Genomic Profiling Filtering and Molecular Analysis of Colorectal Cancer (CRC) using Next Generation Sequencing (NGS): Identifying Somatic Mutations Biomarkers and Patterns for Precision Medicine

Genomic Profiling Filtering and Molecular Analysis of Colorectal Cancer (CRC) using Next Generation Sequencing (NGS): Identifying Somatic Mutations Biomarkers and Patterns for Precision Medicine | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genomic Profiling Filtering and Molecular Analysis of Colorectal Cancer (CRC) using Next Generation Sequencing (NGS): Identifying Somatic Mutations Biomarkers and Patterns for Precision Medicine Hafeez Abiola Afolabi, Salzihan Md Salleh, Zaidi Zakaria, Ewe Seng Ch’ng, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6249524/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Methods Illumina 52-gene-focus panel using NGS was used to detect crucial mutations associated with CRC using Formalin-Fixed Paraffin-Embedded (FFPE) tissue among 21 patients. FastQ data was generated and used to analyze somatic variants. Mutation prediction analysis for clinical consequence interpretation was done using In-Silico-Prediction model, and prognostic factors for CRC was done using logistic regression. Results Overall, 105 variants were detected involving 15 genes and 9 chromosomes. Demographics of successful librariescomprises 12(57.1%) males and 9(42.9%) females. The highest variables in the participants were 71% for “severe group” in comorbidity category; 38% for “one type of comorbidity” in number of comorbidities; 57.1% for “retired group” in employment status; 71.4% for “late stage” in tumour level; 66.7% for “stage-3” in TNM stages; 85.7% for “left side” in tumour location; and 76.2% for “moderately-differentiated” in tumour grading. The five most upregulated genes and chromosomes after filtering are [ALK:34.3%(35/105), FGFR4:18.1%(19/105), NRAS:12.3%(13/105), ERBB3:7.8%(8/105), and KRAS, KIT: 4.8%(5/105) apiece] and [4.3%(36/105) for chr2; 20.0%(21/105) for chr5; 17.1%(18/105) for chr1; 12.4%(13/105) for chr12, and 6.7%(7/105) for chr4]. From 105 variants, 21(20%) were “deleterious/probably-damaging” inferring pathologic effects, 10(10%) were “deleterious/probably-benign” meaning with tumourigenesis tendency, 57(54%) were “tolerated/Benign” implying less likelihood of being pathologic, and 17(16%) as “variant unspecified” meaning clinical consequences yet concluded. Lastly, tumour stages, tumour level, Duke staging and “Charlson-comorbid-severity” represent prognostic factors for CRC. Conclusion NGS sequencing provides comprehensive gene mutation profiling in CRC by identifying biomarkers mutation in CRC. Colorectal cancer (CRC) Next Generation Sequencing (NGS) Formalin-Fixed Paraffin-Embedded (FFPE). Gene mutation Figures Figure 1 Figure 2 1.0 Introduction Worldwide, colorectal cancer (CRC) or colon cancer is the third most prevailing cancer, representing 10% of all cancer diagnoses, and the second leading cause of cancer-associated mortality [ 1 ], [ 2 ]. CRC mostly affects advanced age group of ≥ 50 years old, with reported incidence cases of about 1.9 million in 2020 and about 2.0 million in 2022, and over 930,000 deaths in both years [ 3 ], [ 4 ]. The highest incidence is recorded in countries such as China, the United States, Japan, the Russian Federation, India, Germany, Brazil, Italy, France, and the United Kingdom [ 5 ]–[ 8 ]. The pathogenetic mechanism of CRC involves constellations of several genomic events involving a series of molecular subtypes of microsatellite instability (MIS) and chromosomal instability (CIN) [ 9 ]. The pathogenic contribution of gene mutation at CIN and MIS is acknowledged as the most crucial lead-way in the pathogenesis of cancer mechanisms, including CRC [ 9 ]. OMIC science through comprehensive gene mutation profiling and sequencing analysis, utilizes conventional molecular sequencing techniques to provide the genetic landscape for the CRC mechanism, by highlighting that only a few crucial genes ‘‘mountain mutated variants” are responsible for the cancer pathology effects in large tumour cases, imploring these mountain mutated genes as the most frequently detected variants in the cancer progress [ 10 ]. Recent advances in sequencing technology using high-throughput Next-generation sequencing (NGS) have facilitated the analysis of the entire genome in individual cancers and made the identification of novel genetic alterations possible [ 11 ]. Sequencing-wise, targeted sequencing using NGS technology is a promising tool in clinical application and medical research because NGS increases the coverage depth (compared to the whole exome approach) by decreasing the number of variants analyzed with a similar number of base pairs sequenced [ 12 ]. This ensures the production of reliable sequenced data with satisfactory sequencing depth and quality run in the targeted variants of interest. NGS provides data on the mutational profiling of CRC rapidly and cost-effectively [ 13 ], [ 14 ]. A noteworthy breakthrough is how NGS findings explained that distinct sections of similar tumours revealed different mutation profilings (spatial heterogeneity within tumours) [ 15 ], perhaps because primary tumours and their metastasized tumour could differ in their mutational archetype, thus, indicating sequential heterogeneity [ 16 ]. Identifying the harmony between primary cancers and the metastasized is important for selecting the best therapy options; Why? Because targeted therapies that target variants found in the primary tumour but absent in metastases will not yield an efficient outcome [ 17 ]. The present research established a targeted sequencing profiling using NGS, which includes 15 genes and generates 105 mutation data. This study aimed to identify crucial gene mutations that could serve as screening and prognosticating biomarkers in targeted sequencing profiling of colorectal cancer tissues for clinical application. 2.0 Materials and Method 2.1 Patient and tissue collection This research is a cross-sectional study that recruited patients with confirmed colorectal cancer diagnoses among the patients visiting the pathology unit of Hospital Pakar Universiti Sains Malaysia (HPUSM). The present study recruited 21 patients with the diagnosis of colorectal cancer between 2015 and 2024. Biopsies and resection of formalin-fixed paraffin-embedded tissues were obtained and examined for histopathology examination from primary CRC tissues from the FFPE archives at the Pathology unit of Hospital-USM. Patients were deemed eligible for selection if complete medical data, archived FFPE bloc, treatment outcomes, and histopathology reports were available. Pertinent sample details were retrieved regardless of tumour stage, tumour grade, location or the patient’s age and sex. Following TNM guidelines and histopathological criteria, tissue samples were evaluated and/or diagnosed as sporadic cancerous or adenocarcinoma lesions in the colon by a consultant pathologist. The present study was performed adhering to the Declaration of Helsinki guidelines and approved by the Ethics Committee of the “Ethical Committee of Research” Universiti Sains Malaysia [ethics approval number: USM/JEPeM/21010076]. Informed consents were obtained from eligible participants with the option to opt-out at will. 2.2 DNA extraction Genomic DNA (gDNA) was isolated from 8–10µm-thickness of 4 sessions of macroscopically dissected FFPE CRC tumour tissues. A haematoxylin and eosin-stained slide (H&E) was obtained and employed to estimate the patent for regions with high tumour cellular stains of the conforming slices by two pathologists (SMS and NMM) using a double-headed microscope, thus, ensuring a high tumouricity percentage ranging from 50–90%. To avoid drawbacks from the use of archived FFPE tissues, freshly cut sections from most recent years' archival and quality extraction kits with established good DNA quality and quantity yield from FFPE samples were used. DNA was isolated using the QIAamp DNA FFPE Advanced UNG kit (cat. No 56604, Germany), following the manufacturer’s protocol. The quantity check (concentration) of the extracted DNA was checked using a Qubit 2.0 Fluorometer machine while the quality (purity-check) was done using a Tecan Spectrophotometer NanoQuant Machine, wherein, absorbance ratio of A260/280 and A260/230 between 1.8-2.0 and 2.0 -2.2 respectively are deemed acceptable purity. Also, further quality check of the extracted DNA for desirable quality suitable for library preparation was assessed by deciding the amount of amplifiable gDNA using Infinium HD FFPE QC Assay PCR (Illumina Inc. USA) according to the MIQE guidelines for Real-Time PCR experiments, the threshold cycle (Ct) also called the quantification cycle (Cq) represents the sample quantification value, and all samples with Mean Δ Cq-value (Δ Cq value) below 5 are selected as desirable for use for library preparation. All DNA samples were stored at -20°C (for short-term storage) and − 80 O C 9for long-term storage). 2.3 Library preparation and Next-generation sequencing. For library preparation and pooling, AmpliSeq for Illumina Focus Panel was employed following the AmpliSeq™ for Illumina workflow. 20ng of gDNA was used per library for the pooling. Successful library amplification and library quality of FFPE samples was checked by measuring the concentration of libraries with desirable size (~ 310bp) and short DNA fragments (< 150bp) using the LabChip GX Touch/GXII Touch (PerkinElmer, USA). Libraries with peaks (size) between 250-450bp were considered desirable for sequencing runs on the Illumina MiniSeq machine. After successful sequencing run, fastQ data is generated for biostatistics analysis for variant identification. Analysing NGS data, including demultiplexing, alignment and variant calling for identification of somatic gene mutations, sequenced reads were aligned to the reference sequence in the human reference genome GRCh37 (hg19) with the DNA Amplicon app version 2.1.0 using the BurrowsWheeler Aligner software [ 18 ], [ 19 ]. Subsequently, data were analyzed on the Illumina BaseSpace Annotation software version 1.6.2.0 to identify crucial somatic variants such as synonymous and indels (missense, nonsense, frameshift, in-frame coding indels, and splice sites), while Illumina Variant-Interpreter version 2.6.1.239 (Illumina, https://variantinterpreter.informatics.illumina.com ) were used to simplify and accelerate the analysis and interpretation of genetic variants generated from the NGS run, by allowing visualization and annotating the detected variants with details from online databases of dbSNP, ClinVar, gnomAD, and COSMIC and revealing crucial mutations that can serve as cancer biomarkers. Only sequenced runs with > 100,000 reads and variants with a quality score (phred score) ≥ Q30 are considered as quality runs that were chosen for further analysis. This sequencing run is considered a high-quality run as the required conditions or criteria are met, and they include 1] Phred Quality Score (Q score) or read quality which is the metric that shows the correctness of base calling in sequencing. An acceptable high-quality run with a Q-score of ≥ 30, indicates a 99.9% accuracy for base calls while lower Q-scores reflect more sequencing errors, 2] Depth of coverage tells the number of times a specific region of the genome is sequenced, a depth of 30x is considered standard, 3] Breath of coverage indicates the percentage of the target region that is sequenced at the desired depth, sequence coverage of greater than 95% of the target region is considered high-quality coverage, 4] Read length in the range of 150 bp or 300 bp paired-end is considered satisfactory, however, the degree of the high-quality reads must be high throughout the whole length, 5] Adapter and contaminant should be removed or minimized during quality control steps to make sure the data obtained for analysis are clean. The adapter sequence can be seen during the trimming to remove low-quality bases. In our study’s NGS data, all these quality metrics are met as shown in the amplitude table, by ensuring the set criteria are met, bias and poor runs are removed or prevented. 