Identification of NUDT6 and LINGO1 as key genes for predicting response to neoadjuvant chemoradiotherapy in rectal cancer patients

Identification of NUDT6 and LINGO1 as key genes for predicting response to neoadjuvant chemoradiotherapy in rectal cancer patients | 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 Identification of NUDT6 and LINGO1 as key genes for predicting response to neoadjuvant chemoradiotherapy in rectal cancer patients Qian Wang, Guangpeng Shen, Juntao Wang, Qinsheng Zheng, Xianpeng Qin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8771996/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background Rectal cancer is a highly prevalent cancer worldwide and a common cause of cancer death. Neoadjuvant chemoradiotherapy (nCRT) is the first choice for advanced rectal cancer. In this study, we used bioinformatics approaches to explore key genes affected by nCRT in rectal cancer. Methods A total of 26 samples of rectal cancer patients were collected and divided into the Treatment group (14 patients who received nCRT) and the Control group (12 patients who did not receive nCRT). Key genes were selected by high-throughput sequencing, differential expression analysis, machine learning algorithms, receiver operating characteristic (ROC) curves, and gene expression analysis. Gene Set Enrichment Analysis (GSEA) was used to trace the enrichment pathways of these key genes. Additionally, the relationship between immune cells and these key genes was explored. A nomogram and molecular regulatory network were constructed based on the selected key genes. Results The quality of sequencing data was high for all samples. Overall, 35 differentially expressed genes (DEGs) were discovered. Among them, NUDT6 and LINGO1 had excellent predictive values (both with area under the curve (AUC) > 0.8) and were identified as key genes. A nomogram with good diagnostic performance was constructed. NUDT6 was significantly enriched in the ribosome and oxidative phosphorylation pathways, and was significantly positively correlated with Megakaryocyte-Erythroid Progenitor (MEP) and T helper cell 1 (Th1 cells), and significantly negatively correlated with Myocyte cells. LINGO1 was significantly enriched in the proteasome pathway and significantly positively correlated with Erythrocytes. Additionally, drug prediction analyses indicated that valproic acid was most highly associated with NUDT6, while bisphenol A is most closely linked to LINGO1. Conclusions In this study, the NUDT6 and LINGO1 genes were identified as key genes related to nCRT in rectal cancer. These genes might significantly influence the sensitivity of rectal cancer to nCRT, and the findings could provide valuable insights for developing personalized treatment strategies. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Health sciences/Oncology Rectal cancer Neoadjuvant chemoradiotherapy High-throughput sequencing Machine learning algorithms Immune-related Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Background Colorectal cancer (CRC), one of the most prevalent malignancies of the digestive system, represents a significant global health burden. According to recent epidemiological studies, CRC ranked as the third most lethal malignancy globally in 2022, with rectal adenocarcinoma (READ) comprising approximately 30% of CRC cases [ 1 ] . Colorectal cancer has a hidden onset, with early symptoms not being obvious, and most patients are diagnosed at an advanced stage. Currently, for locally advanced READ patients with T3-4 stage, lymph node negative or positive without distant metastasis, the standard treatment strategy is preoperative chemoradiotherapy (CRT) or combined total neoadjuvant chemotherapy (TNT) followed by total mesorectal excision [ 2 , 3 ] . Although the application of multidisciplinary comprehensive treatment has significantly reduced the local recurrence rate to 5–9% [ 4 ] , the lack of specific biomarkers during the treatment process makes it difficult to accurately monitor treatment effects. Some patients may face issues of overtreatment or undertreatment, which severely affect their quality of life and prognosis [ 5 , 6 ] . Neoadjuvant chemoradiotherapy (nCRT), a multimodal therapy administered prior to surgical resection, is a cornerstone in the management of multiple malignancies. In breast and lung cancers, nCRT demonstrates efficacy in tumor volume reduction, downstaging, and enhancement of both resection rates and survival outcomes. In the field of rectal cancer treatment, nCRT is of great significance for locally advanced patients. It reduces local recurrence and promotes tumor downstaging by inhibiting tumor cell growth, inducing apoptosis, and suppressing angiogenesis. It also increases the rate of anal preservation, greatly improving the quality of life for patients, with some achieving pathological complete response (pCR), leading to extended disease-free survival and overall survival [ 7 ] . However, nCRT still faces many challenges in the treatment of rectal cancer. There is significant individual variability in patient responses to nCRT, with pCR rates only maintained between 15% and 20%, and there is a lack of effective biomarkers to accurately predict patient responses to nCRT [ 8 , 9 ] . This makes it difficult for clinicians to develop personalized treatment plans, affecting treatment outcomes and patient prognosis [ 10 , 11 ] . Therefore, there is an urgent need to identify key genes related to the response of rectal cancer patients to neoadjuvant radiotherapy to provide a theoretical basis and new targets for precise treatment and prognosis improvement in rectal cancer. This study collected 26 rectal cancer samples from Sichuan Provincial People's Hospital, divided into a Treatment group with 14 cases receiving nCRT and a Control group with 12 cases not receiving it. Molecular information was obtained through high-throughput sequencing, followed by RNA extraction, sequencing quality control, DEGs identification, functional, and protein-protein interaction (PPI) analysis. Feature genes were screened using five machine learning algorithms (LASSO, SVM-RFE, Boruta, XGBoost, and RF), and key genes were identified for gene diagnostic assessment. This included constructing nomograms, GSEA, immune infiltration analysis, molecular regulation, and drug-key gene network construction. The aim is to identify key genes predicting the response to neoadjuvant chemoradiotherapy and analyze pathways, providing a basis and targets for precision treatment and prognosis improvement in rectal cancer. 