Oncogenic Hub Genes ANLN and CTHRC1: Implications for Cancer Prognosis and Vaccine-Based Therapeutics

Oncogenic Hub Genes ANLN and CTHRC1: Implications for Cancer Prognosis and Vaccine-Based Therapeutics | 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 Article Oncogenic Hub Genes ANLN and CTHRC1: Implications for Cancer Prognosis and Vaccine-Based Therapeutics Mohammad Shahangir Biswas, Suronjit Kumar Roy, Rubait Hasan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6677557/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 This study explores the transcriptomic, mutational, and immunogenic characteristics linked to significantly differentially expressed genes (DEGs) in colorectal (COAD), liver (LIHC), lung (LUAD), gastric (STAD), and breast (BRCA) cancers. Applying integrated bioinformatics algorithms, we discovered common upregulated and downregulated hub genes and assessed their prognostic importance, genomic modifications, copy number variations, functional enrichment, and pathway engagement. The persistent overexpression of ANLN and CTHRC1 in five cancer types, along with poor survival outcomes, underscores their suitability for multi-epitope vaccine development, emphasizing their antigenic potential and significance as universal therapeutic targets. Five genes—ABCA8, PDK4, MT1M, TMEM100, and LIFR—exhibited consistent downregulation and demonstrated tumor-suppressive characteristics. Genomic analyses demonstrated elevated mutation frequencies in ABCA8 and LIFR, predominantly C > T transitions that suggest age-related mutational signatures. Copy number alterations confirmed oncogenic amplifications (ANLN, CTHRC1) and tumor suppressor deletions (e.g., ABCA8). Functional enrichment associated differentially expressed genes with mitosis, chromosome segregation, and metabolic pathways. A multi-epitope vaccine targeting ANLN and CTHRC1 has been established leveraging predicted B- and T-cell epitopes, cholera toxin B as an adjuvant, and efficient linkers. Structural validation indicated desirable folding, stability, and solubility. The vaccine exhibited significant MHC binding, accomplishing 98.75% global population coverage, alongside strong immune simulation findings. Codon optimization and subsequent cloning into the pET28a(+) vector confirmed the preparation for bacterial expression. ANLN and CTHRC1 demonstrates significant targets for universal immunotherapy. The multi-epitope vaccine demonstrates significant efficacy in silico and has the potential to be widely employed as a cancer immunotherapeutic. Biological sciences/Computational biology and bioinformatics/Genome informatics/Genome assembly algorithms Health sciences/Biomarkers/Predictive markers Gene Expression Profiling Oncogenic Pathways Cancer Biomarkers Immunogenicity Vaccine Design Personalized Immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Introduction Cancer demonstrates an ongoing global health challenge, with approximately 19.98 million new cases and 9.65 million deaths documented worldwide in 2022, emphasizing its extensive influence on morbidity and mortality rates 1 . The Global Cancer Observatory (GLOBOCAN 2022) distinguished the ten most prevalent cancer types worldwide as breast, prostate, lung, colorectal, cervical, stomach, thyroid, liver, uterine corpus, and ovarian cancers. According to the 2022 global cancer statistics report, breast cancer has an incidence rate of 11.5% and a mortality rate of 6.8%. Lung cancer shows an incidence of 12.4% and a mortality rate of 18.7%. Colorectal cancer has a 9.6% incidence and a 9.3% mortality rate. Stomach cancer presents with a 4.8% incidence and a 6.8% mortality rate, while liver cancer has a 4.3% incidence and a 7.8% mortality rate. These cancers are among the most frequently diagnosed and are significant contributors to cancer-related deaths globally 2 . According to projections based on demographics, the number of newly diagnosed cases of cancer would surpass 35 million by the year 2050 3 . The major emphasis of the present research is on these five malignancies, which accounted for 42.6% of all cancer cases worldwide and 49.4% of fatalities attributable to cancer. From this point, we will refer to these cancers collectively as "hub cancers." Therefore, developing viable early diagnostic and prognostic biomarkers is extremely important. These biomarkers have the potential to assist in the discovery of the underlying molecular pathways that are responsible for these aggressive cancers, while simultaneously contributing to the improvement of therapies. Microarray technology combined with bioinformatics analysis has emerged as an effective tool for studying cancer pathophysiology and identifying key genetic and epigenetic alterations involved in carcinogenesis. The application of these methodologies greatly helps in enhancing diagnostic accuracy, assessing prognosis, and formulating effective therapeutic strategies 4 – 6 . Although considerable findings have been made, there has been limited progress in translating these discoveries into robust biomarkers for tumor identification and prognosis. Nevertheless, by examining microarray and RNA-seq data from various cancer datasets and integrating differentially expressed genes (DEGs) across multiple cancer types, it may be possible to uncover universal biomarkers that offer enhanced consistency and greater therapeutic relevance. Furthermore, the poor prognosis observed in patients with hub cancers is primarily driven by the lack of reliable early diagnostic markers, resistance to available treatments, and the adverse effects associated with current chemotherapeutic drugs 7 . Consequently, there is an urgent need for the development of innovative, affordable, safe, and effective cancer treatments 8 . In recent decades, treatment strategies have predominantly depended on surgery, radiation therapy, and chemotherapy, employed either individually or in combination 9 , 10 . Although the results for early-stage malignancies have greatly improved because of these conventional techniques, they are often linked to serious drawbacks, such as systemic toxicity, tumor recurrence, and poor effectiveness against metastasis complications. The introduction of immunotherapies and personalized therapies has revolutionized cancer treatment by providing more individualized and targeted approaches. Combinatorial regimens have demonstrated synergistic benefits, enhancing clinical outcomes in several cancer types by using multiple targeted medicines or combining them with conventional chemotherapeutics such as taxanes and platinum-based drugs 11 . However, despite these innovations, the prognosis for individuals with advanced and metastatic malignancies remains bleak. Recent epidemiological statistics demonstrated that the five-year survival rates for metastatic lung, liver, colorectal, gastric, prostate, and breast cancers were below 30%, with metastatic lung cancer survival at approximately 7% and liver cancer at around 11% 12,13 . Although immune checkpoint inhibitors and other innovative treatments have attained significant success in some cancers, most patients either do not react or ultimately develop resistance to therapy 14 , 15 . The key obstacles in contemporary cancer treatment are tumor immune escape, antigenic heterogeneity, drug resistance, and the ephemeral nature of several therapy-induced responses. The diverse characteristics of malignancies and the heterogeneity in tumor antigen expression across people further complicate treatment outcomes 16 , 17 . Recent findings demonstrated significant intra-tumoral genomic heterogeneity, including mutually exclusive amplifications of EGFR and PDGFRA within the same glioblastoma (GBM) tissue. The amplifications exhibited geographic patterns, with PDGFRA-amplified cells aggregating near endothelial areas. Comparable regional genetic modifications have been identified in other malignancies, such as clear-cell renal cell carcinoma (ccRCC) 18 – 21 . Additionally, intra-tumor heterogeneity extends to gene expression, particularly regarding the factors influencing expression of cluster of differentiation (CD) antigens across various cancer types. Examples include CD34 and CD38 in acute myeloid leukemia (AML) 22 , 23 , CD24/CD44 in breast cancer 24 , 25 , and CD133 in colon and brain tumors [26,27]. Key cancer-related genes like c-KIT 28 , cyclins, Ki67 29 , Bcl-2 30,31 , c-Myc 32 , RAS 33 , and EGFR 34 also show heterogeneous expression within the same cancer. There is a critical need to develop innovative therapeutic strategies that offer broad applicability, reduce the risk of resistance, and provide sustained clinical benefits across multiple cancer types. Cancer vaccines have emerged as a promising therapeutic strategy considering these issues. Vaccines can induce the patient’s immune system to identify and eradicate cancer cells, providing a targeted, systemic, and potentially enduring anti-tumor response with reduced side effects relative to conventional chemotherapeutics 36 , 37 . Recent advancements in immunoinformatics, molecular profiling, and personalized vaccine design have facilitated the creation of cancer vaccines aimed at tumor-specific or tumor-associated antigens. Early-phase clinical trials of neoantigen-based vaccines in melanoma and glioblastoma have shown the potential to elicit robust and lasting T-cell-mediated anti-tumor immunity 38 , 39 . The findings underscore the potential of cancer vaccines as either a complementary or a standalone therapy for solid tumors. This study posits that specific genes are consistently up regulated across various cancer types, suggesting their potential as common targets for immunotherapy, in response to the pressing demand for more effective and widely applicable cancer treatments. Identifying universally overexpressed genes offers a unique opportunity to develop a multi-cancer vaccine capable of eliciting strong immune responses across a diverse patient population, regardless of tumor origin. This study analyzes publicly available microarray gene expression datasets, focusing on five major cancer types: lung, liver, colorectal, gastric, and breast cancers, which together represent a significant portion of global cancer morbidity and mortality. Focusing on the gene(s) that are consistently upregulated across these cancers may address the challenges posed by tumor heterogeneity and interpatient variability, thus providing a more universal and effective treatment strategy. Methods Obtaining Microarray Data from Normal and Cancer Samples Microarray datasets for lung, liver, colorectal, gastric, and breast cancers were sourced from the NCBI Gene Expression Omnibus (GEO), a publicly accessible database for large-scale gene expression data ( http://www.ncbi.nlm.nih.gov/geo/ ). Table 1 summarizes the datasets, which include both tumor tissue samples (primary or metastatic) and their corresponding normal counterparts. Differentially expressed genes (DEGs) were identified using the LIMMA (Linear Models for Microarray Data) package in R (version 4.4.2) 40 . In addition, mRNA expression profiles, copy number variations (CNVs), single-nucleotide variations (SNVs), methylation data, and survival information for seven hub genes in LUAD, BRCA, STAD, COAD, and LIHC patients were retrieved from the National Cancer Institute’s Genomic Data Commons (GDC) portal ( https://gdc.cancer.gov/ ). All GDC data analyses were performed using the GSCA (Gene Set Cancer Analysis) online platform 41 . Table 1 Features of the datasets that were used to identify the genes of differential expression (DEGs) Cancers Dataset Platform Tumor Normal Breast 42 GSE42568 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 104 17 Colorectal 43 GSE110224 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 17 17 Gastric 44 , 45 GSE79973 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 10 10 Hepatocellular 46 GSE84402 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 14 14 Lung (NSCLC) 47 GSE33532 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 80 20 Transcriptional Variation in Hub Genes Across Tumor Stages To investigate potential relationships between hub gene expression and tumor progression, we utilized TCGA 48 data generated through the UALCAN portal 49 to analyze the differential expression patterns of the seven hub genes across tumor stages in LUAD, BRCA, STAD, COAD, and LIHC patients. Patients were classified based on tumor stage (Stage I-IV) and gene expression levels to see if the expression of the hub genes altered drastically as the illness progressed. Boxplots were employed to depict expression patterns; statistically significant was regarded as a p-value < 0.05. Prognostic Evaluation of the Hub Genes We analyzed to explore the relationship between gene expression patterns and patient prognosis by correlating the tumor expression levels of hub genes with overall survival data from patients across different hub cancer types, utilizing the GEPIA2 online tool 50 . Kaplan–Meier plots, accompanied by hazard ratios (HR) and p-values, were generated to explain and assess the survival disparities across cohorts exhibiting superior and downcast expression readings of the hub genes. Analysis of SNVs and CNVs in Hub Genes Using the GSCA We obtained single-nucleotide variation (SNV) data from five cancer types—liver, breast, colon, stomach, and lung cancers—focusing on seven functional mutation types: Missense Mutation, Nonsense Mutation, Frame_Shift_Ins, Splice Site, Frame_Shift_Del, and In_Frame_Del. The data was retrieved from the NCI Genomic Data Commons ( https://gdc.cancer.gov/ , accessed on May 29, 2021), and the analysis was conducted utilizing the GSCA (Gene Set Cancer Analysis) server. The percentage of single nucleotide variants (SNVs) for each gene was calculated by dividing the total number of cancer samples by the number of mutated samples, after an examination of the frequency and distribution of SNVs within the hub genes. Using Mutation Annotation Format (MAF) tools built into the GSCA platform, mutation profiles and waterfall charts were displayed. Moreover, GSCA was utilized to investigate copy number variation (CNV) data, emphasizing four distinct types of CNV: homozygous amplification (Homo Amp; CNV = 2), homozygous deletion (Homo Del; CNV = 2), heterozygous amplification (Hete Amp; CNV = 1), and heterozygous deletion (Hete Del; CNV = 1). Across the five forms of cancer, the rates of these CNV occurrences in the hub genes were demonstrated. SNV and CNV data were combined with overall survival data to determine the clinical importance of these genomic changes. The log-rank test was employed to analyze survival differences between patients exhibiting mutated and non-mutated hub genes (pertaining to SNVs) as well as among CNV subgroups. The threshold for statistical significance was established as a p-value of less than 0.05 41 . Investigation of Methylation in Hub Genes The GSCA (Gene Set Cancer Analysis) server ( https://guolab.wchscu.cn/GSCA/#/ ) was employed for evaluating DNA methylation data associated with the hub genes across five distinct cancer types—liver, breast, colon, gastric, and lung—on May 29, 2021. Genes exhibiting notable hypo- or hyper-methylation patterns were detected by assessing the methylation levels of malignant tumors and normal tissues. We further analyzed the relationship between DNA methylation and gene expression to investigate whether methylation status affects transcriptional activity. Additionally, Kaplan–Meier survival analysis combined with log-rank testing, performed via the GSCA platform, was used to evaluate the association between methylation status and overall survival outcomes. The threshold for statistical significance was determined to be a p-value of under 0.05 41 . Investigation of Functional Enrichment of the Hub Genes Functional enrichment analyses of the differentially expressed genes (DEGs) were performed using Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) using the online database Enrichr ( https://maayanlab.cloud/Enrichr/ , accessed on May 29, 2021) 51 . Statistical significance was characterized by terms associated with the GO and KEGG pathways, with a false discovery rate (FDR) of less than 0.05. The R program cluster Profiler 52 was utilized to illustrate the findings of these investigations. Investigation of Gene-Gene and Protein-Protein Interaction To elucidate the functional interrelations of the identified hub genes, gene–gene interaction (GGI) and protein–protein interaction (PPI) networks were meticulously constructed using the GENEMANIA tool ( https://genemania.org/ , accessed on June 1, 2021) 53 and the STRING database (Search Tool for the Retrieval of Interacting Genes/Proteins; http://string.embl.de/ , accessed on May 23, 2021) 54 , respectively. The PPI network of the differentially expressed genes (DEGs) was generated using STRING, applying a confidence score cutoff of 0.40 to ensure biologically meaningful interactions. Additionally, we examined the elevated genes together with their positively linked genes (Pearson correlation coefficient, PCC; refer to Supplementary Table 01) 55 . Their interaction network was examined utilizing the STRING database and demonstrated using Cytoscape (v3.10.3) 56 . Key hub genes are identified with the CytoHubba plugin through the degree technique 57 . Protein Sequence Retrieval and Analysis In this work, ANLN and CTHRC1 were identified as target proteins owing to their notable overexpression in LUAD, BRCA, STAD, COAD, and LIHC cancer types. The FASTA formatted sequences for these proteins (ANLN UniProt ID: Q9NQW6, and CTHRC1 UniProt ID: Q96CG8) were obtained from the UniProt database 58 . The physicochemical properties of the candidate proteins were analyzed using the ExPASy ProtParam tool [59], and their antigenic potential was assessed through the VaxiJen server version 2.0 60 . Estimation of Immune Epitopes The production of the vaccine primarily focused on B-cell epitopes owing to their capacity to elicit the humoral immune response. Epitopes stimulate antibody production that effectively blocks pathogenic antigens. Potential linear B-cell epitopes were determined from the protein sequences utilising Bepipred Linear Epitope Prediction 2.0, with a suitable cutoff of 0.5 61 . Binding predictions for MHC-I and MHC-II were accomplished employing the Immune Epitope Database (IEDB) tools, concerning human HLA sets. The NetMHCpan EL 4.1 and NetMHCIIpan EL 4.1 algorithms were utilized to discover epitopes exhibiting high affinity for MHC-I and MHC-II molecules, respectively 62 . The chosen epitopes showed higher antigenicity ratings, suggesting that they might be effective vaccine candidates. To ensure the safety and effectiveness of the discovered epitopes, their toxicity, allergenicity, and solubility were assessed in combination with antigenicity predictions. The characteristics were evaluated using the AllerTOP v2.0, ToxinPred, and Innovagen Peptide Calculator applications. Epitope Description To evaluate the possibility of the selected epitopes as vaccine candidates, their antigenic qualities were confirmed employing the VaxiJen v2.0 tool, with a threshold of 0.4 60 . Applying the AllerTOP v2.0 server, the allergenic nature of the epitopes was assessed, AllerTOP applied the k-nearest neighbours (kNN) method with a training dataset comprising known allergens and non-allergens 63 . The peptides' toxicity was assessed using ToxinPred 64 , and their solubility was predicted using the Innovagen Peptide Calculator. Vaccine Construction The vaccine was developed by combining the selected B-cell, MHC-I, and MHC-II epitopes with the cholera toxin B adjuvant 65 , 66 . The epitopes were linked using suitable linkers: GPGPG linkers for MHC-II epitopes, AAY linkers for MHC-I epitopes, and KK linkers for B-cell epitopes. The adjuvant was linked to the epitopes by the EAAAK linker. The use of these linkers ensures the appropriate configuration of the vaccine peptide and facilitates optimal folding and flexibility for efficient immune system identification 67 , 68 . Characteristics and Molecular Conformations of the Vaccine Calculations for the molecular weight, solubility, theoretical isoelectric point (pI), in vitro half-life, and GRAVY (grand average of hydropathy) were obtained using the ExPASy ProtParam software aimed at evaluating the chemical and biological properties of the vaccine that was produced. Employing the SOPMA 69 and PSIPRED servers 70 for prediction of secondary structure. Structure Prediction and Justification The vaccine's three-dimensional structure was forecasted using I-TASSER (Iterative Threading Assembly Refinement) 71 . The expected structure was later improved with the GalaxyRefine2 service 72 . The quality of the 3D structure was evaluated using PROCHECK to generate a Ramachandran plot, which assesses the geometry and stereochemical fidelity of the model. Ramachandran plots were used to validate the protein structure by confirming that its torsional angles fall within energetically favorable regions 73 . Additionally, the Z-score—a measure of the protein’s structural stability—was calculated using the ProSA-web platform 74 . Population Coverage Screening The Population Coverage Tool from IEDB was employed to assess the potential global coverage of the vaccine by analyzing the distribution of MHC-binding alleles across 23 different geographical regions. This study aimed to evaluate the influence of epitope affinity variation for HLA alleles on the efficacy of vaccination among different ethnic groups 75 . Discontinuous B-cell Epitope Assessment Discontinuous B-cell epitopes were predicted using the ElliPro server. The service determines the Protrusion Index (PI) to assess whether residues in the protein structure form a discontinuous epitope. A PI score of 0.9 signifies that 90% of the residues in the epitope are contained within the ellipsoid, which is a favorable characteristic for epitope identification. The distance measure was employed to determine the proximity of residue centers, facilitating the identification of multiple discontinuous epitopes 76 . Docking and Molecular Dynamics Simulation The interaction potential between the designed multi-epitope vaccine and its target receptors, MHC class I and II, was assessed using ClusPro 2.0, a robust platform for predicting protein–protein docking conformations 77 . This automated docking tool