2.4 Statistical analysis All statistical analyses in the present study were done using Statistical Package for the Social Sciences SPSS tool (IBM SPSS Inc., USA, Version 23.0). Cleansing of data was done to ensure no missing data, and filtering was applied to select quality runs. For correlation analysis, the contingency coefficient was calculated and the strength of the association between the variants and chromosome specificity was examined. Also, a logistic regression plot was used to predict the gene mutation-clinicopathology factor association by analyzing the functional relationship between the pathogenic gene as the dependent variable and clinicopathological factors as independent variables, thus, providing an insight into which of the clinicopathological factors are highly predictive of the genetic alterations in our colorectal cancer patients. 3.0 Results Overall, out of the 30 colorectal cancer (CRC) FFPE samples initially recruited for the present research, 21 samples completed the run [19 CRC and 2 non-tumours], the excluded nine samples were due to failed run and/or poor-quality run. The demographic distribution of the successful libraries comprises 12(57.1%) males and 9(42.9%) females with mean age (60.2 + 10). The group of “ > 65 years old” were the most at 57% with a M: F ratio of 9(75%) and 3(33.3%) respectively. Similarly, the variables with the highest values were revealed at 71% for the participants in the “severe group” in the comorbidity category, at 38% for the participants in the group with “one type of comorbidity” in the “number of comorbid” reported for the participants, and lastly, at 57.1% for the “retired group” in “employment status” (Table 1). On clinicopathology distribution, the respective highest data was as follows: at 71.4% vs 28.6% for “late stage” and “early stage" in tumour stage level; at 19% vs 66.7% vs 14.3% patients for “stage-2” and “stage-3”, and “stage-4” in TNM tumour stages respectively; at 85.7% vs 14.3% for the “left side and “right side” in tumour location; and at 76.2% vs 23.8% for “moderately-differentiated” and “well-differentiated” in tumour grading respectively (Table 1). Furthermore, using “Duke Stages” the highest was recorded at 81% for “Duke C” with M: F ratio of 83.3% and 77.8% patients respectively compared to 14% and 4.8% for “Duke B” and “Duke D” respectively (Table 1). Following the filter criteria application to identify variants specific to CRC, from the 21 samples that completed the NGS run, 105 variants (involving 15 genes and 9 different chromosomes) passed the filtering from initially more than 500 variants (Tables 2, 3 & 4, Figure 1). Amongst the 105 variants, the five most upregulated genes in each patient are ALK : 34.3% (35/105), FGFR4: 18.1% (19/105) , NRAS : 12.3% (13/105), ERBB3: 7.8% (8/105), and KRAS, KIT : 4.8%(5/105) apiece. The least regulated genes were ESR1 and IDH1: 1% (1/105) apiece, and APC, FGFR1, FGFR3, MYC, & ERBB2 : 1.9% (2/105) apiece (Table 2) Overall, 9 chromosomes were associated with the 105 variants detected by the NGS. The most commonly involved chromosomes with the associated detected gene mutations revealed chromosomes: chr1, chr2, chr4, chr5 , and chr13 as the most commonly occurring chromosomes involved in CRC gene aberration (Table 3). The most upregulated chromosomes associated with the CRC mutations were expressed at; 34.3% (36/105) for chr2 , 20.0% (21/105) for chr5 , 17.1% (18/105) for chr1 , 12.4% (13/105) for chr12 , and 6.7% (7/105) for chr4 . The two least involved chromosomes were 1.0% (1/105) for chr6 , and 1.9% (2/105) for chr17 (Table 3) Analyzing the CRC genetic changes and the associated patterns involves investigating all the mutations detected from the NGS results with chromosomes specific to each mutated gene. From 21 samples, 15 genes from 105 variants were detected involving 9 chromosomes (Table 4). The mutated genes exhibited a strong and precise chromosome-gene mutation specificity pattern at above 95% specificity in 67%, and 40 - < 60% specificity for 20% of the participants (Figure 1), thus, indicating that certain variants can only be identified on specific chromosomes such as monospecific chromosomes for chr3 and PIK3CA gene, chr6 for ESR1 gene, and chr17 for ERBB2 (all >95% specific pattern) (Figure 1). Normalized heatmap representation showed the most upregulated genes between pink and green colours for ALK, ERBB3, FGFR4, KIT, KRAS, NRAS, and DDR2 (Figure 1). Also, among the 21 samples, 2 samples were normal tissues (non-CRC): samples P6 and P9. These latter two samples have only one similar gene ( FGFR3 ) detected in both samples (Figure 1). The cluster graphical view illustrates the chromosomal pattern of the most upregulated chromosome involvement in each patient, and this is depicted by the heatmap colouration range. Chr2 is the most upregulated as having the highest amount of mutations detected at about 70 - >95% rate in most of the patients (Figure 1). Also, the period of sample selection indicates a better and higher number of variant detections for the most recent year “2021” when compared to other years (Figure 1). Also, notable patterns were identified for certain chromosome specificity for more than one type of variants but still with a peculiar and definite high specificity for a major gene type, and they include Chr12 which harbours two genes namely ERBB3 but with >95% associative specificity and about 65-70% for KRAS genes, Chr8: harbours 2 genes namely FGFR1 and MYC (>95% specific), ch5 harbours FGFR4 (>95% specific) and APC (about 20-25% specificity), and lastly chr1 which harbours NRAS (>95% specific) and DDR2 (40-<60% specific) (Figure 1). To determine whether the pattern of profiling for the involved chromosomes and the genes that accounted for the mutations were associated with one another, a correlation matrix analysis was carried out to determine how each of the genes correlated with the specific chromosomes involved in the detection of the genes in the CRC cases, or whether the chromosome-gene specificity is statistically associated with CRC. Several significant correlations were observed. The results revealed that specific chromosomes have a strong positive statistical correlation with certain genes. For example, chr1 was positively correlated with the DDR2 gene (r = 0.71, p < 0.01) and NRAS gene (r = 0.81, p < 0.001). Chr2 was positively correlated with the ALK gene (r = 0.84, p < 0.001), just as chr3 was also positively correlated with the PIK3CA gene (r = 0.85, p < 0.001). Chr8 was positively correlated with FGFR1 and MYC gene at r = 0.68 and 0.71, p < 0.001 respectively. Additionally, chr4 was positively correlated with KIT gene (r = 0.84, p < 0.01), chr5 was positively correlated with the APC and FGFR1 gene (r = 0.57 and 0.43, p < 0.01) respectively, chr6 was positively correlated with the ESR1 gene (r = 0.86, p < 0.01), chr12 was positively correlated with the ERBB3 gene (r = 0.87, p < 0.01) and KRAS gene (r = 0.78) p < 0.001 for both. Lastly, chr17 was positively correlated with the EBRR2 gene with r = 0.89 and p < 0.01 (Figure 2). The remaining correlations in the matrix showed no significant relationships, implying that the detection of the genes on certain chromosomes might be due to chance, which in this case could be metastasis. Thus, the correlation findings revealed that there is a strong positive correlation of the detection of particular variants on specific chromosomes associated with specific genes in CRC cases (Figure 2). The “In Silico Prediction tool” was used to carry out mutation prediction analysis by reviewing the pattern of the substituted bases and nucleotides to infer, if the mutation patterns portray any tissue-damaging information for CRC. From 105 variants, 21 variants (20%) were identified as “deleterious/probably damaging” inferring pathologic effects. Another 10 variants (10%) were identified as “deleterious/probably benign” meaning with a tendency for tumourigenesis. Also, some 57 variants (54%) were identified as “tolerated/Benign” implying less likelihood of being pathologic. Lastly, there were about 17 variants (16%) that were identified as “variant unspecified” meaning clinical consequences yet concluded (Table 5). A total of 15 genes accounted for the 105 variants; eight genes accounted for the 21 (20%) pathologic variants (A LK, ERBB2, APC , ESR1, FGFR1, KRAS, PIK3CA & NRAS) genes. Furthermore, three genes ( IDH1, KRAS & NRAS) accounted for the 10 (9.5%) pathologic benign cases. Another five genes ( ALK, DDR2, FGFR4, MYC & ERBB3) accounted for the 57 (54.3%) tolerated benign cases. Lastly, four genes ( KIT, FGFR3, FGFR4 & KIT accounted for the 17 (16%) variant unspecified (VUS) cases. Further analysis was performed to examine the mutation clinical consequences by examining the distribution of base substitutions across the data to identify mutation-specific effects on CRC, especially as the In-Silico prediction tool prevents the misinterpretation of variants by identifying how variants are designated as pathologic. At differing locations, there is one “C>A”, one “A>T”, three “G>A”, and two “C>T” nucleotide changes in the eight pathologic genes which leads to a predictive consequence change in the amino acid at the protein level: C >A at position c.3592 in ALK results in leucine changes to Isoleucine, “C >T” at c.2632 in ERBB2 results in Histidine changes to Tyrosine, and at c.748 in FGFR1 results in Arginine changes to Tryptophan. “G >A” at position c.1636 in ESR1 results in Alanine changes to Threonine, at position c.34 in KRAS results in Glycine changes to Serine, and at position c.1633 and c.1637 in PIK3CA results in Glutamic acid change to Lysine and Glutamine change to Arginine respectively. A>G at position c.8 and c.182 in NRAS result in Glutamic acid change to Glycine and Glutamine to Leucine respectively. A deletion at c.4240 in APC results in a Valine change to a Termination consequence. Thus, this amino acid change causes an alteration of the original function of the substituted amino acid, hence, a loss of functional cellular process (Table 5). Table 6 below depicts the analysis of the prognostic factors performed using simple and multiple Cox regression analyses by using the log-rank test to investigate the factors that influence prognosis in CRC. The result reveals that the final model of the multiple logistic regression analysis retained only tumour stages, tumour level, Duke staging and Charlson comorbid severity (CCS). Those with stage 2 were 76% less likely to have a pathologic gene compared to those with stage 4 (AOR = 0.24, P = 0.157) and those with stage 3 were 60% less likely to have a pathologic gene compared to those with stage 4 (AOR = 0.40, P = 0.013). Those in the early stage were 64% less likely to have a pathologic gene compared to those in the late stage (AOR = 0.36, P = 0.037). Those with Duke B were 68% less likely to have a pathologic gene compared to those with Duke D (AOR = 0.32, P = 0.461), and those with Duke C were 51% less likely to have a pathologic gene compared to those with Duke D (AOR = 0.49, P = 0.042). For CCS, the mild were 80% less likely to have a pathologic gene compared to the severe (AOR = 0.20, P = 0.686), and the moderate were 45% less likely to have a pathologic gene compared to the severe (AOR = 0.55, P = 0.015). 