sequencing\u003c/strong\u003e A total amount\n of 1 μg RNA per sample was used as input material for the RNA sample\n preparations. Sequencing libraries were generated using NEBNext® UltraTM\n RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s\n recommendations and index codes were added to attribute sequences to each\n sample. Briefly, mRNA was purified from total RNA using poly-T\n oligo-attached magnetic beads. Fragmentation was carried out using\n divalent cations under elevated temperature in NEBNext First Strand\n Synthesis Reaction Buffer(5X). First strand cDNA was synthesized using\n random hexamer primer and M-MuLV Reverse Transcriptase(RNase H-). Second\n strand cDNA synthesis was subsequently performed using DNA Polymerase I\n and RNase H. Remaining overhangs were converted into blunt ends via\n exonuclease/polymerase activities. After adenylation of 3’ ends of DNA\n fragments, NEBNext Adaptor with hairpin loop structure were ligated to\n prepare for hybridization. In order to select cDNA fragments of\n preferentially 250~300 bp in length, the library fragments were purified\n with AMPure XP system (Beckman Coulter, Beverly, USA). Then 3 μl USER\n Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at\n 37°C for 15 min followed by 5 min at 95 °C before PCR. Then PCR was\n performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers\n and Index (X) Primer. At last, PCR products were purified (AMPure XP\n system) and library quality was assessed on the Agilent Bioanalyzer 2100\n system. \u003cstrong\u003eClustering and sequencing\n (Novogene Experimental Department)\u003c/strong\u003e The\n clustering of the index-coded samples was performed on a cBot Cluster\n Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia)\n according to the manufacturer’s instructions. After cluster generation,\n the library preparations were sequenced on an Illumina Novaseq platform\n and 150 bp paired-end reads were generated.\n \u003cstrong\u003eData Analysis\u003c/strong\u003e\n \u003cstrong\u003eQuality control\u003c/strong\u003e\n Raw data (raw reads) of fastq format were firstly processed\n through in-house perl scripts. In this step, clean data (clean reads) were\n obtained by removing reads containing adapter, reads containing ploy-N and\n low quality reads from raw data. At the same time, Q20, Q30 and GC content\n the clean data were calculated. All the downstream analyses were based on\n the clean data with high quality. \u003cstrong\u003eReads\n mapping to the reference genome\u003c/strong\u003e\n Reference genome and gene model annotation files were downloaded\n from genome website directly. Index of the reference genome was built\n using Hisat2 v2.0.5 and paired-end clean reads were aligned to the\n reference genome using Hisat2 v2.0.5. We selected Hisat2 as the mapping\n tool for that Hisat2 can generate a database of splice junctions based on\n the gene model annotation file and thus a better mapping result than other\n non-splice mapping tools. \u003cstrong\u003eNovel\n transcripts prediction\u003c/strong\u003e The mapped reads\n of each sample were assembled by StringTie (v1.3.3b) (Mihaela Pertea.et\n al. 2015) in a reference-based approach. StringTie uses a novel network\n flow algorithm as well as an optional de novo assembly step to assemble\n and quantitate fulllength transcripts representing multiple splice\n variants for each gene locus.\n \u003cstrong\u003eQuantification of gene expression\n level\u003c/strong\u003e featureCounts v1.5.0-p3 was used\n to count the reads numbers mapped to each gene. And then FPKM of each gene\n was calculated based on the length of the gene and reads count mapped to\n this gene. FPKM, expected number of Fragments Per Kilobase of transcript\n sequence per Millions base pairs sequenced, considers the effect of\n sequencing depth and gene length for the reads count at the same time, and\n is currently the most commonly used method for estimating gene expression\n levels. \u003cstrong\u003eDifferential expression\n analysis\u003c/strong\u003e \u003cem\u003e(For DESeq2 with\n biological replicates) \u003c/em\u003eDifferential expression analysis of two\n conditions/groups (two biological replicates per condition) was performed\n using the DESeq2 R package (1.16.1). DESeq2 provide statistical routines\n for determining differential expression in digital gene expression data\n using a model based on the negative binomial distribution. The resulting\n P-values were adjusted using the Benjamini and Hochberg’s approach for\n controlling the false discovery rate . Genes with an adjusted P-value\n \u0026lt;0.05 found by DESeq2 were assigned as differentially expressed.