2 Methods 2.1 Sample acquisition The sample of this study was obtained from Sichuan Provincial People’s Hospital, comprising a total of 26 samples from rectal cancer patients. Among these, 14 samples were collected from patients who received neoadjuvant chemoradiotherapy (nCRT), referred to as the "Treat" group. The samples in this group include D2, D3, D6, D8, D9, D10, D12, D15, D17, D18, D21, D23, D22, and D25. The remaining 12 samples were from patients with rectal cancer who did not undergo nCRT, designated as the "Control" group, which includes D1, D4, D5, D7, D11, D13, D14, D16, D19, D20, D24, and D26. All samples were taken from rectal adenocarcinoma tumor tissues. The clinical indicators of the patients were recorded in detail, specifically including: height, age, Body Mass Index (BMI), gender, weight, preoperative tumor markers, distance from the anus (mm), involvement of the dentate line (1 for yes, 0 for no), size of the primary foci (long diameter, mm), preoperative magnetic resonance imaging/computed tomography (MRI/CT) results [(Extramural Vascular Invasion, EMVI) and (Mesorectal Fascia, MRF)], preoperative imaging staging (T, N, M), concomitant intestinal obstruction (1 for yes, 0 for no), tumor perforation (1 for yes, 0 for no), degree of differentiation (high intermediate 0, low 1), and postoperative routine pathology results (T, N, M, EMVI, neural fasciculus membrane invasion, and tumor emergence) (Additional files 1–2). All samples were collected according to the protocol approved by the Medical Ethics Committee of the Sichuan Provincial People's Hospital (Ethics Approval Number: [Ethics ID: 2022 − 443]). Informed consent forms were in place for all patients in the study. After sample collection, high-throughput sequencing was performed on all rectal cancer tumor tissue samples to obtain comprehensive molecular information, providing data support for subsequent research. 2.2 Extraction, sequencing, and quality control of RNA Rectal adenocarcinoma tumor tissues from the treat and control groups were subjected to total RNA extraction. Extraction was conducted using TRIzol reagent (Invitrogen, CA, USA). The protocol was as described by the manufacturer. A NanoDrop ND-1000 spectrophotometer (NanoDrop, Wilmington, Delaware, USA) and a Bioanalyzer 2100 (Agilent, California, USA) were used for the assessment of the concentration and quality of the extracted RNA. Following this assessment, the purified RNA was fragmented and reverse transcribed in order to synthesise complementary DNA (cDNA). A 300 bp ± 50 bp insert size cDNA library was constructed using the NEBNext Ultra™ RNA Library Preparation Kit for Illumina (New England Biolabs, USA). Finally, cDNA libraries were paired-end sequenced using Illumina Novaseq™ 6000 platform (LC Bio Technology CO., Ltd., Hangzhou, China). The standard procedure for the appropriate sequencing mode was followed. In this study, fastq software ( https://github.com/OpenGene/fastp ) was used for quality control and data filtering of the raw data. The default parameters were used. The cleaned data were aligned to the human reference genome (GRCh38.p12) using HISAT2 software ( https://ccb.jhu.edu/software/ ) after removing low-quality RNA-Seq reads. Transcripts were then assembled and quantified using StringTie software ( https://ccb.jhu.edu/software/ ) based on the reference human genome annotation file. The number of fragments per kilobase of transcript per million mapped reads (FPKM) was used to measure the relative quantification of transcripts. 2.3 Identification of DEGs To assess intergroup differences in the sequencing data of Treat and Control groups samples as well as within-sample correlations, and to aid in the exclusion of outlier samples. Pearson correlation analysis (|correlation (R)| > 0.3, P < 0.05) was performed on the transcriptome sequencing data of the 2 groups of samples based on gene and FPKM comparisons using the R package "psych". The R package "DESeq2" (v 1.38.0) [ 12 ] was also used to identify DEGs between samples from the Treat and Control groups. DEGs were selected at adj. P-value 0.5. Additionally "ggplot2" packages (v 3.4.4) [ 13 ] and "ComplexHeatmap" packages (v 2.14.0) [ 14 ] were used to create the Volcano maps and density heatmaps for DEGs. 2.4 Functional enrichment and protein-protein interaction (PPI) network of DEGs The R package "clusterProfiler" (v 4.7.1.003) [ 15 ] was utilized for Gene Ontology (GO) analysis of DEGs. The significance threshold was set at P-value < 0.05. To explore the interactions with the corresponding proteins of DEGs, DEGs were entered into search tool for the STRING (retrieval of interacting genes/proteins, https://cn.string-db.org/ ), and the those with an interaction score ≥ 0.15 were selected to form a PPI network. This network was then visualised using Cytoscape (v 3.10.2) [ 16 ] . 