4.0 Discussion Colorectal cancer (CRC) is an intestinal tract tumour and the second most lethal cancer globally [ 20 ], [ 21 ]. CRC arises from the gradual transformation of precancerous polyps to cancerous cells over years then metastasized via blood or lymphatic drainage to various body regions and organs [ 22 ]. In the present study, the overall median age was 53.2 ± 5.2 years old, with a higher proportion of male CRC patients compared to the female counterpart at 60% vs 40% for M: F ratio. Thus, demonstrating a slight male predominance [ 23 ]. This higher male predominance in CRC is as reported globally [ 24 ], [ 25 ]. This latter finding is also reported in W.H.O. reports, that men are 1.4 times more likely to be diagnosed with CRC than women with M: F incidence case ratio at 1.6 million vs 0.83 million [ 2 ]. CRC is regarded as a cancer in late age or late adulthood due to its slow and progressive transformation from precancerous polyps cells to cancerous cells [ 26 ]. In the present study, most CRC cases were identified among the group aged “ ≥ 65 years” at 57.1%, this is regarded as the adult stage [ 27 ], and this rate align with the increasing trend in the adult population, as more studies showed that 50-60-year-old and above individuals have high records of CRC incident rates [ 20 ]. Anatomically, CRCs can arise from three different regions of the colon, namely, the proximal, distal and rectum, and these regions differ in the biological and molecular response in the carcinogenesis mechanism or pathway. In the present study, most of the patients have left-sided tumours at 85.7% compared to 14.3% for the right colon. CRC in the right colon often shows nonspecific symptoms like anaemia, fatigue, and weight loss resulting from being wider, thus allowing extensive growth of the tumour before presenting with symptoms of obstruction [ 28 ]. On the other hand, because the left colon is narrower, CRC presentations are associated with early bowel habit changes such as constipation, diarrhoea, bleeding per rectum, or intestinal obstruction [ 29 ]. This is as reported in previous data [ 30 ]–[ 32 ]. Furthermore, from the clinicopathology characteristics, stage-3 has the highest number of participants with 14/21 (66.7%) patients, while stages-2 and stage-4 recorded 19.0% and 14.3% respectively. This is a similar trend when the patients are categorized using Duke’s staging method. Duke-C recorded the highest number of patients at 17/21 (81.0%), followed by Duke-B at 14.3%. Like in the TNM staging, Duke-D (the equivalent of TNM stage-4) has the lowest number of patients at 4.8%. According to related articles on tumour stages, both TNM and Duke staging methods for stage-3 and Duke-C respectively represent a major but localized spread, with lymph node involvement as the main clue that denotes both as advanced [ 33 ], [ 34 ]. On habitual status, smoking habit has long been described as a main risk factor for tumourigenesis and its progress [ 35 ], and males are reported as the foremostly involved gender [ 36 ]. Although in our study’s outcome, patients with a history of ‘never smoke” habits were the most and this could be due to the religious and cultural norms in this peninsular [ 37 ], however, the “smoking habit” category comprised only the male gender at 42.9% (9/21). This figure is similar to reports from existing studies conducted among the Western population [ 38 ] and the Asian populace [ 39 ]. Colorectal cancer (CRC) originates from the summative impacts of genetic-epigenetic changes, for which the targeted treatment triumph depends on the identification of crucial targets and predictive biogenetic markers or mutations [ 40 ]. Among the 21 successful runs, mutation type was 97% missense type, the rest are frameshift, splice region introns, and introns. In the present study, next-generation sequencing was performed using Illumina AmpliSeq 52-cancer gene panel on CRC FFPE tissues. Overall, 105 mutations were detected involving 15 genes. The 5 most upregulated variants include ALK: 35/105(34.3%), FGFR4: 19/105(18.1%), NRAS: 13/105(12.3%), ERBB3: 8/105(7.6%), and KIT, DDR2, & KRAS: 5/105 (4.8%) apiece [Table 1 ]. This was similarly reported in some published articles [ 41 ]–[ 45 ]. Because CRC is a heterogeneous disease associated with several aberrated genes, the profiling of gene mutation provides vital data on the pathogenesis of CRC and potential novel biomarkers for targeted therapy by identifying mutated genes [ 46 ]. Similar findings in Brazil reported APC, TP53, KRAS, PIK3CA, FBXW7, BRAF, PTEN, NRAS, FGFR4, NF1, and ERBB as the most upregulated variants among 20 genes in their study [ 47 ]. The chromosomal and gene alteration in CRC is the main mechanism in tumorigenesis that uncovers the intricate patterns of genetic instability [ 48 ]. While CIN results in extensive chromosomal changes, involving loss of tumour suppressor genes like APC, SMAD4 and TP53 [ 49 ], [ 50 ], and the amplification of oncogenes such as KRAS, NRAS, and MYC [ 44 ], [ 45 ]. In the present study, a total of 9 chromosomes were associated with the 105 variants detected by the NGS technique, amon these, chromosomes: chr1, chr2, chr4, chr5, and chr12 are the most involved, harbouring 18/105, 36/105, 7/105, 21/105, and 13/105 variants respectively (Table 3 ). While the profound genomic mechanisms for CRC are complex and still undergoing research, some studies have uncovered certain genes located on specific chromosomes that can contribute to CRC development. However, the specific aberrations in these genes can differ in CRC patients [ 51 ], [ 52 ]. The present study identified gene-chromosome-specific patterns wherein, multi-specificity was noted on some chromosomes such as the detection of more than one gene on a specific chromosome i.e., KIT and FGFR3 on chromosome 4, similar to some published data [ 53 ]–[ 55 ]. This similar pattern was shown in some studies indicating the 5 most involved patterns for the gene-chromosome association in CRC are APC on chr5 [ 56 ], [ 57 ], KRAS on chr12 [ 58 ], [ 59 ], FGFR4 on chr5 [ 54 ], and NRAS on chr1 [ 60 ], [ 61 ]. Correlation of specific and unique mutations with CRC requires ensuring precise clinical practice and personalized therapy, because it is paramount to identify underlying mutations and specific patterns that are unique to particular diseases, either exclusively or in combined form, because comprehending these aberrations will explain the genetic-epigenetic properties of such diseases. In the present study, the Pearson correlation coefficient (r) test between the mutated genes and their related chromosomes among colorectal cancer (CRC) patients was carried out to assess the association between the detected gene mutations and the particular chromosomes involved in CRC propagation. From the results, a strong positive correlation was observed between the APC gene and chromosome 5 indicating the alteration on chromosome 5 is statistically significantly associated with the APC mutations. This implies that the loss of chromosomal materials in this region is likely associated with mutations or dysregulation of the APC gene, p < 0.001. Furthermore, a similar strong correlation was reported for ALK mutation specifically on chromosome 2, KRAS mutation on chromosome 12, DDR2 mutation on chromosome 1, NRAS mutation on chromosome 1, and PIK3CA mutation on chromosome 3, all these associations are statistically significant p < 0.001 respectively, indicating alteration of the chromosomal region is linked with these specific gene mutations. The Pearson correlation coefficient (r) near + 1 in our study implies a direct correlation between mutated genes and chromosomal changes observed, thus emphasizing the significance of alteration of chromosomal region in tumorigenesis, including CRC. This is as reported in several studies [ 36 ], [ 62 ]–[ 64 ]. Also, it is worth mentioning that weak or no correlation (r near 0) was observed between some genes and associated chromosomes implying that the chromosomal aberrations in that region may not consistently result from the gene mutations or dysfunction. An example is the relationship between FGFR3 and chromosome 4 that revealed no correlation (r approx. < 0.2), plus, the association is weak and not significant, indicating that the changes in the chromosome and gene mutations are not majorly linked with CRC progression. Outlining the underlying genetic and epigenetic elements in a specific tumour including CRC is effective in pronouncing a definitive diagnosis, and also helps in targeted management for the patient [ 65 ]–[ 67 ]. In our study, insight and categorization of detected variants were investigated for organ-variant specificity in CRC patients by applying the In-Silico prediction tool. From a total of 105 variants, 30.0% (31 variants) are categorized as Deleterious/Probably damaging, clinically implying pathogenic. Various proteins have differing roles and functions, meaning that the clinical impact of the variants depends on the protein's function [ 67 ], [ 68 ]. Different changes in specific nucleotide variants influenced the amino acid sequence and impacted the protein function such as seen in the ALK variant, where leucine was changed into isoleucine due to nucleotide change in the codon from “C to A” at position c.3592. The clinical consequences of leucine changing to isoleucine can vary in sequencing, especially if the change occurs at a certain location or sites such as active sites or binding sites, in some cases at the structural motifs which can alter the protein function because of change in the protein's shape [ 69 ], stability, or ability to interact with other molecules [ 70 ]. Mutation in APC showed an amino acid change from histidine to termination, otherwise referred to as nonsense mutation wherein the mutation introduces a premature stop codon. The genomic implication is the development of a truncated, non-functional APC protein, which primarily results in the loss of the tumour suppressor gene functions to regulate cell growth and retain genomic stability [ 71 ]. Thence, there is a loss of the role of the APC gene due to mutation that results in dysregulated cell proliferation, an early and important stage in the growth of CRC [ 71 ], [ 72 ]. Another dysregulation occur in PIK3CA gene mutation with glutamine changing into lysine, this latter substitution is associated with worsened oncogenic signalling that transforms into an increased aggressive tumour action that negatively affects therapy decisions [ 73 ], [ 74 ]. Also in our study, a logistic regression plot was used to predict the gene mutation-clinicopathology factor association, the clinicopathology factors associated with genetic mutation in CRC that were found to be significant on univariate analysis includes tumour stage (P = 0.044), tumour level (P = 0.05), duke stages (P = 0.039), and comorbid