Time-course transcriptomic dataset of gallic acid-induced human cervical carcinoma HeLa cell death

Time-course transcriptomic dataset of gallic acid-induced human cervical carcinoma HeLa cell death | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Time-course transcriptomic dataset of gallic acid-induced human cervical carcinoma HeLa cell death Ho Man Tang , View ORCID Profile Peter Chi Keung Cheung doi: https://doi.org/10.1101/2025.02.03.636357 Ho Man Tang 1 School of Life Sciences, Chinese University of Hong Kong , Shatin, Hong Kong SAR, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter Chi Keung Cheung 1 School of Life Sciences, Chinese University of Hong Kong , Shatin, Hong Kong SAR, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Peter Chi Keung Cheung For correspondence: petercheung{at}cuhk.edu.hk Abstract Full Text Info/History Metrics Preview PDF Abstract Gallic acid is a natural phenolic acid that displays potent anti-cancer activity in clinically relevant cell culture and rodent models. Although research has focused on determining the efficacy of gallic acid against various types of human cancer cells, the molecular mechanisms governing the anti-cancer properties of gallic acid remains largely unclear, and a transcriptomic study of gallic acid-induced cancer cell death has rarely been reported. Therefore, we performed time-course bulk RNA-sequencing study to elucidate the molecular signature of gallic acid-induced cell death in human cervical cancer HeLa cells, as this is a widely used in vitro model in the field. Our RNA-sequencing dataset covers the early (2 nd hour), middle (4 th , 6 th hour), and late (9 th hour) stages of the cell death process after exposure of HeLa cells to gallic acid, and the untreated (0 th hour) cells served as controls. Differential expression of messenger RNAs (mRNA) and long non-coding RNAs (lncRNA) were identified in each time point in the dataset. In summary, this dataset is a unique and valuable resource with which the scientific community can explore the molecular mechanisms and identify key regulators of the gallic acid-induced cancer cell death process. Background & Summary Gallic acid (GA), also known as 3,4,5-trihydroxybenzoic acid, is a natural bioactive substance that displays anti-cancer properties 1 - 4 . This low molecular weight phenolic compound (C7H6O5) is commonly found in edible fungi, fruits, nuts, vegetables, tea leaves, and herbs 5 , 6 . Accumulating evidence from cell culture and clinically relevant rodent models has demonstrated the anti-cancer bioactivity of gallic acid, and its derivatives, in various human cancer cell types 1 - 4 , such as adenocarcinoma 7 , bone cancer 8 , breast cancer 9 , cervical cancer 10 , colon cancer 11 , gastric cancer 12 , leukemia 13 , liver cancer 11 , lung cancer 14 , lymphoma 15 , ovarian cancer 16 , prostate cancer 17 , and skin cancer 18 . Importantly, gallic acid is well-tolerated by rodent models at the dosages that display anti-cancer effect 19 - 23 . Thus, both in vitro and in vivo studies reveal the pharmacological potential of developing gallic acid as a broad-spectrum anti-cancer agent for humans, one that is expected to be safely applied to treat cancers, with minimal side effects. Resisting cell death is the hallmark of cancer 24 , 25 . To overcome the obstacle in drug resistance, the ideal anti-cancer agents should therefore be able to simultaneously activate multiple pathways that trigger cancer cell death 26 , 27 . The three major targets of cancer therapy are a cell suicide pathway called apoptosis 28 , an iron-dependent form of non-apoptotic cell death called ferroptosis 29 , and a regulated form of necrotic cell death called necroptosis 30 . Interestingly, we have recently shown that gallic acid can trigger concurrent apoptosis, ferroptosis, and necroptosis in human cancer cell lines 31 , 32 . Our finding provides new mechanistic insights into the high potency of gallic acid in killing cancer cells reported by other investigators in a diversity of in vitro and in vivo models 1 - 4 . As apoptosis, ferroptosis, and necroptosis regulate cell death by distinctive mechanisms 33 - 35 , the cancer cells that resist one of those cell death mechanisms can be still killed by gallic acid because it activates all three of these cell death pathways. To unleash the potential of gallic acid in clinical application, it is important to