uses fast Fourier transform (FFT) algorithms combined with Monte Carlo simulations to efficiently sample and evaluate the stability of protein conformations. The crystal structures of MHC class I (PDB ID: 5XS3) and MHC class II (PDB ID: 3L6F) were obtained from the RCSB Protein Data Bank. Prior to docking, both structures were refined and visualized using PyMOL to ensure proper formatting and orientation 78 . The docked complexes with the lowest binding energy scores were chosen to represent the most promising interactions between the vaccine construct and the MHC receptors. To evaluate the dynamic stability and flexibility of these complexes, normal mode analysis (NMA) was carried out using the iMODS web server ( http://imods.chaconlab.org ). The iMODS tool offers comprehensive insights into the structural dynamics of the complexes by analyzing parameters such as deformability, B-factor estimates, eigenvalues indicating molecular flexibility, variance distribution, covariance matrices, and the elastic network model. These evaluations provide valuable information on the atomic-level stability and dynamic motion of the vaccine–receptor complexes, enhancing our understanding of their molecular behavior and interaction fidelity 79 . Immune Simulation The C-ImmSim server was used to assess the immunogenicity of the formulated vaccine. This immune simulator utilizes machine learning techniques and a PSS matrix to model immunological responses inside a dynamic, agent-based simulation. The immunization consisted of three doses of 1000 vaccine proteins, administered at four-week intervals, namely at time points 1, 84, and 168. The simulation had a total of 1050 stages. The settings of the C-ImmSim immune simulator were optimized for enhanced modeling, and the variability of the immune response was assessed using the Simpson index (D), which measures the diversity of clonal selection following repeated antigen exposure. The results of these simulations enabled the forecasting of the likely immunological response triggered by the multi-epitope vaccine, providing insights into its effectiveness 80 . Codon Optimization and Cloning Codon optimization of the vaccine construct was carried out using the Java Codon Adaptation Tool (JCAT) to improve its translational efficiency and expression potential in Escherichia coli [81]. The nucleotide sequence was specifically adapted to align with the usage preferences of the E. coli K12 strain, ensuring efficient translation and expression. The optimized sequence achieved a codon adaptation index (CAI) greater than 0.8, indicating strong compatibility with the E. coli translational machinery and a high likelihood of efficient protein expression. In addition, the GC content was fine-tuned to remain within the optimal range of 30–70%, supporting mRNA stability and enhancing the overall efficiency of bacterial expression. Throughout the optimization process, elements that could hinder expression, such as prokaryotic ribosome binding sites, Rho-independent transcription terminators, and unwanted restriction sites, were deliberately excluded. The optimized gene sequence was then inserted into the E. coli expression vector pET-28a(+) using SnapGene 5.2.4 software for further cloning and expression studies. To enable seamless cloning and ensure efficient expression, restriction sites for EcoRI and EcoRV were introduced at the N- and C-termini of the gene sequence, respectively. This modification allows for easy insertion into the vector, facilitating robust vaccine expression in E. coli for subsequent experimental validation. Results ANLN, CTHRC1, ABCA8, PDK4, MT1M, TMEM100, and LIFR Are Identified as Hub Genes Involved in the Development of Breast, Lung, Colorectal, Liver, and Stomach Cancers Table 1 presents the comprehensive information on the GEO datasets associated with each of the five cancer types, while Fig. 1A depicts the distribution of differentially expressed genes (DEGs) across these cancer types using volcano plots. We identified an overall number of 322 differentially expressed genes (DEGs) in colorectal cancer (124 upregulated, 198 downregulated), 1362 DEGs in breast cancer (533 upregulated, 829 downregulated), 1278 DEGs in lung cancer (419 upregulated, 859 downregulated), 501 DEGs in liver cancer (105 upregulated, 396 downregulated), and 504 DEGs in gastric cancer (223 upregulated, 281 downregulated) (Fig. 1B, and C). We also discovered five consistently downregulated genes (logFC distribution) (ABCA8, PDK4, MT1M, TMEM100, and LIFR) and two consistently upregulated DEGs (logFC distribution) (ANLN and CTHRC1) employing integrated analysis of the five GEO datasets (Fig. 1D). The Hub Genes' Expressions Are Associated with Clinical Prognosis of the Hub Cancer Patients According to our differential expression analysis, the expression levels of the up-regulated hub genes, ANLN and CTHRC1, dramatically change as cancer stages progress, often increasing with higher tumour stages across the five cancer types. In BRCA, LIHC, and LUAD cancer, the expression level of ANLN is likewise significantly overexpressed from stage 1 to stages 2, 3, and 4. Similarly, the expression level of CTHRC1 was significantly higher at each stage (Fig. 2). On the other hand, the expression levels of the five downregulated genes—ABCA8, PDK4, MT1M, TMEM100, and LIFR—showed a gradual reduction with advancing tumor stages.(Fig. 3). When examining the COAD, LIHC, LUAD, and STAD cohorts, it is worth noting that we discovered that the two genes that are often elevated were significantly associated (p 0.05) (Fig. 4). Distribution and Impact of Genetic and Epigenetic Alterations in Hub Genes Across Breast, Lung, Colorectal, Liver, and Stomach Cancers A comprehensive mutational analysis of 267 tumour samples from five cancer types demonstrated genetic alterations in all cases. ABCA8 exhibited the highest alteration frequency at 43%, followed by LIFR at 27% and ANLN at 18%. Missense mutations represented the primary type of alteration. Alterations were noted across all cancer types, with variability in tumour mutational burden (TMB) among samples. The findings indicate that ABCA8 is the most mutated gene, suggesting that these genetic alterations may play a role in tumorigenesis in various cancers (Fig. 5A). A heatmap analysis indicated that ABCA8 exhibited the highest single-nucleotide variant (SNV) frequencies across various cancer types, with mutation rates between 22% and 26%, while only 1% was observed in liver hepatocellular carcinoma (LIHC). LIFR exhibited increased mutation frequencies, notably in COAD (21%) and STAD (14%), while demonstrating reduced rates in other cancer types. Additional genes (ANLN, PDK4, TMEM100, CTHRC1, MT1M) demonstrated low mutation frequencies, each below 10%. ABCA8 and LIFR were identified as the most mutated genes among the analyzed cancers (Fig. 5B). The characterisation of SNV classes indicated that C > T transitions were the most common, implying that spontaneous deamination is a significant mutational mechanism. Missense mutations constituted the predominant variant type, accounting for approximately 300 events, while other mutation types were observed with significantly lower frequency. In alignment with previous analyses, ABCA8 and LIFR demonstrated the highest mutation frequencies among the examined genes (Fig. 5C). The predominant mutations were classified as C > T and T > C transitions, in addition to C > G and C > A transversions (Fig. 5D). In addition, the survival analysis indicated that SNVs in ABCA8 correlated with lower survival rates in the BRCA cohort (Fig. 5E). The methylation levels of all seven hub genes exhibited a negative correlation with their mRNA expression levels across all cancer types (cor < 0, p 0.05) in any cohort (Fig. 5G). CNA analysis indicated that LIFR, ANLN, and CTHRC1 displayed amplifications (both heterozygous and homozygous), whereas MT1M, TMEM100, ABCA8, and PDK4 presented heterozygous deletions and amplifications (Fig. 5H). Correlation analysis between CNV and mRNA expression suggested a positive correlation (p < 0.05) between LIFR and ANLN expression and their CNV status in STAD, LUSC, and LUAD cancers (Fig. 5I). Gene–Gene and Protein–Protein Interactions, and Functional Enrichment of the Hub Genes To predict the biological roles of the identified DEGs, we conducted GO and KEGG pathway enrichment analyses. Our analysis identified differentially expressed genes (DEGs) that were significantly enriched in key biological processes, notably cellular transport, chromosome localisation, and mitotic regulation. The processes encompass ADP and ATP transport, crucial for sustaining energy homeostasis during cell division, alongside protein localisation to condensed chromosomes and the centromeric region, underscoring their significance in chromosomal organisation during mitosis. Moreover, differentially expressed genes (DEGs) related to protein localisation at the kinetochore indicate a function in facilitating accurate chromosome segregation, whereas those linked to cytoskeleton-dependent cytokinesis may govern the concluding phase of cell division. The enrichment of differentially expressed genes (DEGs) in purine ribonucleotide and adenine nucleotide transport suggests their potential involvement in providing essential nucleotides for RNA and DNA synthesis during cellular proliferation. The role of these DEGs in the mitotic spindle assembly checkpoint signalling underscores their significance in preserving genomic stability during cell division. The findings indicate that these DEGs are crucial in regulating cellular division, energy metabolism, and chromosome dynamics, positioning them as potential candidates for further research as biomarkers or therapeutic targets in diseases characterised by dysregulated cell division, including cancer (Fig. 6A). The up-regulated genes were enriched in the Protein digestion and absorption and basal transcription factors pathways according to KEGG analysis. The genes that are down-regulated in the KEGG pathway enrichment analysis are mainly associated with impaired mineral absorption, disrupted JAK-STAT signalling, decreased cytokine-cytokine receptor interactions, and modified regulation of stem cell pluripotency. The observed changes indicate possible immune suppression, metabolic disturbances, and diminished tissue regeneration. Furthermore, the down-regulation of pathways associated with diabetic cardiomyopathy indicates impairments in heart function related to diabetes (Fig. 6B). The analysis of gene-gene interactions indicates that these genes constitute a co-expression network that engages with established oncogenes, underscoring their varied and intricate functions in cancer biology. PTK2 (FAK) and WWTR1 (TAZ) are recognised as classic oncogenes that facilitate tumour growth, invasion, and metastasis through the enhancement of cell signalling and migration pathways. IL6ST and CITED2, although not classified as traditional oncogenes, are integral to cancer progression through the enhancement of inflammatory responses and survival signalling pathways. OGT, which plays a role in protein O-GlcNAcylation, facilitates the metabolic reprogramming necessary for the survival of cancer cells, highlighting the significance of post-translational modifications in tumour biology. It is interesting to note that CEBPD and NR3C1 exhibit context-dependent behaviour, which complicates their potential as therapeutic targets by functioning as either tumour suppressors or oncogenes based on the tissue type and environment. On the other hand, SORBS1 and EPB41L3 mostly serve as tumour suppressors, and cancer is encouraged by their loss rather than activation (Fig. 6D). On the other hand, the PPI network of up- and down-regulated DEGs has 249 edges and 75 nodes, with a mean node degree of 5.88 (Fig. 6C). The top hub genes were determined to be KIF11 (degree = 107), BUB1 (degree = 107), BUB1B (degree = 106), CCNA2 (degree = 105), TTK (degree = 104), DLGAP5 (degree = 103), KIF2C (degree = 102), CCNB1 (degree = 102), ESPL1 (degree = 101), and NDC80 (degree = 100) based on our additional examination of the upregulated gene positive correlation network based on node degree (Fig. 6E). Retrieval of Protein Sequences The elevated human proteins CTHRC1 (UniProt ID: AAQ89273.1) and ANLN (Uniprot ID: AAH70066.1) were chosen for investigation. Protein sequences in FASTA format were obtained from the UniProt database. The VaxiJen v2.0 server was used to assess these proteins' antigenic potential. ANLN scored 0.5930 and CTHRC1 scored 0.4745, both proteins showing antigenicity scores over the threshold value of 0.4. Furthermore, the physicochemical characteristics of the proteins were examined using the ExPASy ProtParam service (Supplementary Tables 2 and 3). Prediction of Linear B-cell Epitopes It was anticipated that B-cell epitopes, which are essential for starting the synthesis of antigen-specific antibodies, would improve humoral immune responses. Immunogenic epitopes cause B cells to differentiate into plasma and memory cells when they attach to B-cell receptors. Memory cells provide long-term immunity, while plasma cells are in charge of the initial antibody response. Consequently, B-cell epitope identification is crucial for the development of epitope-based vaccines. Prediction scores over the 0.5 threshold were employed to determine epitopes. Among the 21 predicted epitopes four met the selection criteria: DEEHGKGSLEEAEAER and VQKPDA from ANLN, TFTKMRSNS and QGSPEMNSTINIHRT from CTHRC1 (Tables 2, and 3). Tables 2. Selected epitopes for vaccine production from ANLN Antigen B-cell Epitopes Antigen Score Allele Solubility Toxicity Allergenicity DEEHGKGSLEEAEAER 0.65 - Soluble Nontoxic Non-Allergen VQKPDA 1.22 - MHC-I Epitopes KLKNEGPQRK 1.34 HLA-A*03:01, HLA-A*30:01 GQNPELLPK 1.20 HLA-A*11:01, HLA-A*03:01 KLLERTRARR 1.15 HLA-A*31:01, HLA-A*03:01 MHC-II Epitopes AYRSQRFKETERPSI 0.98 HLA-DPA1*01:03, HLA-DPA1*03:01, HLA-DPA1*01:03, HLA-DPA1*02:01, HLA-DPA1*02:01 DKVPFLSSLESVEER 1.27 HLA-DRB1*09:01, HLA-DRB1*04:05, HLA-DQA1*03:01 DLLYSIDAYRSQRFK 1.21 HLA-DRB1*15:01, HLA-DRB1*12:01, HLA-DQA1*01:01, HLA-DPA1*01:03, HLA-DPA1*02:01, HLA-DPA1*01:03, HLA-DQA1*04:01 Tables 3. Selected epitopes for vaccine production from CTHRC1 Antigen B-Cell Epitopes Antigen Score Allele Solubility Toxicity Allergenicity TFTKMRSNS 1.1743 - Soluble Non-Toxic Non-Allergen QGSPEMNSTINIHRT 0.5772 - MHC-I Epitopes - ATAASSVKTR 0.9132 HLA-A*11:01, HLA-A*68:01 CEGQNPELL 1.5636 HLA-B*40:01 DTISDSVAV 0.5932 HLA-A*68:02, HLA-A*26:01 MHC-II Epitopes RDGFKGEKGECLRES 1.1328 HLA-DRB1*01:01, HLA-DPA1*03:01, HLA-DPA1*02:01, HLA-DPA1*01:03, HLA-DPA1*02:01, HLA-DPA1*02:01, HLA-DRB1*07:01 PGRDGFKGEKGECLR 1.0345 HLA-DRB1*01:01, HLA-DPA1*03:01 GRDGFKGEKGECLRE 0.9773 HLA-DRB1*01:01, HLA-DPA1*03:01, HLA-DPA1*02:01, HLA-DPA1*01:03, HLA-DPA1*02:01 Prediction of MHC-binding Epitopes The IEDB server was employed to evaluate the binding affinities of epitopes to MHC class I and II molecules to predict those that are capable of activating helper T lymphocytes (HTLs). The NetMHCpan EL 4.1 method was employed to predict MHC-I epitopes, with a focus on 54 prevalent human alleles. The NetMHCIIpan EL 4.1 utility was implemented against 27 alleles for MHC-II prediction (Supplementary Table 4). The anticipated epitopes underwent additional assessment for allergenicity, toxicity, solubility, and antigenicity. Tables 2 and 3 present the top three candidate epitopes and their corresponding MHC alleles. Vaccine Construction, Physicochemical and Secondary Structure Analysis The final multi-epitope vaccine was developed by integrating the cholera toxin B subunit as an adjuvant, linked to the N-terminal end of the construct through an EAAAK linker. B-cell epitopes were connected using KK linkers to enhance effective presentation. MHC class II-binding epitopes were linked using GPGPG linkers to improve antigen processing and presentation through MHC-II pathways. In a similar manner, epitopes that bind to MHC class I were connected through AAY linkers, which enhanced the efficiency of proteasomal cleavage and the presentation of MHC-I. The comprehensive configuration guaranteed the best possible folding, immunogenicity, and stability of the construct. Figure 7A illustrates a graphical representation of the vaccine design. The developed vaccine construct demonstrated an estimated isoelectric point (pI) of 9.37 and a molecular weight of 40,979.39 Da. The predicted in vitro half-life was around 30 hours in mammalian reticulocytes, whereas the in vivo half-life was estimated to be over 20 hours in yeast and about 10 hours in E. coli. The stability analysis produced an instability index of 36.68, indicating that the vaccine demonstrates stability, as values exceeding 40 signify instability. The aliphatic index of 60.48 provided evidence for thermal stability. The construct's hydrophilic characteristics were validated by a GRAVY score of -0.778, and a solubility score of 0.635 demonstrated significant solubility post-expression. The analysis of secondary structure prediction, carried out with the SOPMA tool, indicated that 30.67% of residues were organised into alpha helices, 11.20% into extended strands, and 58.13% into random coils (Fig. 7B and C). Vaccine 3D Structure: Prediction, Refinement, and Validation The preliminary three-dimensional configuration of the vaccine construct was forecasted utilising the I-TASSER server. Out of ten threading templates, five 3D models were produced, exhibiting C-scores that varied from − 1.93 to − 2.89. The C-score, reflecting the model's confidence on a scale from − 5 to 2, was employed to identify the optimal structure. The model exhibiting the highest C-score of − 1.93 was selected for additional refinement. According to the evaluation metrics, this model demonstrated a root-mean-square deviation (RMSD) of 11.2 ± 4.6 Å and a Template Modelling (TM) score of 0.48 ± 0.15 (Fig. 8A), indicating a satisfactory level of structural accuracy. Following this, five enhanced versions of the original model were produced. Model 3 exhibited exceptional structural characteristics, showcasing a Rama favoured region percentage of 88.7%, with only 0.7% weak rotamers. It achieved a GDT-HA score of 0.9640, an RMSD of 0.356 Å, and a MolProbity score of 1.982. Consequently, Model 3 was chosen for an additional examination (Fig. 8B). Analysis of the Ramachandran plot demonstrated that 83.5% of the residues were situated in the most favoured regions, 13.6% in allowed regions, and merely 2.9% in disallowed regions (Fig. 8E), suggesting a high level of stereochemical quality. Additionally, the ERRAT analysis produced an overall quality factor of 64.662% (Fig. 8C), while the Z-score obtained from ProSA-Web was − 2.26 (Fig. 8D), thereby affirming the reliability of the predicted tertiary structure. The structural validation results obtained from RAMPAGE, ERRAT, and ProSA-Web collectively indicate the high quality and stability of the final vaccine model. Population Coverage Investigation Selected T-cell epitopes demonstrate the ability to bind to various HLA supertype alleles, thereby ensuring extensive population coverage. The tool for estimating the geographic distribution of anticipated vaccine responses, which focuses on MHC-I and MHC-II epitope-specific population coverage, was utilized from the IEDB database. This analysis covered 16 geographical regions along with several significant countries. The anticipated vaccine is expected to reach an overall global population coverage of around 98.75%. The United States demonstrated the highest coverage at 100.00%, with North America at 99.99%, Europe at 99.91%, and South America at 99.67%, closely following. Areas like South Asia (99.58%), India (99.48%), and Japan (99.15%) exhibited impressive coverage rates. Conversely, coverage rates that were somewhat lower, yet still significant, were noted in Pakistan (68.67%), Central America (74.54%), and Southwest Asia (76.30%). Figure 9 presents a comprehensive analysis of population coverage across different regions. The results demonstrate that the developed vaccine construct may have significant potential for worldwide use. Discontinuous B-Cell Epitopes A total of 169 residues were identified across six discontinuous B-cell epitopes, as predicted by the ElliPro server. The lengths of these epitopes ranged from greater than 3 to 51 residues, with prediction scores varying between 0.58 and 0.877. The discontinuous epitopes are illustrated in Fig. 10A and 10B. Docking and Molecular Dynamics Simulation The ClusPro 2.0 server was utilized for investigating protein–protein interaction to determine how well the vaccine was designed to attach to human immunological receptors. The vaccine-receptor complex was represented by 30 models produced by the server; the model with the lowest energy value was chosen as the best docking conformation. The vaccine showed a substantial affinity for both MHC-I and MHC-II receptors, as seen in Fig. 11A and B. The binding energies for MHC-I and MHC-II were − 973.4 kcal/mol and − 1046 kcal/mol, respectively, suggesting that the vaccine may interact with both immunological receptors effectively. When the vaccine is administered, the lowest binding affinity raises the possibility of a strong immunological response (Fig. 11). The iMOD server was used to conduct Normal Mode Analysis (NMA) to further evaluate the vaccine's stability in combination with MHC-I and MHC-II. As shown in Figs. 12 and 13, NMA demonstrated notable deformability in the locations with hinge movements, suggesting flexible regions within the vaccine-receptor complexes. The B-factor values, which indicate the structural flexibility, were analyzed in relation to the root mean square deviation (RMSD) obtained from NMA. For the MHC-I complex, the eigenvalues were 3.351124 × 10^7, and for the MHC-II complex, they were 3.833857 × 10^7. According to these eigenvalues, the energy needed for structural deformation in these areas is comparatively low, suggesting that the vaccine can undergo the conformational changes required for immunological recognition. The stability and flexibility of the vaccine in these complexes are supported by the covariance matrix, which displays interactions between residue pairs, and the elastic network model, which illustrates interatomic communication via springs. The vaccine retains long-lasting and persistent