status (P = 0.040). This implies that based on the univariate analysis outcome, tumour stage, tumour level, duke stages, and comorbid status were the important prognostic predicting factors in the pathogenicity of CRC in our patients. A significant finding in the univariate and multivariate analysis showed that CRC patients in the late stage (tumour level), stage 4, Duke D, and in poor comorbid state are strongly associated with a higher likelihood of harbouring more pathogenic mutations. Here, patients in the early stage are 64% less likely to have a pathologic gene compared to patients in the late stage, likewise, patients in stage 2 are 76% less likely to have a pathologic gene compared to those with stage 4, and patients in stage 3 are 60% less likely to have a pathologic gene compared to those with stage 4. This is narrated in some studies to significantly impact such patient treatment and prognosis especially if at the late stage [ 75 ], [ 76 ]. These findings were similarly reported in several published articles [ 77 ], [ 78 ]. Through the regression findings, the outcome of our study’s multivariate logistic regression can serve as a guide for personalised focus treatment plans, and the selection of patients at high risk of CRC based on the utilisation of both the clinicopathology and genomic factors. These crucial clinicopathological factors are reported as CRC-predicting factors [ 46 ], [ 47 ], [ 71 ]. Conclusion, Recommendation and Limitation. NGS has revolutionized CRC research and diagnostics by providing data on the mutational profiling of CRC to improve the understanding of tumour biology, and this is the first NGS molecular study on CRC gene profiling in our institution. Our sample size comprises 40% female and 60% male with “ ≥ 65 years old” more at 57.1% at a mean age of 58.2 ± 10. Overall, prevailing factors such as tumour level, tumour stages, Duke-C, moderately differentiated grades, and poor comorbidity status are pertinent risk factors for CRC propagation. Upon applying the In-Silico prediction model to our study’s variants, 9 genes were identified as pathogenic genes that are significantly associated with CRC pathogenesis. These genes include APC, NRAS, ALK, ERBB2, PIK3CA, KRAS, ESR1, IDH1, FGFR1. Lastly, a statistically significant association was noted between the mutated genes and the specific chromosome on which the mutation is detected. As the first molecular study on gene mutation profiling of colorectal cancer using NGS in our institution, and based on our overall experience in the course of this research, a broader and comprehensive mutation detection that detects a wider range of genetic aberrations is recommended for a broader conclusive comparison and profiling result. Also, we recommend the integration of gene mutation data with artificial intelligence algorithms (machine learning models) to be used in clinical settings to boost predictions of cancer outcomes or therapeutic responses. As the first study on a new NGS device on gene mutation profiling in CRC patients in our facility, the first challenge or limitation encountered was obtaining a quality DNA product from the FFPE samples, which eventually led to more cost of purchasing more extraction kits. Also, the smaller sample size does not permit extensive profiling of the tumour research for broad result comparison with existing results. Another setback is technical failures from machine breakdown during the procedure, leading to delays in loading libraries into the sequencer. Lastly, the lack of advanced bioinformatics tools and specialized bioinformatics expertise poses a severe limitation on NGS application at diagnostic and research levels. This is a major reason NGS use is not considered practicable for routine diagnostics, because of the complexity and cost of running when compared to other simpler and faster methods like the Sanger machine. Declarations Author Contributions : Conceptualization: H.A.A and S.M.S, methodology: all authors, Software: H.A.A, A.A.I and S.B.A.; Validation: E.S.C, A.A.B.A.A. and S.N.M.N, Formal analysis: H.A.A, H.E.A, & J.B.S.; Investigation: J.B.S, S.B.A, and Y.W; Resources: Z.Z, S.M.S, and H.A.A, Data curation: W.M.N.W.Z, H.A.A, and A.A.I, Writing-original draft preparation: J.B.S, A.H, Y.W and S.M.S, Writing-review and editing: H.E.A, S.M.S, S.N.M.N. and Z.Z.; Visualization: W.M.N.W.Z and Z.Z, Supervision: E.S.C, S.M.S, A.A.B.A.A, and S.N.M.N, Project administration: S.M.S. and Z.Z, funding acquisition: S.M.S. All authors have read and agreed to the published version of the manuscript. Funding : The funders had no role in the procedure of the research, data collation, result analysis and interpretation, study draft and write-up, or in the publication decision of the findings. The present work is funded by the Research Universiti RU Top-Down grant Universiti Sains Malaysia, and the School of Medical Sciences, Hospital Pakar Universiti Sains Malaysia support. Approval grant number: 1001/PPSP/8070013. Acknowledgements: The authors appreciate the support of the School of Medical Sciences PPSP, Hospital Pakar Universiti Sains Malaysia, Universiti Sains Malaysia (USM) during this project. This research was supported by the USM Fellowship Scheme and the Malaysia RU Top-grant research scholarship. Consent to Participate Declarations: Not applicable Ethical statement: The research was approved by the Ethics Committee Universiti Sains Malaysia USM Human Research [Jawatankuasa Etika Penyelidikan Manusia USM (JEPeM)] following the Hospital USM and Universiti Sains Malaysia research protocol guidelines and regulations with ethical approval code (USM/JEPem/21010076). Clinical Trial Number: Not applicable Informed Consent Statement : Informed consent was not applicable and the Universiti Sains Malaysia (USM) Ethical Committee approved the study. The research ethical approval number is USM/JEPeM/21010076. Consent to Publish declaration: Not applicable Institutional Review Board Statement : Not applicable. Data Availability Statement : The data for the present study are the properties of the institution of the Universiti Sains Malaysia which can be accessed upon request or research collaboration request to the corresponding author through the institution. However, all pertinent information deemed important for the present study has been included in the manuscript text. Conflicts of Interest : All the authors declare no conflict of interest, and the funders have no role in the design of the study. References Kim S. World Health Organization quality of life (WHOQOL) assessment, in Encyclopedia of quality of life and well-being research . 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Supplementary Files Table16.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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2","display":"","copyAsset":false,"role":"figure","size":139753,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap analysis representation of variants distribution.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6249524/v1/1687cad2afebc2c677d310a9.png"},{"id":85382781,"identity":"c08840b1-4efe-42b3-88e2-275ade046d79","added_by":"auto","created_at":"2025-06-25 09:32:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":921840,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6249524/v1/ac1da92c-858c-48f3-a560-9f46226cfaaf.pdf"},{"id":82117757,"identity":"c1ab3743-94d6-4f1d-afdd-a8a012d5c823","added_by":"auto","created_at":"2025-05-07 03:02:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49469,"visible":true,"origin":"","legend":"","description":"","filename":"Table16.docx","url":"https://assets-eu.researchsquare.com/files/rs-6249524/v1/6d7a45dd64608257c62cf890.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic Profiling Filtering and Molecular Analysis of Colorectal Cancer (CRC) using Next Generation Sequencing (NGS): Identifying Somatic Mutations Biomarkers and Patterns for Precision Medicine","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eWorldwide, colorectal cancer (CRC) or colon cancer is the third most prevailing cancer, representing 10% of all cancer diagnoses, and the second leading cause of cancer-associated mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. CRC mostly affects advanced age group of \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;50 years old, with reported incidence cases of about 1.9\u0026nbsp;million in 2020 and about 2.0\u0026nbsp;million in 2022, and over 930,000 deaths in both years [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The highest incidence is recorded in countries such as China, the United States, Japan, the Russian Federation, India, Germany, Brazil, Italy, France, and the United Kingdom [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The pathogenetic mechanism of CRC involves constellations of several genomic events involving a series of molecular subtypes of microsatellite instability (MIS) and chromosomal instability (CIN) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The pathogenic contribution of gene mutation at CIN and MIS is acknowledged as the most crucial lead-way in the pathogenesis of cancer mechanisms, including CRC [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOMIC science through comprehensive gene mutation profiling and sequencing analysis, utilizes conventional molecular sequencing techniques to provide the genetic landscape for the CRC mechanism, by highlighting that only a few crucial genes \u0026lsquo;\u0026lsquo;mountain mutated variants\u0026rdquo; are responsible for the cancer pathology effects in large tumour cases, imploring these mountain mutated genes as the most frequently detected variants in the cancer progress [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Recent advances in sequencing technology using high-throughput Next-generation sequencing (NGS) have facilitated the analysis of the entire genome in individual cancers and made the identification of novel genetic alterations possible [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSequencing-wise, targeted sequencing using NGS technology is a promising tool in clinical application and medical research because NGS increases the coverage depth (compared to the whole exome approach) by decreasing the number of variants analyzed with a similar number of base pairs sequenced [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This ensures the production of reliable sequenced data with satisfactory sequencing depth and quality run in the targeted variants of interest. NGS provides data on the mutational profiling of CRC rapidly and cost-effectively [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A noteworthy breakthrough is how NGS findings explained that distinct sections of similar tumours revealed different mutation profilings (spatial heterogeneity within tumours) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], perhaps because primary tumours and their metastasized tumour could differ in their mutational archetype, thus, indicating sequential heterogeneity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Identifying the harmony between primary cancers and the metastasized is important for selecting the best therapy options; Why? Because targeted therapies that target variants found in the primary tumour but absent in metastases will not yield an efficient outcome [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The present research established a targeted sequencing profiling using NGS, which includes 15 genes and generates 105 mutation data. This study aimed to identify crucial gene mutations that could serve as screening and prognosticating biomarkers in targeted sequencing profiling of colorectal cancer tissues for clinical application.