2.5 Identification of characterized genes by means of machine learning To obtain the characterized genes associated with chemoradiotherapy in rectal cancer, we employed five machine learning algorithms to analyze the DEGs in all sequencing data from the Treat and Control groups. The "glmnet" (v 4.1.4) ( https://glmnet.stanford.edu ) was used for the Least Absolute Shrinkage and Selection Operator (LASSO) analysis. The "family" parameter was set to "binomia", and the optimal lambda value was determined using 5-fold cross-validation to filter for filter LASSO genes. The support vector machine recursive feature elimination (SVM-RFE) was performed using the R package "caret" (v 6.0–93) ( https://CRAN.R-project.org/package=caret ). Under the parameter "method =svmRadial", the optimal SVM-RFE genes were selected. The R package "Boruta" (v 8.0.0) ( https://gitlab.com/mbq/Boruta/ ) was used to extract Boruta genes from the DEGs by creating shadow features. A XGBoost model based on the DEGs was constructed by the R package "xgboost" (v 1.7.8.1) ( https://github.com/dmlc/xgboost ), and the DEGs corresponding to the optimal model were selected as the XGBoost characterized genes. Random forest (RF) classification model based on the DEGs was built using the R package "randomForest" (v 4.7–1.2) [ 17 ] . The corresponding genes were obtained as RF genes by selecting the ntree value that minimised the out-of-bag error of the model in the range from 1 to 100 with a step size of 1. Finally, the intersection genes among the five sets of genes were obtained using the R package "VennDiagram" (v 1.7.3) [ 18 ] and were defined as the characterized genes for further study. 2.6 Assessment of the diagnostic value and expression levels of characterized genes Characterized genes were assessed using the R package "pROC" (v 1.18.0) [ 19 ] in all samples from the Treat and Control groups. To assess the ability of these characterized genes to discriminate between samples from the treat and control groups, ROC curves were plotted. AUC values were calculated and genes with AUC > 0.8 were designated as candidate key genes. To further validate the differences in the expression of candidate key genes between the Treat and Control groups, Wilcoxon rank sum test was used to assess the significant differences in their expression levels (P < 0.05). Candidate key genes with significant differences between the 2 groups were defined as key genes. Box plots were then produced using the "ggplot2" package (v 3.4.4) [ 13 ] . 2.7 Developing and assessing a nomogram A nomogram model was developed using the "rms" package (v 6.8.1) ( https://CRAN.R-project.org/package=rms ) based on key genes to determine the probability of developing rectal cancer after receiving chemoradiotherapy in all samples from the Treat and Control groups. To evaluate the accuracy of the nomogram, calibration curves were plotted using the R package "ResourceSelection" ( https://github.com/psolymos/ResourceSelection ). Finally, decision curve analysis (DCA) of key genes and nomogram was conducted using the R package "rms" (v 6.8.1) (a net gain above 0 indicated good model predictions). 2.8 GSEA GSEA was conducted using the R package "clusterProfiler" (v 4.7.1.003) [ 15 ] to identify pathways associated with key genes. Furthermore, the Spearman correlation analysis was conducted using the "psych" package (v 2.4.3) [ 20 ] to assess the relationship between the key genes and all the other genes in the Treat and Control groups. The genes were then ranked in ascending order according to the correlation coefficients, and a list of related genes corresponding to each key genes was obtained. Subsequently, a reference gene set, c2.cp.kegg.v7.4.symbols.gmt, was obtained from the MSigDB ( https://www.gsea-msigdb.org/gsea/msigdb ) [ 21 ] for GSEA analysis. A |Normalized Enrichment Score (NES)| > 1 and P < 0.05 indicated statistical significance. The 5 pathways were ranked by P-value using "enrichplot" package (v 1.14.2) [ 22 ] . 2.9 Immune infiltration analysis The immune infiltration scores of 64 immune cell types [ 23 ] were calculated for each sample in the Treat and Control groups using the Xcell algorithm. In addition, Wilcoxon test was used to screen for immune infiltrating cells with significant (P < 0.05) differences between the Treat and Control groups. Subsequently, Spearman correlation analysis of these differentially expressed immune cells and key genes was carried out using the "psych" R package (v 2.2.9) [ 24 ] and "ggplot2" (v 3.4.4) [ 13 ] to visualize the analysis results. 2.10 Molecular Regulatory Network Construction The miRTarBase database ( https://ngdc.cncb.ac.cn/bioproject/ ) and the miRWalk database ( http://mirwalk.umm.uni-heidelberg.de/ ) were used to predict the target miRNAs of the key genes in order to investigate the molecular regulatory mechanisms of the key genes. The R package "VennDiagram" (v 1.7.3) [ 18 ] was then employed to intersect the miRNAs obtained from the 2 databases, aiming to identify the target miRNAs of key genes. Subsequently, miRNet ( https://diana.e-ce.uth.gr/ ) and Starbase ( https://rnasysu.com/encori/ ) databases were utilized to predict the target lncRNAs of the identified miRNAs. By intersecting the results of these 2 predictions, the target lncRNAs of key genes were determined. Finally, based on the identified target miRNAs, target lncRNAs, and key genes (mRNAs), a lncRNA-miRNA-mRNA regulatory network centered around key genes was constructed using the R package "Cytoscape" (v 3.10.2) [ 16 ] . 2.11 Establishment of drug-key genes network To investigate potential therapeutic drugs linked to key genes, we used the Comparative Toxicogenomics Database (CTD, http://ctdbase.org/ ) to identify target drugs for key genes. First, the names of the key genes were entered into the CTD database, and drugs with potential binding relationships with these key genes were identified with the help of database algorithms. After obtaining the prediction results, the binding ability between the predicted drugs and the key genes was ranked according to the binding affinity data provided by the CTD database. For each key gene, the top 20 relevant drugs with the highest binding ability scores were selected. Subsequently, drug-key gene network was constructed using the R package "Cytoscape" (v 3.10.2) [ 16 ] . 2.12 Statistical analysis All statistical analyses were conducted in the R programming environment. The Wilcoxon test was specifically used to conduct comparisons among various groups. A P-value < 0.05 was considered statistically significant, unless otherwise specified. 