connections with both MHC-I and MHC-II receptors, according to the combined findings of the docking and NMA. This may help explain how well the vaccine stimulates immunological responses. Immune Simulation The outcomes of immune simulations using the C-ImmSim server demonstrated a significant improvement in immune responses that were very similar to actual immunological responses. The most important early reaction was an increase in IgM concentration. After subsequent tertiary injections, the B cell population, IgG1 + IgG2 antibodies, and IgG + IgM antibodies all rose while antigen levels gradually declined. Additionally, cytotoxic T (TC) cells and memory cells were incorporated into the vaccination model, contributing to an increase in the population of T-helper (TH) cells. Moreover, repeated exposures led to an increase in IFN-γ and IL-2 production. These findings confirmed the vaccination model’s strong immunogenic and antigenic properties (Fig. 14). Codon Optimization and In Silico Cloning When an amino acid is encoded by various codons in different animals, this phenomenon is known as codon bias. The transcription of identical amino acids using different codons is a result of differences in cellular machinery. In this study, codon adaptation methods were used to anticipate which codons would be most effective in encoding certain amino acids in each organism. The Java Codon Adaptation Tool (JCAT) was employed to maximise the codon utilisation of Escherichia coli strain K12. Additionally, the website identified bacterial ribosome binding sites, rho-independent transcription terminators, and restriction enzyme cleavage sites (such as EaeI and StyI ). In comparison to the native GC content of E. coli (strain K12), which is 50.73%, the optimised sequence demonstrated a Codon Adaptation Index (CAI) of 0.912 and a GC content of 51.38%. EcoRI and EcoRV sites were not detected in the codon-optimized vaccine construct sequence when restriction enzyme recognition sites were subsequently examined. The vaccine design was modified to include these enzyme locations to aid in silico cloning. Finally, the vaccine was inserted into the pET28a(+) vector to produce a viable recombinant clone of 5109 bp, as seen in Fig. 15. Discussion This study employed integrated GEO datasets to discover pivotal hub genes associated with the development and progression of five primary cancer types: colorectal (COAD), liver (LIHC), lung (LUAD), gastric (STAD), and breast (BRCA). Our findings highlight a cohort of consistently differentially expressed genes (DEGs) that serve as both therapeutic targets and biomarkers for cancer progression and prognosis. Upregulated Hub Genes and How They Contribute to the Development of Cancer All five cancer types showed two hub genes, ANLN and CTHRC1, consistently raised. Both genes are part of crucial processes required for cancer cells to survive, migrate, and proliferate 82 – 84 . Anillin (ANLN) is a conserved F-actin binding protein first extracted from Drosophila embryos, where it is crucial for cortical cytoskeletal dynamics during cytokinesis and cellularization. In humans, ANLN governs cell morphology, movement, and proliferative advancement, with its dysregulation seen across many malignancies 85 , 86 . Increased ANLN expression is often linked to enhanced tumor cell motility, invasion, and poor prognosis, indicating its significant involvement in carcinogenesis and metastasis 87 , 88 . Pan-cancer analyses have identified a marked upregulation of ANLN in various cancer types, including those found in the bladder (BLCA), breast (BRCA), cervical (CESC), cholangiocarcinoma (CHOL), colon (COAD), esophagus (ESCA), head and neck (HNSC), kidney chromophobe (KICH), kidney renal clear cell (KIRC), kidney renal papillary cell (KIRP), liver (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreas (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate (PRAD), rectum (READ), sarcoma (SARC), stomach (STAD), thyroid (THCA), and uterine corpus endometrial carcinoma (UCEC). Cox regression analysis and Kaplan–Meier survival curves demonstrate that elevated ANLN expression is strongly correlated with poorer overall survival (OS), disease-free interval (DFI), and progression-free survival (PFS) in KIRP, LIHC, LUAD, and PAAD, further establishing its significance as a prognostic marker. These findings align with studies suggesting ANLN as a potential biomarker in cancer 84 , 89 . Mechanistic investigations have shown that ANLN promotes tumor proliferation and migration via interactions with microRNAs and oncogenes, particularly by targeting miR-217 and HMGA2 90,91 . Furthermore, ANLN stimulates the RHOA-PI3K/AKT signaling pathway, promoting cancer cell proliferation and progression 92 . In lung adenocarcinoma, ANLN is associated with metastasis via the facilitation of epithelial-mesenchymal transition (EMT) 93 , but in gastric cancer, its expression has a favourable correlation with the Wnt/β-catenin signaling pathway 94 . The diverse functional relationships demonstrate ANLN's extensive oncogenic potential across different types of tissue. Conversely, CTHRC1 (Collagen triple helix repeat containing 1) is a secreted glycoprotein predominantly expressed in epithelial-mesenchymal interfaces, such as the epidermis-dermis border, corneal epithelium, airway, oesophagus, choroid plexus, and meninges 95 . Its expression is significantly elevated in several malignancies, exhibiting greatly higher transcription levels in 24 different cancer types compared to normal tissues 96 . Although CTHRC1 is consistently upregulated in several malignancies, its expression is context-dependent; for example, one investigation indicated its absence in pancreatic tumour cells, with localisation confined to the adjacent stroma, including fibroblasts, vasculature, and skeletal muscle 97 . This disparity may be ascribed to many factors: i) CTHRC1 exists in various molecular weight forms due to post-translational modifications, potentially affecting epitope recognition; ii) its interaction with unidentified cytoplasmic proteins may obscure epitopes essential for antibody detection; and iii) variability in antibody specificity among laboratories could lead to inconsistent staining results. The findings indicate that ANLN and CTHRC1 may facilitate tumour progression through both intrinsic mechanisms and their influence on the tumour microenvironment, underscoring the necessity for additional research to elucidate their biological functions and potential as diagnostic or therapeutic targets. The roles of downregulated hub genes as tumour suppressors Conversely, the downregulated genes ABCA8, PDK4, MT1M, TMEM100, and LIFR exhibited similar downregulation across the five cancer types, with expression diminishing as tumours advanced to more advanced stages. These genes are probably implicated in critical tumor-suppressive mechanisms. ABCA8, a member of the ATP-binding cassette (ABC) transporter superfamily, is crucial to cancer biology, especially regarding treatment resistance and cellular homeostasis 98 . ABCA8 expression is often downregulated in many malignancies, including hepatocellular carcinoma, prostate cancer, ovarian cancer, and tongue squamous cell carcinoma 99 – 101 . Recent study has shown simultaneous downregulation of ABCA8 and FABP4 in stomach adenocarcinoma (STAD), with their diminished expression strongly linked to worse prognosis, irrespective of clinical factors such as tumour stage, grade, or nodal metastatic status. These data indicate that both genes may function as prognostic indicators in STAD. Subsequent investigation using the cBioPortal database, which includes 1,365 STAD samples, identified significant genetic abnormalities in ABCA8 and FABP4, reinforcing the concept that their dysregulation—via expression loss or mutation—could facilitate tumor growth 102 . PDK4 (pyruvate dehydrogenase kinase isozyme 4) is often downregulated in several types of cancer, including prostate, breast, lung, and liver malignancies 103 , 104 . It has been shown that PDK4 functions to inhibit tumour cell growth and encourage apoptosis, especially in breast and lung cancer cells 105 . Furthermore, its deficiency seems to have wider effects on the development of cancer; in ovarian cancer, PDK4 downregulation triggers the epithelial-mesenchymal transition (EMT) pathway, which increases the tumour cells' capacity for migration and invasion 106 . Metallothionein (MT) family member MT1M is essential for controlling oxidative stress reactions and metal ion homeostasis. Its downregulation in cancer may accelerate tumor growth and resistance to apoptosis by increasing oxidative damage. MTs are low-molecular-weight, cysteine-rich proteins that play a role in several cancer-related activities, including the management of metal ions, defense against oxidative stress, control over cell division and apoptosis, and resistance to chemotherapy and radiation 107 – 111 . Numerous reports of MT isoforms, particularly MT-1 and MT-2, being downregulated in hepatocellular carcinoma (HCC) raise the possibility that decreased MT expression constitutes an early stage of HCC formation 112 – 113 . This decrease in expression has been linked to epigenetic modifications, including promoter hypermethylation, as seen in rat hepatoma models 114 and human HCC tissues. In particular, promote hypermethylation silences MT1M and MT1G in HCC, supporting the idea that hepatocarcinogenesis is facilitated by epigenetic repression of MTs 115 . The transmembrane protein TMEM100 has been associated with the initiation and progression of several malignancies, and there is increasing evidence that it also acts as a tumor suppressor. Multiple studies have shown that tumors such as lung, liver, prostate, and colorectal cancers have significantly reduced TMEM100 expression. Han et al. 116 indicated that TMEM100 inhibits the proliferation of lung cancer cells, while Ou et al. 117 discovered that its downregulation in hepatocellular carcinoma is significantly associated with increased proliferation and invasion. Likewise, Ye et al. 118 discovered a decreased TMEM100 expression in prostate cancer, which correlates with tumour stage and metastasis. In colorectal cancer (CRC), both Li et al. 119 and later studies [120] demonstrated a large decrease in TMEM100, with its reinstatement deactivating the TGF-β signalling pathway, resulting in substantial suppression of cancer cell proliferation. Moreover, TMEM100 regulates prostate cancer advancement by blocking the PI3K/AKT signalling pathway, therefore restraining cell proliferation, migration, and invasion 121 . The leukaemia inhibitory factor receptor (LIFR), or CD118, is a type I cytokine receptor that interacts with multiple ligands, including leukaemia inhibitory factor (LIF), oncostatin M, and cardiotrophin 1. It forms a heterodimeric complex with glycoprotein 130 (gp130) to facilitate signal transduction within the interleukin-6 (IL-6) cytokine family 122 . LIFR regulates immunological responses, apoptosis, and cell differentiation, with its involvement in tumour biology being tissue- and context-dependent 123 . LIFR functions as a tumour suppressor in hepatocellular carcinoma (HCC), with its expression often diminished owing to promote hypermethylation 124 . The downregulation of LIFR has diagnostic significance, as its expression diminishes progressively from low-grade dysplastic nodules to tiny well-differentiated hepatocellular carcinoma, positioning it as a possible immunomarker for early hepatocarcinogenesis 125 . The ectopic expression of LIFR in HCC cells mechanistically inhibits metastasis by decreasing the PI3K/AKT signalling pathway 126 . The absence of LIFR activates NF-κB signaling via SHP1, which enhances the expression of the iron-sequestering factor LCN2, resulting in hepatic iron depletion and increased resistance to drug-induced ferroptosis 127 . The downregulation of these genes in several malignancies indicates their potential role as tumor suppressors, with their loss perhaps promoting malignant transformation. Therapeutic techniques designed to restore the expression or function of these genes may provide a unique method for treating malignancies characterized by downregulated tumor suppressor genes. Prognostic Implications and Cancer-Specific Variability Our research showed that the elevated hub genes ANLN and CTHRC1 were substantially correlated with worse survival in the COAD, LUAD, LIHC, and STAD populations. Recent research revealed that CTHRC1 (collagen triple helix repeat-containing 1) is overexpressed in all 24 major subtypes of human malignancies, demonstrating its extensive role in carcinogenesis. Elevated CTHRC1 expression is significantly correlated with decreased overall survival (OS) in various malignancies, including head-and-neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC). These results underscore the possible oncogenic function of CTHRC1 in the formation and progression of several cancer types, highlighting its promise as both a predictive biomarker and a therapeutic target in cancer 128 . Similarly, ANLN was seen to be increased in most human malignancies, and its elevated expression was substantially correlated with reduced overall survival across most cancer types. The molecular mechanisms governing these genes may vary according to the cancer subtype, and other genetic or environmental variables may affect their prognostic significance 129 , 130 . Genetic Alterations and Their Influence on Cancer Progression Our study identified ABCA8 as the most often mutated gene among the five cancer types studied, with a mutation frequency of 43%. This result is significant, since ABCA8, a member of the ATP-binding cassette (ABC) transporter family, has lately garnered attention for its possible involvement in cancer biology, especially in lipid metabolism and cellular detoxification 131 . Although more prominent ABC transporters, such as ABCB1, are associated with chemoresistance, ABCA8 has been shown as downregulated in breast and colorectal malignancies, potentially affecting lipid metabolism and cellular signaling 132 . Our discovery that ABCA8 mutations correlate with worse outcomes in breast cancer (BRCA) reinforces the concept that ABCA8 may function as a tumor suppressor or metabolic regulator that is compromised during carcinogenesis. Additionally, LIFR (Leukaemia Inhibitory Factor Receptor) demonstrated elevated mutation rates, especially in colorectal (COAD) and stomach (STAD) malignancies, at 21% and 14%, respectively. This corresponds with previous research identifying LIFR as a tumor suppressor that reduces JAK/STAT3 signaling, thereby decreasing epithelial-mesenchymal transition and metastasis 133 . Loss-of-function mutations or downregulation of LIFR have been linked to worse outcomes in breast and gastrointestinal malignancies, corroborating our finding that LIFR mutations are related to a poorer prognosis in BRCA. The prevalence of C > T transitions in mutations of both ABCA8 and LIFR aligns with COSMIC Signature 1, linked to the spontaneous deamination of 5-methylcytosine, a phenomenon prevalent in ageing tissues and tumors with compromised DNA repair mechanisms 134 . This indicates that these mutations are mostly influenced by intrinsic mutational mechanisms, maybe exacerbated by oxidative stress or inadequacies in base excision repair pathways. Conversely, genes such ANLN, PDK4, TMEM100, CTHRC1, and MT1M, although exhibiting lower mutation frequency (< 10%), have been associated with cancer via non-mutational pathways. ANLN is routinely overexpressed in aggressive malignancies, including breast and lung tumours, where it facilitates cellular proliferation and migration 135 . PDK4 regulates cellular metabolism and facilitates the Warburg effect, playing a role in metabolic reprogramming in cancer 136 . TMEM100, a regulator of vascular formation, has been suggested as a tumour suppressor involved in angiogenesis and TGF-β signaling 137 . CTHRC1 is recognized for facilitating cell migration and metastasis, namely via the Wnt/PCP and TGF-β pathways 138 . MT1M, which plays a role in metal ion homeostasis and oxidative stress responses, has been shown to have tumor-suppressive properties in hepatocellular and breast malignancies 139 . Integration of Copy Number Alterations (CNA) into Gene Function and Cancer Progression Analysis of copy number variations indicated that LIFR, ANLN, and CTHRC1 often experience amplifications, while ABCA8, PDK4, MT1M, and TMEM100 display both deletions and amplifications across several cancer types. The amplification of LIFR and ANLN in stomach adenocarcinoma (STAD), lung squamous cell carcinoma (LUSC), and lung adenocarcinoma (LUAD) correlated with elevated mRNA expression, indicating a gene dosage effect. The positive connection between copy number variation and gene expression substantiates the concept that CNAs directly facilitate oncogene activation, hence enhancing tumour cell proliferation, survival, and metastatic capability 140 , 141 . Conversely, heterozygous deletions identified in ABCA8, PDK4, and MT1M may lead to diminished expression levels, thereby compromising their tumor-suppressive capabilities. ABCA8 is linked to lipid control and cell membrane integrity; its loss may compromise cellular homeostasis, promoting tumour growth. Likewise, the ablation of PDK4, a metabolic regulator, may modify mitochondrial activity and facilitate the glycolytic shift seen in aggressive tumors (Warburg effect) 136 . The absence of MT1M, which plays a role in oxidative stress management and metal ion control, may lead to heightened genomic instability and enhanced adaptability of cancer cells 129 . These results underscore the complex regulatory function of CNAs in cancer biology. In contrast to point mutations or minor insertions/deletions, copy number alterations (CNAs) may significantly influence gene expression and cellular functionality, either amplifying oncogenic pathways or repressing tumor-suppressive mechanisms. The mutation and amplification of genes such as LIFR and ANLN in certain tumours (e.g., STAD and LUAD) indicate synergistic carcinogenic pathways, hence enhancing their potential as therapeutic targets. The dual modification status (amplification and deletion) in some genes, such as TMEM100 and ABCA8, indicates context-dependent functional implications. These genes may exhibit variable functions based on tissue type, concomitant mutations, or microenvironmental influences. This highlights the need for cancer type-specific techniques in assessing the therapeutic potential of CNA-targeted treatments. Functional Enrichment and Pathway Analysis Our functional enrichment analysis indicated that the differentially expressed genes (DEGs) were considerably enriched in biological processes essential for cancer development, namely those related to cellular transport, mitotic control, and chromosomal localization. These activities are essential for maintaining energy balance during cell division and for accurate chromosomal segregation during mitosis. The participation of these DEGs in the mitotic spindle assembly checkpoint signaling indicates their potential role in preserving genomic stability, a characteristic feature of tumor cells. The deregulation of these processes is a recognized characteristic of cancer, since mitotic mistakes may result in chromosomal instability, which promotes carcinogenesis and tumor heterogeneity 142 , 143 . Besides mitotic regulation, the KEGG pathway analysis indicated that the elevated genes were predominantly associated with protein digestion and absorption, along with basal transcription factor pathways. These pathways are essential for facilitating the metabolic reprogramming of cancer cells, enabling them to satisfy the heightened requirements for protein synthesis and energy generation necessary for fast cell proliferation and survival. Cancer cells often experience metabolic alterations, exemplified by the Warburg effect, to emphasize anabolic pathways that support cellular proliferation and division 144 . The increase of these pathways indicates that the detected DEGs may significantly contribute to tumor cells' adaptation to elevated metabolic demands. In contrast, the downregulated genes were linked to modified JAK-STAT signaling, interactions between cytokines and their receptors, and the control of stem cell pluripotency. These alterations may facilitate immune evasion and hinder tissue regeneration, both of which are essential for maintaining tumor proliferation. Dysregulation of JAK-STAT signaling is closely associated with immune suppression and the remodeling of the tumor microenvironment, facilitating tumor evasion of immune surveillance and the preservation of an immunosuppressive niche 145 . Similarly, modifications in cytokine-cytokine receptor connections might further inhibit immune responses while facilitating tumor advancement by amplifying pro-inflammatory signaling pathways. These results illustrate the intricate tumor microenvironment, whereby immune evasion, metabolic reprogramming, and disrupted cell signaling synergistically foster circumstances conducive to cancer growth. Network analysis identifies critical regulators of cancer progression The investigation of gene–gene and protein–protein interaction (PPI) networks demonstrated that existing oncogenes and the discovered hub genes had a highly interconnected architecture, indicating that pathways that promote cancer are regulated in concert. Notably, interactions with WWTR1 (TAZ) and PTK2 (FAK), two essential molecules involved in invasion, migration, and mechano-transduction, demonstrate how hub gene activity is integrated into pathways that regulate tumour spread and metastasis 146 . Similarly, a persistently active tumour microenvironment may enhance its tumor-promoting effects by modulating inflammatory signaling and cell survival pathways via the actions of CITED2 and IL6ST. These results highlight the importance of inflammation-driven carcinogenesis, and the crucial role of cytokine-mediated signaling in cancer development 147 . Another layer of metabolic reprogramming that promotes cancer cell survival under stress is indicated by the participation of OGT (O-GlcNAc transferase), a crucial enzyme that controls protein O-GlcNAcylation. Because it facilitates transcriptional and post-translational regulation in response to altered glucose