\u003c/p\u003e"},{"header":"2.0 Materials and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patient and tissue collection\u003c/h2\u003e \u003cp\u003eThis research is a cross-sectional study that recruited patients with confirmed colorectal cancer diagnoses among the patients visiting the pathology unit of Hospital Pakar Universiti Sains Malaysia (HPUSM). The present study recruited 21 patients with the diagnosis of colorectal cancer between 2015 and 2024. Biopsies and resection of formalin-fixed paraffin-embedded tissues were obtained and examined for histopathology examination from primary CRC tissues from the FFPE archives at the Pathology unit of Hospital-USM. Patients were deemed eligible for selection if complete medical data, archived FFPE bloc, treatment outcomes, and histopathology reports were available. Pertinent sample details were retrieved regardless of tumour stage, tumour grade, location or the patient\u0026rsquo;s age and sex. Following TNM guidelines and histopathological criteria, tissue samples were evaluated and/or diagnosed as sporadic cancerous or adenocarcinoma lesions in the colon by a consultant pathologist. The present study was performed adhering to the Declaration of Helsinki guidelines and approved by the Ethics Committee of the \u0026ldquo;Ethical Committee of Research\u0026rdquo; Universiti Sains Malaysia [ethics approval number: USM/JEPeM/21010076]. Informed consents were obtained from eligible participants with the option to opt-out at will.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 DNA extraction\u003c/h2\u003e \u003cp\u003eGenomic DNA (gDNA) was isolated from 8\u0026ndash;10\u0026micro;m-thickness of 4 sessions of macroscopically dissected FFPE CRC tumour tissues. A haematoxylin and eosin-stained slide (H\u0026amp;E) was obtained and employed to estimate the patent for regions with high tumour cellular stains of the conforming slices by two pathologists (SMS and NMM) using a double-headed microscope, thus, ensuring a high tumouricity percentage ranging from 50\u0026ndash;90%. To avoid drawbacks from the use of archived FFPE tissues, freshly cut sections from most recent years' archival and quality extraction kits with established good DNA quality and quantity yield from FFPE samples were used. DNA was isolated using the QIAamp DNA FFPE Advanced UNG kit (cat. No 56604, Germany), following the manufacturer\u0026rsquo;s protocol. The quantity check (concentration) of the extracted DNA was checked using a Qubit 2.0 Fluorometer machine while the quality (purity-check) was done using a Tecan Spectrophotometer NanoQuant Machine, wherein, absorbance ratio of A260/280 and A260/230 between 1.8-2.0 and 2.0 -2.2 respectively are deemed acceptable purity. Also, further quality check of the extracted DNA for desirable quality suitable for library preparation was assessed by deciding the amount of amplifiable gDNA using Infinium HD FFPE QC Assay PCR (Illumina Inc. USA) according to the MIQE guidelines for Real-Time PCR experiments, the threshold cycle (Ct) also called the quantification cycle (Cq) represents the sample quantification value, and all samples with Mean \u003cb\u003eΔ\u003c/b\u003eCq-value (Δ Cq value) below 5 are selected as desirable for use for library preparation. All DNA samples were stored at -20\u0026deg;C (for short-term storage) and \u0026minus;\u0026thinsp;80\u003csup\u003eO\u003c/sup\u003eC 9for long-term storage).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Library preparation and Next-generation sequencing.\u003c/h2\u003e \u003cp\u003eFor library preparation and pooling, AmpliSeq for Illumina Focus Panel was employed following the AmpliSeq\u0026trade; for Illumina workflow. 20ng of gDNA was used per library for the pooling. Successful library amplification and library quality of FFPE samples was checked by measuring the concentration of libraries with desirable size (~\u0026thinsp;310bp) and short DNA fragments (\u0026lt;\u0026thinsp;150bp) using the LabChip GX Touch/GXII Touch (PerkinElmer, USA). Libraries with peaks (size) between 250-450bp were considered desirable for sequencing runs on the Illumina MiniSeq machine. After successful sequencing run, fastQ data is generated for biostatistics analysis for variant identification.\u003c/p\u003e \u003cp\u003eAnalysing NGS data, including demultiplexing, alignment and variant calling for identification of somatic gene mutations, sequenced reads were aligned to the reference sequence in the human reference genome GRCh37 (hg19) with the DNA Amplicon app version 2.1.0 using the BurrowsWheeler Aligner software [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Subsequently, data were analyzed on the Illumina BaseSpace Annotation software version 1.6.2.0 to identify crucial somatic variants such as synonymous and indels (missense, nonsense, frameshift, in-frame coding indels, and splice sites), while Illumina Variant-Interpreter version 2.6.1.239 (Illumina, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://variantinterpreter.informatics.illumina.com\u003c/span\u003e\u003cspan address=\"https://variantinterpreter.informatics.illumina.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to simplify and accelerate the analysis and interpretation of genetic variants generated from the NGS run, by allowing visualization and annotating the detected variants with details from online databases of dbSNP, ClinVar, gnomAD, and COSMIC and revealing crucial mutations that can serve as cancer biomarkers. Only sequenced runs with \u0026gt;\u0026thinsp;100,000 reads and variants with a quality score (phred score)\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;Q30 are considered as quality runs that were chosen for further analysis.\u003c/p\u003e \u003cp\u003eThis sequencing run is considered a high-quality run as the required conditions or criteria are met, and they include 1] Phred Quality Score (Q score) or read quality which is the metric that shows the correctness of base calling in sequencing. An acceptable high-quality run with a Q-score of \u0026ge;\u0026thinsp;30, indicates a 99.9% accuracy for base calls while lower Q-scores reflect more sequencing errors, 2] Depth of coverage tells the number of times a specific region of the genome is sequenced, a depth of 30x is considered standard, 3] Breath of coverage indicates the percentage of the target region that is sequenced at the desired depth, sequence coverage of greater than 95% of the target region is considered high-quality coverage, 4] Read length in the range of 150 bp or 300 bp paired-end is considered satisfactory, however, the degree of the high-quality reads must be high throughout the whole length, 5] Adapter and contaminant should be removed or minimized during quality control steps to make sure the data obtained for analysis are clean. The adapter sequence can be seen during the trimming to remove low-quality bases. In our study\u0026rsquo;s NGS data, all these quality metrics are met as shown in the amplitude table, by ensuring the set criteria are met, bias and poor runs are removed or prevented.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses in the present study were done using Statistical Package for the Social Sciences SPSS tool (IBM SPSS Inc., USA, Version 23.0). Cleansing of data was done to ensure no missing data, and filtering was applied to select quality runs. For correlation analysis, the contingency coefficient was calculated and the strength of the association between the variants and chromosome specificity was examined. Also, a logistic regression plot was used to predict the gene mutation-clinicopathology factor association by analyzing the functional relationship between the pathogenic gene as the dependent variable and clinicopathological factors as independent variables, thus, providing an insight into which of the clinicopathological factors are highly predictive of the genetic alterations in our colorectal cancer patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cp\u003eOverall, out of the 30 colorectal cancer (CRC) FFPE samples initially recruited for the present research, 21 samples completed the run [19 CRC and 2 non-tumours], the excluded nine samples were due to failed run and/or poor-quality run. The demographic distribution of the successful libraries comprises 12(57.1%) males and 9(42.9%) females with mean age (60.2 + 10). The group of \u0026ldquo;\u003cu\u003e\u0026gt;\u003c/u\u003e 65 years old\u0026rdquo; were the most at 57% with a M: F ratio of 9(75%) and 3(33.3%) respectively. Similarly, the variables with the highest values were revealed at 71% for the participants in the \u0026ldquo;severe group\u0026rdquo; in the comorbidity category, at 38% for the participants in the group with \u0026ldquo;one type of comorbidity\u0026rdquo; in the \u0026ldquo;number of comorbid\u0026rdquo; reported for the participants, and lastly, at 57.1% for the \u0026ldquo;retired group\u0026rdquo; in \u0026ldquo;employment status\u0026rdquo; (Table 1). On clinicopathology distribution, the respective highest data was as follows: at 71.4% vs 28.6% for \u0026ldquo;late stage\u0026rdquo; and \u0026ldquo;early stage\u0026quot; in tumour stage level; at 19% vs 66.7% vs 14.3% patients for \u0026ldquo;stage-2\u0026rdquo; and \u0026ldquo;stage-3\u0026rdquo;, and \u0026ldquo;stage-4\u0026rdquo; in TNM tumour stages respectively; at 85.7% vs 14.3% for the \u0026ldquo;left side and \u0026ldquo;right side\u0026rdquo; in tumour location; and at 76.2% vs 23.8% for \u0026ldquo;moderately-differentiated\u0026rdquo; and \u0026ldquo;well-differentiated\u0026rdquo; in tumour grading respectively (Table 1). Furthermore, using \u0026ldquo;Duke Stages\u0026rdquo; the highest was recorded at 81% for \u0026ldquo;Duke C\u0026rdquo; with M: F ratio of 83.3% and 77.8% patients respectively compared to 14% and 4.8% for \u0026ldquo;Duke B\u0026rdquo; and \u0026ldquo;Duke D\u0026rdquo; respectively (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing the filter criteria application to identify variants specific to CRC, from the 21 samples that completed the NGS run, 105 variants (involving 15 genes and 9 different chromosomes) passed the filtering from initially more than 500 variants (Tables 2, 3 \u0026amp; 4, Figure 1). Amongst the 105 variants, the five most upregulated genes in each patient are \u003cem\u003eALK\u003c/em\u003e: 34.3% (35/105), \u003cem\u003eFGFR4:\u0026nbsp;\u003c/em\u003e18.1% (19/105)\u003cem\u003e, NRAS\u003c/em\u003e: 12.3% (13/105), \u003cem\u003eERBB3:\u0026nbsp;\u003c/em\u003e7.8% (8/105), and \u003cem\u003eKRAS, KIT\u003c/em\u003e: 4.8%(5/105) apiece. The least regulated genes were \u003cem\u003eESR1\u003c/em\u003e and \u003cem\u003eIDH1:\u003c/em\u003e 1% (1/105) apiece, and \u003cem\u003eAPC, FGFR1, FGFR3, MYC, \u0026amp; ERBB2\u003c/em\u003e: 1.9% (2/105) apiece (Table 2)\u003c/p\u003e\n\u003cp\u003eOverall, 9 chromosomes were associated with the 105 variants detected by the NGS. The most commonly involved chromosomes with the associated detected gene mutations revealed chromosomes: \u003cem\u003echr1, chr2, chr4, chr5\u003c/em\u003e, and \u003cem\u003echr13\u003c/em\u003e as the most commonly occurring chromosomes involved in CRC gene aberration (Table 3). The most upregulated chromosomes associated with the CRC mutations were expressed at; 34.3% (36/105) for \u003cem\u003echr2\u003c/em\u003e, 20.0% (21/105) for \u003cem\u003echr5\u003c/em\u003e, 17.1% (18/105) for \u003cem\u003echr1\u003c/em\u003e, 12.4% (13/105) for \u003cem\u003echr12\u003c/em\u003e, and 6.7% (7/105) for \u003cem\u003echr4\u003c/em\u003e. The two least involved chromosomes were 1.0% (1/105) for \u003cem\u003echr6\u003c/em\u003e, and 1.9% (2/105) for \u003cem\u003echr17\u003c/em\u003e (Table 3)\u003c/p\u003e\n\u003cp\u003eAnalyzing the CRC genetic changes and the associated patterns involves investigating all the mutations detected from the NGS results with chromosomes specific to each mutated gene. From 21 samples, 15 genes from 105 variants were detected involving 9 chromosomes (Table 4). The mutated genes exhibited a strong and precise chromosome-gene mutation specificity pattern at above 95% specificity in 67%, and 40 - \u0026lt; 60% specificity for 20% of the participants (Figure 1), thus, indicating that certain variants can only be identified on specific chromosomes such as monospecific chromosomes for chr3 and \u003cem\u003ePIK3CA\u003c/em\u003e gene, chr6 for \u003cem\u003eESR1\u003c/em\u003e gene, and chr17 for \u003cem\u003eERBB2\u003c/em\u003e (all \u0026gt;95% specific pattern) (Figure 1).