3 Results 3.1 High-quality sequencing data In this study, gene expression assays were conducted on 14 rectal cancer samples from the Treat group and 12 rectal cancer samples from the Control group. The results from the quality assessment of the sequencing data indicated that all samples in both groups demonstrated good quality characteristics. Specifically, the Q20 values for all samples were above 99.71%, and the Q30 values exceeded 97.58%, which met the established data quality standards (Additional file 3). Further analysis of the density distribution curves and gene expression comparisons revealed that the overall distribution of gene expression was quite similar between the 2 groups. This strongly supports the high quality of the sequencing data (Fig. 1 a-b). In addition, the distribution and reproducibility of the data between samples were investigated using correlation analysis. The analysis revealed that overall correlation coefficients among the samples exceeded 0.6, demonstrating that gene expression patterns within the groups and replicates were highly similar (Fig. 1 c). 3.2 Identification and GO analysis of DEGs First, 35 DEGs were identified between the Treat and Control groups. Among them, 11 up-regulated genes/24 down-regulated genes were found in the Treatment group compared to the Control group. The difference in the distribution of DEGs was visualized by a volcano plot (Fig. 2 a) and further confirmed by a density heatmap (Fig. 2 b), both of which showed significant differences. GO showed the biological processes enriched for 35 DEGs, including 279 biological processes (BP), 62 molecular functions (MF), and 19 cellular components (CC) (Fig. 2 c, Additional files 4–6). In the BP, DEGs were enriched in phosphatidylcholine metabolic process, axon ensheathment in the central nervous system, phosphatidylcholine biosynthetic process, response to auditory stimulus, and central nervous system myelination; CC was mainly enriched in the hinge region between urothelial plaques of apical plasma membrane, hyaluronan cable, nitric-oxide synthase complex, peroxisomal membrane, and microbody membrane; MF contained vitamin binding, phosphatidyl-N-methylethanolamine N-methyltransferase activity, acylglycerone-phosphate reductase activity, phosphatidylethanolamine N-methyltransferase activity. In addition, the established PPI network existed with 10 isolated proteins, retaining 25 proteins and 28 interaction pairs. For example, GPT2, ATP1A2, and RETNLB had close interactions with other genes (Fig. 2 d). 3.3 NUDT6 and LINGO1 were identified as characterized genes In this study, 5 machine learning algorithms were applied to explore the characterized genes associated with nCRT in rectal cancer. First, DEGs were simplified by LASSO regression. When the lambda value was taken to be optimal, log (lambda. 1se) = -4.3395, and six LASSO genes were successfully acquired, namely, NUDT6, RBP4, PNPLA8, LINGO1, HLA-K, and HYAL1(Fig. 3 a). Then, the SVM-RFE method was adopted, and 14 SVM-RFE genes were selected, including NUDT6, NPW, GPT2, PEMT, RPL7P32, DHRS7B, MTARC1, HLA-K, HES5, LINGO1, PNPLA8, RBP4, SNX9, and MAL (Fig. 3 b). Meanwhile, 14 Boruta genes were identified by the Boruta algorithm, namely, NPW, NUDT6, RASD1, PNPLA8, GPT2, LINGO1, PEMT, HLA-K, DHRS7B, MTARC1, HES5, SNX9, RPL7P32, and MAL (Fig. 3 c). In addition, the XGBoost algorithm was utilized to construct a model based on DEGs. It was found that the model was optimized when the first 6 DEGs were selected. The six XGBoost genes selected at this time were NUDT6, NPW, LINGO1, MTARC1, MUC17, and AQP5 (Fig. 3 d). Finally, the RF algorithm revealed the importance of genes with the help of the relationship between the error rate and the classification tree. Under the condition of ntree of 93, the top 10 DEGs in terms of importance were identified as RF genes, which were NUDT6, NPW, LINGO1, DHRS7B, GPT2, HLA-K, RPL7P32, MAL, HES5, and ATP1A2 (Fig. 3 e). Subsequently, the genes obtained from the above 5 machine learning algorithms were cross-analyzed, and it was finally determined that NUDT6 and LINGO1 could be used as characterized genes associated with rectal cancer nCRT (Fig. 3 f). 3.4 Assessment of the diagnostic value and expression levels of NUDT6 and LINGO1 ROC analysis showed that the AUC values of NUDT6 and LINGO1 exceeded 0.8 in all samples in the Treat and Control groups (Fig. 4 a). The results showed that NUDT6 and LINGO1 demonstrated satisfactory ability in differentiating rectal cancer samples before and after receiving nCRT, suggesting that they had potential diagnostic value. Further validation of expression levels revealed a significant difference in the expression of NUDT6 and LINGO1 between the Treat and Control groups (P < 0.05), with both genes showing down-regulation in the Treat group (Fig. 4 b-c). Consequently, NUDT6 and LINGO1 were identified as key genes in this context. 3.5 Construction and evaluation of a nomogram model with superior predictive power based on 2 key genes A nomogram was created based on 2 genes, NUDT6 and LINGO1, to predict the incidence of rectal cancer after receiving nCRT (Fig. 5 a). The calibration curve showed that the difference between the actual risk of developing rectal cancer after receiving nCRT and the predicted risk of the nomogram was small (Fig. 5 b). In addition, DCA showed that the nomogram had a high clinical benefit and was superior in predictive efficacy to NUDT6 or LINGO1 alone (Fig. 5 c). 