metabolism, O-GlcNAcylation is becoming more widely acknowledged as a hallmark of cancer 148 . Most importantly, the identification of KIF11, BUB1, BUB1B, and CCNA2 as the key upregulated nodes in the PPI network underscores their essential roles in maintaining spindle checkpoint integrity and regulating mitotic progression. These genes are crucial for chromosomal integrity, and their overexpression may lead to uncontrolled cell division—a hallmark of cancer.. They make appealing therapeutic targets because of their prominence in the network and proven role in cell cycle control. Translational potential is shown by the preclinical or clinical study of inhibitors that target KIF11 (e.g., ispinesib) and BUB1/BUB1B 149,150 . Implications of Therapeutic Advances and Future Investigation Identifying ANLN and CTHRC1 as hub genes with elevated expression and antigenicity across several cancer types offers substantial evidence of their role in essential oncogenic processes, including cell migration, extracellular matrix remodeling, and metastasis. These roles render them interesting to target therapeutic targets, especially for therapies designed to inhibit tumor propagation. In accordance with previous results 151 , 152 , our research substantiates the carcinogenic capacity of these genes and validates their selection for vaccine-based immunotherapy. We are developing a multi-epitope vaccine aimed against ANLN and CTHRC1, using their high VaxiJen antigenicity scores (0.5930 and 0.4745, respectively) and reinforcing the notion that overexpressed tumor-associated antigens might elicit strong immune responses. This technique is consistent with past research that has focused on elevated cancer biomarkers for vaccine development. The incorporation of high-affinity B-cell and T-cell epitopes, as predicted by the IEDB server, facilitates the activation of both humoral and cellular immunological responses, specifically targeting CD4⁺ helper T cells and CD8⁺ cytotoxic T cells, which are crucial for robust anti-tumor immunity 153 , 154 . The vaccine design uses cholera toxin B subunit as an adjuvant and integrates KK, GPGPG, and AAY linkers to guarantee epitope separation and appropriate folding. This architecture improves antigen presentation and immune activation, following other vaccine technologies 155 , 156 . The hydrophilic characteristics and advantageous GRAVY score indicate excellent solubility and expression potential, aligning with successful constructions in prostate and lung cancer vaccine studies 157 . The structural model produced by I-TASSER and corroborated by Ramachandran plots, RMSD, ERRAT, and ProSA-web demonstrates a stable, correctly folded protein. These findings are analogous to previous structural investigations of multi-epitope vaccines 158 , 159 and confirm the construct’s suitability for immune recognition and downstream applications. The vaccine succeeds in 98.75% population coverage, demonstrating high projected effectiveness in North America, Europe, and South Asia, thereby underscoring its worldwide significance. Although a marginal reduction in effectiveness was seen in particular countries (e.g., Pakistan, Central America), this underscores the need of integrating region-specific HLA alleles in forthcoming iterations—a developing subject in personalized cancer vaccines 160 , 161 . Molecular docking and normal mode analysis validated robust and adaptable contacts with MHC-I and MHC-II molecules, crucial for triggering immunological responses. The vaccine's capacity for stable binding to immune receptors aligned with previous findings. Furthermore, C-ImmSim immunological simulation demonstrated a robust IgG response, the development of memory cells, and cytokine production, therefore confirming the vaccine's immunogenic potential 162 . Codon optimisation and effective in silico cloning into the pET28a(+) vector provide compatibility with bacterial expression platforms, including E. coli. The use of EcoRI and EcoRV restriction sites enhances the yield of recombinant protein synthesis, which is essential for preclinical testing and subsequent clinical trials 163 . Conclusion This work comprehensively discovered and validated ANLN and CTHRC1 as consistently elevated oncogenic hub genes in five predominant cancers: colorectal, liver, lung, gastric, and breast. Their significant correlation with worse prognosis, elevated mutation and amplification rates, involvement in metastasis, extracellular matrix remodeling, and immune evasion designates them as potential therapeutic targets. Simultaneously, five persistently downregulated genes (ABCA8, PDK4, MT1M, TMEM100, and LIFR) indicate tumor-suppressive roles and diagnostic applicability. Leveraging the significant antigenicity of ANLN and CTHRC1, we developed an innovative multi-epitope cancer vaccine that incorporates immunogenic B- and T-cell epitopes, confirmed for structural stability, immunogenicity, and broad population applicability. In silico tests, including molecular docking, immunological simulations, and codon optimization, substantiate its promise as a beneficial therapeutic candidate on a worldwide scale. These results enhance the basis for personalized immunotherapy approaches and support further experimental validation and preclinical evaluation of this vaccine design. Declarations Acknowledgements The authors would like to thank to the Khwaja Yunus Ali University during the manuscript preparation. Author contributions All authors contributed equally to this work. Competing interests There is no competing interests References Hughes, T., Harper, A., Gupta, S., Frazier, A. L., van der Graaf, W. T., Moreno, F. et al. 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A comprehensive approach to developing a multi-epitope vaccine against Mycobacterium tuberculosis: from in silico design to in vitro immunization evaluation. Front. Immunol. 14, 1280299 (2023). Zhou, Z., Schnake, P., Xiao, L. & Lal, A. A. Enhanced expression of a recombinant malaria candidate vaccine in Escherichia coli by codon optimization. Protein Expr. Purif. 34, 87–94 (2004). Additional Declarations There is NO Competing Interest. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6677557","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":457634930,"identity":"d7f41f65-7184-475c-85cd-105c94a1612b","order_by":0,"name":"Mohammad Shahangir Biswas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACA2YGBiA6AGQyNjB8AFJs7KRoYZwB0sJMSAsDXAuQwcMA4eIF5uzMhz8X1NyRN+df3PbZ5tc2eT5mBsYPH3Nwa7FsZkuTnnHsmeHOGQ+bZ+f23TZsY2Zglpy5DY/DDvOYMfOwHWbccONgM3Nuz21GoBY2Zl78Wow/8/w7bA/WYtlz254YLQbSvG2HEzecb2xmZvhxO5GgFrBfePsOJ2+4wdjM2NtwO7mNmbEZr1/M+Q8f/szz7bDthvPHHzP8+HPbdn5788EPH/FoQQCJBGBstoFYwGRAHOA/ACT+EKl4FIyCUTAKRhQAAPZ+U54AeIIsAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1545-5521","institution":"University of Science \u0026 Technology Chittagong","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Shahangir","lastName":"Biswas","suffix":""},{"id":457634931,"identity":"1406ffc8-0a5e-475d-a96e-76c02b0bb64f","order_by":1,"name":"Suronjit Kumar Roy","email":"","orcid":"","institution":"Khwaja Yunus Ali University","correspondingAuthor":false,"prefix":"","firstName":"Suronjit","middleName":"Kumar","lastName":"Roy","suffix":""},{"id":457634932,"identity":"d076dbee-e0cd-4690-a925-9a6bf70f13d4","order_by":2,"name":"Rubait Hasan","email":"","orcid":"","institution":"Khwaja Yunus Ali University","correspondingAuthor":false,"prefix":"","firstName":"Rubait","middleName":"","lastName":"Hasan","suffix":""}],"badges":[],"createdAt":"2025-05-16 06:10:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6677557/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6677557/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83048699,"identity":"f4ba3580-61d5-4a67-a587-635ceb3c0dbe","added_by":"auto","created_at":"2025-05-19 12:12:14","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":563994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInvestigation of the differential expression of genes (DEGs) in cancer patients compared to healthy controls. (A)\u003c/strong\u003e The volcano plot illustrates the distribution of DEGs between cancer patients and healthy individuals. Genes meeting the thresholds of logFC and \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05 are considered significant. Upregulated genes are revealed in red, and downregulated genes in blue. \u003cstrong\u003e(B)\u003c/strong\u003e Venn diagram showing the overlap of upregulated and downregulated DEGs across five types of cancer. \u003cstrong\u003e(C)\u003c/strong\u003e Venn diagram showing the overlap of down-regulated DEGs across five types of cancer. (D) Heatmap showing the logFC distribution of the 7 overlapping DEGs (2 up-regulated and 5 down-regulated genes) in all five GEO datasets.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/1742af32f94bac4fc45d723b.jpeg"},{"id":83048696,"identity":"8f0003a5-5d50-4557-84b4-b54891c251e0","added_by":"auto","created_at":"2025-05-19 12:12:14","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":250979,"visible":true,"origin":"","legend":"\u003cp\u003eExpression profiles of the two consistently upregulated genes across various stages of five cancer types were analyzed using data from The Cancer Genome Atlas (TCGA) via the GDC portal. The plots illustrate stage-specific expression patterns, highlighting potential characteristics of these genes in cancer progression.\u003c/p\u003e","description":"","filename":"floatimage221.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/618477d9bea019fec10b401f.jpeg"},{"id":83048702,"identity":"85d14180-b1d2-4732-b205-c298f8cd98ea","added_by":"auto","created_at":"2025-05-19 12:12:14","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":623408,"visible":true,"origin":"","legend":"\u003cp\u003eExpression profiles of the five consistently downregulated genes were evaluated across various stages of five cancer types using data from The Cancer Genome Atlas (TCGA) via the GDC portal. The plots depict stage-specific expression trends, suggesting potential involvement of these genes in cancer development and progression.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/fe4f11815a4e316eaf351c51.jpeg"},{"id":83048901,"identity":"0dd49beb-4a42-4e61-ab87-41f4eabe8d31","added_by":"auto","created_at":"2025-05-19 12:20:14","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":716225,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier plots illustrating survival differences between cancer patient cohorts with high and low expression levels of hub genes. Statistically significant differences (p \u0026lt; 0.05) are indicated. HR: Hazard Ratio.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/6d9f59e68b457b605a7eb4f6.jpeg"},{"id":83050460,"identity":"407aecf2-3634-4c6b-b7c1-497602094dbd","added_by":"auto","created_at":"2025-05-19 12:36:14","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":419876,"visible":true,"origin":"","legend":"\u003cp\u003eThe cancer hub genes change genetically and epigenetically in different types of cancer. The waterfall plot (A) demonstrates how the mutations affected the hub genes in the hub cancers. The bar plot in (B) illustrates the fraction of the total mutation frequency that occurs in each of the genes across all five forms of cancer. (C) A list of all the variants found in the sample and genes, along with their counts and types. It demonstrates the number of each type of useful variation in Plot 1 (Variant Classification). The number of each SNV class in the hub gene set in the five types of cancer is shown in Plot 2 (SNV class). Shown in Plot 3 are the most changed genes for each sample (D) The Titv plot exhibits the details of the changes (Ti; A \u0026lt;-\u0026gt; G and C ^-\u0026gt; T) and additions (Tv) to the hub gene set that happen in hub cancers. (E) The bubble plot shows the changes in patient survival between the seven gene hub mutant and wild types in five types of cancer. (F) A bubble plot that shows how methylation and mRNA gene expression are linked. Positive relationships are shown by the blue bubbles, and statistical significance is shown by the size of the bubbles. (G) A bubble plot and a Kaplan–Meir curve highlight the single gene survival rate between groups with high and low methylation levels of the hub genes. The colour changes from blue to red to show the hazard ratio, and the size of the bubble shows how significant the result is. The black outline edge shows genes with a Log-rank of 0.05 or less. (H) A pie chart that exhibits how many kinds of CNV there are in each gene in each type of cancer. (I) Bubble plot of the correlation of CNV with mRNA expression. The color from blue to red and the bubble size represent the statistical significance. The black outline border indicates Log-rank p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/008b3e2dc672f146fb62ac34.jpeg"},{"id":83048898,"identity":"6dc7b27f-9944-40ff-bd4c-9933c1b69571","added_by":"auto","created_at":"2025-05-19 12:20:14","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":427169,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment and interaction analysis of the DEGs. (A) Bubble plot of GO biological enrichment analysis of DEGs. (B) Bubble plot of KEGG pathway enrichment analysis of DEGs. GO and pathways enrichment are ranked by their p value. KEGG, Kyoto Encyclopedia of Genes and Genomes. (C) protein protein interaction network (PPI). Node sizes are based on the degree of connectivity of the nodes. (D) Cycle plot of the DEGs gene–gene. (E) Protein-protein interaction network (PPI) of up-regulated genes with positively correlated genes ranked by degree.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/530ddb3a00d6218b6f3504cc.jpeg"},{"id":83048712,"identity":"41f5bab3-3d6b-449c-8de2-5bf5bf2b8c56","added_by":"auto","created_at":"2025-05-19 12:12:14","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":359024,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The constructed multi-epitope vaccine comprises a total of 407 amino acids. At the N-terminus, an EAAAK linker was employed to connect the cholera toxin B subunit. MHC class II epitopes were interconnected through the utilisation of GPGPG linkers, whereas MHC class I epitopes were associated via AAY linkers. Linear B-cell epitopes were interconnected utilising KK linkers to guarantee adequate separation and flexibility. (B) The solubility and secondary structure of the vaccine construct are predicted by PSIPRED. (C) The vaccine contained 30.67% alpha helices, 11.20% extended strands, and 58.44% random coils, as determined by the SOPMA secondary structure analysis.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/7992178a0743288f4c55ace4.jpeg"},{"id":83048707,"identity":"7e9a9502-8271-4ae0-9800-e762e711e8e5","added_by":"auto","created_at":"2025-05-19 12:12:14","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":278731,"visible":true,"origin":"","legend":"\u003cp\u003eThe prediction, refinement, and validation of vaccine 3D structures. (A) The vaccine's demonstration 3D structure (I-TASSER). (B) The three-dimensional structure has been improved with the assistance of Galaxy Refine. (C) With an ERRAT quality measurement of 64.662%. (D) The E.Z. was -2.26, as per the ProSA-web evaluation. (E) 83.5% of the residues in the Ramachandran plot were classified as favorable, 13.6% as acceptable, and 2.9% as within the disallowed zone.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/fb57f72a6daa56777d9323a5.jpeg"},{"id":83048700,"identity":"ebb3dcb9-4618-4e89-b347-6cf8474ace28","added_by":"auto","created_at":"2025-05-19 12:12:14","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":412808,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation coverage for epitopes by region and country.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/145f4d8b9890431d00d6de23.jpeg"},{"id":83048711,"identity":"68ed0883-d0ed-42b0-a9e8-b8665b5d3aae","added_by":"auto","created_at":"2025-05-19 12:12:14","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":356155,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The ElliPro server anticipates discontinuous B-cell epitopes for multi-epitope vaccination. 1–6. The verdant surface is characterised by discontinuous B-cell epitopes. (B) Score and discontinuous B-cell epitope residues.\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/823b08a60996b58cd3b2911b.jpeg"},{"id":83050129,"identity":"9f1e96ed-bc96-440c-8ffb-ab8d1351db11","added_by":"auto","created_at":"2025-05-19 12:28:14","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":397932,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The interaction between the vaccine candidate and the MHC class I receptor involves chain A of the receptor and chain D of the vaccine construct. (B) In the case of the MHC class II receptor, the vaccine candidate interacts with both chain A and chain D, as well as chain B and chain D. These molecular interactions were analyzed and predicted using the PDBsum web server.\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/f71e9380b6e4b96ebb38a47a.jpeg"},{"id":83048905,"identity":"f5bccc49-9c7a-4923-87b8-dfc2d6e54d9f","added_by":"auto","created_at":"2025-05-19 12:20:14","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":334520,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular dynamics simulation of the vaccine construct in complex with the MHC class I receptor encompassed multiple structural and dynamic analyses: (A) Deformability analysis to assess the flexibility of different regions within the complex; (B) Evaluation of B-factors to investigate atomic displacement and mobility; (C) Eigenvalue analysis, where lower eigenvalues suggest easier deformability and greater flexibility; (D) Variance analysis, with red regions indicating individual residue fluctuations and green regions representing cumulative variance; (E) Covariance matrix analysis, where red indicates correlated motions, white denotes no correlation, and blue represents anti-correlated movements; and (F) Elastic network modeling, where darker regions reflect stiffer and less flexible areas of the complex.\u003c/p\u003e","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/2e5181478eec434d848f1542.jpeg"},{"id":83048731,"identity":"7480db37-a869-49ee-9a60-57415bb91691","added_by":"auto","created_at":"2025-05-19 12:12:15","extension":"jpeg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":333304,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular dynamics simulation of the vaccine construct in complex with the MHC class II receptor included several structural and dynamic evaluations: (A) Deformability analysis to assess the flexibility of residues across the complex; (B) B-factor analysis to measure atomic fluctuations and mobility; (C) Eigenvalue assessment, where lower eigenvalues reflect greater structural flexibility and ease of deformation; (D) Variance analysis, with red indicating individual residue motion and green representing cumulative variance; (E) Covariance matrix mapping, showing correlated motions in red, uncorrelated regions in white, and anti-correlated motions in blue; and (F) Elastic network analysis, where darker shades represent regions of higher rigidity and limited flexibility.\u003c/p\u003e","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/071da9da0524bf17d967c18b.jpeg"},{"id":83048727,"identity":"eb7ac8b1-3072-4708-afe1-146c4054bbac","added_by":"auto","created_at":"2025-05-19 12:12:15","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":878273,"visible":true,"origin":"","legend":"\u003cp\u003eIn silico immune simulations performed using the C-ImmSim server revealed several key immunological responses: (A) Notable increases in IgM and IgG antibody titers, illustrated by cream-colored peaks, along with a decline in antigen levels (black peak) following the second and third vaccine doses; (B) Activation of B-cell populations, indicated by a purple peak; (C) Expansion of memory B cells, depicted by a green peak; (D) Activation of helper T (TH) cells, represented by a purple peak; (E) Generation and enhancement of memory TH cells, shown as green peaks; A T-cell response with Th1 polarization, also represented by a purple peak; and (G) Elevated cytokine levels, including IL-2 (cream peak) and IFN-γ (purple peak), indicating a strong cell-mediated immune response induced by the vaccine candidate.\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/115444bc92c829e2d5d5a7aa.png"},{"id":83048724,"identity":"902f5cf8-84b2-41fa-a903-3b5daf7b54b3","added_by":"auto","created_at":"2025-05-19 12:12:15","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":205155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIn silico\u003c/em\u003e cloning of the vaccine construct into the pET-28a(+) expression vector was performed using SnapGene software. In the simulation, the vector backbone was represented in black, while the inserted vaccine sequence was shown in red. Restriction sites for EcoRI and EcoRV were used to facilitate the cloning process, ensuring proper insertion and orientation of the vaccine gene within the vector.\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/245b39915cb5263b52c98962.png"},{"id":83234875,"identity":"9e3b5efb-a8e3-42ff-b402-ed70681d5e23","added_by":"auto","created_at":"2025-05-21 14:15:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8280142,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/328757c8-f31b-44d6-9e94-a59f22c7e29b.pdf"},{"id":83048698,"identity":"92b49fcd-fc37-46e4-89f5-b540be369e6c","added_by":"auto","created_at":"2025-05-19 12:12:14","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29582,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6677557/v1/6f829487eb80a98153cc717a.