Identification of Colorectal Adenocarcinoma Key Genes to Be Validated as Potential Prognosis Biomarkers

Identification of Colorectal Adenocarcinoma Key Genes to Be Validated as Potential Prognosis Biomarkers | 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 Identification of Colorectal Adenocarcinoma Key Genes to Be Validated as Potential Prognosis Biomarkers Elham Omer Mahgoub, Pooja Prakash Mankar, Amit kumar, Syed Asif Naqv, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5188416/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 Background: Colorectal Adenocarcinoma (COAD). continues to be one of the leading causes of death worldwide. The patient's chance of survival increases with the early prognosis of a malignant tumor. Methods: Advanced bioinformatics methods were used to gain a thorough understanding of the genetic landscape of colorectal cancer. The transcriptome RNA-seq raw data were obtained from PRJEB24758 in the European Nucleotide Archive (ENA) database. Then, the Sequence Read Archive (SRA) runs a selection to download the sequences database. The transcriptomic RNA released data is analyzed by the bioinformatics tool, which also uses online analytic tools to help visualize the results and identify key genes that may be employed as prognosis biomarkers in the future. The annotation pathways have been determined using David's annotation tools, and cluster analysis in Gsea and the c-bioportal database also showed the significance of these pathways. Two hundred fifty miRNA overlapped with the highest two upregulated and downregulated genes that were subjected to screening. The Venn diagram determined the common genes that the immunogenic genes set and cell type gene signature. Results: 1,274 genes with substantial differential expression in colorectal cancer were found using stringent approaches such as HISAT2 alignment and DeSeq2 analysis. This study identified 913 upregulated genes of colorectal adenocarcinoma (COAD). The upregulated genes-expressed profile of COAD was studied. The upregulated genes are controlled by 20 pathways expressed in colorectal adenocarcinoma. David's annotation tool was used to prepare diagrams for the KEGG analysis, enriched genes, and many more diagrams. The resulting miRNA overlapped genes interacted significantly with TF to produce key genes. From a different perspective, fifty-seven upregulated common genes were determined using the Venn diagram, and higher mutated genes were selected. Investigating these key genes for targeted therapy in colorectal cancer therapy is crucial, as the study emphasizes. Conclusion: Thus, the development of novel therapeutic approaches and the identification of key genes of the changed expression of genes implicated in COAD drug resistance are crucial goals for the ongoing advancement of COAD therapy. Colorectal adenocarcinoma RNA sequencing Differential expression genes(DEGs) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction The second most significant cause of cancer-related deaths worldwide. The third most prevalent cancer globally. Obesity, inactivity, and smoking habits, in addition to an older population, raise the risk of Colorectal Adenocarcinoma (COAD). Colorectal Adenocarcinoma continues to be a problem despite improvements in surgical methods, chemotherapy, and radiotherapy. Endoscopic procedures, such as endoscopic submucosal dissection and endoscopic mucosal excision, have been used to treat early-stage COAD. Adenocarcinomas are the most common subtype of Colorectal Adenocarcinoma (COAD) and result from the malignant transformation of glandular epithelial cells in the colon or rectum. These tumors usually grow slowly, starting as benign adenomatous polyps and moving through a sequence of well-researched genetic and epigenetic changes called the adenoma-carcinoma sequence. However, due to the prevalence of lymph node metastases, lymph node dissection is a crucial component of surgical therapy for cancer in its advanced stages. 1 . Once colon cancer develops local or distant metastases, the prognosis is dismal. Researchers or physicians may need to find novel markers for predicting the prognosis and therapy of colon cancer to improve the prognosis of people with the disease. . Colorectal Adenocarcinoma (COAD) patients are most likely to develop new targeted genes to overcome the cancer cell drug resistance to chemotherapy either naturally or as a result of receiving T-DM1 treatment. This study suggests that variations in the expression levels of specific genes may impact the sensitivity or resistance to chemotherapy 3 , 4 , 5 . Biomarkers are essential for the early diagnosis of drug resistance to chemotherapy. Although biomarkers are utilized at different phases of the disease to aid in its management, each has its limits 6 , 7 . Identification of essential genes that could be used as biomarkers for diagnosis of colorectal cancer was a persuasive motive to analyze Differential expression genes 8 , 39 , 10 . The clinical use of biomarkers in COAD is crucial for prognostic stratification, surveillance, and treatment selection, in addition to being necessary for the early diagnosis of the disease. Therefore, there is an urgent need for new, affordable, non-invasive, easily quantifiable, and accurate screening methods for colorectal cancer (COAD). This study uses RNA-Seq data of colorectal cancer (COAD) to analyze differential gene expression and predict possible biomarker for future diagnosis and gene therapy. Methods Data Collection The dataset used in this work is freely available to the public via the European Nucleotide Archive (ENA) under project ID PRJNA649071 (https://www.ebi.ac.uk/ena/browser/home). The transcriptome RNA-seq raw data were obtained from PRJEB24758 in the European Nucleotide Archive (ENA) database. Then, the Sequence Read Archive (SRA) runs a selection to download the sequences database. Samples relevant to Colorectal Adenocarcinoma (COAD) are gathered into this dataset, which includes fresh frozen tissues (FF), formalin-fixed, paraffin-embedded (FFPE) tissues, and several cancer cell lines. The NGS datasets were discovered by using the ENA's search feature associated with colorectal adenocarcinoma. Species (such as Homo sapiens), experimental circumstances, library type (RNA-Seq), and sequencing platform (Illumina) filters were used to limit the results. The Run Accession Numbers for certain transcriptome RNA-seq raw data runs that matched colorectal cancer samples were noted. Europa Galaxy was used to identify the differentially expressed genes (DEGs) between colorectal adenocarcinoma tissues and normal tissues. The inclusion of matched normal and tumor samples from COAD patients provides a valuable resource for comparative analysis. FTP (File Transfer Protocol) was used for effective data retrieval. The raw transcriptome RNA-seq raw data (single-end) files (often in FASTQ format) containing the raw sequencing reads were obtained using FTP links. After the download was finished, the raw data files were arranged on the local storage system. An organized directory including pertinent metadata, sample details, and the name of the dataset was created. Further, the data were filtered according to normal and tumor samples in an Excel worksheet. The data was also filtered to select single-end data, as it contains a larger data set than paired-end data. Quality Control and Processing Tools to process the data in Galaxy, The FTP links were uploaded to the Galaxy database (https://usegalaxy.org/) along with the reference genome (Homo sapiens) from the Ensemble. The sample set for the investigation included 186 runs of tumor samples and 164 runs of normal samples, offering a balanced representation for reliable comparison analyses. This important collection paves the way for future discoveries on the molecular causes of Colorectal Adenocarcinoma (COAD) and enables a thorough investigation of the transcriptional changes linked to the illness. The FastQC tool is used to calculate the quality control of NGS raw data. The quality of sequencing data was checked by clicking on FastQC output datasets under the various quality matrices, including pre-base sequence quality, sequence length distribution, GC content, etc. Using MultiQC, the FastQC report was aggregated into one report. The adapter sequences were trimmed using the TrimGalore tool. The ftp link of reference genome https://ftp.ensembl.org/pub/release110/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.toplevel.fa.gz (Homo sapiens) and GTF file https://ftp.ensembl.org/pub/release-110/gtf/homosapiens/ and Homo_sapiens.GRCh38.110.gtf.gz was copied and uploaded in the galaxy for alignment. The alignment sequence of the data compared with the reference genome process was done using the HISAT2 tool. The Feature Counts tool is used to count how many reads are aligned to each gene (count matrix). The differentially expressed genes from RNA-seq data were identified using the DESeq2 tool, the RNA sequencing summarized in flowchart shown in Figure 1. Statistical Analysis NA values were eliminated by applying the filter to P-adj values in the Excel sheet. The filter was applied to p-adj as ≤0.05 to derive only significant genes. Further, the data were sorted based on fold change values to identify upregulated genes (threshold is ≥2) and to identify downregulated genes (threshold is ≤-2). Differential Gene Expression Analysis Next, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) https://david.ncifcrf.gov/, ver. 11 It was used to identify potential uses for differentially expressed genes (DEGs). The DEGs were analyzed by choosing official gene symbols, annotation tables, and Entrez IDs. Genes were functionally enriched using g-Profiler https://biit.cs.ut.ee/gprofiler/gost , which conducts statistical enrichment analysis and offers interpretative insights into gene lists provided by users. 12 , 11 . In the David annotation tools, the Kyoto Encyclopedia of Genes and Genomes (KEGG), HPA tissues, Upregulated tissues, GOTERM-cc-direct cellular component, and gene enrichment analysis columns were prepared using the lists of the gene's functional annotation tools. Two columns were copied into a new Excel worksheet, sorted from larger to smaller, and the chart was chosen after the columns of each functional annotation. Their related frequency column was selected in the Excel worksheet and inserted using pivotable in Excel. Functional Gene Enrichment The STRING database found the most crucial modules in the protein-protein interaction 13 network. The study's analysis of the molecular causes of colorectal cancer revealed the gene different cluster group and activities in the gene set related to colorectal adenosarcoma. Furthermore, using a string database, co-expression genes are identified as additional information on the gene's connection and regulation. Gene Set Enrichment Analysis (GSEA) The study used the Gene Set Enrichment Analysis (GSEA) database https://www.gsea-msigdb.org/gsea/index.jsp to measure the overlap of gene functions. The Ensembl ID was submitted to the molecular signature database, and the oncogenic dataset was selected to visualize the transcription factor for each enriched gene in the large-scale design. Venn diagram analysis Venn diagram is used to overlap the immunogenic genes set versus cell type gene signature data that is retrieved from the Gsea database. The Venn diagram method draw venn diagrams, result ugent. Be . The common gene expression signatures of the immunogenic genes set versus cell type gene signature are considered the suitable group of genes to be used in further experiments. Mapping of Protein-Protein Network The biomart_mapper script in R uses the Limma package to map 913 upregulated and 906 downregulated genes of COAD and choose the functional protein coding of the RNA sequence data. Even though the original purpose of the R program Limma was to analyze differential expression (DE) in RNA sequencing data. Cytoscape 3.7.1 was used to map the protein-coding gene data that was produced. The obtained data was then used to forecast the heatmap of the top 10 downregulated genes and the highest upregulated genes, demonstrating a considerable up-regulation of COAD relative to normal tissue. miRNA Prediction Using Mirwalk Database the two highest upregulated genes and two highest downregulated genes were submitted to the mirwalk database http://mirwalk.umm.uni-heidelberg.de/ to identify the miRNA that interacts with these genes. The results have been saved in one Excel worksheet. Next, the same list of genes was submitted to the chea3 database https://maayanlab.cloud/chea3/ to identify the overlapped transcription factors (TF), and that was added to the second worksheet. Finally, the resulting TF list was submitted to the mirwalk database, and the overlapped miRNA was documented. The overlapped 250 miRNA in the two listed was prepared using Excel formulas. Cytoscape software was used to merge the three networks of TF-Genes, Genes -TF, and finally, miRNA-TF. One gene was connected to one transcription factor, and both overlapped with the same miRNA. C.bioportal Database The structure of the gene variants and the types of mutations in the enriched genes were examined using the c.bioportal database https://www.cbioportal.org/ . The official _gene_Id set genes are submitted for colorectal adenocarcinoma based on gene selection, and each gene mutation is thereafter examined individually. G-profiler Database In order to determine the functional gene pathways, common group, and enriched genes, the Ensanbl_Id query was submitted to g-profiler https://biit.cs.ut.ee/gprofiler/gost . Besides general annotations, interactions, literature, pathway associations, disease associations, and tissue expression were explored. Results Differential Expression Genes Analysis Differential expression genes analysis resulted from RNA sequencing. As shown in Figure 2, the Genes were processed using David's annotation analysis site. The MA plot shows differentially expressed genes, with blue indicating a departure from the baseline, suggesting substantial transcriptional activity. The distribution of points indicates a complex alteration in gene regulatory networks in colorectal cancer. The dispersion plot shows decreased variability in gene expression as the mean expression level increases, indicating the technical robustness of RNA-Seq data. The volcano plot shows genes exceeding established thresholds for p-value and log2 fold change, underscoring their potential as key players in colorectal cancer pathology. These genes represent promising targets for further validation as biomarkers or therapeutic interventions. Transcription Factors alterations in transcription factors' (TFs') expression and activity affect other genes' levels. TFs regulate essential physiological processes and control the expression of multiple genes at once. Transcription factors are heat mapped using the Gsea database, as shown in Figure 3. As in Figure (3), The TF checkpoint database provided the reported gene functions, and Ensembl genes were utilized to convert the reported Entrez gene IDs to corresponding Ensembl IDs. Transcription factor (TF) enrichment analysis was performed on the differentially expressed genes using the Gsea database web-server application. In short, humanized gene symbols with human differentially expressed genes. 