\u003c/p\u003e\n\u003cp\u003eNormalized heatmap representation showed the most upregulated genes between pink and green colours for \u003cem\u003eALK, ERBB3, FGFR4, KIT, KRAS, NRAS,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;DDR2\u0026nbsp;\u003c/em\u003e(Figure 1).\u003cem\u003e\u0026nbsp;\u003c/em\u003eAlso, among the 21 samples, 2 samples were normal tissues (non-CRC): samples P6 and P9. These latter two samples have only one similar gene (\u003cem\u003eFGFR3\u003c/em\u003e) detected in both samples (Figure 1). The cluster graphical view illustrates the chromosomal pattern of the most upregulated chromosome involvement in each patient, and this is depicted by the heatmap colouration range. Chr2 is the most upregulated as having the highest amount of mutations detected at about 70 - \u0026gt;95% rate in most of the patients (Figure 1). Also, the period of sample selection indicates a better and higher number of variant detections for the most recent year \u0026ldquo;2021\u0026rdquo; when compared to other years (Figure 1).\u003c/p\u003e\n\u003cp\u003eAlso, notable patterns were identified for certain chromosome specificity for more than one type of variants but still with a peculiar and definite high specificity for a major gene type, and they include Chr12 which harbours two genes namely ERBB3 but with \u0026gt;95% associative specificity and about 65-70% for KRAS genes, Chr8: harbours 2 genes namely FGFR1 and MYC (\u0026gt;95% specific), ch5 harbours FGFR4 (\u0026gt;95% specific) and APC (about 20-25% specificity), and lastly chr1 which harbours NRAS (\u0026gt;95% specific) and DDR2 (40-\u0026lt;60% specific) (Figure 1).\u003c/p\u003e\n\u003cp\u003eTo determine whether the pattern of profiling for the involved chromosomes and the genes that accounted for the mutations were associated with one another, a correlation matrix analysis was carried out to determine how each of the genes correlated with the specific chromosomes involved in the detection of the genes in the CRC cases, or whether the chromosome-gene specificity is statistically associated with CRC. Several significant correlations were observed. The results revealed that specific chromosomes have a strong positive statistical correlation with certain genes. For example, chr1 was positively correlated with the \u003cem\u003eDDR2\u003c/em\u003e gene (r = 0.71, p \u0026lt; 0.01) and \u003cem\u003eNRAS\u003c/em\u003e gene (r = 0.81, p \u0026lt; 0.001). Chr2 was positively correlated with the \u003cem\u003eALK\u0026nbsp;\u003c/em\u003egene (r = 0.84, p \u0026lt; 0.001), just as chr3 was also positively correlated with the \u003cem\u003ePIK3CA\u003c/em\u003e gene (r = 0.85, p \u0026lt; 0.001). Chr8 was positively correlated with \u003cem\u003eFGFR1\u003c/em\u003e and \u003cem\u003eMYC\u003c/em\u003e gene at r = 0.68 and 0.71, p \u0026lt; 0.001 respectively. Additionally, chr4 was positively correlated with \u003cem\u003eKIT\u003c/em\u003e gene (r = 0.84, p \u0026lt; 0.01), chr5 was positively correlated with the \u003cem\u003eAPC\u003c/em\u003e and \u003cem\u003eFGFR1\u003c/em\u003e gene (r = 0.57 and 0.43, p \u0026lt; 0.01) respectively, chr6 was positively correlated with the \u003cem\u003eESR1\u003c/em\u003e gene (r = 0.86, p \u0026lt; 0.01), chr12 was positively correlated with the \u003cem\u003eERBB3\u003c/em\u003e gene (r = 0.87, p \u0026lt; 0.01) and \u003cem\u003eKRAS\u0026nbsp;\u003c/em\u003egene (r = 0.78) p \u0026lt; 0.001 for both. Lastly, chr17 was positively correlated with the \u003cem\u003eEBRR2\u003c/em\u003e gene with r = 0.89 and p \u0026lt; 0.01 (Figure 2). The remaining correlations in the matrix showed no significant relationships, implying that the detection of the genes on certain chromosomes might be due to chance, which in this case could be metastasis. Thus, the correlation findings revealed that there is a strong positive correlation of the detection of particular variants on specific chromosomes associated with specific genes in CRC cases (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe \u0026ldquo;In Silico Prediction tool\u0026rdquo; was used to carry out mutation prediction analysis by reviewing the pattern of the substituted bases and nucleotides to infer, if the mutation patterns portray any tissue-damaging information for CRC. From 105 variants, 21 variants (20%) were identified as \u0026ldquo;deleterious/probably damaging\u0026rdquo; inferring pathologic effects. Another 10 variants (10%) were identified as \u0026ldquo;deleterious/probably benign\u0026rdquo;\u0026nbsp;meaning with a tendency for tumourigenesis. Also, some 57 variants (54%) were identified as \u0026ldquo;tolerated/Benign\u0026rdquo; implying less likelihood of being pathologic. Lastly, there were about 17 variants (16%) that were identified as \u0026ldquo;variant unspecified\u0026rdquo; meaning clinical consequences yet concluded (Table 5).\u003c/p\u003e\n\u003cp\u003eA total of 15 genes accounted for the 105 variants; eight genes accounted for the 21 (20%) pathologic variants (A\u003cem\u003eLK, ERBB2, APC\u003c/em\u003e, \u003cem\u003eESR1, FGFR1, KRAS, PIK3CA\u003c/em\u003e \u0026amp; \u003cem\u003eNRAS)\u003c/em\u003e genes. Furthermore, three genes (\u003cem\u003eIDH1,\u003c/em\u003e \u003cem\u003eKRAS\u003c/em\u003e \u0026amp; \u003cem\u003eNRAS)\u003c/em\u003e accounted for the 10 (9.5%) pathologic benign cases. Another five genes (\u003cem\u003eALK, DDR2, FGFR4, MYC\u003c/em\u003e \u0026amp; \u003cem\u003eERBB3)\u003c/em\u003e accounted for the 57 (54.3%) tolerated benign cases. Lastly, four genes (\u003cem\u003eKIT, FGFR3, FGFR4\u003c/em\u003e \u0026amp; \u003cem\u003eKIT\u003c/em\u003e accounted for the 17 (16%) variant unspecified (VUS) cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther analysis was performed to examine the mutation clinical consequences by examining the distribution of base substitutions across the data to identify mutation-specific effects on CRC, especially as the In-Silico prediction tool prevents the misinterpretation of variants by identifying how variants are designated as pathologic. At differing locations, there is one \u0026ldquo;C\u0026gt;A\u0026rdquo;, one \u0026ldquo;A\u0026gt;T\u0026rdquo;, three \u0026ldquo;G\u0026gt;A\u0026rdquo;, and two \u0026ldquo;C\u0026gt;T\u0026rdquo; nucleotide changes in the eight pathologic genes which leads to a predictive consequence change in the amino acid at the protein level: \u0026nbsp;C \u0026gt;A at position c.3592 in \u003cem\u003eALK\u003c/em\u003e results in leucine changes to Isoleucine, \u0026ldquo;C \u0026gt;T\u0026rdquo; at c.2632 in \u003cem\u003eERBB2\u003c/em\u003e results in Histidine changes to Tyrosine, and at c.748 in \u003cem\u003eFGFR1\u003c/em\u003e results in Arginine changes to Tryptophan. \u0026ldquo;G \u0026gt;A\u0026rdquo; at position c.1636 in \u003cem\u003eESR1\u003c/em\u003e results in Alanine changes to Threonine, at position c.34 in \u003cem\u003eKRAS\u003c/em\u003e results in Glycine changes to Serine, and at position c.1633 and c.1637 in \u003cem\u003ePIK3CA\u003c/em\u003e results in Glutamic acid change to Lysine and Glutamine change to Arginine respectively. A\u0026gt;G at position c.8 and c.182 in \u003cem\u003eNRAS\u003c/em\u003e result in Glutamic acid change to Glycine and Glutamine to Leucine respectively. A deletion at c.4240 in \u003cem\u003eAPC\u003c/em\u003e results in a Valine change to a Termination consequence. Thus, this amino acid change causes an alteration of the original function of the substituted amino acid, hence, a loss of functional cellular process (Table 5).\u003c/p\u003e\n\u003cp\u003eTable 6 below depicts the analysis of the prognostic factors performed using simple and multiple Cox regression analyses by using the log-rank test to investigate the factors that influence prognosis in CRC. The result reveals that the final model of the multiple logistic regression analysis retained only tumour stages, tumour level, Duke staging and Charlson comorbid severity (CCS). Those with stage 2 were 76% less likely to have a pathologic gene compared to those with stage 4 (AOR = 0.24, P = 0.157) and those with stage 3 were 60% less likely to have a pathologic gene compared to those with stage 4 (AOR = 0.40, P = 0.013). Those\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ein the early stage were 64% less likely to have a pathologic gene compared to those in the late stage (AOR = 0.36, P = 0.037). Those with Duke B were 68% less likely to have a pathologic gene compared to those with Duke D (AOR = 0.32, P = 0.461), and those with Duke C were 51% less likely to have a pathologic gene compared to those with Duke D (AOR = 0.49, P = 0.042). For CCS, the mild were 80% less likely to have a pathologic gene compared to the severe (AOR = 0.20, P = 0.686), and the moderate were 45% less likely to have a pathologic gene compared to the severe (AOR = 0.55, P = 0.015).