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Oncogenic Hub Genes ANLN and CTHRC1: Implications for Cancer Prognosis and Vaccine-Based Therapeutics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer demonstrates an ongoing global health challenge, with approximately 19.98\u0026nbsp;million new cases and 9.65\u0026nbsp;million deaths documented worldwide in 2022, emphasizing its extensive influence on morbidity and mortality rates\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The Global Cancer Observatory (GLOBOCAN 2022) distinguished the ten most prevalent cancer types worldwide as breast, prostate, lung, colorectal, cervical, stomach, thyroid, liver, uterine corpus, and ovarian cancers. According to the 2022 global cancer statistics report, breast cancer has an incidence rate of 11.5% and a mortality rate of 6.8%. Lung cancer shows an incidence of 12.4% and a mortality rate of 18.7%. Colorectal cancer has a 9.6% incidence and a 9.3% mortality rate. Stomach cancer presents with a 4.8% incidence and a 6.8% mortality rate, while liver cancer has a 4.3% incidence and a 7.8% mortality rate. These cancers are among the most frequently diagnosed and are significant contributors to cancer-related deaths globally\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. According to projections based on demographics, the number of newly diagnosed cases of cancer would surpass 35\u0026nbsp;million by the year 2050\u003csup\u003e3\u003c/sup\u003e. The major emphasis of the present research is on these five malignancies, which accounted for 42.6% of all cancer cases worldwide and 49.4% of fatalities attributable to cancer. From this point, we will refer to these cancers collectively as \"hub cancers.\" Therefore, developing viable early diagnostic and prognostic biomarkers is extremely important. These biomarkers have the potential to assist in the discovery of the underlying molecular pathways that are responsible for these aggressive cancers, while simultaneously contributing to the improvement of therapies.\u003c/p\u003e \u003cp\u003eMicroarray technology combined with bioinformatics analysis has emerged as an effective tool for studying cancer pathophysiology and identifying key genetic and epigenetic alterations involved in carcinogenesis. The application of these methodologies greatly helps in enhancing diagnostic accuracy, assessing prognosis, and formulating effective therapeutic strategies\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Although considerable findings have been made, there has been limited progress in translating these discoveries into robust biomarkers for tumor identification and prognosis. Nevertheless, by examining microarray and RNA-seq data from various cancer datasets and integrating differentially expressed genes (DEGs) across multiple cancer types, it may be possible to uncover universal biomarkers that offer enhanced consistency and greater therapeutic relevance. Furthermore, the poor prognosis observed in patients with hub cancers is primarily driven by the lack of reliable early diagnostic markers, resistance to available treatments, and the adverse effects associated with current chemotherapeutic drugs\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Consequently, there is an urgent need for the development of innovative, affordable, safe, and effective cancer treatments\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent decades, treatment strategies have predominantly depended on surgery, radiation therapy, and chemotherapy, employed either individually or in combination\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Although the results for early-stage malignancies have greatly improved because of these conventional techniques, they are often linked to serious drawbacks, such as systemic toxicity, tumor recurrence, and poor effectiveness against metastasis complications. The introduction of immunotherapies and personalized therapies has revolutionized cancer treatment by providing more individualized and targeted approaches. Combinatorial regimens have demonstrated synergistic benefits, enhancing clinical outcomes in several cancer types by using multiple targeted medicines or combining them with conventional chemotherapeutics such as taxanes and platinum-based drugs\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, despite these innovations, the prognosis for individuals with advanced and metastatic malignancies remains bleak. Recent epidemiological statistics demonstrated that the five-year survival rates for metastatic lung, liver, colorectal, gastric, prostate, and breast cancers were below 30%, with metastatic lung cancer survival at approximately 7% and liver cancer at around 11%\u003csup\u003e12,13\u003c/sup\u003e. Although immune checkpoint inhibitors and other innovative treatments have attained significant success in some cancers, most patients either do not react or ultimately develop resistance to therapy\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe key obstacles in contemporary cancer treatment are tumor immune escape, antigenic heterogeneity, drug resistance, and the ephemeral nature of several therapy-induced responses. The diverse characteristics of malignancies and the heterogeneity in tumor antigen expression across people further complicate treatment outcomes\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Recent findings demonstrated significant intra-tumoral genomic heterogeneity, including mutually exclusive amplifications of EGFR and PDGFRA within the same glioblastoma (GBM) tissue. The amplifications exhibited geographic patterns, with PDGFRA-amplified cells aggregating near endothelial areas. Comparable regional genetic modifications have been identified in other malignancies, such as clear-cell renal cell carcinoma (ccRCC)\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Additionally, intra-tumor heterogeneity extends to gene expression, particularly regarding the factors influencing expression of cluster of differentiation (CD) antigens across various cancer types. Examples include CD34 and CD38 in acute myeloid leukemia (AML)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, CD24/CD44 in breast cancer\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and CD133 in colon and brain tumors [26,27]. Key cancer-related genes like c-KIT\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, cyclins, Ki67\u003csup\u003e29\u003c/sup\u003e, Bcl-2\u003csup\u003e30,31\u003c/sup\u003e, c-Myc\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, RAS\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and EGFR\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e also show heterogeneous expression within the same cancer. There is a critical need to develop innovative therapeutic strategies that offer broad applicability, reduce the risk of resistance, and provide sustained clinical benefits across multiple cancer types.\u003c/p\u003e \u003cp\u003eCancer vaccines have emerged as a promising therapeutic strategy considering these issues. Vaccines can induce the patient\u0026rsquo;s immune system to identify and eradicate cancer cells, providing a targeted, systemic, and potentially enduring anti-tumor response with reduced side effects relative to conventional chemotherapeutics\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Recent advancements in immunoinformatics, molecular profiling, and personalized vaccine design have facilitated the creation of cancer vaccines aimed at tumor-specific or tumor-associated antigens. Early-phase clinical trials of neoantigen-based vaccines in melanoma and glioblastoma have shown the potential to elicit robust and lasting T-cell-mediated anti-tumor immunity\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The findings underscore the potential of cancer vaccines as either a complementary or a standalone therapy for solid tumors. This study posits that specific genes are consistently up regulated across various cancer types, suggesting their potential as common targets for immunotherapy, in response to the pressing demand for more effective and widely applicable cancer treatments. Identifying universally overexpressed genes offers a unique opportunity to develop a multi-cancer vaccine capable of eliciting strong immune responses across a diverse patient population, regardless of tumor origin. This study analyzes publicly available microarray gene expression datasets, focusing on five major cancer types: lung, liver, colorectal, gastric, and breast cancers, which together represent a significant portion of global cancer morbidity and mortality. Focusing on the gene(s) that are consistently upregulated across these cancers may address the challenges posed by tumor heterogeneity and interpatient variability, thus providing a more universal and effective treatment strategy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eObtaining Microarray Data from Normal and Cancer Samples\u003c/h2\u003e \u003cp\u003eMicroarray datasets for lung, liver, colorectal, gastric, and breast cancers were sourced from the NCBI Gene Expression Omnibus (GEO), a publicly accessible database for large-scale gene expression data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Table\u0026nbsp;1 summarizes the datasets, which include both tumor tissue samples (primary or metastatic) and their corresponding normal counterparts. Differentially expressed genes (DEGs) were identified using the LIMMA (Linear Models for Microarray Data) package in R (version 4.4.2)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In addition, mRNA expression profiles, copy number variations (CNVs), single-nucleotide variations (SNVs), methylation data, and survival information for seven hub genes in LUAD, BRCA, STAD, COAD, and LIHC patients were retrieved from the National Cancer Institute\u0026rsquo;s Genomic Data Commons (GDC) portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All GDC data analyses were performed using the GSCA (Gene Set Cancer Analysis) online platform\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFeatures of the datasets that were used to identify the genes of differential expression (DEGs)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTumor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE42568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE110224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastric\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE79973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatocellular\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE84402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung (NSCLC)\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE33532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTranscriptional Variation in Hub Genes Across Tumor Stages\u003c/h3\u003e\n\u003cp\u003eTo investigate potential relationships between hub gene expression and tumor progression, we utilized TCGA\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e data generated through the UALCAN portal\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e to analyze the differential expression patterns of the seven hub genes across tumor stages in LUAD, BRCA, STAD, COAD, and LIHC patients. Patients were classified based on tumor stage (Stage I-IV) and gene expression levels to see if the expression of the hub genes altered drastically as the illness progressed. Boxplots were employed to depict expression patterns; statistically significant was regarded as a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003ePrognostic Evaluation of the Hub Genes\u003c/h3\u003e\n\u003cp\u003eWe analyzed to explore the relationship between gene expression patterns and patient prognosis by correlating the tumor expression levels of hub genes with overall survival data from patients across different hub cancer types, utilizing the GEPIA2 online tool\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Kaplan\u0026ndash;Meier plots, accompanied by hazard ratios (HR) and p-values, were generated to explain and assess the survival disparities across cohorts exhibiting superior and downcast expression readings of the hub genes.\u003c/p\u003e\n\u003ch3\u003eAnalysis of SNVs and CNVs in Hub Genes Using the GSCA\u003c/h3\u003e\n\u003cp\u003eWe obtained single-nucleotide variation (SNV) data from five cancer types\u0026mdash;liver, breast, colon, stomach, and lung cancers\u0026mdash;focusing on seven functional mutation types: Missense Mutation, Nonsense Mutation, Frame_Shift_Ins, Splice Site, Frame_Shift_Del, and In_Frame_Del. The data was retrieved from the NCI Genomic Data Commons (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on May 29, 2021), and the analysis was conducted utilizing the GSCA (Gene Set Cancer Analysis) server. The percentage of single nucleotide variants (SNVs) for each gene was calculated by dividing the total number of cancer samples by the number of mutated samples, after an examination of the frequency and distribution of SNVs within the hub genes. Using Mutation Annotation Format (MAF) tools built into the GSCA platform, mutation profiles and waterfall charts were displayed. Moreover, GSCA was utilized to investigate copy number variation (CNV) data, emphasizing four distinct types of CNV: homozygous amplification (Homo Amp; CNV\u0026thinsp;=\u0026thinsp;2), homozygous deletion (Homo Del; CNV\u0026thinsp;=\u0026thinsp;2), heterozygous amplification (Hete Amp; CNV\u0026thinsp;=\u0026thinsp;1), and heterozygous deletion (Hete Del; CNV\u0026thinsp;=\u0026thinsp;1). Across the five forms of cancer, the rates of these CNV occurrences in the hub genes were demonstrated. SNV and CNV data were combined with overall survival data to determine the clinical importance of these genomic changes. The log-rank test was employed to analyze survival differences between patients exhibiting mutated and non-mutated hub genes (pertaining to SNVs) as well as among CNV subgroups. The threshold for statistical significance was established as a p-value of less than 0.05\u003csup\u003e41\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eInvestigation of Methylation in Hub Genes\u003c/h3\u003e\n\u003cp\u003eThe GSCA (Gene Set Cancer Analysis) server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guolab.wchscu.cn/GSCA/#/\u003c/span\u003e\u003cspan address=\"https://guolab.wchscu.cn/GSCA/#/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed for evaluating DNA methylation data associated with the hub genes across five distinct cancer types\u0026mdash;liver, breast, colon, gastric, and lung\u0026mdash;on May 29, 2021. Genes exhibiting notable hypo- or hyper-methylation patterns were detected by assessing the methylation levels of malignant tumors and normal tissues. We further analyzed the relationship between DNA methylation and gene expression to investigate whether methylation status affects transcriptional activity. Additionally, Kaplan\u0026ndash;Meier survival analysis combined with log-rank testing, performed via the GSCA platform, was used to evaluate the association between methylation status and overall survival outcomes. The threshold for statistical significance was determined to be a p-value of under 0.05\u003csup\u003e41\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInvestigation of Functional Enrichment of the Hub Genes\u003c/h2\u003e \u003cp\u003eFunctional enrichment analyses of the differentially expressed genes (DEGs) were performed using Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) using the online database Enrichr (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on May 29, 2021)\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Statistical significance was characterized by terms associated with the GO and KEGG pathways, with a false discovery rate (FDR) of less than 0.05. The R program cluster Profiler\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e was utilized to illustrate the findings of these investigations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInvestigation of Gene-Gene and Protein-Protein Interaction\u003c/h3\u003e\n\u003cp\u003eTo elucidate the functional interrelations of the identified hub genes, gene\u0026ndash;gene interaction (GGI) and protein\u0026ndash;protein interaction (PPI) networks were meticulously constructed using the GENEMANIA tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genemania.org/\u003c/span\u003e\u003cspan address=\"https://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on June 1, 2021)\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e and the STRING database (Search Tool for the Retrieval of Interacting Genes/Proteins; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string.embl.de/\u003c/span\u003e\u003cspan address=\"http://string.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on May 23, 2021)\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, respectively. The PPI network of the differentially expressed genes (DEGs) was generated using STRING, applying a confidence score cutoff of 0.40 to ensure biologically meaningful interactions. Additionally, we examined the elevated genes together with their positively linked genes (Pearson correlation coefficient, PCC; refer to Supplementary Table\u0026nbsp;01)\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Their interaction network was examined utilizing the STRING database and demonstrated using Cytoscape (v3.10.3)\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Key hub genes are identified with the CytoHubba plugin through the degree technique\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eProtein Sequence Retrieval and Analysis\u003c/h3\u003e\n\u003cp\u003eIn this work, ANLN and CTHRC1 were identified as target proteins owing to their notable overexpression in LUAD, BRCA, STAD, COAD, and LIHC cancer types. The FASTA formatted sequences for these proteins (ANLN UniProt ID: Q9NQW6, and CTHRC1 UniProt ID: Q96CG8) were obtained from the UniProt database\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. The physicochemical properties of the candidate proteins were analyzed using the ExPASy ProtParam tool [59], and their antigenic potential was assessed through the VaxiJen server version 2.0\u003csup\u003e60\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEstimation of Immune Epitopes\u003c/h2\u003e \u003cp\u003eThe production of the vaccine primarily focused on B-cell epitopes owing to their capacity to elicit the humoral immune response. Epitopes stimulate antibody production that effectively blocks pathogenic antigens. Potential linear B-cell epitopes were determined from the protein sequences utilising Bepipred Linear Epitope Prediction 2.0, with a suitable cutoff of 0.5\u003csup\u003e61\u003c/sup\u003e. Binding predictions for MHC-I and MHC-II were accomplished employing the Immune Epitope Database (IEDB) tools, concerning human HLA sets. The NetMHCpan EL 4.1 and NetMHCIIpan EL 4.1 algorithms were utilized to discover epitopes exhibiting high affinity for MHC-I and MHC-II molecules, respectively \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. The chosen epitopes showed higher antigenicity ratings, suggesting that they might be effective vaccine candidates. To ensure the safety and effectiveness of the discovered epitopes, their toxicity, allergenicity, and solubility were assessed in combination with antigenicity predictions. The characteristics were evaluated using the AllerTOP v2.0, ToxinPred, and Innovagen Peptide Calculator applications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEpitope Description\u003c/h2\u003e \u003cp\u003eTo evaluate the possibility of the selected epitopes as vaccine candidates, their antigenic qualities were confirmed employing the VaxiJen v2.0 tool, with a threshold of 0.4\u003csup\u003e60\u003c/sup\u003e. Applying the AllerTOP v2.0 server, the allergenic nature of the epitopes was assessed, AllerTOP applied the k-nearest neighbours (kNN) method with a training dataset comprising known allergens and non-allergens\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. The peptides' toxicity was assessed using ToxinPred\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, and their solubility was predicted using the Innovagen Peptide Calculator.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eVaccine Construction\u003c/h2\u003e \u003cp\u003eThe vaccine was developed by combining the selected B-cell, MHC-I, and MHC-II epitopes with the cholera toxin B adjuvant\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. The epitopes were linked using suitable linkers: GPGPG linkers for MHC-II epitopes, AAY linkers for MHC-I epitopes, and KK linkers for B-cell epitopes. The adjuvant was linked to the epitopes by the EAAAK linker. The use of these linkers ensures the appropriate configuration of the vaccine peptide and facilitates optimal folding and flexibility for efficient immune system identification\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics and Molecular Conformations of the Vaccine\u003c/h2\u003e \u003cp\u003eCalculations for the molecular weight, solubility, theoretical isoelectric point (pI), in vitro half-life, and GRAVY (grand average of hydropathy) were obtained using the ExPASy ProtParam software aimed at evaluating the chemical and biological properties of the vaccine that was produced. Employing the SOPMA\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e and PSIPRED servers\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e for prediction of secondary structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStructure Prediction and Justification\u003c/h2\u003e \u003cp\u003eThe vaccine's three-dimensional structure was forecasted using I-TASSER (Iterative Threading Assembly Refinement)\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. The expected structure was later improved with the GalaxyRefine2 service\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. The quality of the 3D structure was evaluated using PROCHECK to generate a Ramachandran plot, which assesses the geometry and stereochemical fidelity of the model. Ramachandran plots were used to validate the protein structure by confirming that its torsional angles fall within energetically favorable regions\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Additionally, the Z-score\u0026mdash;a measure of the protein\u0026rsquo;s structural stability\u0026mdash;was calculated using the ProSA-web platform\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Coverage Screening\u003c/h2\u003e \u003cp\u003eThe Population Coverage Tool from IEDB was employed to assess the potential global coverage of the vaccine by analyzing the distribution of MHC-binding alleles across 23 different geographical regions. This study aimed to evaluate the influence of epitope affinity variation for HLA alleles on the efficacy of vaccination among different ethnic groups\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDiscontinuous B-cell Epitope Assessment\u003c/h2\u003e \u003cp\u003eDiscontinuous B-cell epitopes were predicted using the ElliPro server. The service determines the Protrusion Index (PI) to assess whether residues in the protein structure form a discontinuous epitope. A PI score of 0.9 signifies that 90% of the residues in the epitope are contained within the ellipsoid, which is a favorable characteristic for epitope identification. The distance measure was employed to determine the proximity of residue centers, facilitating the identification of multiple discontinuous epitopes\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDocking and Molecular Dynamics Simulation\u003c/h2\u003e \u003cp\u003eThe interaction potential between the designed multi-epitope vaccine and its target receptors, MHC class I and II, was assessed using ClusPro 2.0, a robust platform for predicting protein\u0026ndash;protein docking conformations\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. This automated docking tool uses fast Fourier transform (FFT) algorithms combined with Monte Carlo simulations to efficiently sample and evaluate the stability of protein conformations. The crystal structures of MHC class I (PDB ID: 5XS3) and MHC class II (PDB ID: 3L6F) were obtained from the RCSB Protein Data Bank. Prior to docking, both structures were refined and visualized using PyMOL to ensure proper formatting and orientation\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. The docked complexes with the lowest binding energy scores were chosen to represent the most promising interactions between the vaccine construct and the MHC receptors. To evaluate the dynamic stability and flexibility of these complexes, normal mode analysis (NMA) was carried out using the iMODS web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://imods.chaconlab.org\u003c/span\u003e\u003cspan address=\"http://imods.chaconlab.