14 . Gene Enrichment Analysis One technique for examining genomic data, especially extensive transcriptome data, is Gene Ontology (GO) analysis. As explained in Figure (5) using The Database for Annotation, Visualization, and Integrated Discovery (DAVID, https://david.ncifcrf.gov/, ver. 6.8). Ten pathways of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were visualized. Gene Ontology (GO) enrichment analysis to ascertain the biological function of cancer-related protein pathways 15 . For additional visualization, the enriched GO keywords and pathways with a False Discovery Rate (FDR) of less than 0.01 were chosen. Plotting of KEGG pathways, bubble graphs, and the top 20 relevant GO terms (BP, CC, and MF) was done using available methods in Microsoft Excel. Cytoscape, Enrichment Analysis As shown in Figure (6)(A) Cytoscape software, the network illustrates how the DEGs found from COAD interact with one another on a protein-protein level using Cytoscape. As shown in Figure 6 (A) and (B), The network visualizes functional groups based on the most significant (leading) phrase for each group, offering a clear picture of how upregulating and downregulating relate to one another. Functional groups can be created utilizing the GO relationships as an alternative to the kappa score grouping method. Meanwhile, the protein-protein network of the group 913 upregulated genes of colorectal adenocarcinoma generated from the NDeX database shows a very strong network The Heatmap of Top Ten Genes As shown in Figure 6 D, ten higher upregulated genes and ten higher downregulated genes are in a heatmap predicted by the Limma package in R. The green color represents the normal sample, and the pink color represents the tumor samples. Correlation Networks In Figure 7(D), a Venn diagram was used to identify 57 common genes, and the Pearson correlation coefficient analysis was performed on these genes. Figure 7 (A) shows a Venn diagram comparing immunogenic genes with cell type gene signatures, revealing 57 common genes between the two. (B) String protein-protein interaction classified the upregulated genes into five clusters. (C) Depicts the regulatory network of miRNA-Gene-TF in COAD, highlighting 250 differentially expressed mRNAs. The green circles represent the differentially expressed target mRNAs. (D) Shows that the key gene TIMP2 has significant interactions with TF and miRNA. G- profiler and Gene Ontology Analysis the g-profiler database recognized the functional enrichment of GO: MF, GO: BP, GO: CC, KEGG, and TF for each common group, as shown in Table 1 and Figure 8. Gene fusion events can produce new proteins with modified functionalities or regulatory characteristics, which may have an impact on the development of diseases like colorectal cancer. With a fusion score of 0.003, the string network displays the fusion of the RPL12 and RPL3 genes.study's analysis of the molecular regulations of colorectal cancer with g: Profiler revealed overrepresented biological processes and activities in the gene set related to colorectal cancer. The results underscore the significance of comprehending the basic foundations of the disease by offering insights into the pathophysiological mechanisms involved. The g- profiler database arranges the enriched gene in 20 pathways beside the KEGG, WP, TF, MIRNA, HPA, and CORUM pathways had been reported in Figure 8(A). In Figure 8(B), the enrichment analysis explained a significant overrepresentation of gene ontological terms associated with protein binding (GO:0005515), structural molecule activity (GO:0005198), and protein-containing complex binding (GO:0044877). Moreover, there was a noticeable enrichment in "cytoplasmic translation" (GO:0002181) and "cell adhesion" (GO:0007155), which suggests modifications to the cellular structure and translation machinery. Table 1: Common genes upregulated in functional enrichment of GO: MF, GO: BP, GO: CC, KEGG and TF Genes GO:MF GO:BP GO:CC KEGG TF RPL12 IPI NAS,IC HDA KEGG TF RPL23 IPI NAS,IC HDA KEGG TF RPL26 IPI IBA,IC HDA KEGG TF RPL3 IPI IC HDA KEGG TF RPL24 IPI,HDA IDA,IBA,NAS,IC HDA KEGG - RPL31 IPI IDA,IBA,NAS,IC HDA KEGG - RPL37A IPI NAS,IC HDA KEGG - RPS19 IPI NAS,IC HDA KEGG - RPS4X IPI IC HDA KEGG - RPS8 IPI NAS,IC HDA KEGG - YBX1 IPI IDA IDA,HDA,TAS,IEA - TF RPS7 IPI NAS, IC - KEGG TF RPL7 IPI NAS,IC - KEGG TF RPL38 IPI IDA,NAS,IC - KEGG TF The Mutants Genes As mentioned in Figure 9, the highest mutated genes are MYLK, SYNPO2, PCSK5, JCHAIN, ABCA8, and SLC26A3, which were selected from the 57 common genes identified using the Venn diagram tool. Discussion Identifying patients with colon adenocarcinoma (COAD) who may benefit from immunotherapy and whose tumor microenvironment (TME) was required for reprogramming to advantageous immune-mediated responses requires precise immune molecular characterization due to cancer cell mutations. On the other hand, little has been discovered regarding COAD's immunological characteristics. Despite the current gaps in our understanding of COAD's Immunological characteristics, there is hope. Our research is paving the way for the development of accurate and reliable biomarkers for COAD diagnosis and prognosis. By observing co-expression correlations, regulatory relations, and physical binding interactions, we are rehabilitating the colorectal adenocarcinoma network and opening new doors for patient care. This study identified 913 upregulated genes of colorectal adenocarcinoma (COAD). The gene-expressed profile of COAD was studied, and many findings were noted. The upregulated genes are controlled by 20 pathways expressed in colorectal adenocarcinoma, as shown in Figure 2, analyzed by cluster profiler. RNA-sequence expression of COAD was characterized using David annotation analysis, and the resulting genes were compared with each published COAD gene database. Besides, the known disease-associated gene sets rehabilitate the colon adenocarcinoma network observed. Also, physical binding interactions, regulatory associations, and co-expression correlations were analyzed. Nevertheless, there are no sensitive and accurate biomarkers for colorectal adenocarcinoma diagnosis or prognosis. The enriched genes regulate Cellular biology, which contains transcription factors (TFs), as shown in Figure 3 in a sum of functions. TFs are key players in the growth and tumorigenesis of colorectal adenocarcinoma since they are the molecules that make up the terminal effector signal pathway. 16 . A rising amount of evidence indicates that peculiar TFs have a role in the development of COAD, its clinicopathological characteristics, and its prognosis. 17 , 18 . That finding provides new insight into the basic pathogenesis, potential biomarkers, and therapeutic targets. Prior research has demonstrated the extensive distribution of this local gene co-expression, as shown in Figure 4, over numerous chromosomes and tissues. However, it is still unclear if this co-expression results from transcription processes in individual cells because previous studies have only employed bulk RNA-seq, averaging gene expression values over millions of cells as this study. Here, but in a previous study 19 , take advantage of the co-occurrence of transcription events in single cells to uncover gene co-expression across cells, unbiased by cell-type heterogeneity. the study utilized single cell datasets spanning over 85 individuals. find correlations between genes and regulators and show that over 95% of co-expressed gene pairs have similar regulatory elements. co-expression in single cell gene regulatory networks and call for a more thorough examination of common regulatory components to help understand why diseases affect multiple genes at once 20 . As shown in Figure 5, Functional enrichments were performed; common enriched diseases are malignant neoplasm of the breast and liver Grrhosis, which is related to colorectal cancer. Identification of core gene families linked to colorectal cancer metastases to the liver and regulatory functions in tumor cell immune infiltration 21 , 22 . The discovery came when the CCL cluster, as a part of core genes, was primarily enriched in GO-BP pathways linked to immune cell migration and chemotaxis, including neutrophils, monocytes, and leucocytes. The leucocyte carries anticancer chemicals or medications to the targeted tissues in a moving manner, which allows it to carry out its anticancer functions, where consider the hand of the immune system to fight cancer in the human body 23 , 24 . KEGG: the upregulated genes expressed in colorectal adenocarcinoma depend on these pathways. In Figure 5 B, C HPA tissues: The gene expression enriched in adipose tissues, adipocytes, and adrenal gland. The HPA tissues continued to function during the prolonged stress. Increases the response to stress in COAD cells and tumor tissues at both the mRNA and protein levels, as observed in Figure 5 D: Upregulated tissues: the primary tissues that express the upregulated genes are the Brain, Testis, Kidney, and lung. as mentioned in many studies that HPA axis continued to function during the prolonged stress 25 . Increase the response to stress in CRC cells and tumor tissues at both the mRNA and protein levels. These facts proved that the tumor microenvironment is linked to the effectiveness of immunotherapy and patient survival in colorectal cancer patients. 26 . Meanwhile, in Figure 5 E: GOTERM-CC- direct cellular component, relation extraction between gene and other entities, F gene enrichment analysis, the main biological functional enriched genes, the main gain involved in the regulation of the transcriptome genes, and genes silencing by miRNA. These gene enrichment analyses are used to identify differentially expressed key genes associated with colorectal cancer that can be used in tumor diagnosis and targeted gene therapy. 27 , 28 . This work investigates the potential activities of upregulated and downregulated genes in COAD, including 913 upregulated and 906 downregulated genes, to provide a comprehensive knowledge of the genomic organization of COAD, as seen in Figure 6. A and B The distribution of the Differentially Expressed 1274 nodes and 7665 edges characterize genes (DEGs) in Cytoscape software. The heatmap generated using an R script, as shown in Figure 6(D), shows ten higher upregulated genes and ten higher downregulated genes, indicating considerable DEGs of COAD relative to normal tissue. The heatmap provides a clear visual representation of how gene expression profiles are distributed among both normal patients and those with tumor patients. 29 . The String protein-protein interaction database recognizes 5 cluster networks of the upregulated genes shown in Figure 7 (A)) Furthermore, there are 57 upregulated common genes shown in Figure 7 (A), the Venn diagram that finds the common gene between the two sets of the immunogenic genes set and cell type gene signature. There have been several reports of immune cell transcriptome signatures, which consist of common gene lists indicating the presence of a specific immune cell population. Most of these gene signatures have been identified by examining separate blood cells [24]. A network-based deconvolution technique was used to obtain the set of immune gene signatures directly from tissue transcriptomics data, as mentioned in many studies. 30 , 31 . As shown in Figure 7 (C and D), Overexpressed microRNAs regulate receptor signaling pathways. Conversely, downregulated microRNAs target most of the functions linked to the mitotic cell cycle in human organs. The characteristics of cancer, maintaining proliferative signals, avoiding growth suppressors, stopping cell death, starting invasion and metastasis, and starting angiogenesis, among other things., have been demonstrated to be impacted by dysregulated miRNAs 32 . Two hundred and fifty miRNAs shown in Figure 6 (C) are the overexpressed miRNAs that interacted with the higher upregulated genes and downregulated genes. As shown in Figure 7 (D), the biomarkers of the TIMP2 target gene are connected to its regulator (ZEBED1) transcription factors TF, and both are connected with miRNA (has-miR-6822-3p). Recent studies have demonstrated that there may be a negative correlation between the prognosis of COAD patients and the expression levels of TIMP1, according to Song et al. 33 TIMP1 suppression may lead to enhanced apoptosis and decreased invasion, migration, and proliferation in vitro while weakening tumorigenesis and metastasis in vivo through the FAK-PI3K/Akt and MAPK pathways. When serum TIMP1 levels were higher, patients with colorectal cancer had shorter overall survival periods and lower tumor grades. 34 , 35 , 36 . As shown in Figure 8 and Table 1, the protein-protein connection is further explained using ontologies to express biological knowledge. Gene Ontology (GO) is a resource that provides information about the function of gene products. These ontologies cover three domains: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). The process of assigning functional information to genes that are relevant to the literature using gene annotation GO 37 . All five of the chosen criteria were shown to exhibit upregulation of a total of 14 genes. Gene Ontology Molecular Function (GO: MF), Gene Ontology Biological Process (GO: BP), Gene Ontology Cellular Component (GO: CC), Kyoto Encyclopedia of Genes and Genomes (KEGG), and transcription factor (TF) analyses revealed the following genes as the most frequently upregulated genes as presented in table1 and Figure (5). KEGG pathways and GO keywords were used in gene enrichment analysis to identify genes that were consistently elevated in a variety of functional domains. Significantly, ribosomal function. It was found that these upregulated genes are significantly associated with immune-related biological processes and pathways. This finding suggests a potential impact on the immune system in the context of colorectal adenocarcinoma (COAD). Furthermore, as explained in Table 1, the differentiation, progression, or metastasis of colorectal cancer are linked to the expression patterns of Ribosomal Proteins (RPs)2 38 . RPL3 (60S ribosomal protein L3, a component of the large subunit of cytoplasmic ribosomes), RPL12 (60S ribosomal protein L12, directly binding to 26S ribosomal RNA), RPL23 (Ribosomal protein L23), and RPL26 (60S ribosomal protein L26, a part of the large ribosomal subunit). The nucleolus initiates the development of functioning ribosomes, which are necessary for coordinated ribosomal RNA (rRNA) processing and ribosomal protein 31 assembly. This process is often hyperactivated to support the need for protein synthesis, which is necessary to survive the unrelenting expansion of cancer cells 38 . All categories show upregulation of RPL24 (Ribosomal protein L24; Belongs to the eukaryotic ribosomal protein eL24 family), which is a possible therapeutic target whose acetylation or depletion prevents the formation of polysomes and the development of cancer cells 39 . Ribosomal protein L31 (RPL31) is known to have the capacity to inhibit the proliferation and migration of Gastric cancer cells 30 . RPL31 (Ribosomal protein L31), RPL37A (Ribosomal protein L37a), RPS19 (40S ribosomal protein S19; Required for pre-rRNA processing and maturation of 40S ribosomal subunits in colorectal cancer (CRC); Belongs to the eukaryotic ribosomal protein eS19 family), The eukaryotic protein uS19 is encoded in human disorders and is a part of the ribosome decoding site 31 .RPS4X (Ribosomal protein S4 X-linked; Belongs to the eukaryotic ribosomal protein eS4 family). The overexpressed multifunctional protein YB-1 has been found to be a partner of the X-linked ribosomal protein S4 (RPS4X), which is involved in cellular translation and proliferation in a number of breast cancer cells. In these cell lines, depletion of RPS4X consistently leads to cisplatin resistance. 