\n \u003cem\u003e(For edgeR without biological replicates) \u003c/em\u003ePrior to\n differential gene expression analysis, for each sequenced library, the\n read counts were adjusted by edgeR program package through one scaling\n normalized factor. Differential expression analysis of two conditions was\n performed using the edgeR R package (3.18.1). The P values were adjusted\n using the Benjamini \u0026amp; Hochberg method. Corrected P-value of 0.05\n and absolute foldchange of 2 were set as the threshold for significantly\n differential expression. \u003cstrong\u003eGO and KEGG\n enrichment analysis of differentially expressed\n genes\u003c/strong\u003e Gene Ontology (GO) enrichment\n analysis of differentially expressed genes was implemented by the\n clusterProfiler R package, in which gene length bias wascorrected. GO\n terms with corrected Pvalue less than 0.05 were considered significantly\n enriched by differential expressed genes. KEGG is a database resource for\n understanding high-level functions and utilities of the biological system,\n such as the cell, the organism and the ecosystem, from molecular-level\n information, especially large-scale molecular datasets generated by genome\n sequencing and other high-through put experimental technologies\n (http://www.genome.jp/kegg/). We used clusterProfiler R package to test\n the statistical enrichment of differential expression genes in KEGG\n pathways.\n \u003cstrong\u003eMetabolomics \u003c/strong\u003e\n \u003cstrong\u003eSample Extraction Method\u003c/strong\u003e\n After slowly thawing the sample at 4°C, an appropriate amount of\n the sample is taken and added to a pre-cooled methanol/acetonitrile/water\n solution (2:2:1, v/v). The mixture is vortexed for mixing, followed by\n low-temperature ultrasonic treatment for 30 minutes. The sample is then\n allowed to stand at -20°C for 10 minutes and centrifuged at 14,000 g at\n 4°C for 20 minutes. The supernatant is collected and vacuum-dried. For\n mass spectrometry analysis, 100 μL of acetonitrile/water solution\n (acetonitrile:water = 1:1, v/v) is added to redissolve the dried sample,\n which is then vortexed. The sample is centrifuged again at 14,000 g at 4°C\n for 15 minutes, and the supernatant is taken for injection and\n analysis. \u003cstrong\u003eChromatographic\n Conditions\u003c/strong\u003e The samples were separated\n using an Agilent 1290 Infinity LC Ultra-High-Performance Liquid\n Chromatography (UHPLC) system with a HILIC column. The column temperature\n was maintained at 25°C, and the flow rate was set to 0.5 mL/min. The\n injection volume was 2 μL. The mobile phase composition was as follows: A\n = water + 25 mM ammonium acetate + 25 mM ammonia solution, and B =\n acetonitrile. The gradient elution program was as follows: 0 to 0.5 min,\n 95% B; 0.5 to 7 min, a linear decrease from 95% B to 65% B; 7 to 8 min, a\n linear decrease from 65% B to 40% B; 8 to 9 min, B maintained at 40%; 9 to\n 9.1 min, a linear increase from 40% B to 95% B; and 9.1 to 12 min, B\n maintained at 95%. During the entire analysis, the samples were kept in a\n 4°C autosampler. To avoid the impact of fluctuations in instrument\n detection signals, the samples were analyzed consecutively in a random\n order. Quality control (QC) samples were inserted into the sample queue to\n monitor and evaluate the stability of the system and the reliability of\n the experimental data. \u003cstrong\u003eQ-TOF Mass\n Spectrometry Conditions\u003c/strong\u003e The AB Triple\n TOF 6600 mass spectrometer was used to collect the first-order and\n second-order spectra of the samples. The ESI source\n conditions after HILIC chromatographic separation were as follows: Ion\n Source Gas1 (Gas1): 60, Ion Source Gas2 (Gas2): 60, Curtain gas (CUR): 30,\n source temperature: 600°C, IonSpray Voltage Floating (ISVF): ±5500 V (both\n positive and negative modes); TOF MS scan m/z range: 60-1000 Da, product\n ion scan m/z range: 25-1000 Da, TOF MS scan accumulation time: 0.20\n s/spectrum, product ion scan accumulation time: 0.05 s/spectrum. The\n second-order mass spectrometry was acquired using information-dependent\n acquisition (IDA) in high sensitivity mode. The Declustering