elucidate the molecular mechanisms that govern gallic acid-induced cancer cell death. Therefore, we performed a time-course RNA-sequencing (RNA-Seq) to reveal the transcriptomic profile of the early (2 nd hour), middle (4 th , 6 th hour), and late (9 th hour) stages of gallic acid-induced cell death process 32 . We used human cervical cancer HeLa cells as the study model here, because this cell line is commonly used in studies of gallic acid and cancer biology 1 , 36 . We applied 50μg/mL of gallic acid to trigger HeLa cell death in this study, as this is the physiologically relevant dosage that is lower than the no-observed-adverse-effect level (NOAEL) in the rodent models used for studying gallic acid 19 - 21 , and this dosage displays anti-cancer activity to a wide range of cancer cell types in vivo and in vitro 1 - 4 . Of particular interest, our RNA-Seq dataset 32 covers both the well characterized protein-coding RNAs/ messenger RNAs (mRNA) 37 , and the long non-coding RNAs (lncRNA) 38 in which their functions are being emerged. Therefore, our dataset will serve as a unique and valuable resource for further studying the molecular mechanisms that regulate the anti-cancer activity of gallic acid, thereby facilitating the translation of this natural anti-cancer agent into clinical applications. Methods Cell culture The human cervical cancer HeLa cell line was obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA; catalog # CCL-2). This cell line was cultured in Dulbecco’s Modified Eagle Medium/Nutrient Mixture F-12 (DMEM/F-12; catalog # 10565042) supplemented with 10% heat inactivated fetal bovine serum (FBS; catalog # 16140071), 2mM GlutaMAX (catalog # 35050061), 100 U/mL penicillin and 100 μg/mL streptomycin (catalog # 15070063) purchased from Thermo Fisher Scientific (Waltham, MA, USA), at 37 degree Celsius (°C) under an atmosphere of 5% carbon dioxide (CO2). Before the start of the experiment, HeLa cells were plated onto a 100 mm Corning tissue culture dish (Corning, NY, USA; catalog # 430167) for 1 day to reach 80% cell confluency. Cell death induction by gallic acid Gallic acid (catalog # G7384) dissolved in dimethyl sulfoxide (DMSO; catalog # D2438) purchased from Sigma-Aldrich (Burlington, MA, USA) was to make a 20 mg/mL stock solution. This was then further diluted into the cell culture medium (described above) to achieve the working concentration of 50 μg/mL, before being applied to the cells to induce cell death. The stock solution was freshly made before each cell death induction experiment. Total RNA isolation Total RNA was isolated from the HeLa cells that were exposed to 50 μg/mL gallic acid for 0, 2, 4, 6, and 9 hours ( Fig. 1 ). At each of the timepoints, the cell culture medium was removed from the cell culture dish, and the cells were lysed on the dish using 2 mL of ice-cooled TRIzol by thorough pipetting. Total RNA was extracted using the Direct-zol™ RNA MiniPrep kit (Zymo Research, Irvine, CA, USA; catalog # R2070T) according to the manufacturer’s instructions, eluted in nuclease free water, quantified by a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA), and stored at -80°C. Download figure Open in new tab Figure 1. Schematic of the workflow for the time-course RNA-sequencing study. Human cervical cancer HeLa cells were treated with 50μg/mL gallic acid for 2 hours (GA2hr), 4 hours (GA4hr), 6 hours (GA6hr), and 9 hours (GA9hr) to induce the cell death process. Untreated cells served as control (GA0hr). Cell samples of three biological replicates from each condition mentioned above were collected for total RNA extraction, analysed using Illumina HiSeq 4000 for RNA-sequencing, and subjected to pre-alignment and post-alignment quality checks. The raw RNA-sequencing dataset of all 15 samples is publicly available at the Gene Expression Omnibus (GEO accession: GSE158788) 49 . RNA-sequencing The RNA-sequencing analysis was conducted at the Novogene Core Facility (Novogene Corporation Inc., Sacramento, CA, USA) using the standard protocols from Novogene and Illumina (San Diego, CA, USA) 32 . The RNA concentration was measured by using a NanoDrop spectrophotometer and Qubit RNA Assay Kit in a Qubit 2.0 fluorometer (Thermo Fisher Scientific; catalog # Q32852). The RNA purity was accessed using the NanoPhotometer spectrophotometer (IMPLEN, Westlake Village, CA, USA). The RNA integrity was determined using the RNA Nano 600 Assay Kit