30 . It is observed that approximately 25% of human RPs show tissue-specific expression, and the most intricate patterns of RP expression are found in primary hematopoietic cells 30 , which transcriptional regulators probably influence with context restrictions. Surprisingly, the study also demonstrated a pattern of dysregulated expression of particular RPs across cancer types, patterns that are prognostic of disease development and derive from copy number differences. Such as RPS8 (Ribosomal protein S8; Belongs to the eukaryotic ribosomal protein eS8 family. Genes, except for transcription factor (TF) studies. RPS7 (40S ribosomal protein S7; Required for rRNA maturation), RPL7 (60S ribosomal protein L7; Component of the large ribosomal subunit, binds to G-rich structures in 28S rRNA and mRNAs. It is observed that approximately 25% of human RPs show tissue-specific expression, and the most intricate patterns of RP expression are found in primary hematopoietic cells, which transcriptional regulators probably influence with context restrictions. Surprisingly, the study also demonstrated a pattern of dysregulated expression of particular RPs across cancer types, patterns that are prognostic of disease development and derive from copy number differences 40 . Many studies observe that anticancer medicines that target mRNA translation are anticipated to become possible. That can be explained by the knowledge that a significant part of the pathophysiology of many malignancies is the abnormal translation of mRNAs into proteins, which involves gene transcriptions and post-translational modifications of proteins. That is described by the regulation of protein translation by the interplay between mRNA modifications and ncRNAs 40 . Upregulate gene plays a role in the translation apparatus; inhibits cell-free translation of mRNAs and RPL38. 40 (Ribosomal protein L38; Belongs to the eukaryotic ribosomal protein eL38 family) genes upregulated in all the categories except in the Cellular Component (CC). Apart from KEGG pathways, all categories showed an upregulation of the YBX1 (Y-box-binding protein 1; DNA- and RNA-binding protein involved in various processes, such as translational repression. The first is the traditional general program that produces ribosomes, and the second is a gene-specific function that regulates the number of ribosomal proteins generated by each distinct ribosomal gene. This particular control of ribosomal protein genes is mainly accomplished by variations in pre-mRNA splicing efficiency and mRNA stability, in contrast to the general system, which is primarily regulated at the level of transcription and translation. 41 . RNA stabilization, mRNA splicing, DNA repair, and transcription regulation. Predominantly acts as an RNA-binding protein: binds preferentially to the 5'-[CU]CUGCG-3' RNA motif and specifically recognizes mRNA transcripts modified by C5-methylcytosine) gene (Table 1). genes RPL3, RPL12, RPL23, RPL26, RPL2415, RPL31, RPL37A, RPS19, RPS4X, RPS8, RPS7, RPL7, RPL3816 42 , and YBX1 were constantly elevated, indicating that they may be targets for therapy. In various studies, the RPL313 43 and RPL717 Upregulation were detected in colorectal cancer. After delving into gene fusion occurrences, the RPL12 and RPL3 genes showed a fusion score of 0.003, suggesting that these changes might lead to altered regulatory features or capabilities. Except for YBX1 (0.4), the STRING network showed significant co-expression scores (0.8 to 0.9) for all genes, as observed in Figure 8. The pathogenesis of Colorectal Adenocarcinoma (COAD) is maintained in part by the pathways that interact between factors and cytokine receptors. These pathways have been connected with the development and progression of colorectal cancer of COAD) and share MYLK, SYNPO2, PCSK5, JCHAIN, ABCA8, and SLC26A3 as shown in Figure 9. Those genes happen to have many variants, including nonsense, insertion, frameshift, deletion, and splice variants. Differential routes were also noted at some phases, such as leukocyte migration across the endothelium and the emergence of Toll-like receptor signaling pathways. In Figure 9, the mutated genes were identified using c-bioportal. A study of Bladder cancer revealed that promoters like MYLK promote the invasion and migration of various solid tumors. Therefore, MYLK overexpression can serve as a biomarker for colorectal adenocarcinoma 44 . On the other hand, overexpression of the SYNPO2 gene is considered a poor prognostic factor for patients with nasopharyngeal cancer. Genes that showed a significant positive correlation with SYNPO2, based on GO biological process keywords, were primarily linked to cellular processes. Thus, SYNPO2 is an important indicator of nasopharyngeal cancer prognosis 45 . Meanwhile, PCSK5 downregulation enhances the inhibitory action of andrographolide on glioblastoma by controlling STAT3. Higher expression of PCSK5 was associated with a poorer prognosis for patients with advanced GBM stages. PCSK5 knockdown lessened the IL-6-induced epithelial-mesenchymal transition (EMT) -like characteristics of GBM cells. Additionally, when combined with Andro therapy, PCSK5 knockdown significantly reduced both the in vivo tumor development and the in vitro invasion and proliferation of GBM cells 46 . A recent study has identified JCHAIN as a novel B-cell prognostic biomarker that can predict overall survival in Head and Neck Squamous Cell Carcinoma (HNSCC) patients. In the low-risk group, JCHAIN was considered a highly expressed protective gene. A single-cell dataset study showed that B cells had significant expression levels of JCHAIN 47 . Additionally, Through the Wnt/β-catenin pathway, the tumor suppressor ABCA8 prevents colorectal cancer from progressing malignantly using ABCA8 as targeted gene therapy can prevent the growth and spread of xenograft tumors in vivo and reverse the epithelial-mesenchymal transition. 48 . Moreover, overexpression of ABCA8 inhibited the invasion of cancer cells 49 . The SLC26A3 gene has been found to have 98 variants, and public databases suggested that the SLC26A3 promoter may interact with p65 49 , which led researchers to investigate whether NF-κB/p65 directly binds to the SLC26A3 gene promoter element and controls the expression of colorectal adenocarcinoma. In the mammalian intestines, the PI3K/AKT pathway may have an impact on the SLC26A3 gene. Hence, these highly mutated genes also function as promoters for the development and progression of cancer. Conclusion In summary, the study offers an identification of key genes for colorectal adenocarcinoma to be validated as potential prognosis biomarkers. The discovered key genes, particularly those connected to fusion events and ribosomal functions, need to be looked into further to see how they contribute to the development of disease and to determine whether they should be the focus of targeted gene therapies. Twelve gene regulatory pathways and six diagnostic biomarkers (MYLK, SYNPO2, PCSK5, JCHAIN, ABCA8, and SLC26A3) were described in this study. mRNA transcripts included RPL3, RPL12, RPL23, RPL26, RPL2415, RPL31, RPL37A, RPS19, RPS4X, RPS8, RPS7, RPL7, RPL3816, and YBX1, which were shown to be consistently elevated. and particularly identifies mRNA transcripts modified by C5-methylcytosine). As the text demonstrates, both in vivo and invitro investigations have confirmed that the TIMP2 target gene is linked to its regulator (ZEBED1) transcription factors TF, and both are linked to miRNA (has-miR-6822-3p), which was chosen and promoted as key genes. Future Directions: The investigation of key genes in the prognosis of colorectal adenocarcinoma and targeted gene therapy should focus on elucidating molecular pathways, creating novel treatment strategies to improve patient outcomes, and implementing the study's findings in clinical settings in the future. Verification in larger patient cohorts with colorectal adenocarcinoma is necessary to confirm the significance and dependability of the identified genes and fusion events as potential therapeutic targets. It is possible to create hypotheses for further study using enrichment maps. The parameters used in the pathway enrichment study, such as the minimum and maximum pathway size or the chosen route databases, should be changed in order to assess how robust the conclusions are. These parameters are suggested to have the potential to impact the findings. Declarations Author Contribution The authors, E. O. M, participated in the conception, design, analysis, and interpretation of the data, besides writing and editing the manuscript (2) P. M participated in the conception, design, analysis, and interpretation of the data besides writing and editing the manuscript (3) A. K. participated in the conception design, analysis, and interpretation of the data besides writing and editing the manuscript (4) S. N. participated in technical support and data analysis and project managing (5) B.M. participated in the conception design, analysis, and interpretation of the data supervising. This manuscript has not been submitted, nor is it under review at another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. The following authors have no affiliations with organizations with a direct or indirect financial interest in the subject matter discussed in the manuscript. Disclosure statement The authors reported no potential conflict of interest. Funder Qatar Foundation, Qatar National Library, grand award number NPRP9-350-3-074 Email [email protected] References Shinji, S., T. Yamada, A. Matsuda, et al., Recent Advances in the Treatment of Colorectal Cancer: A Review. J Nippon Med Sch, 2022. 89 (3): p. 246-254,doi: 10.1272/jnms.JNMS.2022_89-310. He, S., X. Li, X. Zhou, et al., Role of epithelial cell-mesenchymal transition regulators in molecular typing and prognosis of colon cancer. J Gastrointest Oncol, 2023. 14 (2): p. 744-757,doi: 10.21037/jgo-23-49. Dai, Y., Z. Jiang, Y. Qiu, et al., Identification of Key Carcinogenic Genes in Colon Adenocarcinoma. Iran J Public Health, 2022. 51 (2): p. 364-374,doi: 10.18502/ijph.v51i2.8689. Yazdani, F., N. Mottaghi-Dastjerdi, A. Ghorbani, et al., Identification of Key Genes and Pathways Governing T-DM1 Resistance in OE-19 Esophageal Cancer Cells through Bioinformatics Analysis. 2023, doi.org/10.21203/rs.3.rs-3423572/v1. Mahgoub, E.O., W.C. Cho, M. Sharifi, et al., Role of functional genomics in identifying cancer drug resistance and overcoming cancer therapy relapse. Heliyon %@ 2405-8440, 2023,doi: 10.1016/j.heliyon.2023.e22095. Chen, L., D. Lu, K. Sun, et al., Identification of biomarkers associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis. Gene, 2019. 692 : p. 119-125,doi: 10.1016/j.gene.2019.01.001. Mahgoub, I., A.K. Bolad and M. Mergani, Generation and immune-characterization of single chain fragment variable (scFv) antibody recognize breast cancer cells line (MCF-7). Journal for ImmunoTherapy of Cancer, 2014. 2 (Suppl 1): p. P6 %@ 2051-1426. doi: 10.1016/j.gene.2019.01.001. Pournoor, E., Z. Mousavian, A.N. Dalini, et al., Identification of Key Components in Colon Adenocarcinoma Using Transcriptome to Interactome Multilayer Framework. Sci Rep, 2020. 10 (1): p. 4991,doi: 10.1038/s41598-020-59605-z. Xu, M., J. Chang, W. Wang, et al., Classification of colon adenocarcinoma based on immunological characterizations: Implications for prognosis and immunotherapy. Front Immunol, 2022. 13 : p. 934083,doi: 10.3389/fimmu.2022.934083. Mahgoub, E.O. and S. Kulkarni, Structural Analyses of An Epidermal Growth Factor Receptor-Specific Single-Chain Fragment Variable via An In Silico Approach. J Vis Exp, 2023(201): p. e65894 %@ 1940-087X,doi: 10.3791/65894. Raudvere, U., L. Kolberg, I. Kuzmin, et al., g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res, 2019. 47 (W1): p. W191-W198,doi: 10.1093/nar/gkz369. Kolberg, L., U. Raudvere, I. Kuzmin, et al., g:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update). Nucleic Acids Res, 2023. 51 (W1): p. W207-W212,doi: 10.1093/nar/gkad347. Gureghian, V., H. Herbst, I. Kozar, et al., A multi-omics integrative approach unravels novel genes and pathways associated with senescence escape after targeted therapy in NRAS mutant melanoma. Cancer Gene Ther, 2023. 30 (10): p. 1330-1345,doi: 10.1038/s41417-023-00640-z. Khan, A.A., T.J. Huat, A. Al Mutery, et al., Significant transcriptomic changes are associated with differentiation of bone marrow-derived mesenchymal stem cells into neural progenitor-like cells in the presence of bFGF and EGF. Cell Biosci, 2020. 10 : p. 126,doi: 10.1186/s13578-020-00487-z. Manoochehri, H., A. Jalali, H. Tanzadehpanah, et al., Identification of Key Gene Targets for Sensitizing Colorectal Cancer to Chemoradiation: an Integrative Network Analysis on Multiple Transcriptomics Data. J Gastrointest Cancer, 2022. 53 (3): p. 649-668,doi: 10.1007/s12029-021-00690-2. Sajadi, M., M. Fazilti, H. Nazem, et al., The expression changes of transcription factors including ANKZF1, LEF1, CASZ1, and ATOH1 as a predictor of survival rate in colorectal cancer: a large-scale analysis. Cancer Cell Int, 2022. 22 (1): p. 339,doi: 10.1186/s12935-022-02751-3. Shalem, O., N.E. Sanjana, E. Hartenian, et al., Genome-scale CRISPR-Cas9 knockout screening in human cells. Science, 2014. 343 (6166): p. 84-87,doi: 10.1126/science.1247005. Lin, J., Z. Cao, D. Yu, et al., Identification of Transcription Factor-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma. Front Genet, 2021. 12 : p. 709133,doi: 10.3389/fgene.2021.709133. D, M.R., C. Ziyani and O. Delaneau, Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis. Commun Biol, 2022. 5 (1): p. 876,doi: 10.1038/s42003-022-03831-w. AbuQamar, S.F., K.A. El-Tarabily and A. Sham, Co-expression Networks in Predicting Transcriptional Gene Regulation. Methods Mol Biol, 2021. 2328 : p. 1-11,doi: 10.1007/978-1-0716-1534-8_1. Liu, W.Q., W.L. Li, S.M. Ma, et al., Discovery of core gene families associated with liver metastasis in colorectal cancer and regulatory roles in tumor cell immune infiltration. Transl Oncol, 2021. 14 (3): p. 101011,doi: 10.1016/j.tranon.2021.101011. Mahgoub, E.O., Single chain fragment variables antibody binding to EGF receptor in the surface of MCF7 breast cancer cell line: application and production review. Open Journal of Genetics, 2017. 7 (2): p. 84-103, doi: 10.4236/ojgen.2017.72008. Mitchell, M.J. and M.R. King, Leukocytes as carriers for targeted cancer drug delivery. Expert Opin Drug Deliv, 2015. 12 (3): p. 375-92,doi: 10.1517/17425247.2015.966684. Mahgoub, E.O. and A.K. Bolad, Construction, expression and characterisation of a single chain variable fragment in the Escherichia coli periplasmic that recognise MCF-7 breast cancer cell line. J Cancer Res Ther, 2014. 10 (2): p. 265-73,doi: 10.4103/0973-1482.136551. Ilaslan, E., M.P. Sajek, J. Jaruzelska, et al., Emerging Roles of NANOS RNA-Binding Proteins in Cancer. Int J Mol Sci, 2022. 23 (16): p. 9408 %@ 1422-0067,doi: 10.3390/ijms23169408. Zhu, C., S. Wang, Y. Du, et al., Tumor microenvironment-related gene selenium-binding protein 1 (SELENBP1) is associated with immunotherapy efficacy and survival in colorectal cancer. BMC Gastroenterol, 2022. 22 (1): p. 437,doi: 10.1186/s12876-022-02532-2. Xu, F., L. Jiang, Q. Zhao, et al., Whole-transcriptome and proteome analyses identify key differentially expressed mRNAs, miRNAs, lncRNAs and circRNAs associated with HCC. Oncogene, 2021. 40 (29): p. 4820-4831,doi: 10.1038/s41388-021-01908-0. Mahgoub, I.O. Design, expression and characterization of a single chain fragment variable anti-mcf-7 antibody; A humanized antibody derived from monoclonal antibody . 