Potential\n (DP) was set to ±60 V (both positive and negative modes), and the\n Collision Energy was 35 ± 15 eV. The IDA settings were as follows:\n isotopes within 4 Da were excluded, and 10 candidate ions were monitored\n per cycle. \u003cstrong\u003eData Analysis\n Workflow\u003c/strong\u003e The raw data in Wiff format is\n converted to .mzXML format using ProteoWizard. Subsequently, XCMS software\n is utilized for peak alignment, retention time correction, and peak area\n extraction. The data obtained through XCMS extraction undergoes initial\n metabolite structure identification and data preprocessing. Following\n this, the quality of the experimental data is evaluated, and finally, the\n data analysis is conducted. The data analysis encompasses univariate\n statistical analysis, multivariate statistical analysis, differential\n metabolite screening, correlation analysis of differential metabolites,\n and KEGG pathway analysis.","descriptionType":"Methods"},{"description":"# Data from: TIPE drives a cancer stem-like phenotype by promoting\n glycolysis via PKM2/HIF-1α axis in melanoma\n [https://doi.org/10.5061/dryad.ghx3ffc05](https://doi.org/10.5061/dryad.ghx3ffc05) ## Description of the data and file structure Transcriptomics Data We conducted RNA sequencing analysis by overexpressing TIPE in G361 cells, which comprised three experimental groups and three control groups. Metabolomics Data We conducted a metabolomic analysis after interfering with TIPE in A375 cells, which consisted of six experimental groups and six control groups. ### Files and variables #### **File: Metabolomics\\_Data.xlsx** **Description:**  ID: The identification number of the metabolite in mass spectrometry detection m/z: Mass-to-charge ratio rt (s): Retention time of the metabolite on the chromatography, also known as the peak emergence time, with the unit being seconds Name: The name of the metabolite Adduct: Information on the adduct ions of the compound Score: A scoring value ranging from 0 to 1. The higher the value, the higher the degree of matching and the more reliable the qualitative result HMDB: The HMDB number can be used to obtain detailed annotation information about the metabolite on the HMDB official website KEGG: The corresponding C number in KEGG. The C number can be used to query detailed information about the metabolite and the metabolic pathways it participates in on the KEGG official website Superclass: Parent class, representing the classification of the metabolite Class: Subclass, further classifying the metabolite Subclass: Sub-subclass, providing an even more detailed classification of the metabolite Sample: The relative content of each metabolite in the corresponding sample. In this test, control group including NC1, NC2, NC3, NC4, NC5, NC6, and the treatment group including KD1, KD2, KD3, KD4, KD5, KD6 The missing values are displayed as \"blank cells\" #### **File: Transcriptomics\\_Data.xlsx** **Description:**  gene_id: A unique identifier for each gene. samples: The standardized readcount values for each sample. The control group including N1, N2, and N3. The treatment group(overexpression of TIPE) including V1, V2, and V3. log2FoldChange: The ratio of gene expression levels between the treatment group and the control group, followed by taking the logarithm to the base 2. pvalue: P-value of significance test padj: Adjusted p-value for multiple hypothesis testing gene_name: The name assigned to the gene. gene_chr: Chromosome name where the gene is located. gene_start: Start position of the gene on the chromosome. gene_end: End position of the gene on the chromosome. gene_strand: Positive or negative strand information of the chromosome where the gene is located. gene_length: The sum of all non-overlapping exon regions from the start to the end of the gene. gene_biotype: Gene type, such as protein-coding gene, long non-coding RNA, etc. gene_description: Functional description of the gene. gene_tf_family: Transcription factor family annotation of the gene. Used NA\" to indicate missing values.","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Natural Science Foundation of China","awardNumber":"81972002","funderIdentifier":"https://ror.org/01h0zpd94","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Natural Science Foundation of China","awardNumber":"12304241","funderIdentifier":"https://ror.org/01h0zpd94","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Natural