on the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Ribosomal RNA depletion of the samples was performed using Ribo-zero Magnetic Gold Kit (Illumina; catalog # 20037135). Sequencing libraries were generated using NEBNext ® Ultra™ II Directional RNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA; catalog # E7760L), and were then sequenced using Illumina HiSeq 4000 sequencer for 150 bp read length in paired-end mode (paired-end 150, 2×150). Corresponding raw RNA-sequencing data was generated by the sequencer as FASTQ files for 15 RNA samples ( Table 1 ) 32 . View this table: View inline View popup Table 1. Gene Expression Omnibus (GEO) accession number of individual samples in the time-course transcriptome dataset for gallic acid-induced HeLa cell death. GEO accession number of the dataset is GSE158788 49 with 15 samples 68 - 82 . Pre-alignment quality check of dataset The quality of raw RNA reads in all FASTQ files was evaluated using MultiQC analysis 39 though the Illumina’s DRAGEN (Dynamic Read Analysis for GENomics) platform (Code Availability 1-2). This evaluation includes per-sequence ( Fig. 2A ) and per-position ( Fig. 2B ) Phred quality score (Q Score) which indicates the accuracy of the raw reads generated by a sequencer 40 , 41 , the percentage of mapped reads per read group of each sample ( Fig. 2C ) which indicates the quality of the paired-end sequencing 37 , and the total number of sequencing reads obtained per sample ( Fig. 2D ) which reveals an overview of the read depth of the dataset 37 . Download figure Open in new tab Figure 2. Pre-alignment quality check of dataset. The quality of the raw RNA reads in all FASTQ files of 15 samples in the dataset GSE158788 was evaluated to determine (A) mean per-sequence Phred quality scores, (B) mean per-position Phred quality scores, (C) percentage of paired reads, and (D) total number of reads of each sample. Post-alignment quality check of dataset The post-alignment quality check of the RNA reads in all the FASTQ files was accessed using Partek Flow (Partek Inc., St. Louis, MO, USA) (Code Availability 3) with STAR 2.7.8a aligner package (Code Availability 4) to perform transcript alignment of each sample with the human reference genome hg38 42 . This quality check includes distribution of raw counts of all samples ( Fig. 3A ), coverage breakdown of the counts to reveal the percentage of the reads that fully or partly align with exons or introns ( Fig. 3B ), and sample box plot that illustrates the 10 th , 25 th , 50 th , 75 th , and 90 th percentiles of the counts to show the distribution of normalized gene expression level of each sample ( Fig. 3C ). Download figure Open in new tab Figure 3. Post-alignment quality check of dataset. The post-alignment quality check of RNA-sequencing data from all 15 samples in the dataset GSE158788 was evaluated to determine (A) frequency distribution of raw counts, (B) coverage breakdown of the counts, and (C) sample box plot that shows 10 th , 25 th , 50 th , 75 th , and 90 th percentiles of the counts after normalization of each sample. Differential gene expression analysis The characteristics of the dataset and the differential gene expression of coding RNA (messenger RNA, mRNA) and long non-coding RNA (lncRNA) were accessed using Partek Flow (Code Availability 3) with DESeq2 statistical package 43 (Code Availability 5). These analyses include the three-dimensional scatterplot of principal component analysis (3DPCA) 44 , 45 that illustrates the similarity or difference of each sample in the overall dataset with percentage listed on the planes of “X”, “Y”, and “Z” that refer to their corresponding contribution rate ( Fig. 4A ), the hierarchical dendrogram 40 , 46 - 48 that reveals the similarity of gene expression pattens between samples ( Fig.4B ), and differential gene expression of all transcripts ( Fig. 4C ), mRNA ( Fig. 4D ), and lncRNA ( Fig. 4E ) at the 2 nd hour ( i GA2hr vs GA0hr), 4 th hour ( ii GA4hr vs GA0hr), 6 th hour ( iii GA6hr vs GA0hr) and 9 th hour ( iv GA9hr vs GA0hr) of the gallic acid-induced HeLa cell death process in the dataset. Download figure Open in new tab Figure 4. Time-course transcriptomic profiling of gallic acid-induced HeLa cell death. (A) Three-dimensional scatterplot of principal component analysis (3DPCA) and (B) hierarchical dendrogram of all 15 samples in the dataset GSE158788, and (C-E) volcano plots to