2014. Hamad bin Khalifa University Press (HBKU Press). doi.org/10.5339/qfarc.2014.HBPP0705. Miao, Y., J. Wang, X. Ma, et al., Identification prognosis-associated immune genes in colon adenocarcinoma. Biosci Rep, 2020. 40 (11): p. BSR20201734,doi: 10.1042/BSR20201734. Wu, F., Y. Liu, S. Hu, et al., Ribosomal protein L31 (RPL31) inhibits the proliferation and migration of gastric cancer cells. Heliyon, 2023. 9 (2): p. e13076,doi: 10.1016/j.heliyon.2023.e13076. Graifer, D. and G. Karpova, Eukaryotic protein uS19: a component of the decoding site of ribosomes and a player in human diseases. Biochem J, 2021. 478 (5): p. 997-1008,doi: 10.1042/BCJ20200950. Peng, Y. and C.M. Croce, The role of MicroRNAs in human cancer. Signal Transduct Target Ther, 2016. 1 (1): p. 15004,doi: 10.1038/sigtrans.2015.4. Song, G., S. Xu, H. Zhang, et al., TIMP1 is a prognostic marker for the progression and metastasis of colon cancer through FAK-PI3K/AKT and MAPK pathway. J Exp Clin Cancer Res, 2016. 35 (1): p. 148,doi: 10.1186/s13046-016-0427-7. Giaginis, C., N. Nikiteas, A. Margeli, et al., Serum tissue inhibitor of metalloproteinase 1 and 2 (TIMP-1 and TIMP-2) levels in colorectal cancer patients: associations with clinicopathological variables and patient survival. Int J Biol Markers, 2009. 24 (4): p. 245-52,doi: 10.1177/172460080902400405. Liu, Y., C. Li, L. Dong, et al., Identification and verification of three key genes associated with survival and prognosis of COAD patients via integrated bioinformatics analysis. Biosci Rep, 2020. 40 (9): p. BSR20200141,doi: 10.1042/BSR20200141. Mahgoub, E.O., Y. Haik and S. Qadri, Comparison study of exosomes molecules driven from (NCI1975) NSCLC cell culture supernatant isolation and characterization techniques. The FASEB Journal, 2019. 33 (S1): p. 647.22-647.22 %@ 0892-6638, doi: 10.1042/BSR20200141. Ding, R., Y. Qu, C.H. Wu, et al., Automatic gene annotation using GO terms from cellular component domain. BMC Med Inform Decis Mak, 2018. 18 (Suppl 5): p. 119,doi: 10.1186/s12911-018-0694-7. Nait Slimane, S., V. Marcel, T. Fenouil, et al., Ribosome Biogenesis Alterations in Colorectal Cancer. Cells, 2020. 9 (11): p. 2361 %@ 2073-4409,doi: 10.3390/cells9112361. Wilson-Edell, K.A., A. Kehasse, G.K. Scott, et al., RPL24: a potential therapeutic target whose depletion or acetylation inhibits polysome assembly and cancer cell growth. Oncotarget, 2014. 5 (13): p. 5165-76,doi: 10.18632/oncotarget.2099, 10.18632/oncotarget.2099.. Kang, J., N. Brajanovski, K.T. Chan, et al., Ribosomal proteins and human diseases: molecular mechanisms and targeted therapy. Signal Transduct Target Ther, 2021. 6 (1): p. 323,doi: 10.1038/s41392-021-00728-8. Petibon, C., M. Malik Ghulam, M. Catala, et al., Regulation of ribosomal protein genes: An ordered anarchy. Wiley Interdiscip Rev RNA, 2021. 12 (3): p. e1632,doi: 10.1002/wrna.1632. Qin, Y., L. Liu, X. Hu, et al., Expression and bioinformatics analysis of RPL38 protein and mRNA in gastric cancer. Cell Mol Biol (Noisy-le-grand), 2023. 69 (13): p. 256-261,doi: 10.14715/cmb/2023.69.13.38. Pan, R., C. Yu, Y. Shao, et al., Identification of Key Genes and Pathways Involved in Circulating Tumor Cells in Colorectal Cancer. Anal Cell Pathol (Amst), 2022. 2022 : p. 9943571,doi: 10.1155/2022/9943571. Jin, H., B. Liu, X. Guo, et al., MYLK and CALD1 as molecular targets in bladder cancer. Medicine (Baltimore), 2023. 102 (47): p. e36302,doi: 10.1097/MD.0000000000036302. Ye, G., L. Tu, Z. Li, et al., SYNPO2 promotes the development of BLCA by upregulating the infiltration of resting mast cells and increasing the resistance to immunotherapy. Oncol Rep, 2024. 51 (1): p. 1-13,doi: 10.3892/or.2023.8673. Gong, H., X. Yang, L. An, et al., PCSK5 downregulation promotes the inhibitory effect of andrographolide on glioblastoma through regulating STAT3. Mol Cell Biochem, 2024: p. 1-13,doi: 10.1007/s11010-024-04977-3. Li, K., C. Zhang, R. Zhou, et al., Single cell analysis unveils B cell-dominated immune subtypes in HNSCC for enhanced prognostic and therapeutic stratification. Int J Oral Sci, 2024. 16 (1): p. 29,doi: 10.1038/s41368-024-00292-1. Yang, K., X. Li, Z. Jiang, et al., Tumour suppressor ABCA8 inhibits malignant progression of colorectal cancer via Wnt/beta-catenin pathway. Dig Liver Dis, 2024. 56 (5): p. 880-893,doi: 10.1016/j.dld.2023.10.026. Lv, C., H. Yang, J. Yu, et al., ABCA8 inhibits breast cancer cell proliferation by regulating the AMP activated protein kinase/mammalian target of rapamycin signaling pathway. Environ Toxicol, 2022. 37 (6): p. 1423-1431,doi: 10.1002/tox.23495. Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5188416","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":380978503,"identity":"8d26589e-a5a8-4f11-bd96-8d266fe25a5c","order_by":0,"name":"Elham Omer Mahgoub","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBAC+XYeEFXHwAeiPkAEDfBqYWMGaznMwAYkGWeQoOUAWAuETVgL78FPNxgOyLOJHX722KbGTraBvXmbBEOZHR4tfMnSOQx1hm3SaebGOceSjRt4jpVJMJxLxucwA6AWZsY26QQz6dyGA4kNEjlmEoxtzPi0GP8GarFvk07/Jm0J0iL/BqSlHp8WM5AtiW3SOWbSjGBbeEBaDuPVYp1jcDgZqKVMsgfolzaetGKLhHPHcWqRb+8xvp1TUWfbL52+TeIHMMT62Q9vvPGhrBqnFghAigjGNhCZwEZABzJgbIA4mAQto2AUjIJRMNwBAHa+Q3rlyb1aAAAAAElFTkSuQmCC","orcid":"","institution":"Community College of Qatar","correspondingAuthor":true,"prefix":"","firstName":"Elham","middleName":"Omer","lastName":"Mahgoub","suffix":""},{"id":380978505,"identity":"8f57d5d8-187d-4721-9331-4cd95615ea3b","order_by":1,"name":"Pooja Prakash Mankar","email":"","orcid":"","institution":"Shri Shivaji College of Arts, Commerce and Science","correspondingAuthor":false,"prefix":"","firstName":"Pooja","middleName":"Prakash","lastName":"Mankar","suffix":""},{"id":380978507,"identity":"7c942c41-9f65-40e3-8290-52d3c7c5ee21","order_by":2,"name":"Amit kumar","email":"","orcid":"","institution":"ICMR","correspondingAuthor":false,"prefix":"","firstName":"Amit","middleName":"","lastName":"kumar","suffix":""},{"id":380978509,"identity":"52d093b3-6431-43d2-9b13-f3d474900ebc","order_by":3,"name":"Syed Asif Naqv","email":"","orcid":"","institution":"Australia Affiliation- BBR PTY LTD 4, D502- Amity Akruthi","correspondingAuthor":false,"prefix":"","firstName":"Syed","middleName":"Asif","lastName":"Naqv","suffix":""},{"id":380978510,"identity":"6443250f-d8d3-40a9-a53d-d3620e41f582","order_by":4,"name":"Bharti Mittal","email":"","orcid":"","institution":"ICMR","correspondingAuthor":false,"prefix":"","firstName":"Bharti","middleName":"","lastName":"Mittal","suffix":""}],"badges":[],"createdAt":"2024-10-01 15:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5188416/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5188416/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73779004,"identity":"90d77b12-3ad0-444e-81ac-1c9e19e4a5cd","added_by":"auto","created_at":"2025-01-14 15:05:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80778,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart \u0026nbsp;\u0026nbsp;of RNA sequencing pipeline.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5188416/v1/b9685340d81df7e83d2310ca.png"},{"id":73779001,"identity":"57c6a68f-1169-4e39-9ad1-d49a4b26efcb","added_by":"auto","created_at":"2025-01-14 15:05:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":497829,"visible":true,"origin":"","legend":"\u003cp\u003e(A)PC1, \u0026nbsp;\u0026nbsp;the principal component 1 in the x-axes, and PC2, the principal component 2 \u0026nbsp;\u0026nbsp;in the y-axes, to characterize the variants in the genes\u003c/p\u003e\n\u003cp\u003e(B)The \u0026nbsp;\u0026nbsp;volcano plot shows the distribution of the genes depending on their \u0026nbsp;\u0026nbsp;expression of the genes. The upregulated genes are shown in blue, while the \u0026nbsp;\u0026nbsp;down-regulated genes are shown in grey.\u003c/p\u003e\n\u003cp\u003e(C)Box-blot \u0026nbsp;\u0026nbsp;of feature genes counts as normal genes and tumor genes\u003c/p\u003e\n\u003cp\u003e(D)the \u0026nbsp;\u0026nbsp;normalized count represents the upregulated genes and downregulated genes.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5188416/v1/a177539b31b86c459900faf5.png"},{"id":73779006,"identity":"238b162e-8f1f-4d4c-8117-115cd063d7f3","added_by":"auto","created_at":"2025-01-14 15:05:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":364627,"visible":true,"origin":"","legend":"\u003cp\u003eshows \u0026nbsp;\u0026nbsp;the transcription factors (TF) for each upregulated gene in the Gsea \u0026nbsp;\u0026nbsp;database. TFs are key players in the growth and tumorigenesis of colorectal \u0026nbsp;\u0026nbsp;adenocarcinoma.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5188416/v1/be7c5a09e68ae33672abe67c.png"},{"id":73779009,"identity":"0d0e048c-f2b2-4855-b6ef-e960a6120581","added_by":"auto","created_at":"2025-01-14 15:05:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":258617,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expression of upregulated genes based on RNA expression patterns and protein co-regulation derived from String database. Co-expression is shown by a red square: a more intense square color represents a higher association score of the expression data.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5188416/v1/24ba9a42d36d784823ada8bb.png"},{"id":73779002,"identity":"5bd2cb30-8006-47e5-9ec0-29d707d29283","added_by":"auto","created_at":"2025-01-14 15:05:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116090,"visible":true,"origin":"","legend":"\u003cp\u003e(A)shows Functional enrichments performed; common enriched diseases \u0026nbsp;\u0026nbsp;are malignant neoplasms of the breast, liver Fibrosis, and colorectal cancer.\u003c/p\u003e\n\u003cp\u003e(B)KEGG: \u0026nbsp;\u0026nbsp;the upregulated genes expressed in colorectal adenocarcinoma depend on these \u0026nbsp;\u0026nbsp;pathways.\u003c/p\u003e\n\u003cp\u003e(C)HPA tissues: the gene expression enriched in adipose tissues, \u0026nbsp;\u0026nbsp;adipocytes, and adrenal glands.\u003c/p\u003e\n\u003cp\u003e(D)Upregulated \u0026nbsp;\u0026nbsp;tissues: The primary tissues that express the upregulated genes are the \u0026nbsp;\u0026nbsp;Brain, Testis, Kidney, and lung. They are involved in cytoplasmic translation \u0026nbsp;\u0026nbsp;and translation.\u003c/p\u003e\n\u003cp\u003e(E)GOTERM-cc- \u0026nbsp;\u0026nbsp;Direct cellular component, relation extraction between gene and other \u0026nbsp;\u0026nbsp;entities.\u003c/p\u003e\n\u003cp\u003e(F)gene \u0026nbsp;\u0026nbsp;enrichment analysis, the primary biological functional enriched genes, the \u0026nbsp;\u0026nbsp;main gain involved in the regulation of the transcriptome genes, and genes \u0026nbsp;\u0026nbsp;silencing by miRNA.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5188416/v1/7dd3111d459ff8c63a34546d.png"},{"id":73780595,"identity":"46174d66-dbef-44f2-afa1-fd3ef7650ca5","added_by":"auto","created_at":"2025-01-14 15:13:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1031590,"visible":true,"origin":"","legend":"\u003cp\u003e(A)Cytoscape \u0026nbsp;\u0026nbsp;software, the network illustrates how the DEGs found from COAD interact \u0026nbsp;\u0026nbsp;with one another on a protein-protein level using Cytoscape. The edges are \u0026nbsp;\u0026nbsp;shown as lines (gray), and the nodes are ellipses (Robin's green).\u003c/p\u003e\n\u003cp\u003e(B)upregulated \u0026nbsp;\u0026nbsp;genes and downregulated genes in the regulated circular.\u003c/p\u003e\n\u003cp\u003e(C)Enrichment \u0026nbsp;\u0026nbsp;Analysis and Network images of the group 913 upregulated genes of colorectal \u0026nbsp;\u0026nbsp;adenocarcinoma generated from the NDeX database\u003c/p\u003e\n\u003cp\u003e(D)Ten \u0026nbsp;\u0026nbsp;higher upregulated genes and ten higher downregulated genes are in a heatmap \u0026nbsp;\u0026nbsp;that is predicted by the limma package in R. The green color represents the \u0026nbsp;\u0026nbsp;normal sample. The pink color represents the tumor samples.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5188416/v1/1dc0d318b184c354c1d39a8b.png"},{"id":73779010,"identity":"f7eaa373-cc5b-49db-83cf-5f70388dc637","added_by":"auto","created_at":"2025-01-14 15:05:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":606700,"visible":true,"origin":"","legend":"\u003cp\u003e(A)Venn diagram immunogenic genes set versus cell type gene \u0026nbsp;\u0026nbsp;signature; the common genes were 57 common genes between cell type \u0026nbsp;\u0026nbsp;signature genes versus immunogenic genes.\u003c/p\u003e\n\u003cp\u003e(B)String protein-protein interaction divided \u0026nbsp;\u0026nbsp;the upregulated genes into five clusters.\u003c/p\u003e\n\u003cp\u003e(C)Regulatory \u0026nbsp;\u0026nbsp;network of miRNA-Gene-TF in COAD between 250 differentially \u0026nbsp;\u0026nbsp;expressed mRNAs Green circles represent the differentially expressed target \u0026nbsp;\u0026nbsp;mRNAs.\u003c/p\u003e\n\u003cp\u003e(D)The key \u0026nbsp;\u0026nbsp;gene TIMP2 has substantial TF and miRNA interactions.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5188416/v1/c37db370570b00f292d003bf.png"},{"id":73779012,"identity":"c3130360-5760-4f65-b01c-712982f80420","added_by":"auto","created_at":"2025-01-14 15:05:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":260291,"visible":true,"origin":"","legend":"\u003cp\u003e(A)Figure \u0026nbsp;\u0026nbsp;(8) g-profiler database: (A) identified each \u0026nbsp;\u0026nbsp;common group's functional enrichment for GO: MF, GO: BP, GO: CC, KEGG, and \u0026nbsp;\u0026nbsp;TF.\u003c/p\u003e\n\u003cp\u003e(B)viewed \u0026nbsp;\u0026nbsp;the 20 pathways of enriched genes and mutation types of gene structure. \u0026nbsp;\u0026nbsp;Those pathways are controlled by the upregulated genes expressed in \u0026nbsp;\u0026nbsp;colorectal adenocarcinoma.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5188416/v1/4505f5f7f87bca409f9aa2b2.png"},{"id":73780596,"identity":"74db0ccc-09c3-437f-8a18-229b61fc16ed","added_by":"auto","created_at":"2025-01-14 15:13:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":917884,"visible":true,"origin":"","legend":"\u003cp\u003e(A)C-bioportal, Boxplot Comparative between the \u0026nbsp;\u0026nbsp;mutational and non-mutational part of MYLK genes.\u003c/p\u003e\n\u003cp\u003e(B)Boxplot \u0026nbsp;\u0026nbsp;Comparative between the mutational and non-mutational parts of SYNPO2 gene.\u003c/p\u003e\n\u003cp\u003e(C)Boxplot \u0026nbsp;\u0026nbsp;Comparative between the mutational and non-mutational parts of PCSK5 gene.\u003c/p\u003e\n\u003cp\u003e(D)Boxplot \u0026nbsp;\u0026nbsp;Comparative between the mutational and non-mutational parts of JCHAIN gene.\u003c/p\u003e\n\u003cp\u003e(E)Boxplot \u0026nbsp;\u0026nbsp;Comparative between the mutational and non-mutational parts of ABCA8 gene.\u003c/p\u003e\n\u003cp\u003e(F)Boxplot \u0026nbsp;\u0026nbsp;Comparative between the mutational and non-mutational parts of SLC26A3 gene.