Science Foundation of China","awardNumber":"81971491","funderIdentifier":"https://ror.org/01h0zpd94","funderIdentifierType":"ROR"}],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.5061/DRYAD.GHX3FFC05</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Zhao, Peiqing</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9311-1927</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/04n3h0p93" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Central Hospital of Zibo</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Tian, Maojin</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/04n3h0p93" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Central Hospital of Zibo</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Du, Jianxin</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/04n3h0p93" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Central Hospital of Zibo</affiliation>
    </creator>
  </creators>
  <titles>
    <title>
      Data from: TIPE drives a cancer stem-like phenotype by promoting
      glycolysis via PKM2/HIF-1α axis in melanoma
    </title>
  </titles>
  <publisher publisherIdentifier="https://ror.org/00x6h5n95" publisherIdentifierScheme="ROR" schemeURI="https://ror.org/">Dryad</publisher>
  <resourceType resourceTypeGeneral="Dataset">dataset</resourceType>
  <publicationYear>2024</publicationYear>
  <subjects>
    <subject subjectScheme="fos" schemeURI="https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf">FOS: Basic medicine</subject>
    <subject>transcriptomics</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Metabolomics</subject>
    <subject>Gene interference and overexpression</subject>
  </subjects>
  <fundingReferences>
    <fundingReference>
      <funderName>National Natural Science Foundation of China</funderName>
      <funderIdentifier funderIdentifierType="ROR">https://ror.org/01h0zpd94</funderIdentifier>
      <awardNumber>81972002</awardNumber>
    </fundingReference>
    <fundingReference>
      <funderName>National Natural Science Foundation of China</funderName>
      <funderIdentifier funderIdentifierType="ROR">https://ror.org/01h0zpd94</funderIdentifier>
      <awardNumber>12304241</awardNumber>
    </fundingReference>
    <fundingReference>
      <funderName>National Natural Science Foundation of China</funderName>
      <funderIdentifier funderIdentifierType="ROR">https://ror.org/01h0zpd94</funderIdentifier>
      <awardNumber>81971491</awardNumber>
    </fundingReference>
  </fundingReferences>
  <dates>
    <date dateType="Created">2024-11-04T03:10:05Z</date>
    <date dateType="Submitted">2024-11-04T03:10:38Z</date>
    <date dateType="Issued">2024-12-13T00:00:00Z</date>
    <date dateType="Available">2024-12-13T00:00:00Z</date>
  </dates>
  <language>en</language>
  <relatedIdentifiers>
    <relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">https://doi.org/10.7554/eLife.92741.2</relatedIdentifier>
  </relatedIdentifiers>
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    <rights rightsURI="https://spdx.org/licenses/CC0-1.0.html">Creative Commons Zero v1.0 Universal</rights>
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  <descriptions>
    <description descriptionType="Abstract">
      TIPE (TNFAIP8) has been identified as an oncogene and participates in
      tumor biology. However, how its role in the metabolism of tumor cells
      during melanoma development remains unclear. Here, we demonstrated that
      TIPE promoted glycolysis by interacting with pyruvate kinase M2 (PKM2) in
      melanoma. We found that TIPE induced PKM2 dimerization, thereby
      facilitating its translocation from the cytoplasm to the nucleus.
      TIPE-mediated PKM2 dimerization consequently promoted HIF-1α activation
      and glycolysis, which contributed to melanoma progression and increased
      its stemness features. Notably, TIPE specifically phosphorylated PKM2 at
      Ser 37 in an ERK-dependent manner. Consistently, the expression of TIPE
      was positively correlated with the levels of PKM2 Ser37 phosphorylation
      and cancer stem cell markers in melanoma tissues from clinical samples and
      tumor bearing mice. In summary, our findings indicate that the
      TIPE/PKM2/HIF-1α signaling pathway plays a pivotal role in promoting
      cancer stem cell properties by facilitating the glycolysis, which would
      provide a promising therapeutic target for melanoma intervention.