3.6 Crucial functional pathways and differential immune microenvironment GSEA revealed that NUDT6 was significantly enriched in 84 KEGG pathways, particularly in the ribosome, oxidative phosphorylation, Huntington's disease pathways, focal adhesion, and Extracellular Matrix (ECM)-receptor interaction (Fig. 6 a, Additional file 7). Similarly, LINGO1 was also enriched in 86 KEGG pathways, with notable enrichment in the proteasome, systemic lupus erythematosus, cell cycle, cytokine receptor interaction, and DNA replication pathways (Fig. 6 b, Additional file 8). These findings offer valuable insights into the potential mechanisms by which nCRT may affect the pathogenesis of rectal cancer. The tumor microenvironment plays a crucial role in the diagnosis, survival, and treatment response of malignant tumors. To investigate how key genes affect the progression of rectal cancer following nCRT, we analyzed the relationship between the expression of these key genes and immune cell infiltration in the tumor The heatmap showed the distribution of immune scores for 64 immune cell types in the Treat and Control groups, with varying degrees of infiltration for each immune cell type (Fig. 6 c). A comparison of immune cell infiltration between the Treatment and Control groups revealed significant differences in the levels of erythrocytes, megakaryocyte-erythroid progenitors (MEPs), myocytes, and T helper cell 1 (Th1 cells) (P < 0.05) (Fig. 6 d). In the Treat group, Erythrocytes, MEP, and Th1 cells were significantly lower than in the Control group, while Myocytes were significantly higher than in the Control group. Additionally, the relationship between immune cells and key genes (NUDT6 and LINGO1) was further investigated. The findings revealed a significant positive correlation between LINGO1 and erythrocytes (R = 0.63, P < 0.01). NUDT6 showed a significant positive correlation with megakaryocyte-erythroid progenitors (MEP) (R = 0.50, P < 0.05) and Th1 cells (R = 0.50, P < 0.05). Conversely, NUDT6 exhibited a significant negative correlation with myocytes (R = -0.55, P < 0.05) (Fig. 6 e). 3.7 The lncRNA-miRNA-mRNA network and drug prediction based on key genes A sum of 3 target miRNAs and 38 target lncRNAs of LINGO1 were predicted, and a lncRNA-miRNA-mRNA regulatory network with 3 miRNAs, 1 mRNA, 38 lncRNAs, and 42 interaction pairs was constructed (Fig. 7 a). In addition, drugs targeting NUDT6 and LINGO1 were predicted based on the CTD database, including 75 drugs targeting LINGO1 and 66 drugs targeting NUDT6. Notably, the drug with the highest correlation with LINGO1 was bisphenol A, while valproic acid had the strongest correlation with NUDT6 (Fig. 7 b, Additional files 9–10). It was hypothesized that these bisphenol A and valproic acid drugs could be potential drugs for nCRT-targeted therapy in rectal cancer patients. 4 Discussion Rectal cancer is a common malignant tumor of the colorectal region, posing significant challenges in clinical management due to its complex pathophysiology and variable treatment responses [ 25 ] . Standard treatment methods typically involve neoadjuvant chemoradiotherapy (nCRT), aimed at reducing tumor size and improving surgical outcomes [ 11 ] . However, the efficacy of nCRT can vary greatly among patients, necessitating the identification of reliable biomarkers to predict treatment responses and guide therapeutic strategies [ 26 ] . Understanding the molecular basis of cancer, particularly in the context of nCRT, is crucial for developing targeted interventions and improving patient prognosis [ 27 ] . This study collected samples from 26 rectal cancer patients (Treat group and Control group) and utilized transcriptome sequencing technology and bioinformatics methods to explore key genes and their enriched pathways associated with neoadjuvant chemoradiotherapy in rectal cancer. Ultimately, NUDT6 and LINGO1 were identified as key genes that can predict nCRT association in rectal cancer patients. Further analysis examined the biological pathways involving these key genes and their correlation with immune cells, exploring the potential mechanisms by which nCRT affects the onset of rectal cancer and predicting potential therapeutic drugs, providing new reference points for the precise diagnosis and personalized treatment of rectal cancer. NUDT6(Nudix-type motif 6) has been identified as a fibroblast growth factor 2 (FGF2) antisense gene, playing a significant role in regulating FGF2 expression. NUDT6 is implicated in various cellular processes, including cell proliferation and differentiation. Studies have demonstrated that NUDT6 can influence the stability and expression of FGF2 mRNA, thereby modulating its biological activity [ 28 ] . In the context of rectal cancer, NUDT6's role in cell proliferation is particularly relevant. Enhanced expression of NUDT6 has been associated with increased cell proliferation, which may contribute to tumor growth and resistance to therapy. Furthermore, NUDT6 has been shown to interact with key signaling pathways such as the extracellular signal-regulated kinase (ERK) and p38MAPK pathways, which are crucial for cell survival and proliferation [ 29 ] . These interactions suggest that NUDT6 could be a potential target for therapeutic intervention, particularly in enhancing the efficacy of chemoradiotherapy by modulating its expression or function. Additionally, NUDT6's involvement in the regulation of FGF2 indicates that it could play a role in the tumor microenvironment, influencing processes such as angiogenesis and immune cell infiltration, which are critical for tumor progression and response to treatment [ 30 ] . LINGO1, or Leucine-rich repeat and Ig domain-containing Nogo receptor-interacting protein 1, is a transmembrane protein that plays a crucial role in the central nervous system, particularly in neuronal survival, axonal regeneration, and oligodendrocyte maturation. It has been implicated in various neurological disorders, including Parkinson's disease and essential tremor, due to its regulatory effects on neuronal pathways [ 31 ] . In the context of rectal cancer, LINGO1's role in modulating immune responses and cellular signaling pathways is of particular interest. Studies have shown that LINGO1 can influence the activity of large conductance Ca 2+ -activated K + (BK) channels, which are involved in regulating cell proliferation and apoptosis [ 32 ] . This regulatory function suggests that LINGO1 could impact tumor growth and response to therapy by modulating these