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5188416/v1/af8985ee23735dd2c9753810.png"},{"id":76438845,"identity":"5f5be7dc-d86c-4e67-ab4a-f81b79f679cb","added_by":"auto","created_at":"2025-02-17 07:54:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4560849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5188416/v1/03da16d9-7313-450f-973e-37423178819e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Colorectal Adenocarcinoma Key Genes to Be Validated as Potential Prognosis Biomarkers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe second most significant cause of cancer-related deaths worldwide. The third most prevalent cancer globally. Obesity, inactivity, and smoking habits, in addition to an older population, raise the risk of Colorectal Adenocarcinoma (COAD). Colorectal Adenocarcinoma continues to be a problem despite improvements in surgical methods, chemotherapy, and radiotherapy. Endoscopic procedures, such as endoscopic submucosal dissection and endoscopic mucosal excision, have been used to treat early-stage COAD. Adenocarcinomas are the most common subtype of Colorectal Adenocarcinoma (COAD) and result from the malignant transformation of glandular epithelial cells in the colon or rectum. These tumors usually grow slowly, starting as benign adenomatous polyps and moving through a sequence of well-researched genetic and epigenetic changes called the adenoma-carcinoma sequence. However, due to the prevalence of lymph node metastases, lymph node dissection is a crucial component of surgical therapy for cancer in its advanced stages. \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Once colon cancer develops local or distant metastases, the prognosis is dismal. Researchers or physicians may need to find novel markers for predicting the prognosis and therapy of colon cancer to improve the prognosis of people with the disease. .\u003c/p\u003e \u003cp\u003eColorectal Adenocarcinoma (COAD) patients are most likely to develop new targeted genes to overcome the cancer cell drug resistance to chemotherapy either naturally or as a result of receiving T-DM1 treatment. This study suggests that variations in the expression levels of specific genes may impact the sensitivity or resistance to chemotherapy \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Biomarkers are essential for the early diagnosis of drug resistance to chemotherapy. Although biomarkers are utilized at different phases of the disease to aid in its management, each has its limits \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Identification of essential genes that could be used as biomarkers for diagnosis of colorectal cancer was a persuasive motive to analyze Differential expression genes \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e ,\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe clinical use of biomarkers in COAD is crucial for prognostic stratification, surveillance, and treatment selection, in addition to being necessary for the early diagnosis of the disease. Therefore, there is an urgent need for new, affordable, non-invasive, easily quantifiable, and accurate screening methods for colorectal cancer (COAD). This study uses RNA-Seq data of colorectal cancer (COAD) to analyze differential gene expression and predict possible biomarker for future diagnosis and gene therapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used in this work is freely available to the public via the European Nucleotide Archive (ENA) under project ID PRJNA649071 (https://www.ebi.ac.uk/ena/browser/home). The transcriptome RNA-seq raw data were obtained from PRJEB24758 in the European Nucleotide Archive (ENA) database. Then, the Sequence Read Archive (SRA) runs a selection to download the sequences database. Samples relevant to Colorectal Adenocarcinoma (COAD) are gathered into this dataset, which includes fresh frozen tissues (FF), formalin-fixed, paraffin-embedded (FFPE) tissues, and several cancer cell lines.\u003c/p\u003e\n\u003cp\u003eThe NGS datasets were discovered by using the ENA\u0026apos;s search feature associated with colorectal adenocarcinoma. Species (such as Homo sapiens), experimental circumstances, library type (RNA-Seq), and sequencing platform (Illumina) filters were used to limit the results. The Run Accession Numbers for certain transcriptome RNA-seq raw data runs that matched colorectal cancer samples were noted. Europa Galaxy was used to identify the differentially expressed genes (DEGs) between colorectal adenocarcinoma tissues and normal tissues. The inclusion of matched normal and tumor samples from COAD patients provides a valuable resource for comparative analysis. FTP (File Transfer Protocol) was used for effective data retrieval. The raw transcriptome RNA-seq raw data (single-end) files (often in FASTQ format) containing the raw sequencing reads were obtained using FTP links. After the download was finished, the raw data files were arranged on the local storage system. An organized directory including pertinent metadata, sample details, and the name of the dataset was created.\u003c/p\u003e\n\u003cp\u003eFurther, the data were filtered according to normal and tumor samples in an Excel worksheet. The data was also filtered to select single-end data, as it contains a larger data set than paired-end data. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality Control and Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTools to process the data in Galaxy, The FTP links were uploaded to the Galaxy database (https://usegalaxy.org/) along with the reference genome (Homo sapiens) from the Ensemble. The sample set for the investigation included 186 runs of tumor samples and 164 runs of normal samples, offering a balanced representation for reliable comparison analyses. This important collection paves the way for future discoveries on the molecular causes of Colorectal Adenocarcinoma (COAD) and enables a thorough investigation of the transcriptional changes linked to the illness.\u003c/p\u003e\n\u003cp\u003eThe FastQC tool is used to calculate the quality control of NGS raw data. The quality of sequencing data was checked by clicking on FastQC output datasets under the various quality matrices, including pre-base sequence quality, sequence length distribution, GC content, etc. Using MultiQC, the FastQC report was aggregated into one report. The adapter sequences were trimmed using the TrimGalore tool. The ftp link of reference genome https://ftp.ensembl.org/pub/release110/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.toplevel.fa.gz (Homo sapiens) and GTF file https://ftp.ensembl.org/pub/release-110/gtf/homosapiens/ and \u003cu\u003eHomo_sapiens.GRCh38.110.gtf.gz \u003c/u\u003ewas copied and uploaded in the galaxy for alignment. The alignment sequence of the data compared with the reference genome process was done using the HISAT2 tool. The Feature Counts tool is used to count how many reads are aligned to each gene (count matrix). The differentially expressed genes from RNA-seq data were identified using the DESeq2 tool, the RNA sequencing summarized in flowchart shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA values were eliminated by applying the filter to P-adj values in the Excel sheet. The filter was applied to p-adj as \u0026le;0.05 to derive only significant genes. Further, the data were sorted based on fold change values to identify upregulated genes (threshold is \u0026ge;2) and to identify downregulated genes (threshold is \u0026le;-2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential Gene Expression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) \u003cu\u003ehttps://david.ncifcrf.gov/, ver. \u003c/u\u003e\u003cu\u003e\u003csup\u003e11\u003c/sup\u003e\u003c/u\u003eIt was used to identify potential uses for differentially expressed genes (DEGs). The DEGs were analyzed by choosing official gene symbols, annotation tables, and Entrez IDs. Genes were functionally enriched using g-Profiler \u003cu\u003ehttps://biit.cs.ut.ee/gprofiler/gost\u003c/u\u003e, which conducts statistical enrichment analysis and offers interpretative insights into gene lists provided by users. \u003csup\u003e12\u003c/sup\u003e,\u003csup\u003e11\u003c/sup\u003e. In the David annotation tools, the Kyoto Encyclopedia of Genes and Genomes (KEGG), HPA tissues, Upregulated tissues, GOTERM-cc-direct cellular component, and gene enrichment analysis columns were prepared using the lists of the gene\u0026apos;s functional annotation tools. Two columns were copied into a new Excel worksheet, sorted from larger to smaller, and the chart was chosen after the columns of each functional annotation. Their related frequency column was selected in the Excel worksheet and inserted using pivotable in Excel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Gene Enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe STRING database found the most crucial modules in the protein-protein interaction \u003csup\u003e13\u003c/sup\u003e network. The study\u0026apos;s analysis of the molecular causes of colorectal cancer revealed the gene different cluster group and activities in the gene set related to colorectal adenosarcoma. Furthermore, using a string database, co-expression genes are identified as additional information on the gene\u0026apos;s connection and regulation. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis (GSEA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study used the Gene Set Enrichment Analysis (GSEA) database \u003cu\u003ehttps://www.gsea-msigdb.org/gsea/index.jsp\u003c/u\u003e to measure the overlap of gene functions. The Ensembl ID was submitted to the molecular signature database, and the oncogenic dataset was selected to visualize the transcription factor for each enriched gene in the large-scale design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVenn diagram analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenn diagram is used to overlap the immunogenic genes set versus cell type gene signature data that is retrieved from the Gsea database. The Venn diagram method\u003cu\u003e draw venn diagrams, result ugent. Be\u003c/u\u003e. The common gene expression signatures of the immunogenic genes set versus cell type gene signature are considered the suitable group of genes to be used in further experiments. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMapping of Protein-Protein Network \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe biomart_mapper script in R uses the Limma package to map 913 upregulated and 906 downregulated genes of COAD and choose the functional protein coding of the RNA sequence data. Even though the original purpose of the R program Limma was to analyze differential expression (DE) in RNA sequencing data. Cytoscape 3.7.1 was used to map the protein-coding gene data that was produced. The obtained data was then used to forecast the heatmap of the top 10 downregulated genes and the highest upregulated genes, demonstrating a considerable up-regulation of COAD relative to normal tissue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emiRNA Prediction Using Mirwalk Database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethe two highest upregulated genes and two highest downregulated genes were submitted to the mirwalk database \u003cu\u003ehttp://mirwalk.umm.uni-heidelberg.de/\u003c/u\u003e to identify the miRNA that interacts with these genes. The results have been saved in one Excel worksheet. Next, the same list of genes was submitted to the chea3 database \u003cu\u003ehttps://maayanlab.cloud/chea3/\u003c/u\u003eto identify the overlapped transcription factors (TF), and that was added to the second worksheet. Finally, the resulting TF list was submitted to the mirwalk database, and the overlapped miRNA was documented. The overlapped 250 miRNA in the two listed was prepared using Excel formulas. Cytoscape software was used to merge the three networks of TF-Genes, Genes -TF, and finally, miRNA-TF. One gene was connected to one transcription factor, and both overlapped with the same miRNA. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.bioportal Database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe structure of the gene variants and the types of mutations in the enriched genes were examined using the c.bioportal database\u003cu\u003e https://www.cbioportal.org/\u003c/u\u003e. The official _gene_Id set genes are submitted for colorectal adenocarcinoma based on gene selection, and each gene mutation is thereafter examined individually. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG-profiler Database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to determine the functional gene pathways, common group, and enriched genes, the Ensanbl_Id query was submitted to g-profiler \u003cu\u003ehttps://biit.cs.ut.ee/gprofiler/gost\u003c/u\u003e. Besides general annotations, interactions, literature, pathway associations, disease associations, and tissue expression were explored.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDifferential Expression Genes Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential expression genes analysis resulted from RNA sequencing. As shown in Figure 2, the Genes were processed using David\u0026apos;s annotation analysis site. The MA plot shows differentially expressed genes, with blue indicating a departure from the baseline, suggesting substantial transcriptional activity. The distribution of points indicates a complex alteration in gene regulatory networks in colorectal cancer. The dispersion plot shows decreased variability in gene expression as the mean expression level increases, indicating the technical robustness of RNA-Seq data. The volcano plot shows genes exceeding established thresholds for p-value and log2 fold change, underscoring their potential as key players in colorectal cancer pathology. These genes represent promising targets for further validation as biomarkers or therapeutic interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscription Factors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ealterations in transcription factors\u0026apos; (TFs\u0026apos;) expression and activity affect other genes\u0026apos; levels. TFs regulate essential physiological processes and control the expression of multiple genes at once. Transcription factors are heat mapped using the Gsea database, as shown in Figure 3. As in Figure (3), The TF checkpoint database provided the reported gene functions, and Ensembl genes were utilized to convert the reported Entrez gene IDs to corresponding Ensembl IDs. Transcription factor (TF) enrichment analysis was performed on the differentially expressed genes using the Gsea database web-server application. In short, humanized gene symbols with human differentially expressed genes.\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne technique for examining genomic data, especially extensive transcriptome data, is Gene Ontology (GO) analysis. As explained in Figure (5) using The Database for Annotation, Visualization, and Integrated Discovery (DAVID, https://david.ncifcrf.gov/, ver. 6.8). Ten pathways of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were visualized. Gene Ontology (GO) enrichment analysis to ascertain the biological function of cancer-related protein pathways\u003csup\u003e15\u003c/sup\u003e. For additional visualization, the enriched GO keywords and pathways with a False Discovery Rate (FDR) of less than 0.01 were chosen. Plotting of KEGG pathways, bubble graphs, and the top 20 relevant GO terms (BP, CC, and MF) was done using available methods in Microsoft Excel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Cytoscape, Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAs shown in Figure (6)(A)\u0026nbsp;\u003c/strong\u003eCytoscape software, the network illustrates how the DEGs found from COAD interact with one another on a protein-protein level using Cytoscape. As shown in Figure 6 (A) and (B), The network visualizes functional groups based on the most significant (leading) phrase for each group, offering a clear picture of how upregulating and downregulating relate to one another. Functional groups can be created utilizing the GO relationships as an alternative to the kappa score grouping method. Meanwhile, the protein-protein network of the group 913 upregulated genes of colorectal adenocarcinoma generated from the NDeX database shows a very strong network\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Heatmap of Top Ten Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 6 D, ten higher upregulated genes and ten higher downregulated genes are in a heatmap predicted by the Limma package in R. The green color represents the normal sample, and the pink color represents the tumor samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Networks\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Figure 7(D), a Venn diagram was used to identify 57 common genes, and the Pearson correlation coefficient analysis was performed on these genes. Figure 7 (A) shows a Venn diagram comparing immunogenic genes with cell type gene signatures, revealing 57 common genes between the two. (B) String protein-protein interaction classified the upregulated genes into five clusters. (C) Depicts the regulatory network of miRNA-Gene-TF in COAD, highlighting 250 differentially expressed mRNAs. The green circles represent the differentially expressed target mRNAs. (D) Shows that the key gene TIMP2 has significant interactions with TF and miRNA.