\u003c/p\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eColorectal cancer (CRC) is an intestinal tract tumour and the second most lethal cancer globally [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. CRC arises from the gradual transformation of precancerous polyps to cancerous cells over years then metastasized via blood or lymphatic drainage to various body regions and organs [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In the present study, the overall median age was 53.2\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;5.2 years old, with a higher proportion of male CRC patients compared to the female counterpart at 60% vs 40% for M: F ratio. Thus, demonstrating a slight male predominance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This higher male predominance in CRC is as reported globally [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This latter finding is also reported in W.H.O. reports, that men are 1.4 times more likely to be diagnosed with CRC than women with M: F incidence case ratio at 1.6\u0026nbsp;million vs 0.83\u0026nbsp;million [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. CRC is regarded as a cancer in late age or late adulthood due to its slow and progressive transformation from precancerous polyps cells to cancerous cells [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the present study, most CRC cases were identified among the group aged \u0026ldquo;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e 65 years\u0026rdquo; at 57.1%, this is regarded as the adult stage [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and this rate align with the increasing trend in the adult population, as more studies showed that 50-60-year-old and above individuals have high records of CRC incident rates [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnatomically, CRCs can arise from three different regions of the colon, namely, the proximal, distal and rectum, and these regions differ in the biological and molecular response in the carcinogenesis mechanism or pathway. In the present study, most of the patients have left-sided tumours at 85.7% compared to 14.3% for the right colon. CRC in the right colon often shows nonspecific symptoms like anaemia, fatigue, and weight loss resulting from being wider, thus allowing extensive growth of the tumour before presenting with symptoms of obstruction [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. On the other hand, because the left colon is narrower, CRC presentations are associated with early bowel habit changes such as constipation, diarrhoea, bleeding per rectum, or intestinal obstruction [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This is as reported in previous data [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, from the clinicopathology characteristics, stage-3 has the highest number of participants with 14/21 (66.7%) patients, while stages-2 and stage-4 recorded 19.0% and 14.3% respectively. This is a similar trend when the patients are categorized using Duke\u0026rsquo;s staging method. Duke-C recorded the highest number of patients at 17/21 (81.0%), followed by Duke-B at 14.3%. Like in the TNM staging, Duke-D (the equivalent of TNM stage-4) has the lowest number of patients at 4.8%. According to related articles on tumour stages, both TNM and Duke staging methods for stage-3 and Duke-C respectively represent a major but localized spread, with lymph node involvement as the main clue that denotes both as advanced [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. On habitual status, smoking habit has long been described as a main risk factor for tumourigenesis and its progress [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and males are reported as the foremostly involved gender [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Although in our study\u0026rsquo;s outcome, patients with a history of \u0026lsquo;never smoke\u0026rdquo; habits were the most and this could be due to the religious and cultural norms in this peninsular [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], however, the \u0026ldquo;smoking habit\u0026rdquo; category comprised only the male gender at 42.9% (9/21). This figure is similar to reports from existing studies conducted among the Western population [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and the Asian populace [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eColorectal cancer (CRC) originates from the summative impacts of genetic-epigenetic changes, for which the targeted treatment triumph depends on the identification of crucial targets and predictive biogenetic markers or mutations [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Among the 21 successful runs, mutation type was 97% missense type, the rest are frameshift, splice region introns, and introns. In the present study, next-generation sequencing was performed using Illumina AmpliSeq 52-cancer gene panel on CRC FFPE tissues. Overall, 105 mutations were detected involving 15 genes. The 5 most upregulated variants include ALK: 35/105(34.3%), FGFR4: 19/105(18.1%), NRAS: 13/105(12.3%), ERBB3: 8/105(7.6%), and KIT, DDR2, \u0026amp; KRAS: 5/105 (4.8%) apiece [Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]. This was similarly reported in some published articles [\u003cspan additionalcitationids=\"CR42 CR43 CR44\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Because CRC is a heterogeneous disease associated with several aberrated genes, the profiling of gene mutation provides vital data on the pathogenesis of CRC and potential novel biomarkers for targeted therapy by identifying mutated genes [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Similar findings in Brazil reported APC, TP53, KRAS, PIK3CA, FBXW7, BRAF, PTEN, NRAS, FGFR4, NF1, and ERBB as the most upregulated variants among 20 genes in their study [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe chromosomal and gene alteration in CRC is the main mechanism in tumorigenesis that uncovers the intricate patterns of genetic instability [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. While CIN results in extensive chromosomal changes, involving loss of tumour suppressor genes like APC, SMAD4 and TP53 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], and the amplification of oncogenes such as KRAS, NRAS, and MYC [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In the present study, a total of 9 chromosomes were associated with the 105 variants detected by the NGS technique, amon these, chromosomes: chr1, chr2, chr4, chr5, and chr12 are the most involved, harbouring 18/105, 36/105, 7/105, 21/105, and 13/105 variants respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). While the profound genomic mechanisms for CRC are complex and still undergoing research, some studies have uncovered certain genes located on specific chromosomes that can contribute to CRC development. However, the specific aberrations in these genes can differ in CRC patients [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The present study identified gene-chromosome-specific patterns wherein, multi-specificity was noted on some chromosomes such as the detection of more than one gene on a specific chromosome i.e., \u003cem\u003eKIT\u003c/em\u003e and FGFR3 on chromosome 4, similar to some published data [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This similar pattern was shown in some studies indicating the 5 most involved patterns for the gene-chromosome association in CRC are \u003cem\u003eAPC\u003c/em\u003e on chr5 [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], \u003cem\u003eKRAS\u003c/em\u003e on chr12 [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], \u003cem\u003eFGFR4\u003c/em\u003e on chr5 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and \u003cem\u003eNRAS\u003c/em\u003e on chr1 [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCorrelation of specific and unique mutations with CRC requires ensuring precise clinical practice and personalized therapy, because it is paramount to identify underlying mutations and specific patterns that are unique to particular diseases, either exclusively or in combined form, because comprehending these aberrations will explain the genetic-epigenetic properties of such diseases. In the present study, the Pearson correlation coefficient (r) test between the mutated genes and their related chromosomes among colorectal cancer (CRC) patients was carried out to assess the association between the detected gene mutations and the particular chromosomes involved in CRC propagation. From the results, a strong positive correlation was observed between the \u003cem\u003eAPC\u003c/em\u003e gene and chromosome 5 indicating the alteration on chromosome 5 is statistically significantly associated with the APC mutations. This implies that the loss of chromosomal materials in this region is likely associated with mutations or dysregulation of the \u003cem\u003eAPC\u003c/em\u003e gene, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Furthermore, a similar strong correlation was reported for ALK mutation specifically on chromosome 2, \u003cem\u003eKRAS\u003c/em\u003e mutation on chromosome 12, \u003cem\u003eDDR2\u003c/em\u003e mutation on chromosome 1, \u003cem\u003eNRAS\u003c/em\u003e mutation on chromosome 1, and \u003cem\u003ePIK3CA\u003c/em\u003e mutation on chromosome 3, all these associations are statistically significant p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 respectively, indicating alteration of the chromosomal region is linked with these specific gene mutations. The Pearson correlation coefficient (r) near +\u0026thinsp;1 in our study implies a direct correlation between mutated genes and chromosomal changes observed, thus emphasizing the significance of alteration of chromosomal region in tumorigenesis, including CRC. This is as reported in several studies [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], [\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Also, it is worth mentioning that weak or no correlation (r near 0) was observed between some genes and associated chromosomes implying that the chromosomal aberrations in that region may not consistently result from the gene mutations or dysfunction. An example is the relationship between FGFR3 and chromosome 4 that revealed no correlation (r approx. \u0026lt; 0.2), plus, the association is weak and not significant, indicating that the changes in the chromosome and gene mutations are not majorly linked with CRC progression.\u003c/p\u003e \u003cp\u003eOutlining the underlying genetic and epigenetic elements in a specific tumour including CRC is effective in pronouncing a definitive diagnosis, and also helps in targeted management for the patient [\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. In our study, insight and categorization of detected variants were investigated for organ-variant specificity in CRC patients by applying the In-Silico prediction tool. From a total of 105 variants, 30.0% (31 variants) are categorized as Deleterious/Probably damaging, clinically implying pathogenic. Various proteins have differing roles and functions, meaning that the clinical impact of the variants depends on the protein's function [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Different changes in specific nucleotide variants influenced the amino acid sequence and impacted the protein function such as seen in the ALK variant, where leucine was changed into isoleucine due to nucleotide change in the codon from \u0026ldquo;C to A\u0026rdquo; at position c.3592. The clinical consequences of leucine changing to isoleucine can vary in sequencing, especially if the change occurs at a certain location or sites such as active sites or binding sites, in some cases at the structural motifs which can alter the protein function because of change in the protein's shape [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], stability, or ability to interact with other molecules [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Mutation in APC showed an amino acid change from histidine to termination, otherwise referred to as nonsense mutation wherein the mutation introduces a premature stop codon. The genomic implication is the development of a truncated, non-functional APC protein, which primarily results in the loss of the tumour suppressor gene functions to regulate cell growth and retain genomic stability [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Thence, there is a loss of the role of the \u003cem\u003eAPC\u003c/em\u003e gene due to mutation that results in dysregulated