    </description>
    <description descriptionType="Methods">
      &lt;p&gt;Transcriptomics &lt;/p&gt; &lt;p&gt;Sample collection and
      preparation&lt;/p&gt; &lt;p&gt;RNA quantification and
      qualification&lt;/p&gt; &lt;p&gt;①     RNA degradation and contamination
      was monitored on 1% agarose gels.&lt;/p&gt; &lt;p&gt;②     RNA purity was
      checked using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA)
      .&lt;/p&gt; &lt;p&gt;③     RNA integrity was assessed using the RNA Nano
      6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA,
      USA).&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Library preparation for
      Transcriptome sequencing&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;A total amount
      of 1 μg RNA per sample was used as input material for the RNA sample
      preparations. Sequencing libraries were generated using NEBNext® UltraTM
      RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s
      recommendations and index codes were added to attribute sequences to each
      sample. Briefly, mRNA was purified from total RNA using poly-T
      oligo-attached magnetic beads. Fragmentation was carried out using
      divalent cations under elevated temperature in NEBNext First Strand
      Synthesis Reaction Buffer(5X). First strand cDNA was synthesized using
      random hexamer primer and M-MuLV Reverse Transcriptase(RNase H-). Second
      strand cDNA synthesis was subsequently performed using DNA Polymerase I
      and RNase H. Remaining overhangs were converted into blunt ends via
      exonuclease/polymerase activities. After adenylation of 3’ ends of DNA
      fragments, NEBNext Adaptor with hairpin loop structure were ligated to
      prepare for hybridization. In order to select cDNA fragments of
      preferentially 250~300 bp in length, the library fragments were purified
      with AMPure XP system (Beckman Coulter, Beverly, USA). Then 3 μl USER
      Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at
      37°C for 15 min followed by 5 min at 95 °C before PCR. Then PCR was
      performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers
      and Index (X) Primer. At last, PCR products were purified (AMPure XP
      system) and library quality was assessed on the Agilent Bioanalyzer 2100
      system.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Clustering and sequencing
      (Novogene Experimental Department)&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The
      clustering of the index-coded samples was performed on a cBot Cluster
      Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia)
      according to the manufacturer’s instructions. After cluster generation,
      the library preparations were sequenced on an Illumina Novaseq platform
      and 150 bp paired-end reads were generated.&lt;/p&gt;
      &lt;p&gt;&lt;strong&gt;Data Analysis&lt;/strong&gt;&lt;/p&gt;
      &lt;p&gt;&lt;strong&gt;Quality control&lt;/strong&gt;&lt;/p&gt;
      &lt;p&gt;Raw data (raw reads) of fastq format were firstly processed
      through in-house perl scripts. In this step, clean data (clean reads) were
      obtained by removing reads containing adapter, reads containing ploy-N and
      low quality reads from raw data. At the same time, Q20, Q30 and GC content
      the clean data were calculated. All the downstream analyses were based on
      the clean data with high quality.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Reads
      mapping to the reference genome&lt;/strong&gt;&lt;/p&gt;
      &lt;p&gt;Reference genome and gene model annotation files were downloaded
      from genome website directly. Index of the reference genome was built
      using Hisat2 v2.0.5 and paired-end clean reads were aligned to the
      reference genome using Hisat2 v2.0.5. We selected Hisat2 as the mapping
      tool for that Hisat2 can generate a database of splice junctions based on
      the gene model annotation file and thus a better mapping result than other
      non-splice mapping tools.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Novel
      transcripts prediction&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The mapped reads
      of each sample were assembled by StringTie (v1.3.3b) (Mihaela Pertea.et
      al. 2015) in a reference-based approach. StringTie uses a novel network
      flow algorithm as well as an optional de novo assembly step to assemble
      and quantitate fulllength transcripts representing multiple splice
      variants for each gene locus.&lt;/p&gt;
      &lt;p&gt;&lt;strong&gt;Quantification of gene expression
      level&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;featureCounts v1.5.0-p3 was used
      to count the reads numbers mapped to each gene. And then FPKM of each gene
      was calculated based on the length of the gene and reads count mapped to
      this gene. FPKM, expected number of Fragments Per Kilobase of transcript
      sequence per Millions base pairs sequenced, considers the effect of
      sequencing depth and gene length for the reads count at the same time, and
      is currently the most commonly used method for estimating gene expression
      levels.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Differential expression
      analysis&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;&lt;em&gt;(For DESeq2 with
      biological replicates) &lt;/em&gt;Differential expression analysis of two
      conditions/groups (two biological replicates per condition) was performed
      using the DESeq2 R package (1.16.1). DESeq2 provide statistical routines
      for determining differential expression in digital gene expression data
      using a model based on the negative binomial distribution. The resulting
      P-values were adjusted using the Benjamini and Hochberg’s approach for
      controlling the false discovery rate . Genes with an adjusted P-value
      &amp;lt;0.05 found by DESeq2 were assigned as differentially expressed.