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG- profiler and Gene Ontology Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethe g-profiler database recognized the functional enrichment of GO: MF, GO: BP, GO: CC, KEGG, and TF for each common group, as shown in Table 1 and Figure 8. Gene fusion events can produce new proteins with modified functionalities or regulatory characteristics, which may have an impact on the development of diseases like colorectal cancer. With a fusion score of 0.003, the string network displays the fusion of the RPL12 and RPL3 genes.study\u0026apos;s analysis of the molecular regulations of colorectal cancer with g: Profiler revealed overrepresented biological processes and activities in the gene set related to colorectal cancer. The results underscore the significance of comprehending the basic foundations of the disease by offering insights into the pathophysiological mechanisms involved. The g- profiler database arranges the enriched gene in 20 pathways beside the KEGG, WP, TF, MIRNA, HPA, and CORUM pathways had been reported in Figure 8(A). In Figure 8(B), the enrichment analysis explained a significant overrepresentation of gene ontological terms associated with protein binding (GO:0005515), structural molecule activity (GO:0005198), and protein-containing complex binding (GO:0044877). Moreover, there was a noticeable enrichment in \u0026quot;cytoplasmic translation\u0026quot; (GO:0002181) and \u0026quot;cell adhesion\u0026quot; (GO:0007155), which suggests modifications to the cellular structure and translation machinery.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 548px;\"\u003e\n \u003cp\u003eTable 1: Common genes upregulated in functional enrichment of GO: MF, GO: BP, GO: CC, KEGG and TF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eGO:MF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eGO:BP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003eGO:CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPL12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eNAS,IC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPL23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eNAS,IC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPL26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eIBA,IC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPL24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI,HDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eIDA,IBA,NAS,IC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPL31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eIDA,IBA,NAS,IC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPL37A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eNAS,IC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPS19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eNAS,IC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPS4X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPS8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eNAS,IC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eYBX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eIDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003eIDA,HDA,TAS,IEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPS7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eNAS, IC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPL7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003eNAS,IC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eRPL38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eIDA,NAS,IC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eThe Mutants Genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs mentioned in Figure 9, the highest mutated genes are MYLK, SYNPO2, PCSK5, JCHAIN, ABCA8, and SLC26A3, which were selected from the 57 common genes identified using the Venn diagram tool.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIdentifying patients with colon adenocarcinoma (COAD) who may benefit from immunotherapy and whose tumor microenvironment (TME) was required for reprogramming to advantageous immune-mediated responses requires precise immune molecular characterization due to cancer cell mutations. On the other hand, little has been discovered regarding COAD\u0026apos;s immunological\u0026nbsp;characteristics. Despite the current gaps in our understanding of COAD\u0026apos;s Immunological characteristics, there is hope. Our research is paving the way for the development of accurate and reliable biomarkers for COAD diagnosis and prognosis. By observing co-expression correlations, regulatory relations, and physical binding interactions, we are rehabilitating the colorectal adenocarcinoma network and opening new doors for patient care.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study identified 913 upregulated genes of colorectal adenocarcinoma (COAD). The gene-expressed profile of COAD was studied, and many findings were noted. The upregulated genes are controlled by 20 pathways expressed in colorectal adenocarcinoma, as shown in Figure 2, analyzed by cluster profiler. RNA-sequence expression of\u0026nbsp;COAD\u0026nbsp;was characterized using David annotation analysis, and the resulting genes were compared with each published\u0026nbsp;COAD\u0026nbsp;gene database. Besides, the known disease-associated gene sets rehabilitate the colon adenocarcinoma network observed. Also, physical binding interactions, regulatory associations, and co-expression correlations were analyzed.\u003c/p\u003e\n\u003cp\u003eNevertheless, there are no sensitive and accurate biomarkers for colorectal adenocarcinoma diagnosis or prognosis. The enriched genes regulate Cellular biology, which contains transcription factors (TFs), as shown in Figure 3 in a sum of functions. TFs are key players in the growth and tumorigenesis of colorectal adenocarcinoma since they are the molecules that make up the terminal effector signal pathway.\u003csup\u003e16\u003c/sup\u003e. A rising amount of evidence indicates that peculiar TFs have a role in the development of\u0026nbsp;COAD, its clinicopathological characteristics, and its prognosis.\u0026nbsp;\u003csup\u003e17\u003c/sup\u003e,\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e. That finding provides new insight into the basic pathogenesis, potential biomarkers, and therapeutic targets.\u003c/p\u003e\n\u003cp\u003ePrior research has demonstrated the extensive distribution of this local gene co-expression, as shown in Figure 4, over numerous chromosomes and tissues. However, it is still unclear if this co-expression results from transcription processes in individual cells because previous studies have only employed bulk RNA-seq, averaging gene expression values over millions of cells as this study. Here, but in a previous study\u003csup\u003e19\u003c/sup\u003e,\u0026nbsp;take advantage of the co-occurrence of transcription events in single cells to uncover gene co-expression across cells, unbiased by cell-type heterogeneity. the study\u0026nbsp;utilized single cell datasets spanning over 85 individuals. find correlations between genes and regulators and show that over 95% of co-expressed gene pairs have similar regulatory elements. co-expression in single cell gene regulatory networks and call for a more thorough examination of common regulatory components to help understand why diseases affect multiple genes at once\u003csup\u003e20\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 5, Functional enrichments were performed;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ecommon enriched diseases are malignant neoplasm of the breast and liver Grrhosis, which is related to colorectal cancer. Identification of core gene families linked to colorectal cancer metastases to the liver and regulatory functions in tumor cell immune infiltration\u003csup\u003e21\u003c/sup\u003e,\u003csup\u003e22\u003c/sup\u003e. The discovery came when the CCL cluster, as a part of core genes, was primarily enriched in GO-BP pathways linked to immune cell migration and chemotaxis, including neutrophils, monocytes, and leucocytes. The leucocyte carries anticancer chemicals or medications to the targeted tissues in a moving manner, which allows it to carry out its anticancer functions, where consider the hand of the immune system to fight cancer in the human body\u003csup\u003e23\u003c/sup\u003e,\u003csup\u003e24\u003c/sup\u003e. KEGG: the upregulated genes expressed in colorectal adenocarcinoma depend on these pathways.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In Figure 5 B, C HPA tissues: The gene expression enriched in adipose tissues, adipocytes, and adrenal gland. The HPA tissues continued to function during the prolonged stress. Increases the response to stress in COAD cells and tumor tissues at both the mRNA and protein levels, as observed in Figure 5 D: Upregulated tissues: the primary tissues that express the upregulated genes are the Brain, Testis, Kidney, and lung. as mentioned in many studies that HPA axis continued to function during the prolonged stress \u003csup\u003e25\u003c/sup\u003e. Increase\u0026nbsp;the\u0026nbsp;response to stress in CRC cells and tumor tissues at both the mRNA and protein levels. These facts proved that the tumor microenvironment is linked to the effectiveness of immunotherapy and patient survival in colorectal cancer patients.\u003csup\u003e26\u003c/sup\u003e. Meanwhile, in Figure 5 E: GOTERM-CC- direct cellular component, relation extraction between gene and other entities, F gene enrichment analysis, the main biological functional enriched genes, the main gain involved in the regulation of the transcriptome genes, and genes silencing by miRNA. These gene enrichment analyses are used to identify differentially expressed key genes associated with colorectal cancer that can be used in tumor diagnosis and targeted gene therapy.\u003csup\u003e27\u003c/sup\u003e,\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis work investigates the potential activities of upregulated and downregulated genes in COAD, including 913 upregulated and 906 downregulated genes, to provide a comprehensive knowledge of the genomic organization of COAD, as seen in Figure 6. A and B The distribution of the Differentially Expressed 1274 nodes and 7665 edges characterize genes (DEGs) in Cytoscape software. The heatmap generated using an R script, as shown in Figure 6(D), shows ten higher upregulated genes and ten higher downregulated genes, indicating considerable DEGs of COAD relative to normal tissue. The heatmap provides a clear visual representation of how gene expression profiles are distributed among both normal patients and those with tumor patients.\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe String protein-protein interaction database recognizes 5 cluster networks of the upregulated genes shown in Figure 7 (A)) Furthermore, there are 57 upregulated common genes shown in Figure 7 (A), the Venn diagram that finds the common gene between the two sets of the immunogenic genes set and cell type gene signature. There have been several reports of immune cell transcriptome signatures, which consist of common gene lists indicating the presence of a specific immune cell population. Most of these gene signatures have been identified by examining separate blood cells [24]. A network-based deconvolution technique was used to obtain the set of immune gene signatures directly from tissue transcriptomics data, as mentioned in many studies. \u003csup\u003e30\u003c/sup\u003e,\u003csup\u003e31\u003c/sup\u003e. As shown in Figure 7 (C and D), Overexpressed microRNAs regulate receptor signaling pathways. Conversely, downregulated microRNAs target most of the functions linked to the mitotic cell cycle in human organs. The characteristics of cancer, maintaining proliferative signals, avoiding growth suppressors, stopping cell death, starting invasion and metastasis, and starting angiogenesis, among other things., have been demonstrated to be impacted by dysregulated miRNAs \u003csup\u003e32\u003c/sup\u003e. Two hundred and fifty miRNAs shown in Figure 6 (C) are the overexpressed miRNAs that interacted with the higher upregulated genes and downregulated genes. As shown in Figure 7 (D), the biomarkers of the TIMP2 target gene are connected to its regulator (ZEBED1) transcription factors TF, and both are connected with miRNA (has-miR-6822-3p). Recent studies have demonstrated that there may be a negative correlation between the prognosis of COAD patients and the expression levels of TIMP1, according to Song et al. \u003csup\u003e33\u003c/sup\u003eTIMP1 suppression may lead to enhanced apoptosis and decreased invasion, migration, and proliferation in vitro while weakening tumorigenesis and metastasis in vivo through the FAK-PI3K/Akt and MAPK pathways. When serum TIMP1 levels were higher, patients with colorectal cancer had shorter overall survival periods and lower tumor grades.\u003csup\u003e34\u003c/sup\u003e,\u003csup\u003e35\u003c/sup\u003e,\u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 8 and Table 1, the protein-protein connection is further explained using ontologies to express biological knowledge. Gene Ontology (GO) is a resource that provides information about the function of gene products. These ontologies cover three domains: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). The process of assigning functional information to genes that are relevant to the literature using gene annotation GO\u003csup\u003e37\u003c/sup\u003e. All five of the chosen criteria were shown to exhibit upregulation of a total of 14 genes. Gene Ontology Molecular Function (GO: MF), Gene Ontology Biological Process (GO: BP), Gene Ontology Cellular Component (GO: CC), Kyoto Encyclopedia of Genes and Genomes (KEGG), and transcription factor (TF) analyses revealed the following genes as the most frequently upregulated genes as presented in table1 and Figure (5). KEGG pathways and GO keywords were used in gene enrichment analysis to identify genes that were consistently elevated in a variety of functional domains. Significantly, ribosomal function. It was found that these upregulated genes are significantly associated with immune-related biological processes and pathways. This finding suggests a potential impact on the immune system in the context of colorectal adenocarcinoma (COAD).