pathways. Additionally, LINGO1's involvement in the immune system, particularly in the regulation of T-cell responses, highlights its potential role in the tumor microenvironment and immune evasion mechanisms [ 33 ] . Given these multifaceted roles, targeting LINGO1 could provide a novel approach to enhancing the efficacy of chemoradiotherapy in rectal cancer by modulating immune responses and cellular signaling pathways. In our study, we identified NUDT6 and LINGO1 as critical genes associated with the response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer. The enrichment analysis revealed that NUDT6 and LINGO1 are involved in several key pathways that could elucidate their roles in rectal cancer progression and treatment response. NUDT6, a member of the Nudix hydrolase family, is downregulated by green tea catechins, which suppress its proliferative activity in colorectal cancer cells [ 28 ] . Additionally, NUDT6 has been implicated in angiogenesis regulation, as knockdown of PROX1, a transcription factor, leads to upregulation of angiogenic factors including NUDT6, suggesting its role in tumor vascularization [ 34 ] . LINGO1, on the other hand, has been identified as a prognostic gene in various cancers. In glioblastoma, LINGO1 was found to be upregulated and associated with poor prognosis, indicating its potential role in tumor aggressiveness [ 35 ] . Furthermore, LINGO1 has been explored as a target for chimeric antigen receptor (CAR) T cell therapy in Ewing sarcoma, highlighting its relevance as a tumor-specific antigen [ 36 ] . The cell surface proteome analysis of Ewing sarcoma revealed LINGO1 as a unique therapeutic target, emphasizing its potential in targeted cancer therapies [ 37 ] . The involvement of these genes in multiple cancer-related pathways, including those regulating cell proliferation, angiogenesis, and immune response, underscores their significance in rectal cancer. The good diagnostic performance of NUDT6 and LINGO1, with AUC values exceeding 0.8, further validates their potential as biomarkers for assessing nCRT response. The construction of a nomogram model based on these genes offers a robust tool for predicting rectal cancer recurrence post-nCRT, providing a valuable asset for personalized treatment strategies. In summary, our findings not only highlight the importance of NUDT6 and LINGO1 in rectal cancer but also open new avenues for therapeutic interventions targeting these pathways. The integration of gene expression analysis with machine learning and immune profiling presents a comprehensive approach to understanding and combating rectal cancer. In our study, the immune cell infiltration analysis revealed significant differences between the treatment group (Treat) and the control group (Control), particularly in the levels of erythrocytes, megakaryocyte-erythroid progenitors (MEP), myocytes, and T helper 1 (Th1) cells. The observed decrease in erythrocytes and MEP in the treatment group aligns with previous research indicating that neoadjuvant chemoradiotherapy (nCRT) can alter the tumor microenvironment, potentially reducing the overall immune cell infiltration [ 38 ] . Additionally, the significant reduction in Th1 cells in the treatment group suggests a suppression of the immune response, which is crucial for anti-tumor immunity. Th1 cells are known for their role in producing interferon-gamma (IFN-γ), which activates macrophages and enhances the cytotoxic activities of natural killer (NK) cells and CD8 + T cells [ 39 ] . Conversely, the increase in myocytes in the treatment group may indicate a tissue remodeling process post-nCRT, which has been observed in other studies where radiochemotherapy induced changes in the tumor stroma and surrounding tissues [ 40 ] . The correlation of LINGO1 with erythrocytes and NUDT6 with MEP and Th1 cells further underscores the potential regulatory roles these genes play in modulating the immune microenvironment in response to nCRT. For instance, the positive correlation of NUDT6 with MEP and Th1 cells suggests that this gene might be involved in promoting the differentiation and proliferation of these immune cells, thereby influencing the overall immune landscape [ 41 ] . These findings are significant as they highlight the impact of nCRT on the immune cell composition within the tumor microenvironment, which could have implications for the efficacy of immunotherapies. The modulation of immune cell infiltration by nCRT indicates a potential window for combining immunotherapy with nCRT to enhance anti-tumor immune responses. Moreover, understanding the molecular mechanisms by which NUDT6 and LINGO1 influence immune cell dynamics could provide new therapeutic targets to improve treatment outcomes for rectal cancer patients undergoing nCRT [ 42 ] . 5 Conclusions This study identifies NUDT6 and LINGO1 as novel biomarkers for predicting the efficacy of neoadjuvant chemoradiotherapy (nCRT) in rectal cancer for the first time through transcriptome analysis and clinically applicable nomograms. These findings not only reveal the association between key signaling pathways and immune regulation but also provide potential intervention targets for precise tumor treatment strategies. However, the study has certain limitations: a single-center small sample size (n = 26), insufficient adjustment for clinical covariates, and the research on drug-gene interaction mechanisms is still in its early stages. To advance subsequent research, it is recommended to focus on the following tasks: (1) validating the universality of the biomarkers using multi-center large sample cohorts; (2) elucidating the molecular mechanisms of NUDT6/LINGO1 in nCRT resistance/sensitivity through functional experiments such as CRISPR screening or patient-derived organoids; (3) conducting translational medicine research to integrate biomarkers with emerging therapies like immunotherapy in prospective clinical trials. By addressing these key issues, it is expected that the exploratory findings of this study can be transformed into clinically valuable diagnostic and therapeutic tools. Declarations Ethics approval and consent to participate The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Medical Ethics Committee of the Sichuan Provincial People's Hospital (Ethics Approval Number: [Ethics ID: 2022 − 443], Approval Date: November 28, 2022). Written informed consent was obtained from individual or guardian participants. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding Supported by Sichuan Science and Technology Program (No.2022YFS0223 to Zhou Yang). Author Contribution The authors’ contributions are as follows—Zhou Yang and Qian Wang contributed to the umbrella review design. All authors conducted the literature search, extracted the data, and performed the analysis. Qian Wang wrote the first draft of the manuscript and Zhou Yang is the guarantor of the article. All authors wrote and reviewed the manuscript, and all authors read and approved the final manuscript. Qian Wang and Guangpeng Shen contributed equally to this work. Acknowledgements Not applicable. Data Availability The datasets generated and/or analysed during the current study are available in the NCBI Sequence Read Archive (SRA) repository, [http://www.ncbi.nlm.nih.gov/bioproject/1423203, Accession: PRJNA1423203]. References Siegel, R. L. et al. Cancer statistics, 2022. CA: A Cancer. J. Clin. 72 , 7–33 (2022). Teng, H. et al. Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer. Cancer Cell. 41 , 124–138e6 (2023). Bosset, J-F. et al. Chemotherapy with preoperative radiotherapy in rectal cancer. N. Engl. J. Med. 355 , 1114–1123 (2006). Keller, D. S. et al. The multidisciplinary management of rectal cancer. Nat. Rev. Gastroenterol. Hepatol. 17 , 414–429 (2020). Wen, L., Han, Z. & Du, Y. Identification of gene biomarkers and immune cell infiltration characteristics in rectal cancer. Journal of Gastrointestinal Oncology; 12. Epub ahead of print June 2021. 10.21037/jgo-21-255 Yang, K. et al. Development and validation of a novel hypoxia score for predicting prognosis and immune microenvironment in rectal cancer. Front. Surg. ; 9 . Epub ahead of print 25 April 2022. 10.3389/fsurg.2022.881554 Wang, Y. et al. Neoadjuvant chemoradiotherapy combined with immunotherapy for locally advanced rectal cancer: A new era for anal preservation. Front. Immunol. ; 13 . Epub ahead of print 8 December 2022. 10.3389/fimmu.2022.1067036 Gambacorta, M. A. et al. Timing to achieve the highest rate of pCR after preoperative radiochemotherapy in rectal cancer: A pooled analysis of 3085 patients from 7 randomized trials. Radiother. Oncol. 154 , 154–160 (2021). Omejc, M. & Potisek, M. Prognostic significance of tumor regression in locally advanced rectal cancer after preoperative radiochemotherapy. Radiol. Oncol. 52 , 30–35 (2017). Wang, H. et al. Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy in patients with rectal cancer. Nat. Commun. 13 , 7802 (2022). Zhao, P. et al. Identification of hub genes and potential molecular mechanisms related to radiotherapy sensitivity in rectal cancer based on multiple datasets. J. Translational Med. 21 , 176 (2023). Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15 , 550 (2014). Ito, K. & Murphy, D. Application of ggplot2 to pharmacometric graphics. CPT: Pharmacometrics Syst. Pharmacol. 2 , 79 (2013). Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32 , 2847–2849 (2016). Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov. 2 , 100141 (2021). Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13 , 2498–2504 (2003). Lim, J. et al. Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis. BMC Complement. Med. Ther. 23 , 409 (2023). Chen, H. & Boutros, P. C. VennDiagram: A package for the generation of highly-customizable venn and euler diagrams in R. BMC Bioinform. 12 , 35 (2011). Robin, X. et al. pROC: An open-source package for R and S + to analyze and compare ROC curves. BMC Bioinform. 12 , 77 (2011). Zhan, Z-Q. et al. Gastroesophageal reflux disease with 6 neurodegenerative and psychiatric disorders: Genetic correlations, causality, and potential molecular mechanisms. J. Psychiatr. Res. 172 , 244–253 (2024). Chen, L., Hua, J. & He, X. Bioinformatics analysis identifies a key gene HLA_DPA1 in severe influenza-associated immune infiltration. BMC Genom. 25 , 257 (2024). Zhang, C. et al. Genome-wide mutation profiling and related risk signature for prognosis of papillary renal cell carcinoma. Annals Translational Med. 7 , 427–427 (2019). Lu, K. et al. Bioinformatics analysis identifies immune-related gene signatures and subtypes in diabetic nephropathy. Front Endocrinol; 13. Epub ahead of print 7 December 2022. 10.3389/fendo.2022.1048139 Saidmamatov, O. et al. Translation and adaptation of the adult developmental coordination disorder/dyspraxia checklist (ADC) into asian uzbekistan. Sports 11 , 135 (2023). Wei, F-Z. et al. Development and validation of a nomogram and a comprehensive prognostic analysis of an LncRNA-associated competitive endogenous RNA network based on immune-related genes for locally advanced rectal cancer with neoadjuvant therapy. Front Oncol; 11. Epub ahead of print 19 July 2021. 10.3389/fonc.2021.697948 Shu, P. et al. An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence. Front. Oncol. ; 14 . Epub ahead of print 9 February 2024. 10.3389/fonc.2024.1294440 Ji, D. et al. Somatic mutations and immune alternation in rectal cancer following neoadjuvant chemoradiotherapy. Cancer Immunol. Res. 6 , 1401–1416 (2018). Sukhthankar, M. et al. A potential proliferative gene, NUDT6, is down-regulated by green tea catechins at the posttranscriptional level. J. Nutr. Biochem. 21 , 98–106 (2010). Baguma-Nibasheka, M., MacFarlane, L. A. & Murphy, P. R. Regulation of fibroblast growth factor-2 expression and cell cycle progression by an endogenous antisense RNA. Genes 3 , 505–520 (2012). Winter, H. et al. Targeting long non-coding RNA NUDT6 enhances smooth muscle cell survival and limits vascular disease progression. Mol. Ther. 31 , 1775–1790 (2023). Stefansson, H. et al. Variant in the sequence of the LINGO1 gene confers risk of essential tremor. Nat. Genet. 