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The iMODS tool offers comprehensive insights into the structural dynamics of the complexes by analyzing parameters such as deformability, B-factor estimates, eigenvalues indicating molecular flexibility, variance distribution, covariance matrices, and the elastic network model. These evaluations provide valuable information on the atomic-level stability and dynamic motion of the vaccine\u0026ndash;receptor complexes, enhancing our understanding of their molecular behavior and interaction fidelity\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImmune Simulation\u003c/h2\u003e \u003cp\u003eThe C-ImmSim server was used to assess the immunogenicity of the formulated vaccine. This immune simulator utilizes machine learning techniques and a PSS matrix to model immunological responses inside a dynamic, agent-based simulation. The immunization consisted of three doses of 1000 vaccine proteins, administered at four-week intervals, namely at time points 1, 84, and 168. The simulation had a total of 1050 stages. The settings of the C-ImmSim immune simulator were optimized for enhanced modeling, and the variability of the immune response was assessed using the Simpson index (D), which measures the diversity of clonal selection following repeated antigen exposure. The results of these simulations enabled the forecasting of the likely immunological response triggered by the multi-epitope vaccine, providing insights into its effectiveness\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCodon Optimization and Cloning\u003c/h2\u003e \u003cp\u003eCodon optimization of the vaccine construct was carried out using the Java Codon Adaptation Tool (JCAT) to improve its translational efficiency and expression potential in \u003cem\u003eEscherichia coli\u003c/em\u003e [81]. The nucleotide sequence was specifically adapted to align with the usage preferences of the \u003cem\u003eE. coli\u003c/em\u003e K12 strain, ensuring efficient translation and expression. The optimized sequence achieved a codon adaptation index (CAI) greater than 0.8, indicating strong compatibility with the \u003cem\u003eE. coli\u003c/em\u003e translational machinery and a high likelihood of efficient protein expression. In addition, the GC content was fine-tuned to remain within the optimal range of 30\u0026ndash;70%, supporting mRNA stability and enhancing the overall efficiency of bacterial expression. Throughout the optimization process, elements that could hinder expression, such as prokaryotic ribosome binding sites, Rho-independent transcription terminators, and unwanted restriction sites, were deliberately excluded. The optimized gene sequence was then inserted into the \u003cem\u003eE. coli\u003c/em\u003e expression vector pET-28a(+) using SnapGene 5.2.4 software for further cloning and expression studies. To enable seamless cloning and ensure efficient expression, restriction sites for \u003cem\u003eEcoRI\u003c/em\u003e and \u003cem\u003eEcoRV\u003c/em\u003e were introduced at the N- and C-termini of the gene sequence, respectively. This modification allows for easy insertion into the vector, facilitating robust vaccine expression in \u003cem\u003eE. coli\u003c/em\u003e for subsequent experimental validation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eANLN, CTHRC1, ABCA8, PDK4, MT1M, TMEM100, and LIFR Are Identified as Hub Genes Involved in the Development of Breast, Lung, Colorectal, Liver, and Stomach Cancers\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;1 presents the comprehensive information on the GEO datasets associated with each of the five cancer types, while Fig.\u0026nbsp;1A depicts the distribution of differentially expressed genes (DEGs) across these cancer types using volcano plots. We identified an overall number of 322 differentially expressed genes (DEGs) in colorectal cancer (124 upregulated, 198 downregulated), 1362 DEGs in breast cancer (533 upregulated, 829 downregulated), 1278 DEGs in lung cancer (419 upregulated, 859 downregulated), 501 DEGs in liver cancer (105 upregulated, 396 downregulated), and 504 DEGs in gastric cancer (223 upregulated, 281 downregulated) (Fig.\u0026nbsp;1B, and C). We also discovered five consistently downregulated genes (logFC distribution) (ABCA8, PDK4, MT1M, TMEM100, and LIFR) and two consistently upregulated DEGs (logFC distribution) (ANLN and CTHRC1) employing integrated analysis of the five GEO datasets (Fig.\u0026nbsp;1D).\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eThe Hub Genes' Expressions Are Associated with Clinical Prognosis of the Hub Cancer Patients\u003c/h2\u003e \u003cp\u003eAccording to our differential expression analysis, the expression levels of the up-regulated hub genes, ANLN and CTHRC1, dramatically change as cancer stages progress, often increasing with higher tumour stages across the five cancer types. In BRCA, LIHC, and LUAD cancer, the expression level of ANLN is likewise significantly overexpressed from stage 1 to stages 2, 3, and 4. Similarly, the expression level of CTHRC1 was significantly higher at each stage (Fig.\u0026nbsp;2). On the other hand, the expression levels of the five downregulated genes\u0026mdash;ABCA8, PDK4, MT1M, TMEM100, and LIFR\u0026mdash;showed a gradual reduction with advancing tumor stages.(Fig.\u0026nbsp;3). When examining the COAD, LIHC, LUAD, and STAD cohorts, it is worth noting that we discovered that the two genes that are often elevated were significantly associated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with shorter life lengths. However, none of the genes that were elevated exhibited a significant connection with prognosis in the BRCA and LUSC cohorts (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDistribution and Impact of Genetic and Epigenetic Alterations in Hub Genes Across Breast, Lung, Colorectal, Liver, and Stomach Cancers\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA comprehensive mutational analysis of 267 tumour samples from five cancer types demonstrated genetic alterations in all cases. ABCA8 exhibited the highest alteration frequency at 43%, followed by LIFR at 27% and ANLN at 18%. Missense mutations represented the primary type of alteration. Alterations were noted across all cancer types, with variability in tumour mutational burden (TMB) among samples. The findings indicate that ABCA8 is the most mutated gene, suggesting that these genetic alterations may play a role in tumorigenesis in various cancers (Fig.\u0026nbsp;5A). A heatmap analysis indicated that ABCA8 exhibited the highest single-nucleotide variant (SNV) frequencies across various cancer types, with mutation rates between 22% and 26%, while only 1% was observed in liver hepatocellular carcinoma (LIHC). LIFR exhibited increased mutation frequencies, notably in COAD (21%) and STAD (14%), while demonstrating reduced rates in other cancer types. Additional genes (ANLN, PDK4, TMEM100, CTHRC1, MT1M) demonstrated low mutation frequencies, each below 10%. ABCA8 and LIFR were identified as the most mutated genes among the analyzed cancers (Fig.\u0026nbsp;5B). The characterisation of SNV classes indicated that C\u0026thinsp;\u0026gt;\u0026thinsp;T transitions were the most common, implying that spontaneous deamination is a significant mutational mechanism. Missense mutations constituted the predominant variant type, accounting for approximately 300 events, while other mutation types were observed with significantly lower frequency. In alignment with previous analyses, ABCA8 and LIFR demonstrated the highest mutation frequencies among the examined genes (Fig.\u0026nbsp;5C).\u003c/p\u003e\u003cp\u003eThe predominant mutations were classified as C\u0026thinsp;\u0026gt;\u0026thinsp;T and T\u0026thinsp;\u0026gt;\u0026thinsp;C transitions, in addition to C\u0026thinsp;\u0026gt;\u0026thinsp;G and C\u0026thinsp;\u0026gt;\u0026thinsp;A transversions (Fig.\u0026nbsp;5D). In addition, the survival analysis indicated that SNVs in ABCA8 correlated with lower survival rates in the BRCA cohort (Fig.\u0026nbsp;5E). The methylation levels of all seven hub genes exhibited a negative correlation with their mRNA expression levels across all cancer types (cor\u0026thinsp;\u0026lt;\u0026thinsp;0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;5F). However, the methylation status of none of the hub genes significantly predicted poor prognosis (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in any cohort (Fig.\u0026nbsp;5G). CNA analysis indicated that LIFR, ANLN, and CTHRC1 displayed amplifications (both heterozygous and homozygous), whereas MT1M, TMEM100, ABCA8, and PDK4 presented heterozygous deletions and amplifications (Fig.\u0026nbsp;5H). Correlation analysis between CNV and mRNA expression suggested a positive correlation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between LIFR and ANLN expression and their CNV status in STAD, LUSC, and LUAD cancers (Fig.\u0026nbsp;5I).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eGene\u0026ndash;Gene and Protein\u0026ndash;Protein Interactions, and Functional Enrichment of the Hub Genes\u003c/h2\u003e \u003cp\u003eTo predict the biological roles of the identified DEGs, we conducted GO and KEGG pathway enrichment analyses. Our analysis identified differentially expressed genes (DEGs) that were significantly enriched in key biological processes, notably cellular transport, chromosome localisation, and mitotic regulation. The processes encompass ADP and ATP transport, crucial for sustaining energy homeostasis during cell division, alongside protein localisation to condensed chromosomes and the centromeric region, underscoring their significance in chromosomal organisation during mitosis. Moreover, differentially expressed genes (DEGs) related to protein localisation at the kinetochore indicate a function in facilitating accurate chromosome segregation, whereas those linked to cytoskeleton-dependent cytokinesis may govern the concluding phase of cell division. The enrichment of differentially expressed genes (DEGs) in purine ribonucleotide and adenine nucleotide transport suggests their potential involvement in providing essential nucleotides for RNA and DNA synthesis during cellular proliferation. The role of these DEGs in the mitotic spindle assembly checkpoint signalling underscores their significance in preserving genomic stability during cell division. The findings indicate that these DEGs are crucial in regulating cellular division, energy metabolism, and chromosome dynamics, positioning them as potential candidates for further research as biomarkers or therapeutic targets in diseases characterised by dysregulated cell division, including cancer (Fig.\u0026nbsp;6A). The up-regulated genes were enriched in the Protein digestion and absorption and basal transcription factors pathways according to KEGG analysis. The genes that are down-regulated in the KEGG pathway enrichment analysis are mainly associated with impaired mineral absorption, disrupted JAK-STAT signalling, decreased cytokine-cytokine receptor interactions, and modified regulation of stem cell pluripotency. The observed changes indicate possible immune suppression, metabolic disturbances, and diminished tissue regeneration. Furthermore, the down-regulation of pathways associated with diabetic cardiomyopathy indicates impairments in heart function related to diabetes (Fig.\u0026nbsp;6B).\u003c/p\u003e \u003cp\u003eThe analysis of gene-gene interactions indicates that these genes constitute a co-expression network that engages with established oncogenes, underscoring their varied and intricate functions in cancer biology. PTK2 (FAK) and WWTR1 (TAZ) are recognised as classic oncogenes that facilitate tumour growth, invasion, and metastasis through the enhancement of cell signalling and migration pathways. IL6ST and CITED2, although not classified as traditional oncogenes, are integral to cancer progression through the enhancement of inflammatory responses and survival signalling pathways. OGT, which plays a role in protein O-GlcNAcylation, facilitates the metabolic reprogramming necessary for the survival of cancer cells, highlighting the significance of post-translational modifications in tumour biology. It is interesting to note that CEBPD and NR3C1 exhibit context-dependent behaviour, which complicates their potential as therapeutic targets by functioning as either tumour suppressors or oncogenes based on the tissue type and environment. On the other hand, SORBS1 and EPB41L3 mostly serve as tumour suppressors, and cancer is encouraged by their loss rather than activation (Fig.\u0026nbsp;6D). On the other hand, the PPI network of up- and down-regulated DEGs has 249 edges and 75 nodes, with a mean node degree of 5.88 (Fig.\u0026nbsp;6C). The top hub genes were determined to be KIF11 (degree\u0026thinsp;=\u0026thinsp;107), BUB1 (degree\u0026thinsp;=\u0026thinsp;107), BUB1B (degree\u0026thinsp;=\u0026thinsp;106), CCNA2 (degree\u0026thinsp;=\u0026thinsp;105), TTK (degree\u0026thinsp;=\u0026thinsp;104), DLGAP5 (degree\u0026thinsp;=\u0026thinsp;103), KIF2C (degree\u0026thinsp;=\u0026thinsp;102), CCNB1 (degree\u0026thinsp;=\u0026thinsp;102), ESPL1 (degree\u0026thinsp;=\u0026thinsp;101), and NDC80 (degree\u0026thinsp;=\u0026thinsp;100) based on our additional examination of the upregulated gene positive correlation network based on node degree (Fig.\u0026nbsp;6E).\u003c/p\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eRetrieval of Protein Sequences\u003c/h2\u003e \u003cp\u003eThe elevated human proteins CTHRC1 (UniProt ID: AAQ89273.1) and ANLN (Uniprot ID: AAH70066.1) were chosen for investigation. Protein sequences in FASTA format were obtained from the UniProt database. The VaxiJen v2.0 server was used to assess these proteins' antigenic potential. ANLN scored 0.5930 and CTHRC1 scored 0.4745, both proteins showing antigenicity scores over the threshold value of 0.4. Furthermore, the physicochemical characteristics of the proteins were examined using the ExPASy ProtParam service (Supplementary Tables\u0026nbsp;2 and 3).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003ePrediction of Linear B-cell Epitopes\u003c/h2\u003e \u003cp\u003eIt was anticipated that B-cell epitopes, which are essential for starting the synthesis of antigen-specific antibodies, would improve humoral immune responses. Immunogenic epitopes cause B cells to differentiate into plasma and memory cells when they attach to B-cell receptors. Memory cells provide long-term immunity, while plasma cells are in charge of the initial antibody response. Consequently, B-cell epitope identification is crucial for the development of epitope-based vaccines. Prediction scores over the 0.5 threshold were employed to determine epitopes. Among the 21 predicted epitopes four met the selection criteria: DEEHGKGSLEEAEAER and VQKPDA from ANLN, TFTKMRSNS and QGSPEMNSTINIHRT from CTHRC1 (Tables\u0026nbsp;2, and 3).\u003c/p\u003e \u003cp\u003eTables\u0026nbsp;2. Selected epitopes for vaccine production from ANLN Antigen\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-cell Epitopes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntigen\u003c/p\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAllele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSolubility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eToxicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAllergenicity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEEHGKGSLEEAEAER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eSoluble\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eNontoxic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eNon-Allergen\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVQKPDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMHC-I Epitopes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKLKNEGPQRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*03:01,\u003c/p\u003e \u003cp\u003eHLA-A*30:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGQNPELLPK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*11:01,\u003c/p\u003e \u003cp\u003eHLA-A*03:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKLLERTRARR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*31:01,\u003c/p\u003e \u003cp\u003eHLA-A*03:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMHC-II Epitopes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAYRSQRFKETERPSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-DPA1*01:03,\u003c/p\u003e \u003cp\u003eHLA-DPA1*03:01,\u003c/p\u003e \u003cp\u003eHLA-DPA1*01:03,\u003c/p\u003e \u003cp\u003eHLA-DPA1*02:01,\u003c/p\u003e \u003cp\u003eHLA-DPA1*02:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDKVPFLSSLESVEER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-DRB1*09:01,\u003c/p\u003e \u003cp\u003eHLA-DRB1*04:05,\u003c/p\u003e \u003cp\u003eHLA-DQA1*03:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLLYSIDAYRSQRFK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-DRB1*15:01,\u003c/p\u003e \u003cp\u003eHLA-DRB1*12:01,\u003c/p\u003e \u003cp\u003eHLA-DQA1*01:01,\u003c/p\u003e \u003cp\u003eHLA-DPA1*01:03,\u003c/p\u003e \u003cp\u003eHLA-DPA1*02:01,\u003c/p\u003e \u003cp\u003eHLA-DPA1*01:03,\u003c/p\u003e \u003cp\u003eHLA-DQA1*04:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTables\u0026nbsp;3. Selected epitopes for vaccine production from CTHRC1 Antigen\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-Cell Epitopes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntigen\u003c/p\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAllele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSolubility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eToxicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAllergenicity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTFTKMRSNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eSoluble\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eNon-Toxic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eNon-Allergen\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQGSPEMNSTINIHRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMHC-I Epitopes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATAASSVKTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*11:01,\u003c/p\u003e \u003cp\u003eHLA-A*68:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEGQNPELL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-B*40:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTISDSVAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*68:02,\u003c/p\u003e \u003cp\u003eHLA-A*26:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMHC-II Epitopes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDGFKGEKGECLRES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-DRB1*01:01,\u003c/p\u003e \u003cp\u003eHLA-DPA1*03:01,\u003c/p\u003e \u003cp\u003eHLA-DPA1*02:01,\u003c/p\u003e \u003cp\u003eHLA-DPA1*01:03,\u003c/p\u003e \u003cp\u003eHLA-DPA1*02:01,\u003c/p\u003e \u003cp\u003eHLA-DPA1*02:01,\u003c/p\u003e \u003cp\u003eHLA-DRB1*07:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGRDGFKGEKGECLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-DRB1*01:01,\u003c/p\u003e \u003cp\u003eHLA-DPA1*03:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRDGFKGEKGECLRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-DRB1*01:01,\u003c/p\u003e \u003cp\u003eHLA-DPA1*03:01,\u003c/p\u003e \u003cp\u003eHLA-DPA1*02:01,\u003c/p\u003e \u003cp\u003eHLA-DPA1*01:03,\u003c/p\u003e \u003cp\u003eHLA-DPA1*02:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003ePrediction of MHC-binding Epitopes\u003c/h2\u003e \u003cp\u003eThe IEDB server was employed to evaluate the binding affinities of epitopes to MHC class I and II molecules to predict those that are capable of activating helper T lymphocytes (HTLs). The NetMHCpan EL 4.1 method was employed to predict MHC-I epitopes, with a focus on 54 prevalent human alleles. The NetMHCIIpan EL 4.1 utility was implemented against 27 alleles for MHC-II prediction (Supplementary Table\u0026nbsp;4). The anticipated epitopes underwent additional assessment for allergenicity, toxicity, solubility, and antigenicity. Tables\u0026nbsp;2 and 3 present the top three candidate epitopes and their corresponding MHC alleles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eVaccine Construction, Physicochemical and Secondary Structure Analysis\u003c/h2\u003e \u003cp\u003eThe final multi-epitope vaccine was developed by integrating the cholera toxin B subunit as an adjuvant, linked to the N-terminal end of the construct through an EAAAK linker. B-cell epitopes were connected using KK linkers to enhance effective presentation. MHC class II-binding epitopes were linked using GPGPG linkers to improve antigen processing and presentation through MHC-II pathways. In a similar manner, epitopes that bind to MHC class I were connected through AAY linkers, which enhanced the efficiency of proteasomal cleavage and the presentation of MHC-I. The comprehensive configuration guaranteed the best possible folding, immunogenicity, and stability of the construct. Figure\u0026nbsp;7A illustrates a graphical representation of the vaccine design.