\u003c/p\u003e\n\u003cp\u003eFurthermore, as explained in Table 1, the differentiation, progression, or metastasis of colorectal cancer are linked to the expression patterns of Ribosomal Proteins (RPs)2 \u003csup\u003e38\u003c/sup\u003e. RPL3 (60S ribosomal protein L3, a component of the large subunit of cytoplasmic ribosomes), RPL12 (60S ribosomal protein L12, directly binding to 26S ribosomal RNA), RPL23 (Ribosomal protein L23), and RPL26 (60S ribosomal protein L26, a part of the large ribosomal subunit). The nucleolus initiates the development of functioning ribosomes, which are necessary for coordinated ribosomal RNA (rRNA) processing and ribosomal protein \u003csup\u003e31\u003c/sup\u003e assembly. This process is often hyperactivated to support the need for protein synthesis, which is necessary to survive the unrelenting expansion of cancer cells \u003csup\u003e38\u003c/sup\u003e. All categories show upregulation of RPL24 (Ribosomal protein L24; Belongs to the eukaryotic ribosomal protein eL24 family), which is a possible therapeutic target whose acetylation or depletion prevents the formation of polysomes and the development of cancer cells \u003csup\u003e39\u003c/sup\u003e. Ribosomal\u0026nbsp;protein\u0026nbsp;L31\u0026nbsp;(RPL31) is known\u0026nbsp;to have the capacity to inhibit\u0026nbsp;the proliferation\u0026nbsp;and migration\u0026nbsp;of Gastric\u0026nbsp;cancer\u0026nbsp;cells\u003csup\u003e30\u003c/sup\u003e. RPL31 (Ribosomal protein L31), RPL37A (Ribosomal protein L37a), RPS19 (40S ribosomal protein S19; Required for pre-rRNA processing and maturation of 40S ribosomal subunits in colorectal cancer (CRC); Belongs to the eukaryotic ribosomal protein eS19 family), The eukaryotic protein uS19 is encoded in human disorders and is a part of the ribosome decoding site \u003csup\u003e31\u003c/sup\u003e.RPS4X (Ribosomal protein S4 X-linked; Belongs to the eukaryotic ribosomal protein eS4 family). The overexpressed multifunctional protein YB-1 has been found to be a partner of the X-linked ribosomal protein S4 (RPS4X), which is involved in cellular translation and proliferation in a number of breast cancer cells. In these cell lines, depletion of RPS4X consistently leads to cisplatin resistance. \u003csup\u003e30\u003c/sup\u003e. It is observed that approximately 25% of human RPs show tissue-specific expression, and the most intricate patterns of RP expression are found in primary hematopoietic cells\u003csup\u003e30\u003c/sup\u003e, which transcriptional regulators probably influence with context restrictions. Surprisingly, the study also demonstrated a pattern of dysregulated expression of particular RPs across cancer types, patterns that are prognostic of disease development and derive from copy number differences. Such as RPS8 (Ribosomal protein S8; Belongs to the eukaryotic ribosomal protein eS8 family. Genes, except for transcription factor (TF) studies. RPS7 (40S ribosomal protein S7; Required for rRNA maturation), RPL7 (60S ribosomal protein L7; Component of the large ribosomal subunit, binds to G-rich structures in 28S rRNA and mRNAs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt is observed that approximately 25% of human RPs show tissue-specific expression, and the most intricate patterns of RP expression are found in primary hematopoietic cells, which transcriptional regulators probably influence\u0026nbsp;with context restrictions. Surprisingly, the study also demonstrated a\u0026nbsp;pattern of dysregulated expression\u0026nbsp;of particular RPs across cancer types, patterns that are prognostic of disease development and derive from copy number differences\u003csup\u003e40\u003c/sup\u003e. Many studies observe that anticancer medicines that target mRNA translation are anticipated to become possible. That can be explained by the knowledge that a significant part of the pathophysiology of many malignancies is the abnormal translation of mRNAs into proteins, which involves gene transcriptions and post-translational modifications of proteins. That is described by the regulation of protein translation by the interplay between mRNA modifications and ncRNAs\u003csup\u003e40\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eUpregulate gene plays a role in the translation apparatus; inhibits cell-free translation of mRNAs and RPL38. \u003csup\u003e40\u003c/sup\u003e (Ribosomal protein L38; Belongs to the eukaryotic ribosomal protein eL38 family) genes upregulated in all the categories except in the Cellular Component (CC). Apart from KEGG pathways, all categories showed an upregulation of the YBX1 (Y-box-binding protein 1; DNA- and RNA-binding protein involved in various processes, such as translational repression. The first is the traditional general program that produces ribosomes, and the second is a gene-specific function that regulates the number of ribosomal proteins generated by each distinct ribosomal gene. This particular control of ribosomal protein genes is mainly accomplished by variations in pre-mRNA splicing efficiency and mRNA stability, in contrast to the general system, which is primarily regulated at the level of transcription and translation.\u003csup\u003e41\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;RNA stabilization, mRNA splicing, DNA repair, and transcription regulation. Predominantly acts as an RNA-binding protein: binds preferentially to the 5\u0026apos;-[CU]CUGCG-3\u0026apos; RNA motif and specifically recognizes mRNA transcripts modified by C5-methylcytosine) gene (Table 1). genes RPL3, RPL12, RPL23, RPL26, RPL2415, RPL31, RPL37A, RPS19, RPS4X, RPS8, RPS7, RPL7, RPL3816\u0026nbsp;\u003csup\u003e42\u003c/sup\u003e, and YBX1 were constantly elevated, indicating that they may be targets for therapy. In various studies, the RPL313\u0026nbsp;\u003csup\u003e43\u003c/sup\u003e and RPL717 Upregulation were detected in colorectal cancer. After delving into gene fusion occurrences, the RPL12 and RPL3 genes showed a fusion score of 0.003, suggesting that these changes might lead to altered regulatory features or capabilities. Except for YBX1 (0.4), the STRING network showed significant co-expression scores (0.8 to 0.9) for all genes, as observed in Figure 8.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe pathogenesis of Colorectal Adenocarcinoma (COAD) is maintained in part by the pathways that interact between factors and cytokine receptors. These pathways have been connected with the development and progression of colorectal cancer of COAD) and share MYLK, SYNPO2, PCSK5, JCHAIN, ABCA8, and SLC26A3 as shown in Figure 9. Those genes happen to have many variants, including nonsense, insertion, frameshift, deletion, and splice variants. Differential routes were also noted at some phases, such as leukocyte migration across the endothelium and the emergence of Toll-like receptor signaling pathways. In Figure 9, the mutated genes were identified using c-bioportal. A study of Bladder cancer revealed that promoters like MYLK promote the invasion and migration of various solid tumors. Therefore, MYLK overexpression can serve as a biomarker for colorectal adenocarcinoma \u003csup\u003e44\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOn the other hand, overexpression of the SYNPO2 gene is considered a poor prognostic factor for patients with nasopharyngeal cancer. Genes that showed a significant positive correlation with SYNPO2, based on GO biological process keywords, were primarily linked to cellular processes. Thus, SYNPO2 is an important indicator of nasopharyngeal cancer prognosis\u003csup\u003e45\u003c/sup\u003e . Meanwhile, PCSK5 downregulation enhances the inhibitory action of andrographolide on glioblastoma by controlling STAT3. Higher expression of PCSK5 was associated with a poorer prognosis for patients with advanced GBM stages. PCSK5 knockdown lessened the IL-6-induced epithelial-mesenchymal transition (EMT) -like characteristics of GBM cells. Additionally, when combined with Andro therapy, PCSK5 knockdown significantly reduced both the in vivo tumor development and the in vitro invasion and proliferation of GBM cells \u003csup\u003e46\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA recent study has identified JCHAIN as a novel B-cell prognostic biomarker that can predict overall survival in Head and Neck Squamous Cell Carcinoma (HNSCC) patients. In the low-risk group, JCHAIN was considered a highly expressed protective gene. A single-cell dataset study showed that B cells had significant expression levels of JCHAIN\u003csup\u003e47\u003c/sup\u003e. Additionally, Through the Wnt/\u0026beta;-catenin pathway, the tumor suppressor ABCA8 prevents colorectal cancer from progressing malignantly using ABCA8 as targeted gene therapy can prevent the growth and spread of xenograft tumors in vivo and reverse the epithelial-mesenchymal transition.\u003csup\u003e48\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMoreover, overexpression of ABCA8 inhibited the invasion of cancer cells\u003csup\u003e49\u003c/sup\u003e. The SLC26A3 gene has been found to have 98 variants, and public databases suggested that the SLC26A3 promoter may interact with p65\u003csup\u003e49\u003c/sup\u003e, which led researchers to investigate whether NF-\u0026kappa;B/p65 directly binds to the SLC26A3 gene promoter element and controls the expression of colorectal adenocarcinoma. In the mammalian intestines, the PI3K/AKT pathway may have an impact on the SLC26A3 gene. Hence, these highly mutated genes also function as promoters for the development and progression of cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, the study offers an identification of key genes for colorectal adenocarcinoma to be validated as potential prognosis biomarkers. The discovered key genes, particularly those connected to fusion events and ribosomal functions, need to be looked into further to see how they contribute to the development of disease and to determine whether they should be the focus of targeted gene therapies. Twelve gene regulatory pathways and six diagnostic biomarkers (MYLK, SYNPO2, PCSK5, JCHAIN, ABCA8, and SLC26A3) were described in this study. mRNA transcripts included RPL3, RPL12, RPL23, RPL26, RPL2415, RPL31, RPL37A, RPS19, RPS4X, RPS8, RPS7, RPL7, RPL3816, and YBX1, which were shown to be consistently elevated. and particularly identifies mRNA transcripts modified by C5-methylcytosine). As the text demonstrates, both in vivo and invitro investigations have confirmed that the TIMP2 target gene is linked to its regulator (ZEBED1) transcription factors TF, and both are linked to miRNA (has-miR-6822-3p), which was chosen and promoted as key\u0026nbsp;genes.\u003c/p\u003e\n\u003cp\u003eFuture Directions: The investigation of key genes in the prognosis of colorectal adenocarcinoma and targeted gene therapy should focus on elucidating molecular pathways, creating novel treatment strategies to improve patient outcomes, and implementing the study\u0026apos;s findings in clinical settings in the future. Verification in larger patient cohorts with colorectal adenocarcinoma is necessary to confirm the significance and dependability of the identified genes and fusion events as potential therapeutic targets. It is possible to create hypotheses for further study using enrichment maps. The parameters used in the pathway enrichment study, such as the minimum and maximum pathway size or the chosen route databases, should be changed in order to assess how robust the conclusions are. These parameters are suggested to have the potential to impact the findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe authors, E. O. M, participated in the conception, design, analysis, and interpretation of the data, besides writing and editing the manuscript (2) P. M participated in the conception, design, analysis, and interpretation of the data besides writing and editing the manuscript (3) A. K. participated in the conception design, analysis, and interpretation of the data besides writing and editing the manuscript (4) S. N. participated in technical support and data analysis and project managing (5) B.M. participated in the conception design, analysis, and interpretation of the data supervising. This manuscript has not been submitted, nor is it under review at another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. The following authors have no affiliations with organizations with a direct or indirect financial interest in the subject matter discussed in the manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors reported no potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunder\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQatar Foundation, Qatar National Library, grand award number \u003cstrong\u003eNPRP9-350-3-074\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmail\u0026nbsp;\u003c/strong\u003e\u003cu\[email protected]\u003c/u\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShinji, S., T. Yamada, A. Matsuda, et al., \u003cem\u003eRecent Advances in the Treatment of Colorectal Cancer: A Review.\u003c/em\u003e J Nippon Med Sch, 2022. \u003cstrong\u003e89\u003c/strong\u003e(3): p. 246-254,doi: 10.1272/jnms.JNMS.2022_89-310.\u003c/li\u003e\n\u003cli\u003eHe, S., X. Li, X. Zhou, et al., \u003cem\u003eRole of epithelial cell-mesenchymal transition regulators in molecular typing and prognosis of colon cancer.\u003c/em\u003e J Gastrointest Oncol, 2023. \u003cstrong\u003e14\u003c/strong\u003e(2): p. 744-757,doi: 10.21037/jgo-23-49.\u003c/li\u003e\n\u003cli\u003eDai, Y., Z. Jiang, Y. Qiu, et al., \u003cem\u003eIdentification of Key Carcinogenic Genes in Colon Adenocarcinoma.\u003c/em\u003e Iran J Public Health, 2022. \u003cstrong\u003e51\u003c/strong\u003e(2): p. 364-374,doi: 10.18502/ijph.v51i2.8689.\u003c/li\u003e\n\u003cli\u003eYazdani, F., N. Mottaghi-Dastjerdi, A. Ghorbani, et al., \u003cem\u003eIdentification of Key Genes and Pathways Governing T-DM1 Resistance in OE-19 Esophageal Cancer Cells through Bioinformatics Analysis.\u003c/em\u003e 2023, doi.org/10.21203/rs.3.rs-3423572/v1.\u003c/li\u003e\n\u003cli\u003eMahgoub, E.O., W.C. Cho, M. Sharifi, et al., \u003cem\u003eRole of functional genomics in identifying cancer drug resistance and overcoming cancer therapy relapse.\u003c/em\u003e Heliyon %@ 2405-8440, 2023,doi: 10.1016/j.heliyon.2023.e22095.\u003c/li\u003e\n\u003cli\u003eChen, L., D. Lu, K. Sun, et al., \u003cem\u003eIdentification of biomarkers associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis.\u003c/em\u003e Gene, 2019. \u003cstrong\u003e692\u003c/strong\u003e: p. 119-125,doi: 10.1016/j.gene.2019.01.001.\u003c/li\u003e\n\u003cli\u003eMahgoub, I., A.K. Bolad and M. Mergani, \u003cem\u003eGeneration and immune-characterization of single chain fragment variable (scFv) antibody recognize breast cancer cells line (MCF-7).\u003c/em\u003e Journal for ImmunoTherapy of Cancer, 2014. \u003cstrong\u003e2\u003c/strong\u003e(Suppl 1): p. P6 %@ 2051-1426. doi: 10.1016/j.gene.2019.01.001.\u003c/li\u003e\n\u003cli\u003ePournoor, E., Z. Mousavian, A.N. Dalini, et al., \u003cem\u003eIdentification of Key Components in Colon Adenocarcinoma Using Transcriptome to Interactome Multilayer Framework.\u003c/em\u003e Sci Rep, 2020. \u003cstrong\u003e10\u003c/strong\u003e(1): p. 4991,doi: 10.1038/s41598-020-59605-z.\u003c/li\u003e\n\u003cli\u003eXu, M., J. Chang, W. Wang, et al., \u003cem\u003eClassification of colon adenocarcinoma based on immunological characterizations: Implications for prognosis and immunotherapy.\u003c/em\u003e Front Immunol, 2022. \u003cstrong\u003e13\u003c/strong\u003e: p. 934083,doi: 10.3389/fimmu.2022.934083.\u003c/li\u003e\n\u003cli\u003eMahgoub, E.O. and S. Kulkarni, \u003cem\u003eStructural Analyses of An Epidermal Growth Factor Receptor-Specific Single-Chain Fragment Variable via An In Silico Approach.\u003c/em\u003e J Vis Exp, 2023(201): p. e65894 %@ 1940-087X,doi: 10.3791/65894.