show the differential gene expression of (C) all transcripts, (D) mRNA, and (E) lncRNA of the 2 nd hour ( i GA2hr vs GA0hr), 4 th hour ( ii GA4hr vs GA0hr), 6 th hour ( iii GA6hr vs GA0hr) and 9 th hour ( iv GA9hr vs GA0hr) of the gallic acid-induced HeLa cell death process. Data Records All the raw FASTQ files of the dataset for Gene Expression Profile Analysis of Gallic Acid-induced Cell Death Process 32 was deposited to the Gene Expression Omnibus (GEO) database with the accession number GSE158788 49 . The dataset is available for public access through this link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158788 The GEO accession numbers of the raw FASTQ files for all individual samples are listed in Table 1 . Technical Validation We performed quality controls on all the 15 samples in our dataset ( Fig. 1 and Table 1 , with 5 timepoints, 3 replicates for each timepoint, covering GA0hr, GA2hr, GA4hr, GA6hr, and GA9hr) to evaluate the accuracy of raw RNA-sequencing reads and read depth ( Fig. 2 ), distribution and coverage breakdown of raw counts ( Fig. 3 ), and sample clustering and differential gene expression analyses ( Fig. 4 ) of the dataset 49 . Accuracy of raw RNA sequences We conducted a pre-alignment quality check of our dataset using MultiQC analysis ( Fig. 2 ). The Phred quality score (Q Score) is a standard method widely used to measure the base quality in RNA-sequencing 40 , 41 . In our dataset, the per-sequence Q Score ( Fig. 2A ) and per-position Q Score ( Fig. 2B ) of most raw reads were significantly higher than Q30 (correct base call by the sequencer is 99.9%, standard from Illumina) and is approaching to Q40 (99.99%). The result of the high correct base call is further validated by the high rate of mapped reads per read group analysis of the paired-end sequencing ( Fig. 2C ), which was higher than 90% for each of the 15 samples. Read depth, and coverage Our dataset contains at least 78.8 million reads per sample ( Fig. 2D ), which indicates a high read depth that is suitable for studying the molecular mechanisms of cellular process such as differential gene expression 50 . The post-alignment quality check was performed using Partek Flow analysis, which revealed the wide distribution of raw counts ( Fig. 3A ), and the even distribution of the reads that were aligned within exon (e.g., mature RNA) and intron (premature RNA) regions ( Fig. 3B ). Furthermore, the sample box plot illustrates the similar distribution of the counts of all samples in the dataset ( Fig. 3C ). In summary, these validation analyses indicate the high quality of the raw RNA-sequencing dataset in our study. Sample clustering To illustrate the overall characteristic of the dataset, we evaluated all 15 samples using the three-dimensional scatterplot of principal component analysis (3DPCA) to determine the similarity or difference of each sample in the overall dataset. Our data reveals that all samples from the same time points (biological replicates) closely cluster, while samples belonging to different time points are distinct from others ( Fig. 4A ) 32 . This validation analysis indicates that the gene expression profile of samples at the same time point is similar to each other, and is distinct from that at the different time points. This result is also supported by our hierarchical dendrogram ( Fig. 4B ) 32 which demonstrates that all the biological replicates are connected with the shortest branch at their corresponding time points, which indicates the highest similarity in their gene expression patterns. Differential gene expression To validate that our dataset 32 is suitable for differential gene expression analysis, we applied volcano plots to illustrate the differential expression of all transcripts ( Fig. 4C ) for all four individual time points (GA2hr, GA4hr, GA6hr, GA9hr) for comparison with the control (GA0hr). Our further validation reveals that the differential expression occurs in both mRNA ( Fig. 4D ) and lncRNA ( Fig. 4E ) at each time point. These analyses demonstrate that the gene expression profile at each stage of gallic acid-induced cell death process is distinct from the control cells that have not been exposed to the gallic acid, thereby indicating that our dataset is suitable for differential gene expression analysis to study the molecular signature of this cell death process. Usage Notes The present study demonstrates that we