\u003c/p\u003e \u003cp\u003eThe developed vaccine construct demonstrated an estimated isoelectric point (pI) of 9.37 and a molecular weight of 40,979.39 Da. The predicted in vitro half-life was around 30 hours in mammalian reticulocytes, whereas the in vivo half-life was estimated to be over 20 hours in yeast and about 10 hours in E. coli. The stability analysis produced an instability index of 36.68, indicating that the vaccine demonstrates stability, as values exceeding 40 signify instability. The aliphatic index of 60.48 provided evidence for thermal stability. The construct's hydrophilic characteristics were validated by a GRAVY score of -0.778, and a solubility score of 0.635 demonstrated significant solubility post-expression. The analysis of secondary structure prediction, carried out with the SOPMA tool, indicated that 30.67% of residues were organised into alpha helices, 11.20% into extended strands, and 58.13% into random coils (Fig.\u0026nbsp;7B and C).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eVaccine 3D Structure: Prediction, Refinement, and Validation\u003c/h2\u003e \u003cp\u003eThe preliminary three-dimensional configuration of the vaccine construct was forecasted utilising the I-TASSER server. Out of ten threading templates, five 3D models were produced, exhibiting C-scores that varied from \u0026minus;\u0026thinsp;1.93 to \u0026minus;\u0026thinsp;2.89. The C-score, reflecting the model's confidence on a scale from \u0026minus;\u0026thinsp;5 to 2, was employed to identify the optimal structure. The model exhibiting the highest C-score of \u0026minus;\u0026thinsp;1.93 was selected for additional refinement. According to the evaluation metrics, this model demonstrated a root-mean-square deviation (RMSD) of 11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 \u0026Aring; and a Template Modelling (TM) score of 0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 (Fig.\u0026nbsp;8A), indicating a satisfactory level of structural accuracy. Following this, five enhanced versions of the original model were produced. Model 3 exhibited exceptional structural characteristics, showcasing a Rama favoured region percentage of 88.7%, with only 0.7% weak rotamers. It achieved a GDT-HA score of 0.9640, an RMSD of 0.356 \u0026Aring;, and a MolProbity score of 1.982. Consequently, Model 3 was chosen for an additional examination (Fig.\u0026nbsp;8B). Analysis of the Ramachandran plot demonstrated that 83.5% of the residues were situated in the most favoured regions, 13.6% in allowed regions, and merely 2.9% in disallowed regions (Fig.\u0026nbsp;8E), suggesting a high level of stereochemical quality. Additionally, the ERRAT analysis produced an overall quality factor of 64.662% (Fig.\u0026nbsp;8C), while the Z-score obtained from ProSA-Web was \u0026minus;\u0026thinsp;2.26 (Fig.\u0026nbsp;8D), thereby affirming the reliability of the predicted tertiary structure. The structural validation results obtained from RAMPAGE, ERRAT, and ProSA-Web collectively indicate the high quality and stability of the final vaccine model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Coverage Investigation\u003c/h2\u003e \u003cp\u003eSelected T-cell epitopes demonstrate the ability to bind to various HLA supertype alleles, thereby ensuring extensive population coverage. The tool for estimating the geographic distribution of anticipated vaccine responses, which focuses on MHC-I and MHC-II epitope-specific population coverage, was utilized from the IEDB database. This analysis covered 16 geographical regions along with several significant countries. The anticipated vaccine is expected to reach an overall global population coverage of around 98.75%. The United States demonstrated the highest coverage at 100.00%, with North America at 99.99%, Europe at 99.91%, and South America at 99.67%, closely following. Areas like South Asia (99.58%), India (99.48%), and Japan (99.15%) exhibited impressive coverage rates. Conversely, coverage rates that were somewhat lower, yet still significant, were noted in Pakistan (68.67%), Central America (74.54%), and Southwest Asia (76.30%). Figure\u0026nbsp;9 presents a comprehensive analysis of population coverage across different regions. The results demonstrate that the developed vaccine construct may have significant potential for worldwide use.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiscontinuous B-Cell Epitopes\u003c/h3\u003e\n\u003cp\u003eA total of 169 residues were identified across six discontinuous B-cell epitopes, as predicted by the ElliPro server. The lengths of these epitopes ranged from greater than 3 to 51 residues, with prediction scores varying between 0.58 and 0.877. The discontinuous epitopes are illustrated in Fig.\u0026nbsp;10A and 10B.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eDocking and Molecular Dynamics Simulation\u003c/h2\u003e \u003cp\u003eThe ClusPro 2.0 server was utilized for investigating protein\u0026ndash;protein interaction to determine how well the vaccine was designed to attach to human immunological receptors. The vaccine-receptor complex was represented by 30 models produced by the server; the model with the lowest energy value was chosen as the best docking conformation. The vaccine showed a substantial affinity for both MHC-I and MHC-II receptors, as seen in Fig.\u0026nbsp;11A and B. The binding energies for MHC-I and MHC-II were \u0026minus;\u0026thinsp;973.4 kcal/mol and \u0026minus;\u0026thinsp;1046 kcal/mol, respectively, suggesting that the vaccine may interact with both immunological receptors effectively. When the vaccine is administered, the lowest binding affinity raises the possibility of a strong immunological response (Fig.\u0026nbsp;11).\u003c/p\u003e \u003cp\u003eThe iMOD server was used to conduct Normal Mode Analysis (NMA) to further evaluate the vaccine's stability in combination with MHC-I and MHC-II. As shown in Figs.\u0026nbsp;12 and 13, NMA demonstrated notable deformability in the locations with hinge movements, suggesting flexible regions within the vaccine-receptor complexes. The B-factor values, which indicate the structural flexibility, were analyzed in relation to the root mean square deviation (RMSD) obtained from NMA. For the MHC-I complex, the eigenvalues were 3.351124 \u0026times; 10^7, and for the MHC-II complex, they were 3.833857 \u0026times; 10^7. According to these eigenvalues, the energy needed for structural deformation in these areas is comparatively low, suggesting that the vaccine can undergo the conformational changes required for immunological recognition. The stability and flexibility of the vaccine in these complexes are supported by the covariance matrix, which displays interactions between residue pairs, and the elastic network model, which illustrates interatomic communication via springs. The vaccine retains long-lasting and persistent connections with both MHC-I and MHC-II receptors, according to the combined findings of the docking and NMA. This may help explain how well the vaccine stimulates immunological responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eImmune Simulation\u003c/h2\u003e \u003cp\u003eThe outcomes of immune simulations using the C-ImmSim server demonstrated a significant improvement in immune responses that were very similar to actual immunological responses. The most important early reaction was an increase in IgM concentration. After subsequent tertiary injections, the B cell population, IgG1\u0026thinsp;+\u0026thinsp;IgG2 antibodies, and IgG\u0026thinsp;+\u0026thinsp;IgM antibodies all rose while antigen levels gradually declined. Additionally, cytotoxic T (TC) cells and memory cells were incorporated into the vaccination model, contributing to an increase in the population of T-helper (TH) cells. Moreover, repeated exposures led to an increase in IFN-γ and IL-2 production. These findings confirmed the vaccination model\u0026rsquo;s strong immunogenic and antigenic properties (Fig.\u0026nbsp;14).\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eCodon Optimization and In Silico Cloning\u003c/h2\u003e \u003cp\u003eWhen an amino acid is encoded by various codons in different animals, this phenomenon is known as codon bias. The transcription of identical amino acids using different codons is a result of differences in cellular machinery. In this study, codon adaptation methods were used to anticipate which codons would be most effective in encoding certain amino acids in each organism. The Java Codon Adaptation Tool (JCAT) was employed to maximise the codon utilisation of \u003cem\u003eEscherichia coli\u003c/em\u003e strain K12. Additionally, the website identified bacterial ribosome binding sites, rho-independent transcription terminators, and restriction enzyme cleavage sites (such as \u003cem\u003eEaeI\u003c/em\u003e and \u003cem\u003eStyI\u003c/em\u003e). In comparison to the native GC content of \u003cem\u003eE. coli\u003c/em\u003e (strain K12), which is 50.73%, the optimised sequence demonstrated a Codon Adaptation Index (CAI) of 0.912 and a GC content of 51.38%. \u003cem\u003eEcoRI\u003c/em\u003e and \u003cem\u003eEcoRV\u003c/em\u003e sites were not detected in the codon-optimized vaccine construct sequence when restriction enzyme recognition sites were subsequently examined. The vaccine design was modified to include these enzyme locations to aid in silico cloning. Finally, the vaccine was inserted into the pET28a(+) vector to produce a viable recombinant clone of 5109 bp, as seen in Fig.\u0026nbsp;15.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study employed integrated GEO datasets to discover pivotal hub genes associated with the development and progression of five primary cancer types: colorectal (COAD), liver (LIHC), lung (LUAD), gastric (STAD), and breast (BRCA). Our findings highlight a cohort of consistently differentially expressed genes (DEGs) that serve as both therapeutic targets and biomarkers for cancer progression and prognosis.\u003c/p\u003e\n\u003ch3\u003eUpregulated Hub Genes and How They Contribute to the Development of Cancer\u003c/h3\u003e\n\u003cp\u003eAll five cancer types showed two hub genes, ANLN and CTHRC1, consistently raised. Both genes are part of crucial processes required for cancer cells to survive, migrate, and proliferate\u003csup\u003e\u003cspan additionalcitationids=\"CR83\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. Anillin (ANLN) is a conserved F-actin binding protein first extracted from Drosophila embryos, where it is crucial for cortical cytoskeletal dynamics during cytokinesis and cellularization. In humans, ANLN governs cell morphology, movement, and proliferative advancement, with its dysregulation seen across many malignancies\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Increased ANLN expression is often linked to enhanced tumor cell motility, invasion, and poor prognosis, indicating its significant involvement in carcinogenesis and metastasis\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. Pan-cancer analyses have identified a marked upregulation of ANLN in various cancer types, including those found in the bladder (BLCA), breast (BRCA), cervical (CESC), cholangiocarcinoma (CHOL), colon (COAD), esophagus (ESCA), head and neck (HNSC), kidney chromophobe (KICH), kidney renal clear cell (KIRC), kidney renal papillary cell (KIRP), liver (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreas (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate (PRAD), rectum (READ), sarcoma (SARC), stomach (STAD), thyroid (THCA), and uterine corpus endometrial carcinoma (UCEC). Cox regression analysis and Kaplan\u0026ndash;Meier survival curves demonstrate that elevated ANLN expression is strongly correlated with poorer overall survival (OS), disease-free interval (DFI), and progression-free survival (PFS) in KIRP, LIHC, LUAD, and PAAD, further establishing its significance as a prognostic marker. These findings align with studies suggesting ANLN as a potential biomarker in cancer\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. Mechanistic investigations have shown that ANLN promotes tumor proliferation and migration via interactions with microRNAs and oncogenes, particularly by targeting miR-217 and HMGA2\u003csup\u003e90,91\u003c/sup\u003e. Furthermore, ANLN stimulates the RHOA-PI3K/AKT signaling pathway, promoting cancer cell proliferation and progression\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. In lung adenocarcinoma, ANLN is associated with metastasis via the facilitation of epithelial-mesenchymal transition (EMT)\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e, but in gastric cancer, its expression has a favourable correlation with the Wnt/β-catenin signaling pathway\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. The diverse functional relationships demonstrate ANLN's extensive oncogenic potential across different types of tissue.\u003c/p\u003e \u003cp\u003eConversely, CTHRC1 (Collagen triple helix repeat containing 1) is a secreted glycoprotein predominantly expressed in epithelial-mesenchymal interfaces, such as the epidermis-dermis border, corneal epithelium, airway, oesophagus, choroid plexus, and meninges\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. Its expression is significantly elevated in several malignancies, exhibiting greatly higher transcription levels in 24 different cancer types compared to normal tissues\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. Although CTHRC1 is consistently upregulated in several malignancies, its expression is context-dependent; for example, one investigation indicated its absence in pancreatic tumour cells, with localisation confined to the adjacent stroma, including fibroblasts, vasculature, and skeletal muscle\u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e. This disparity may be ascribed to many factors: i) CTHRC1 exists in various molecular weight forms due to post-translational modifications, potentially affecting epitope recognition; ii) its interaction with unidentified cytoplasmic proteins may obscure epitopes essential for antibody detection; and iii) variability in antibody specificity among laboratories could lead to inconsistent staining results. The findings indicate that ANLN and CTHRC1 may facilitate tumour progression through both intrinsic mechanisms and their influence on the tumour microenvironment, underscoring the necessity for additional research to elucidate their biological functions and potential as diagnostic or therapeutic targets.\u003c/p\u003e\n\u003ch3\u003eThe roles of downregulated hub genes as tumour suppressors\u003c/h3\u003e\n\u003cp\u003eConversely, the downregulated genes ABCA8, PDK4, MT1M, TMEM100, and LIFR exhibited similar downregulation across the five cancer types, with expression diminishing as tumours advanced to more advanced stages. These genes are probably implicated in critical tumor-suppressive mechanisms. ABCA8, a member of the ATP-binding cassette (ABC) transporter superfamily, is crucial to cancer biology, especially regarding treatment resistance and cellular homeostasis\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. ABCA8 expression is often downregulated in many malignancies, including hepatocellular carcinoma, prostate cancer, ovarian cancer, and tongue squamous cell carcinoma\u003csup\u003e\u003cspan additionalcitationids=\"CR100\" citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. Recent study has shown simultaneous downregulation of ABCA8 and FABP4 in stomach adenocarcinoma (STAD), with their diminished expression strongly linked to worse prognosis, irrespective of clinical factors such as tumour stage, grade, or nodal metastatic status. These data indicate that both genes may function as prognostic indicators in STAD. Subsequent investigation using the cBioPortal database, which includes 1,365 STAD samples, identified significant genetic abnormalities in ABCA8 and FABP4, reinforcing the concept that their dysregulation\u0026mdash;via expression loss or mutation\u0026mdash;could facilitate tumor growth\u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. PDK4 (pyruvate dehydrogenase kinase isozyme 4) is often downregulated in several types of cancer, including prostate, breast, lung, and liver malignancies\u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e,\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. It has been shown that PDK4 functions to inhibit tumour cell growth and encourage apoptosis, especially in breast and lung cancer cells \u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e. Furthermore, its deficiency seems to have wider effects on the development of cancer; in ovarian cancer, PDK4 downregulation triggers the epithelial-mesenchymal transition (EMT) pathway, which increases the tumour cells' capacity for migration and invasion\u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMetallothionein (MT) family member MT1M is essential for controlling oxidative stress reactions and metal ion homeostasis. Its downregulation in cancer may accelerate tumor growth and resistance to apoptosis by increasing oxidative damage. MTs are low-molecular-weight, cysteine-rich proteins that play a role in several cancer-related activities, including the management of metal ions, defense against oxidative stress, control over cell division and apoptosis, and resistance to chemotherapy and radiation\u003csup\u003e\u003cspan additionalcitationids=\"CR108 CR109 CR110\" citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e. Numerous reports of MT isoforms, particularly MT-1 and MT-2, being downregulated in hepatocellular carcinoma (HCC) raise the possibility that decreased MT expression constitutes an early stage of HCC formation\u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e. This decrease in expression has been linked to epigenetic modifications, including promoter hypermethylation, as seen in rat hepatoma models\u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e and human HCC tissues. In particular, promote hypermethylation silences MT1M and MT1G in HCC, supporting the idea that hepatocarcinogenesis is facilitated by epigenetic repression of MTs\u003csup\u003e\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe transmembrane protein TMEM100 has been associated with the initiation and progression of several malignancies, and there is increasing evidence that it also acts as a tumor suppressor. Multiple studies have shown that tumors such as lung, liver, prostate, and colorectal cancers have significantly reduced TMEM100 expression. Han et al.\u003csup\u003e\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u003c/sup\u003e indicated that TMEM100 inhibits the proliferation of lung cancer cells, while Ou et al.\u003csup\u003e\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/sup\u003e discovered that its downregulation in hepatocellular carcinoma is significantly associated with increased proliferation and invasion. Likewise, Ye et al.\u003csup\u003e\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e\u003c/sup\u003e discovered a decreased TMEM100 expression in prostate cancer, which correlates with tumour stage and metastasis. In colorectal cancer (CRC), both Li et al.\u003csup\u003e\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e\u003c/sup\u003e and later studies [120] demonstrated a large decrease in TMEM100, with its reinstatement deactivating the TGF-β signalling pathway, resulting in substantial suppression of cancer cell proliferation. Moreover, TMEM100 regulates prostate cancer advancement by blocking the PI3K/AKT signalling pathway, therefore restraining cell proliferation, migration, and invasion\u003csup\u003e\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe leukaemia inhibitory factor receptor (LIFR), or CD118, is a type I cytokine receptor that interacts with multiple ligands, including leukaemia inhibitory factor (LIF), oncostatin M, and cardiotrophin 1. It forms a heterodimeric complex with glycoprotein 130 (gp130) to facilitate signal transduction within the interleukin-6 (IL-6) cytokine family\u003csup\u003e\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e\u003c/sup\u003e. LIFR regulates immunological responses, apoptosis, and cell differentiation, with its involvement in tumour biology being tissue- and context-dependent\u003csup\u003e\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e\u003c/sup\u003e. LIFR functions as a tumour suppressor in hepatocellular carcinoma (HCC), with its expression often diminished owing to promote hypermethylation\u003csup\u003e\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e\u003c/sup\u003e. The downregulation of LIFR has diagnostic significance, as its expression diminishes progressively from low-grade dysplastic nodules to tiny well-differentiated hepatocellular carcinoma, positioning it as a possible immunomarker for early hepatocarcinogenesis\u003csup\u003e\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e\u003c/sup\u003e. The ectopic expression of LIFR in HCC cells mechanistically inhibits metastasis by decreasing the PI3K/AKT signalling pathway\u003csup\u003e\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e\u003c/sup\u003e. The absence of LIFR activates NF-κB signaling via SHP1, which enhances the expression of the iron-sequestering factor LCN2, resulting in hepatic iron depletion and increased resistance to drug-induced ferroptosis\u003csup\u003e\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e\u003c/sup\u003e. The downregulation of these genes in several malignancies indicates their potential role as tumor suppressors, with their loss perhaps promoting malignant transformation. Therapeutic techniques designed to restore the expression or function of these genes may provide a unique method for treating malignancies characterized by downregulated tumor suppressor genes.