      &lt;em&gt;(For edgeR without biological replicates) &lt;/em&gt;Prior to
      differential gene expression analysis, for each sequenced library, the
      read counts were adjusted by edgeR program package through one scaling
      normalized factor. Differential expression analysis of two conditions was
      performed using the edgeR R package (3.18.1). The P values were adjusted
      using the Benjamini &amp;amp; Hochberg method. Corrected P-value of 0.05
      and absolute foldchange of 2 were set as the threshold for significantly
      differential expression.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;GO and KEGG
      enrichment analysis of differentially expressed
      genes&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Gene Ontology (GO) enrichment
      analysis of differentially expressed genes was implemented by the
      clusterProfiler R package, in which gene length bias wascorrected. GO
      terms with corrected Pvalue less than 0.05 were considered significantly
      enriched by differential expressed genes. KEGG is a database resource for
      understanding high-level functions and utilities of the biological system,
      such as the cell, the organism and the ecosystem, from molecular-level
      information, especially large-scale molecular datasets generated by genome
      sequencing and other high-through put experimental technologies
      (http://www.genome.jp/kegg/). We used clusterProfiler R package to test
      the statistical enrichment of differential expression genes in KEGG
      pathways.&lt;/p&gt;
      &lt;p&gt;&lt;strong&gt;Metabolomics &lt;/strong&gt;&lt;/p&gt;
      &lt;p&gt;&lt;strong&gt;Sample Extraction Method&lt;/strong&gt;&lt;/p&gt;
      &lt;p&gt;After slowly thawing the sample at 4°C, an appropriate amount of
      the sample is taken and added to a pre-cooled methanol/acetonitrile/water
      solution (2:2:1, v/v). The mixture is vortexed for mixing, followed by
      low-temperature ultrasonic treatment for 30 minutes. The sample is then
      allowed to stand at -20°C for 10 minutes and centrifuged at 14,000 g at
      4°C for 20 minutes. The supernatant is collected and vacuum-dried. For
      mass spectrometry analysis, 100 μL of acetonitrile/water solution
      (acetonitrile:water = 1:1, v/v) is added to redissolve the dried sample,
      which is then vortexed. The sample is centrifuged again at 14,000 g at 4°C
      for 15 minutes, and the supernatant is taken for injection and
      analysis.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Chromatographic
      Conditions&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The samples were separated
      using an Agilent 1290 Infinity LC Ultra-High-Performance Liquid
      Chromatography (UHPLC) system with a HILIC column. The column temperature
      was maintained at 25°C, and the flow rate was set to 0.5 mL/min. The
      injection volume was 2 μL. The mobile phase composition was as follows: A
      = water + 25 mM ammonium acetate + 25 mM ammonia solution, and B =
      acetonitrile. The gradient elution program was as follows: 0 to 0.5 min,
      95% B; 0.5 to 7 min, a linear decrease from 95% B to 65% B; 7 to 8 min, a
      linear decrease from 65% B to 40% B; 8 to 9 min, B maintained at 40%; 9 to
      9.1 min, a linear increase from 40% B to 95% B; and 9.1 to 12 min, B
      maintained at 95%. During the entire analysis, the samples were kept in a
      4°C autosampler. To avoid the impact of fluctuations in instrument
      detection signals, the samples were analyzed consecutively in a random
      order. Quality control (QC) samples were inserted into the sample queue to
      monitor and evaluate the stability of the system and the reliability of
      the experimental data.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Q-TOF Mass
      Spectrometry Conditions&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The AB Triple
      TOF 6600 mass spectrometer was used to collect the first-order and
      second-order spectra of the samples.&lt;/p&gt; &lt;p&gt;The ESI source
      conditions after HILIC chromatographic separation were as follows: Ion
      Source Gas1 (Gas1): 60, Ion Source Gas2 (Gas2): 60, Curtain gas (CUR): 30,
      source temperature: 600°C, IonSpray Voltage Floating (ISVF): ±5500 V (both
      positive and negative modes); TOF MS scan m/z range: 60-1000 Da, product
      ion scan m/z range: 25-1000 Da, TOF MS scan accumulation time: 0.20