cell proliferation, an early and important stage in the growth of CRC [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Another dysregulation occur in \u003cem\u003ePIK3CA\u003c/em\u003e gene mutation with glutamine changing into lysine, this latter substitution is associated with worsened oncogenic signalling that transforms into an increased aggressive tumour action that negatively affects therapy decisions [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlso in our study, a logistic regression plot was used to predict the gene mutation-clinicopathology factor association, the clinicopathology factors associated with genetic mutation in CRC that were found to be significant on univariate analysis includes tumour stage (P\u0026thinsp;=\u0026thinsp;0.044), tumour level (P\u0026thinsp;=\u0026thinsp;0.05), duke stages (P\u0026thinsp;=\u0026thinsp;0.039), and comorbid status (P\u0026thinsp;=\u0026thinsp;0.040). This implies that based on the univariate analysis outcome, tumour stage, tumour level, duke stages, and comorbid status were the important prognostic predicting factors in the pathogenicity of CRC in our patients. A significant finding in the univariate and multivariate analysis showed that CRC patients in the late stage (tumour level), stage 4, Duke D, and in poor comorbid state are strongly associated with a higher likelihood of harbouring more pathogenic mutations. Here, patients in the early stage are 64% less likely to have a pathologic gene compared to patients in the late stage, likewise, patients in stage 2 are 76% less likely to have a pathologic gene compared to those with stage 4, and patients in stage 3 are 60% less likely to have a pathologic gene compared to those with stage 4. This is narrated in some studies to significantly impact such patient treatment and prognosis especially if at the late stage [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. These findings were similarly reported in several published articles [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Through the regression findings, the outcome of our study\u0026rsquo;s multivariate logistic regression can serve as a guide for personalised focus treatment plans, and the selection of patients at high risk of CRC based on the utilisation of both the clinicopathology and genomic factors. These crucial clinicopathological factors are reported as CRC-predicting factors [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion, Recommendation and Limitation.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNGS has revolutionized CRC research and diagnostics by providing data on the mutational profiling of CRC to improve the understanding of tumour biology, and this is the first NGS molecular study on CRC gene profiling in our institution. Our sample size comprises 40% female and 60% male with \u0026ldquo;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e65 years old\u0026rdquo; more at 57.1% at a mean age of 58.2\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;10. Overall, prevailing factors such as tumour level, tumour stages, Duke-C, moderately differentiated grades, and poor comorbidity status are pertinent risk factors for CRC propagation. Upon applying the In-Silico prediction model to our study\u0026rsquo;s variants, 9 genes were identified as pathogenic genes that are significantly associated with CRC pathogenesis. These genes include APC, NRAS, ALK, ERBB2, PIK3CA, KRAS, ESR1, IDH1, FGFR1. Lastly, a statistically significant association was noted between the mutated genes and the specific chromosome on which the mutation is detected.\u003c/p\u003e \u003cp\u003eAs the first molecular study on gene mutation profiling of colorectal cancer using NGS in our institution, and based on our overall experience in the course of this research, a broader and comprehensive mutation detection that detects a wider range of genetic aberrations is recommended for a broader conclusive comparison and profiling result. Also, we recommend the integration of gene mutation data with artificial intelligence algorithms (machine learning models) to be used in clinical settings to boost predictions of cancer outcomes or therapeutic responses.\u003c/p\u003e \u003cp\u003eAs the first study on a new NGS device on gene mutation profiling in CRC patients in our facility, the first challenge or limitation encountered was obtaining a quality DNA product from the FFPE samples, which eventually led to more cost of purchasing more extraction kits. Also, the smaller sample size does not permit extensive profiling of the tumour research for broad result comparison with existing results. Another setback is technical failures from machine breakdown during the procedure, leading to delays in loading libraries into the sequencer. Lastly, the lack of advanced bioinformatics tools and specialized bioinformatics expertise poses a severe limitation on NGS application at diagnostic and research levels. This is a major reason NGS use is not considered practicable for routine diagnostics, because of the complexity and cost of running when compared to other simpler and faster methods like the Sanger machine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: Conceptualization: H.A.A and S.M.S, methodology: all authors, Software: H.A.A, A.A.I and S.B.A.; Validation: E.S.C, A.A.B.A.A. and S.N.M.N, Formal analysis: H.A.A, H.E.A, \u0026amp; J.B.S.; Investigation: J.B.S, S.B.A, and Y.W; Resources: Z.Z, S.M.S, and H.A.A, Data curation: W.M.N.W.Z, H.A.A, and A.A.I, Writing-original draft preparation: J.B.S, \u0026nbsp;A.H, Y.W and S.M.S, Writing-review and editing: H.E.A, S.M.S, S.N.M.N. and Z.Z.; Visualization: W.M.N.W.Z and Z.Z, Supervision: E.S.C, S.M.S, A.A.B.A.A, and S.N.M.N, Project administration: S.M.S. and Z.Z, funding acquisition: S.M.S. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: The funders had no role in the procedure of the research, data collation, result analysis and interpretation, study draft and write-up, or in the publication decision of the findings. The present work is funded by the Research Universiti RU Top-Down grant Universiti Sains Malaysia, and the School of Medical Sciences, Hospital Pakar Universiti Sains Malaysia support. Approval grant number: 1001/PPSP/8070013.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The authors appreciate the support of the School of Medical Sciences PPSP, Hospital Pakar Universiti Sains Malaysia, Universiti Sains Malaysia (USM) during this project. This research was supported by the USM Fellowship Scheme and the Malaysia RU Top-grant research scholarship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate Declarations:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement:\u003c/strong\u003e The research was approved by the Ethics Committee Universiti Sains Malaysia USM Human Research [Jawatankuasa Etika Penyelidikan Manusia USM (JEPeM)] following the Hospital USM and Universiti Sains Malaysia research protocol guidelines and regulations with ethical approval code (USM/JEPem/21010076).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e: Informed consent was not applicable and the Universiti Sains Malaysia (USM) Ethical Committee approved the study. The research ethical approval number is USM/JEPeM/21010076.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: The data for the present study are the properties of the institution of the Universiti Sains Malaysia which can be accessed upon request or research collaboration request to the corresponding author through the institution. However, all pertinent information deemed important for the present study has been included in the manuscript text.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e: All the authors declare no conflict of interest, and the funders have no role in the design of the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKim S. World Health Organization quality of life (WHOQOL) assessment, in \u003cem\u003eEncyclopedia of quality of life and well-being research\u003c/em\u003e. Springer; 2024. pp. 7866\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization IARC, Population factsheets. 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Cancer Lett. 2002;184(2):215\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 6 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer (CRC), Next Generation Sequencing (NGS), Formalin-Fixed Paraffin-Embedded (FFPE). Gene mutation","lastPublishedDoi":"10.21203/rs.3.rs-6249524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6249524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIllumina 52-gene-focus panel using NGS was used to detect crucial mutations associated with CRC using Formalin-Fixed Paraffin-Embedded (FFPE) tissue among 21 patients. FastQ data was generated and used to analyze somatic variants. Mutation prediction analysis for clinical consequence interpretation was done using In-Silico-Prediction model, and prognostic factors for CRC was done using logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 105 variants were detected involving 15 genes and 9 chromosomes. Demographics of successful librariescomprises 12(57.1%) males and 9(42.9%) females. The highest variables in the participants were 71% for \u0026ldquo;severe group\u0026rdquo; in comorbidity category; 38% for \u0026ldquo;one type of comorbidity\u0026rdquo; in number of comorbidities; 57.1% for \u0026ldquo;retired group\u0026rdquo; in employment status; 71.4% for \u0026ldquo;late stage\u0026rdquo; in tumour level; 66.7% for \u0026ldquo;stage-3\u0026rdquo; in TNM stages; 85.7% for \u0026ldquo;left side\u0026rdquo; in tumour location; and 76.2% for \u0026ldquo;moderately-differentiated\u0026rdquo; in tumour grading. The five most upregulated genes and chromosomes after filtering are [ALK:34.3%(35/105), FGFR4:18.1%(19/105), NRAS:12.3%(13/105), ERBB3:7.8%(8/105), and KRAS, KIT: 4.8%(5/105) apiece] and [4.3%(36/105) for chr2; 20.0%(21/105) for chr5; 17.1%(18/105) for chr1; 12.4%(13/105) for chr12, and 6.7%(7/105) for chr4]. From 105 variants, 21(20%) were \u0026ldquo;deleterious/probably-damaging\u0026rdquo; inferring pathologic effects, 10(10%) were \u0026ldquo;deleterious/probably-benign\u0026rdquo; meaning with tumourigenesis tendency, 57(54%) were \u0026ldquo;tolerated/Benign\u0026rdquo; implying less likelihood of being pathologic, and 17(16%) as \u0026ldquo;variant unspecified\u0026rdquo; meaning clinical consequences yet concluded. Lastly, tumour stages, tumour level, Duke staging and \u0026ldquo;Charlson-comorbid-severity\u0026rdquo; represent prognostic factors for CRC.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNGS sequencing provides comprehensive gene mutation profiling in CRC by identifying biomarkers mutation in CRC.\u003c/p\u003e","manuscriptTitle":"Genomic Profiling Filtering and Molecular Analysis of Colorectal Cancer (CRC) using Next Generation Sequencing (NGS): Identifying Somatic Mutations Biomarkers and Patterns for Precision Medicine","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 03:02:47","doi":"10.21203/rs.3.rs-6249524/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ee83210-094f-4a16-8390-86176ebbaaf4","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-25T09:24:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 03:02:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6249524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6249524","identity":"rs-6249524","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}