have generated a high-quality time-course RNA-sequencing dataset revealing the gene expression profile of gallic acid-induced HeLa cell death 32 . Gallic acid is a broad-spectrum anti-cancer compound 1 - 4 . By comparing the molecular signature of the present dataset generated with human cervical cancer HeLa cells and with those generated in the past and in the future for the cell death response of other human cancer cell types 19 - 23 to gallic acid, it will be able to reveal the molecular signature shared by different cancer models, and therefore identify the key regulators and therapeutic targets that mediate gallic acid-induced cancer cell death mechanisms. Multiple factors could impact the transcriptomic changes that should be taken into consideration for studying the molecular signature of gallic acid. For example, different cancer cell types may respond to gallic acid differently 1 - 4 . In our present studies, our RNA-Seq detected striking changes in transcription of genes at the early (2 nd hour), middle (4 th , 6 th hour), and late (9 th hour) stages of cell death in HeLa cells that were exposed to 50μg/mL of gallic acid, in which apoptosis, ferroptosis, and necroptosis were identified 32 . However, differential gene expression was detected in the colon cancer HCT116 cells treated with 50μM of gallic acid for extended time (12 th and 24 th hour), in which activation of apoptotic cell death pathways was identified 51 . Heterogeneity is the hallmark of cancer cells 25 , and could cause different cellular responses to gallic acid. Dimethyl sulfoxide (DMSO), a common solvent of gallic acid and numerous agents for biomedical use, could also impact on transcriptomic changes and may cause cytotoxicity. For example, treatment of mouse neuroblastoma N2a cells with 2% DMSO or HeLa cells with 5% DMSO at the final concentration in cell culture medium could affect RNA synthesis 52 (of note: 0.25% of DMSO was used in our present study). Besides, low concentrations of DMSO (2-4%) could induce cytotoxicity such as in rat retinal ganglion RGC-5 cells in vitro , and the high sensitivity of rat retinal cells to DMSO in vivo was reported 53 . Therefore, proper solvent control is essential for studying the cytotoxicity of gallic acid, and using other solvents for gallic acid such as ethanol could be considered. The molecular signature of gallic acid-induced cell death can be illustrated by determining the differential gene expression of the datasets using TopHat 54 , Cufflinks 55 , 56 , HISAT 57 , StringTie 58 , Ballgown 59 , 60 , and HTSeq 61 . Functional and pathway analyses can be performed using Kyoto Encyclopedia of Genes and Genomes (KEGG) 62 , 63 , Gene Ontology (GO) 64 , 65 , Ingenuity Pathway Analysis (IPA) 66 , and WebGestalt 67 . Future analyses of the transcriptomic changes in response gallic acid in different types of cancer cells will reveal the molecular mechanisms governing the anti-cancer property of gallic acid. Therefore, our dataset is a valuable resource with high reuse potential for future studies, and thus facilitate the translation of this natural anti-cancer compound into clinical cancer treatment. Code Availability The pre-alignment quality check, post-alignment quality check, cell clustering, and differential gene expression data analyses were performed using the following software and their default parameters. No custom code was used in the present study. 1) MultiQC, version 1.18: https://multiqc.info/ 2) Illumina’s DRAGEN, version 4.2: https://www.illumina.com/products/by-type/informatics-products/dragen-secondary-analysis.html 3) Partek® Flow®, version 10.0: https://www.partek.com/partek-flow/ 4) STAR aligner, version 2.7.8a: https://github.com/alexdobin/STAR 5) DESeq2, version 3.18: https://www.bioconductor.org/packages/release/bioc/html/DESeq2.html Author contributions H.M.T. and P.C.K.C. designed the research work, analysed the data, and wrote the manuscript. H.M.T. performed the experiments. Competing interests The authors declare no competing interests. Acknowledgements This study was supported by the Direct Grant (4053367) of the Chinese University of Hong Kong (CUHK) Research Committee to Dr. Peter Chi Keung Cheung. References 1. ↵ Subramanian , A.P. et al. Gallic acid: prospects and molecular mechanisms of its anticancer activity . Rsc Advances 5 , 35608 – 35621 ( 2015 ). 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