      s/spectrum, product ion scan accumulation time: 0.05 s/spectrum. The
      second-order mass spectrometry was acquired using information-dependent
      acquisition (IDA) in high sensitivity mode. The Declustering Potential
      (DP) was set to ±60 V (both positive and negative modes), and the
      Collision Energy was 35 ± 15 eV. The IDA settings were as follows:
      isotopes within 4 Da were excluded, and 10 candidate ions were monitored
      per cycle.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Data Analysis
      Workflow&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The raw data in Wiff format is
      converted to .mzXML format using ProteoWizard. Subsequently, XCMS software
      is utilized for peak alignment, retention time correction, and peak area
      extraction. The data obtained through XCMS extraction undergoes initial
      metabolite structure identification and data preprocessing. Following
      this, the quality of the experimental data is evaluated, and finally, the
      data analysis is conducted. The data analysis encompasses univariate
      statistical analysis, multivariate statistical analysis, differential
      metabolite screening, correlation analysis of differential metabolites,
      and KEGG pathway analysis.&lt;/p&gt;
    </description>
    <description descriptionType="TechnicalInfo">
      # Data from: TIPE drives a cancer stem-like phenotype by promoting
      glycolysis via PKM2/HIF-1α axis in melanoma
      [https://doi.org/10.5061/dryad.ghx3ffc05](https://doi.org/10.5061/dryad.ghx3ffc05) ## Description of the data and file structure Transcriptomics Data We conducted RNA sequencing analysis by overexpressing TIPE in G361 cells, which comprised three experimental groups and three control groups. Metabolomics Data We conducted a metabolomic analysis after interfering with TIPE in A375 cells, which consisted of six experimental groups and six control groups. ### Files and variables #### **File: Metabolomics\_Data.xlsx** **Description:**  ID: The identification number of the metabolite in mass spectrometry detection m/z: Mass-to-charge ratio rt (s): Retention time of the metabolite on the chromatography, also known as the peak emergence time, with the unit being seconds Name: The name of the metabolite Adduct: Information on the adduct ions of the compound Score: A scoring value ranging from 0 to 1. The higher the value, the higher the degree of matching and the more reliable the qualitative result HMDB: The HMDB number can be used to obtain detailed annotation information about the metabolite on the HMDB official website KEGG: The corresponding C number in KEGG. The C number can be used to query detailed information about the metabolite and the metabolic pathways it participates in on the KEGG official website Superclass: Parent class, representing the classification of the metabolite Class: Subclass, further classifying the metabolite Subclass: Sub-subclass, providing an even more detailed classification of the metabolite Sample: The relative content of each metabolite in the corresponding sample. In this test, control group including NC1, NC2, NC3, NC4, NC5, NC6, and the treatment group including KD1, KD2, KD3, KD4, KD5, KD6 The missing values are displayed as "blank cells" #### **File: Transcriptomics\_Data.xlsx** **Description:**  gene_id: A unique identifier for each gene. samples: The standardized readcount values for each sample. The control group including N1, N2, and N3. The treatment group(overexpression of TIPE) including V1, V2, and V3. log2FoldChange: The ratio of gene expression levels between the treatment group and the control group, followed by taking the logarithm to the base 2. pvalue: P-value of significance test padj: Adjusted p-value for multiple hypothesis testing gene_name: The name assigned to the gene. gene_chr: Chromosome name where the gene is located. gene_start: Start position of the gene on the chromosome. gene_end: End position of the gene on the chromosome. gene_strand: Positive or negative strand information of the chromosome where the gene is located. gene_length: The sum of all non-overlapping exon regions from the start to the end of the gene. gene_biotype: Gene type, such as protein-coding gene, long non-coding RNA, etc. gene_description: Functional description of the gene. gene_tf_family: Transcription factor family annotation of the gene. Used NA" to indicate missing values.
    </description>
  </descriptions>
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