\u003c/li\u003e\n\u003cli\u003eRaudvere, U., L. Kolberg, I. Kuzmin, et al., \u003cem\u003eg:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update).\u003c/em\u003e Nucleic Acids Res, 2019. \u003cstrong\u003e47\u003c/strong\u003e(W1): p. W191-W198,doi: 10.1093/nar/gkz369.\u003c/li\u003e\n\u003cli\u003eKolberg, L., U. Raudvere, I. Kuzmin, et al., \u003cem\u003eg:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update).\u003c/em\u003e Nucleic Acids Res, 2023. \u003cstrong\u003e51\u003c/strong\u003e(W1): p. W207-W212,doi: 10.1093/nar/gkad347.\u003c/li\u003e\n\u003cli\u003eGureghian, V., H. Herbst, I. Kozar, et al., \u003cem\u003eA multi-omics integrative approach unravels novel genes and pathways associated with senescence escape after targeted therapy in NRAS mutant melanoma.\u003c/em\u003e Cancer Gene Ther, 2023. \u003cstrong\u003e30\u003c/strong\u003e(10): p. 1330-1345,doi: 10.1038/s41417-023-00640-z.\u003c/li\u003e\n\u003cli\u003eKhan, A.A., T.J. Huat, A. Al Mutery, et al., \u003cem\u003eSignificant transcriptomic changes are associated with differentiation of bone marrow-derived mesenchymal stem cells into neural progenitor-like cells in the presence of bFGF and EGF.\u003c/em\u003e Cell Biosci, 2020. \u003cstrong\u003e10\u003c/strong\u003e: p. 126,doi: 10.1186/s13578-020-00487-z.\u003c/li\u003e\n\u003cli\u003eManoochehri, H., A. Jalali, H. Tanzadehpanah, et al., \u003cem\u003eIdentification of Key Gene Targets for Sensitizing Colorectal Cancer to Chemoradiation: an Integrative Network Analysis on Multiple Transcriptomics Data.\u003c/em\u003e J Gastrointest Cancer, 2022. \u003cstrong\u003e53\u003c/strong\u003e(3): p. 649-668,doi: 10.1007/s12029-021-00690-2.\u003c/li\u003e\n\u003cli\u003eSajadi, M., M. Fazilti, H. Nazem, et al., \u003cem\u003eThe expression changes of transcription factors including ANKZF1, LEF1, CASZ1, and ATOH1 as a predictor of survival rate in colorectal cancer: a large-scale analysis.\u003c/em\u003e Cancer Cell Int, 2022. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 339,doi: 10.1186/s12935-022-02751-3.\u003c/li\u003e\n\u003cli\u003eShalem, O., N.E. Sanjana, E. Hartenian, et al., \u003cem\u003eGenome-scale CRISPR-Cas9 knockout screening in human cells.\u003c/em\u003e Science, 2014. \u003cstrong\u003e343\u003c/strong\u003e(6166): p. 84-87,doi: 10.1126/science.1247005.\u003c/li\u003e\n\u003cli\u003eLin, J., Z. Cao, D. Yu, et al., \u003cem\u003eIdentification of Transcription Factor-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma.\u003c/em\u003e Front Genet, 2021. \u003cstrong\u003e12\u003c/strong\u003e: p. 709133,doi: 10.3389/fgene.2021.709133.\u003c/li\u003e\n\u003cli\u003eD, M.R., C. Ziyani and O. Delaneau, \u003cem\u003eShared regulation and functional relevance of local gene co-expression revealed by single cell analysis.\u003c/em\u003e Commun Biol, 2022. \u003cstrong\u003e5\u003c/strong\u003e(1): p. 876,doi: 10.1038/s42003-022-03831-w.\u003c/li\u003e\n\u003cli\u003eAbuQamar, S.F., K.A. El-Tarabily and A. Sham, \u003cem\u003eCo-expression Networks in Predicting Transcriptional Gene Regulation.\u003c/em\u003e Methods Mol Biol, 2021. \u003cstrong\u003e2328\u003c/strong\u003e: p. 1-11,doi: 10.1007/978-1-0716-1534-8_1.\u003c/li\u003e\n\u003cli\u003eLiu, W.Q., W.L. Li, S.M. Ma, et al., \u003cem\u003eDiscovery of core gene families associated with liver metastasis in colorectal cancer and regulatory roles in tumor cell immune infiltration.\u003c/em\u003e Transl Oncol, 2021. \u003cstrong\u003e14\u003c/strong\u003e(3): p. 101011,doi: 10.1016/j.tranon.2021.101011.\u003c/li\u003e\n\u003cli\u003eMahgoub, E.O., \u003cem\u003eSingle chain fragment variables antibody binding to EGF receptor in the surface of MCF7 breast cancer cell line: application and production review.\u003c/em\u003e Open Journal of Genetics, 2017. \u003cstrong\u003e7\u003c/strong\u003e(2): p. 84-103, doi: 10.4236/ojgen.2017.72008.\u003c/li\u003e\n\u003cli\u003eMitchell, M.J. and M.R. King, \u003cem\u003eLeukocytes as carriers for targeted cancer drug delivery.\u003c/em\u003e Expert Opin Drug Deliv, 2015. \u003cstrong\u003e12\u003c/strong\u003e(3): p. 375-92,doi: 10.1517/17425247.2015.966684.\u003c/li\u003e\n\u003cli\u003eMahgoub, E.O. and A.K. Bolad, \u003cem\u003eConstruction, expression and characterisation of a single chain variable fragment in the Escherichia coli periplasmic that recognise MCF-7 breast cancer cell line.\u003c/em\u003e J Cancer Res Ther, 2014. \u003cstrong\u003e10\u003c/strong\u003e(2): p. 265-73,doi: 10.4103/0973-1482.136551.\u003c/li\u003e\n\u003cli\u003eIlaslan, E., M.P. Sajek, J. Jaruzelska, et al., \u003cem\u003eEmerging Roles of NANOS RNA-Binding Proteins in Cancer.\u003c/em\u003e Int J Mol Sci, 2022. \u003cstrong\u003e23\u003c/strong\u003e(16): p. 9408 %@ 1422-0067,doi: 10.3390/ijms23169408.\u003c/li\u003e\n\u003cli\u003eZhu, C., S. Wang, Y. Du, et al., \u003cem\u003eTumor microenvironment-related gene selenium-binding protein 1 (SELENBP1) is associated with immunotherapy efficacy and survival in colorectal cancer.\u003c/em\u003e BMC Gastroenterol, 2022. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 437,doi: 10.1186/s12876-022-02532-2.\u003c/li\u003e\n\u003cli\u003eXu, F., L. Jiang, Q. Zhao, et al., \u003cem\u003eWhole-transcriptome and proteome analyses identify key differentially expressed mRNAs, miRNAs, lncRNAs and circRNAs associated with HCC.\u003c/em\u003e Oncogene, 2021. \u003cstrong\u003e40\u003c/strong\u003e(29): p. 4820-4831,doi: 10.1038/s41388-021-01908-0.\u003c/li\u003e\n\u003cli\u003eMahgoub, I.O. \u003cem\u003eDesign, expression and characterization of a single chain fragment variable anti-mcf-7 antibody; A humanized antibody derived from monoclonal antibody\u003c/em\u003e. 2014. Hamad bin Khalifa University Press (HBKU Press). doi.org/10.5339/qfarc.2014.HBPP0705.\u003c/li\u003e\n\u003cli\u003eMiao, Y., J. Wang, X. Ma, et al., \u003cem\u003eIdentification prognosis-associated immune genes in colon adenocarcinoma.\u003c/em\u003e Biosci Rep, 2020. \u003cstrong\u003e40\u003c/strong\u003e(11): p. BSR20201734,doi: 10.1042/BSR20201734.\u003c/li\u003e\n\u003cli\u003eWu, F., Y. Liu, S. Hu, et al., \u003cem\u003eRibosomal protein L31 (RPL31) inhibits the proliferation and migration of gastric cancer cells.\u003c/em\u003e Heliyon, 2023. \u003cstrong\u003e9\u003c/strong\u003e(2): p. e13076,doi: 10.1016/j.heliyon.2023.e13076.\u003c/li\u003e\n\u003cli\u003eGraifer, D. and G. Karpova, \u003cem\u003eEukaryotic protein uS19: a component of the decoding site of ribosomes and a player in human diseases.\u003c/em\u003e Biochem J, 2021. \u003cstrong\u003e478\u003c/strong\u003e(5): p. 997-1008,doi: 10.1042/BCJ20200950.\u003c/li\u003e\n\u003cli\u003ePeng, Y. and C.M. Croce, \u003cem\u003eThe role of MicroRNAs in human cancer.\u003c/em\u003e Signal Transduct Target Ther, 2016. \u003cstrong\u003e1\u003c/strong\u003e(1): p. 15004,doi: 10.1038/sigtrans.2015.4.\u003c/li\u003e\n\u003cli\u003eSong, G., S. Xu, H. Zhang, et al., \u003cem\u003eTIMP1 is a prognostic marker for the progression and metastasis of colon cancer through FAK-PI3K/AKT and MAPK pathway.\u003c/em\u003e J Exp Clin Cancer Res, 2016. \u003cstrong\u003e35\u003c/strong\u003e(1): p. 148,doi: 10.1186/s13046-016-0427-7.\u003c/li\u003e\n\u003cli\u003eGiaginis, C., N. Nikiteas, A. Margeli, et al., \u003cem\u003eSerum tissue inhibitor of metalloproteinase 1 and 2 (TIMP-1 and TIMP-2) levels in colorectal cancer patients: associations with clinicopathological variables and patient survival.\u003c/em\u003e Int J Biol Markers, 2009. \u003cstrong\u003e24\u003c/strong\u003e(4): p. 245-52,doi: 10.1177/172460080902400405.\u003c/li\u003e\n\u003cli\u003eLiu, Y., C. Li, L. Dong, et al., \u003cem\u003eIdentification and verification of three key genes associated with survival and prognosis of COAD patients via integrated bioinformatics analysis.\u003c/em\u003e Biosci Rep, 2020. \u003cstrong\u003e40\u003c/strong\u003e(9): p. BSR20200141,doi: 10.1042/BSR20200141.\u003c/li\u003e\n\u003cli\u003eMahgoub, E.O., Y. Haik and S. Qadri, \u003cem\u003eComparison study of exosomes molecules driven from (NCI1975) NSCLC cell culture supernatant isolation and characterization techniques.\u003c/em\u003e The FASEB Journal, 2019. \u003cstrong\u003e33\u003c/strong\u003e(S1): p. 647.22-647.22 %@ 0892-6638, doi: 10.1042/BSR20200141.\u003c/li\u003e\n\u003cli\u003eDing, R., Y. Qu, C.H. Wu, et al., \u003cem\u003eAutomatic gene annotation using GO terms from cellular component domain.\u003c/em\u003e BMC Med Inform Decis Mak, 2018. \u003cstrong\u003e18\u003c/strong\u003e(Suppl 5): p. 119,doi: 10.1186/s12911-018-0694-7.\u003c/li\u003e\n\u003cli\u003eNait Slimane, S., V. Marcel, T. Fenouil, et al., \u003cem\u003eRibosome Biogenesis Alterations in Colorectal Cancer.\u003c/em\u003e Cells, 2020. \u003cstrong\u003e9\u003c/strong\u003e(11): p. 2361 %@ 2073-4409,doi: 10.3390/cells9112361.\u003c/li\u003e\n\u003cli\u003eWilson-Edell, K.A., A. Kehasse, G.K. Scott, et al., \u003cem\u003eRPL24: a potential therapeutic target whose depletion or acetylation inhibits polysome assembly and cancer cell growth.\u003c/em\u003e Oncotarget, 2014. \u003cstrong\u003e5\u003c/strong\u003e(13): p. 5165-76,doi: 10.18632/oncotarget.2099, 10.18632/oncotarget.2099..\u003c/li\u003e\n\u003cli\u003eKang, J., N. Brajanovski, K.T. Chan, et al., \u003cem\u003eRibosomal proteins and human diseases: molecular mechanisms and targeted therapy.\u003c/em\u003e Signal Transduct Target Ther, 2021. \u003cstrong\u003e6\u003c/strong\u003e(1): p. 323,doi: 10.1038/s41392-021-00728-8.\u003c/li\u003e\n\u003cli\u003ePetibon, C., M. Malik Ghulam, M. Catala, et al., \u003cem\u003eRegulation of ribosomal protein genes: An ordered anarchy.\u003c/em\u003e Wiley Interdiscip Rev RNA, 2021. \u003cstrong\u003e12\u003c/strong\u003e(3): p. e1632,doi: 10.1002/wrna.1632.\u003c/li\u003e\n\u003cli\u003eQin, Y., L. Liu, X. Hu, et al., \u003cem\u003eExpression and bioinformatics analysis of RPL38 protein and mRNA in gastric cancer.\u003c/em\u003e Cell Mol Biol (Noisy-le-grand), 2023. \u003cstrong\u003e69\u003c/strong\u003e(13): p. 256-261,doi: 10.14715/cmb/2023.69.13.38.\u003c/li\u003e\n\u003cli\u003ePan, R., C. Yu, Y. Shao, et al., \u003cem\u003eIdentification of Key Genes and Pathways Involved in Circulating Tumor Cells in Colorectal Cancer.\u003c/em\u003e Anal Cell Pathol (Amst), 2022. \u003cstrong\u003e2022\u003c/strong\u003e: p. 9943571,doi: 10.1155/2022/9943571.\u003c/li\u003e\n\u003cli\u003eJin, H., B. Liu, X. Guo, et al., \u003cem\u003eMYLK and CALD1 as molecular targets in bladder cancer.\u003c/em\u003e Medicine (Baltimore), 2023. \u003cstrong\u003e102\u003c/strong\u003e(47): p. e36302,doi: 10.1097/MD.0000000000036302.\u003c/li\u003e\n\u003cli\u003eYe, G., L. Tu, Z. Li, et al., \u003cem\u003eSYNPO2 promotes the development of BLCA by upregulating the infiltration of resting mast cells and increasing the resistance to immunotherapy.\u003c/em\u003e Oncol Rep, 2024. \u003cstrong\u003e51\u003c/strong\u003e(1): p. 1-13,doi: 10.3892/or.2023.8673.\u003c/li\u003e\n\u003cli\u003eGong, H., X. Yang, L. An, et al., \u003cem\u003ePCSK5 downregulation promotes the inhibitory effect of andrographolide on glioblastoma through regulating STAT3.\u003c/em\u003e Mol Cell Biochem, 2024: p. 1-13,doi: 10.1007/s11010-024-04977-3.\u003c/li\u003e\n\u003cli\u003eLi, K., C. Zhang, R. Zhou, et al., \u003cem\u003eSingle cell analysis unveils B cell-dominated immune subtypes in HNSCC for enhanced prognostic and therapeutic stratification.\u003c/em\u003e Int J Oral Sci, 2024. \u003cstrong\u003e16\u003c/strong\u003e(1): p. 29,doi: 10.1038/s41368-024-00292-1.\u003c/li\u003e\n\u003cli\u003eYang, K., X. Li, Z. Jiang, et al., \u003cem\u003eTumour suppressor ABCA8 inhibits malignant progression of colorectal cancer via Wnt/beta-catenin pathway.\u003c/em\u003e Dig Liver Dis, 2024. \u003cstrong\u003e56\u003c/strong\u003e(5): p. 880-893,doi: 10.1016/j.dld.2023.10.026.\u003c/li\u003e\n\u003cli\u003eLv, C., H. Yang, J. Yu, et al., \u003cem\u003eABCA8 inhibits breast cancer cell proliferation by regulating the AMP activated protein kinase/mammalian target of rapamycin signaling pathway.\u003c/em\u003e Environ Toxicol, 2022. \u003cstrong\u003e37\u003c/strong\u003e(6): p. 1423-1431,doi: 10.1002/tox.23495.\u003c/li\u003e\n\u003c/ol\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 adenocarcinoma, RNA sequencing, Differential expression genes(DEGs)","lastPublishedDoi":"10.21203/rs.3.rs-5188416/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5188416/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eColorectal Adenocarcinoma (COAD). continues to be one of the leading causes of death worldwide. The patient's chance of survival increases with the early prognosis of a malignant tumor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eAdvanced bioinformatics methods were used to gain a thorough understanding of the genetic landscape of colorectal cancer.\u003cstrong\u003e \u003c/strong\u003eThe transcriptome RNA-seq raw data were obtained from PRJEB24758 in the European Nucleotide Archive (ENA) database. Then, the Sequence Read Archive (SRA) runs a selection to download the sequences database. The transcriptomic RNA released data is analyzed by the bioinformatics tool, which also uses online analytic tools to help visualize the results and identify key genes that may be employed as prognosis biomarkers in the future. The annotation pathways have been determined using David's annotation tools, and cluster analysis in Gsea and the c-bioportal database also showed the significance of these pathways. Two hundred fifty miRNA overlapped with the highest two upregulated and downregulated genes that were subjected to screening. The Venn diagram determined the common genes that the immunogenic genes set and cell type gene signature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e 1,274 genes with substantial differential expression in colorectal cancer were found using stringent approaches such as HISAT2 alignment and DeSeq2 analysis. This study identified 913 upregulated genes of colorectal adenocarcinoma (COAD). The upregulated genes-expressed profile of COAD was studied. The upregulated genes are controlled by 20 pathways expressed in colorectal adenocarcinoma. David's annotation tool was used to prepare diagrams for the KEGG analysis, enriched genes, and many more diagrams. The resulting miRNA overlapped genes interacted significantly with TF to produce key genes. From a different perspective, fifty-seven upregulated common genes were determined using the Venn diagram, and higher mutated genes were selected. Investigating these key genes for targeted therapy in colorectal cancer therapy is crucial, as the study emphasizes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThus, the development of novel therapeutic approaches and the identification of key genes of the changed expression of genes implicated in COAD drug resistance are crucial goals for the ongoing advancement of COAD therapy.\u003c/p\u003e","manuscriptTitle":"Identification of Colorectal Adenocarcinoma Key Genes to Be Validated as Potential Prognosis Biomarkers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-14 15:05:01","doi":"10.21203/rs.3.rs-5188416/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":"5894810e-2ebc-4cee-b18b-c4a9f0be9633","owner":[],"postedDate":"January 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T07:54:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-14 15:05:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5188416","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5188416","identity":"rs-5188416","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}