\u003c/p\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Implications and Cancer-Specific Variability\u003c/h2\u003e \u003cp\u003eOur research showed that the elevated hub genes ANLN and CTHRC1 were substantially correlated with worse survival in the COAD, LUAD, LIHC, and STAD populations. Recent research revealed that CTHRC1 (collagen triple helix repeat-containing 1) is overexpressed in all 24 major subtypes of human malignancies, demonstrating its extensive role in carcinogenesis. Elevated CTHRC1 expression is significantly correlated with decreased overall survival (OS) in various malignancies, including head-and-neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC). These results underscore the possible oncogenic function of CTHRC1 in the formation and progression of several cancer types, highlighting its promise as both a predictive biomarker and a therapeutic target in cancer\u003csup\u003e\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e\u003c/sup\u003e. Similarly, ANLN was seen to be increased in most human malignancies, and its elevated expression was substantially correlated with reduced overall survival across most cancer types. The molecular mechanisms governing these genes may vary according to the cancer subtype, and other genetic or environmental variables may affect their prognostic significance\u003csup\u003e\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e,\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003eGenetic Alterations and Their Influence on Cancer Progression\u003c/h2\u003e \u003cp\u003eOur study identified ABCA8 as the most often mutated gene among the five cancer types studied, with a mutation frequency of 43%. This result is significant, since ABCA8, a member of the ATP-binding cassette (ABC) transporter family, has lately garnered attention for its possible involvement in cancer biology, especially in lipid metabolism and cellular detoxification\u003csup\u003e\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e\u003c/sup\u003e. Although more prominent ABC transporters, such as ABCB1, are associated with chemoresistance, ABCA8 has been shown as downregulated in breast and colorectal malignancies, potentially affecting lipid metabolism and cellular signaling\u003csup\u003e\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e\u003c/sup\u003e. Our discovery that ABCA8 mutations correlate with worse outcomes in breast cancer (BRCA) reinforces the concept that ABCA8 may function as a tumor suppressor or metabolic regulator that is compromised during carcinogenesis.\u003c/p\u003e \u003cp\u003eAdditionally, LIFR (Leukaemia Inhibitory Factor Receptor) demonstrated elevated mutation rates, especially in colorectal (COAD) and stomach (STAD) malignancies, at 21% and 14%, respectively. This corresponds with previous research identifying LIFR as a tumor suppressor that reduces JAK/STAT3 signaling, thereby decreasing epithelial-mesenchymal transition and metastasis\u003csup\u003e\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e\u003c/sup\u003e. Loss-of-function mutations or downregulation of LIFR have been linked to worse outcomes in breast and gastrointestinal malignancies, corroborating our finding that LIFR mutations are related to a poorer prognosis in BRCA. The prevalence of C\u0026thinsp;\u0026gt;\u0026thinsp;T transitions in mutations of both ABCA8 and LIFR aligns with COSMIC Signature 1, linked to the spontaneous deamination of 5-methylcytosine, a phenomenon prevalent in ageing tissues and tumors with compromised DNA repair mechanisms\u003csup\u003e\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e\u003c/sup\u003e. This indicates that these mutations are mostly influenced by intrinsic mutational mechanisms, maybe exacerbated by oxidative stress or inadequacies in base excision repair pathways. Conversely, genes such ANLN, PDK4, TMEM100, CTHRC1, and MT1M, although exhibiting lower mutation frequency (\u0026lt;\u0026thinsp;10%), have been associated with cancer via non-mutational pathways. ANLN is routinely overexpressed in aggressive malignancies, including breast and lung tumours, where it facilitates cellular proliferation and migration\u003csup\u003e\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e\u003c/sup\u003e. PDK4 regulates cellular metabolism and facilitates the Warburg effect, playing a role in metabolic reprogramming in cancer\u003csup\u003e\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e\u003c/sup\u003e. TMEM100, a regulator of vascular formation, has been suggested as a tumour suppressor involved in angiogenesis and TGF-β signaling\u003csup\u003e\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e\u003c/sup\u003e. CTHRC1 is recognized for facilitating cell migration and metastasis, namely via the Wnt/PCP and TGF-β pathways\u003csup\u003e\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e\u003c/sup\u003e. MT1M, which plays a role in metal ion homeostasis and oxidative stress responses, has been shown to have tumor-suppressive properties in hepatocellular and breast malignancies\u003csup\u003e\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003eIntegration of Copy Number Alterations (CNA) into Gene Function and Cancer Progression\u003c/h2\u003e \u003cp\u003eAnalysis of copy number variations indicated that LIFR, ANLN, and CTHRC1 often experience amplifications, while ABCA8, PDK4, MT1M, and TMEM100 display both deletions and amplifications across several cancer types. The amplification of LIFR and ANLN in stomach adenocarcinoma (STAD), lung squamous cell carcinoma (LUSC), and lung adenocarcinoma (LUAD) correlated with elevated mRNA expression, indicating a gene dosage effect. The positive connection between copy number variation and gene expression substantiates the concept that CNAs directly facilitate oncogene activation, hence enhancing tumour cell proliferation, survival, and metastatic capability\u003csup\u003e\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e,\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e\u003c/sup\u003e. Conversely, heterozygous deletions identified in ABCA8, PDK4, and MT1M may lead to diminished expression levels, thereby compromising their tumor-suppressive capabilities. ABCA8 is linked to lipid control and cell membrane integrity; its loss may compromise cellular homeostasis, promoting tumour growth. Likewise, the ablation of PDK4, a metabolic regulator, may modify mitochondrial activity and facilitate the glycolytic shift seen in aggressive tumors (Warburg effect)\u003csup\u003e\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e\u003c/sup\u003e. The absence of MT1M, which plays a role in oxidative stress management and metal ion control, may lead to heightened genomic instability and enhanced adaptability of cancer cells\u003csup\u003e\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e\u003c/sup\u003e. These results underscore the complex regulatory function of CNAs in cancer biology. In contrast to point mutations or minor insertions/deletions, copy number alterations (CNAs) may significantly influence gene expression and cellular functionality, either amplifying oncogenic pathways or repressing tumor-suppressive mechanisms. The mutation and amplification of genes such as LIFR and ANLN in certain tumours (e.g., STAD and LUAD) indicate synergistic carcinogenic pathways, hence enhancing their potential as therapeutic targets. The dual modification status (amplification and deletion) in some genes, such as TMEM100 and ABCA8, indicates context-dependent functional implications. These genes may exhibit variable functions based on tissue type, concomitant mutations, or microenvironmental influences. This highlights the need for cancer type-specific techniques in assessing the therapeutic potential of CNA-targeted treatments.\u003c/p\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003eFunctional Enrichment and Pathway Analysis\u003c/h2\u003e \u003cp\u003eOur functional enrichment analysis indicated that the differentially expressed genes (DEGs) were considerably enriched in biological processes essential for cancer development, namely those related to cellular transport, mitotic control, and chromosomal localization. These activities are essential for maintaining energy balance during cell division and for accurate chromosomal segregation during mitosis. The participation of these DEGs in the mitotic spindle assembly checkpoint signaling indicates their potential role in preserving genomic stability, a characteristic feature of tumor cells. The deregulation of these processes is a recognized characteristic of cancer, since mitotic mistakes may result in chromosomal instability, which promotes carcinogenesis and tumor heterogeneity\u003csup\u003e\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e,\u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e\u003c/sup\u003e. Besides mitotic regulation, the KEGG pathway analysis indicated that the elevated genes were predominantly associated with protein digestion and absorption, along with basal transcription factor pathways. These pathways are essential for facilitating the metabolic reprogramming of cancer cells, enabling them to satisfy the heightened requirements for protein synthesis and energy generation necessary for fast cell proliferation and survival. Cancer cells often experience metabolic alterations, exemplified by the Warburg effect, to emphasize anabolic pathways that support cellular proliferation and division\u003csup\u003e\u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e\u003c/sup\u003e. The increase of these pathways indicates that the detected DEGs may significantly contribute to tumor cells' adaptation to elevated metabolic demands. In contrast, the downregulated genes were linked to modified JAK-STAT signaling, interactions between cytokines and their receptors, and the control of stem cell pluripotency. These alterations may facilitate immune evasion and hinder tissue regeneration, both of which are essential for maintaining tumor proliferation. Dysregulation of JAK-STAT signaling is closely associated with immune suppression and the remodeling of the tumor microenvironment, facilitating tumor evasion of immune surveillance and the preservation of an immunosuppressive niche\u003csup\u003e\u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e\u003c/sup\u003e. Similarly, modifications in cytokine-cytokine receptor connections might further inhibit immune responses while facilitating tumor advancement by amplifying pro-inflammatory signaling pathways. These results illustrate the intricate tumor microenvironment, whereby immune evasion, metabolic reprogramming, and disrupted cell signaling synergistically foster circumstances conducive to cancer growth.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eNetwork analysis identifies critical regulators of cancer progression\u003c/h3\u003e\n\u003cp\u003eThe investigation of gene\u0026ndash;gene and protein\u0026ndash;protein interaction (PPI) networks demonstrated that existing oncogenes and the discovered hub genes had a highly interconnected architecture, indicating that pathways that promote cancer are regulated in concert. Notably, interactions with WWTR1 (TAZ) and PTK2 (FAK), two essential molecules involved in invasion, migration, and mechano-transduction, demonstrate how hub gene activity is integrated into pathways that regulate tumour spread and metastasis\u003csup\u003e\u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e146\u003c/span\u003e\u003c/sup\u003e. Similarly, a persistently active tumour microenvironment may enhance its tumor-promoting effects by modulating inflammatory signaling and cell survival pathways via the actions of CITED2 and IL6ST. These results highlight the importance of inflammation-driven carcinogenesis, and the crucial role of cytokine-mediated signaling in cancer development\u003csup\u003e\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e\u003c/sup\u003e. Another layer of metabolic reprogramming that promotes cancer cell survival under stress is indicated by the participation of OGT (O-GlcNAc transferase), a crucial enzyme that controls protein O-GlcNAcylation. Because it facilitates transcriptional and post-translational regulation in response to altered glucose metabolism, O-GlcNAcylation is becoming more widely acknowledged as a hallmark of cancer\u003csup\u003e\u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e148\u003c/span\u003e\u003c/sup\u003e. Most importantly, the identification of KIF11, BUB1, BUB1B, and CCNA2 as the key upregulated nodes in the PPI network underscores their essential roles in maintaining spindle checkpoint integrity and regulating mitotic progression. These genes are crucial for chromosomal integrity, and their overexpression may lead to uncontrolled cell division\u0026mdash;a hallmark of cancer.. They make appealing therapeutic targets because of their prominence in the network and proven role in cell cycle control. Translational potential is shown by the preclinical or clinical study of inhibitors that target KIF11 (e.g., ispinesib) and BUB1/BUB1B\u003csup\u003e149,150\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eImplications of Therapeutic Advances and Future Investigation\u003c/h3\u003e\n\u003cp\u003eIdentifying ANLN and CTHRC1 as hub genes with elevated expression and antigenicity across several cancer types offers substantial evidence of their role in essential oncogenic processes, including cell migration, extracellular matrix remodeling, and metastasis. These roles render them interesting to target therapeutic targets, especially for therapies designed to inhibit tumor propagation. In accordance with previous results\u003csup\u003e\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e151\u003c/span\u003e,\u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e152\u003c/span\u003e\u003c/sup\u003e, our research substantiates the carcinogenic capacity of these genes and validates their selection for vaccine-based immunotherapy.\u003c/p\u003e \u003cp\u003eWe are developing a multi-epitope vaccine aimed against ANLN and CTHRC1, using their high VaxiJen antigenicity scores (0.5930 and 0.4745, respectively) and reinforcing the notion that overexpressed tumor-associated antigens might elicit strong immune responses. This technique is consistent with past research that has focused on elevated cancer biomarkers for vaccine development. The incorporation of high-affinity B-cell and T-cell epitopes, as predicted by the IEDB server, facilitates the activation of both humoral and cellular immunological responses, specifically targeting CD4⁺ helper T cells and CD8⁺ cytotoxic T cells, which are crucial for robust anti-tumor immunity\u003csup\u003e\u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e,\u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e154\u003c/span\u003e\u003c/sup\u003e. The vaccine design uses cholera toxin B subunit as an adjuvant and integrates KK, GPGPG, and AAY linkers to guarantee epitope separation and appropriate folding. This architecture improves antigen presentation and immune activation, following other vaccine technologies\u003csup\u003e\u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e155\u003c/span\u003e,\u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e156\u003c/span\u003e\u003c/sup\u003e. The hydrophilic characteristics and advantageous GRAVY score indicate excellent solubility and expression potential, aligning with successful constructions in prostate and lung cancer vaccine studies\u003csup\u003e\u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e157\u003c/span\u003e\u003c/sup\u003e. The structural model produced by I-TASSER and corroborated by Ramachandran plots, RMSD, ERRAT, and ProSA-web demonstrates a stable, correctly folded protein. These findings are analogous to previous structural investigations of multi-epitope vaccines\u003csup\u003e\u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e158\u003c/span\u003e,\u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e159\u003c/span\u003e\u003c/sup\u003e and confirm the construct\u0026rsquo;s suitability for immune recognition and downstream applications. The vaccine succeeds in 98.75% population coverage, demonstrating high projected effectiveness in North America, Europe, and South Asia, thereby underscoring its worldwide significance. Although a marginal reduction in effectiveness was seen in particular countries (e.g., Pakistan, Central America), this underscores the need of integrating region-specific HLA alleles in forthcoming iterations\u0026mdash;a developing subject in personalized cancer vaccines\u003csup\u003e\u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e160\u003c/span\u003e,\u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e161\u003c/span\u003e\u003c/sup\u003e. Molecular docking and normal mode analysis validated robust and adaptable contacts with MHC-I and MHC-II molecules, crucial for triggering immunological responses. The vaccine's capacity for stable binding to immune receptors aligned with previous findings. Furthermore, C-ImmSim immunological simulation demonstrated a robust IgG response, the development of memory cells, and cytokine production, therefore confirming the vaccine's immunogenic potential\u003csup\u003e\u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e162\u003c/span\u003e\u003c/sup\u003e. Codon optimisation and effective in silico cloning into the pET28a(+) vector provide compatibility with bacterial expression platforms, including E. coli. The use of EcoRI and EcoRV restriction sites enhances the yield of recombinant protein synthesis, which is essential for preclinical testing and subsequent clinical trials\u003csup\u003e\u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e163\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis work comprehensively discovered and validated ANLN and CTHRC1 as consistently elevated oncogenic hub genes in five predominant cancers: colorectal, liver, lung, gastric, and breast. Their significant correlation with worse prognosis, elevated mutation and amplification rates, involvement in metastasis, extracellular matrix remodeling, and immune evasion designates them as potential therapeutic targets. Simultaneously, five persistently downregulated genes (ABCA8, PDK4, MT1M, TMEM100, and LIFR) indicate tumor-suppressive roles and diagnostic applicability. Leveraging the significant antigenicity of ANLN and CTHRC1, we developed an innovative multi-epitope cancer vaccine that incorporates immunogenic B- and T-cell epitopes, confirmed for structural stability, immunogenicity, and broad population applicability. In silico tests, including molecular docking, immunological simulations, and codon optimization, substantiate its promise as a beneficial therapeutic candidate on a worldwide scale. These results enhance the basis for personalized immunotherapy approaches and support further experimental validation and preclinical evaluation of this vaccine design.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank to the Khwaja Yunus Ali University during the manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHughes, T., Harper, A., Gupta, S., Frazier, A. L., van der Graaf, W. T., Moreno, F. et al. 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Purif. 34, 87\u0026ndash;94 (2004).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Gene Expression Profiling, Oncogenic Pathways, Cancer Biomarkers, Immunogenicity, Vaccine Design, Personalized Immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-6677557/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6677557/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores the transcriptomic, mutational, and immunogenic characteristics linked to significantly differentially expressed genes (DEGs) in colorectal (COAD), liver (LIHC), lung (LUAD), gastric (STAD), and breast (BRCA) cancers. Applying integrated bioinformatics algorithms, we discovered common upregulated and downregulated hub genes and assessed their prognostic importance, genomic modifications, copy number variations, functional enrichment, and pathway engagement. The persistent overexpression of ANLN and CTHRC1 in five cancer types, along with poor survival outcomes, underscores their suitability for multi-epitope vaccine development, emphasizing their antigenic potential and significance as universal therapeutic targets. Five genes\u0026mdash;ABCA8, PDK4, MT1M, TMEM100, and LIFR\u0026mdash;exhibited consistent downregulation and demonstrated tumor-suppressive characteristics. Genomic analyses demonstrated elevated mutation frequencies in ABCA8 and LIFR, predominantly C\u0026thinsp;\u0026gt;\u0026thinsp;T transitions that suggest age-related mutational signatures. Copy number alterations confirmed oncogenic amplifications (ANLN, CTHRC1) and tumor suppressor deletions (e.g., ABCA8). Functional enrichment associated differentially expressed genes with mitosis, chromosome segregation, and metabolic pathways. A multi-epitope vaccine targeting ANLN and CTHRC1 has been established leveraging predicted B- and T-cell epitopes, cholera toxin B as an adjuvant, and efficient linkers. Structural validation indicated desirable folding, stability, and solubility. The vaccine exhibited significant MHC binding, accomplishing 98.75% global population coverage, alongside strong immune simulation findings. Codon optimization and subsequent cloning into the pET28a(+) vector confirmed the preparation for bacterial expression. ANLN and CTHRC1 demonstrates significant targets for universal immunotherapy. The multi-epitope vaccine demonstrates significant efficacy in silico and has the potential to be widely employed as a cancer immunotherapeutic.\u003c/p\u003e","manuscriptTitle":"Oncogenic Hub Genes ANLN and CTHRC1: Implications for Cancer Prognosis and Vaccine-Based Therapeutics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-19 12:12:09","doi":"10.21203/rs.3.rs-6677557/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":"0a7be305-ca90-4259-85b3-c85652e123c6","owner":[],"postedDate":"May 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48636780,"name":"Biological sciences/Computational biology and bioinformatics/Genome informatics/Genome assembly algorithms"},{"id":48636781,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2025-05-21T14:06:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-19 12:12:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6677557","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6677557","identity":"rs-6677557","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}