41 , 277–279 (2009). Dudem, S. et al. LINGO1 is a regulatory subunit of large conductance, Ca2+-activated potassium channels. Proceedings of the National Academy of Sciences. ; 117: 2194–2200. (2020). Vilariño-Güell, C. et al. LINGO1 and LINGO2 variants are associated with essential tremor and parkinson disease. Neurogenetics 11 , 401–408 (2010). Rudzińska, M. et al. Transcription factor prospero homeobox 1 (PROX1) as a potential angiogenic regulator of follicular thyroid cancer dissemination. Int. J. Mol. Sci. 20 , 5619 (2019). Liu, S., Xu, Y. & Zhang, S. LINGO1, C7orf31 and VEGFA are prognostic genes of primary glioblastoma: Analysis of gene expression microarray. neo. ; 65: 532–541. (2018). Morales, E. A. et al. Restricting CAR T cell trafficking expands targetable antigen space. Epub ahead of print 11 February 2024. 10.1101/2024.02.08.579002 Town, J. et al. Exploring the surfaceome of ewing sarcoma identifies a new and unique therapeutic target. Proceedings of the National Academy of Sciences. ; 113: 3603–3608. (2016). Jarosch, A. et al. Neoadjuvant radiochemotherapy decreases the total amount of tumor infiltrating lymphocytes, but increases the number of CD8+/granzyme B+ (GrzB) cytotoxic T-cells in rectal cancer. Oncoimmunology 7 , e1393133 (2018). Däster, S. et al. High frequency of CD8 positive lymphocyte infiltration correlates with lack of lymph node involvement in early rectal cancer. Disease Markers. ; 2014: 792183. (2014). Wagner, F. et al. Neoadjuvant radiochemotherapy significantly alters the phenotype of plasmacytoid dendritic cells and 6-sulfo LacNAc+ monocytes in rectal cancer. Front Immunol; 10. Epub ahead of print 29 March 2019. 10.3389/fimmu.2019.00602 He, L. et al. Identification of four immune subtypes in locally advanced rectal cancer treated with neoadjuvant chemotherapy for predicting the efficacy of subsequent immune checkpoint blockade. Front. Immunol. ; 13 . Epub ahead of print 27 September 2022. 10.3389/fimmu.2022.955187 Koukourakis, I. M. et al. Immune response and immune checkpoint molecules in patients with rectal cancer undergoing neoadjuvant chemoradiotherapy: A review. Curr. Issues. Mol. Biol. 45 , 4495–4517 (2023). Kanehisa, M. et al. KEGG: Biological systems database as a model of the real world. Nucleic Acids Res. 53 , D672–D677 (2025). Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28 , 27–30 (2000). Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28 , 1947–1951 (2019). Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xls Additionalfile2.xls Additionalfile3.xls Additionalfile4.xls Additionalfile5.xls Additionalfile6.xls Additionalfile7.xls Additionalfile8.xls Additionalfile9.xls Additionalfile10.xls Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 31 Mar, 2026 Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers invited by journal 16 Feb, 2026 Editor assigned by journal 16 Feb, 2026 Editor invited by journal 16 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 13 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8771996","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":592419832,"identity":"d54c3204-e7f2-4325-a5f4-47caae0ef77c","order_by":0,"name":"Qian Wang","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Wang","suffix":""},{"id":592419834,"identity":"a6705404-2562-4654-9e8a-8a317618b930","order_by":1,"name":"Guangpeng Shen","email":"","orcid":"","institution":"Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guangpeng","middleName":"","lastName":"Shen","suffix":""},{"id":592419835,"identity":"635282c9-0a6c-4a0e-809b-b721183790d7","order_by":2,"name":"Juntao Wang","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Juntao","middleName":"","lastName":"Wang","suffix":""},{"id":592419838,"identity":"f32ff350-b12c-4cf6-9b26-d210942204d5","order_by":3,"name":"Qinsheng Zheng","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qinsheng","middleName":"","lastName":"Zheng","suffix":""},{"id":592419839,"identity":"f58f55c4-ea73-426f-bb4d-32b85e5cacef","order_by":4,"name":"Xianpeng Qin","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Xianpeng","middleName":"","lastName":"Qin","suffix":""},{"id":592419843,"identity":"40dd2cba-d642-4893-aebc-7a06a23de866","order_by":5,"name":"Zhou Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACdsaGDxUQJhsQ2/DwszcQ0MLM2DjjDEJLmoxkzwFCWhgYkbUctjG44YBfB38zc2PDAYbD8ub8y589+Nh2nofhBgPjh485uLVIHGYEazHcOeNBuuHMtts8jLMbmCVnbsOtxYCZsf3xB4bDjBtuHDgmzQvUwixzgI2ZF78WsC32G24cbANqOcfDJpFAnJbEDeeb2YBaDvDwENIC9Ut68oYbbGySM84l80jwHGzG6xf+9vaHQC3WthvOH38m8aHMzt7+ePPBDx/xaAEDxn/NQPsS4NwGAurBoA5o3wFiFI6CUTAKRsFIBAAo8VYMom9bMgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2026-02-03 06:53:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8771996/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8771996/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103505206,"identity":"5514be69-1b7f-492c-ae26-65abb8b94d72","added_by":"auto","created_at":"2026-02-26 13:27:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4247355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. Density distribution curve of gene expression levels in the treatment group and control group samples. b. Box plot of gene expression levels in the treatment group and control group samples. c. Heatmap of gene expression correlation between samples.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/2b49d2a6914a6babb4a93bdf.png"},{"id":103258967,"identity":"9648da41-bc03-426f-85a5-966b5e19445c","added_by":"auto","created_at":"2026-02-23 17:34:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1953828,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDEGs and GO analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. Volcano plot of differentially expressed genes (DEGs) between the Treatment and Control groups. b. Hierarchical clustering heatmap of DEG expression patterns. c. GO enrichment analysis of DEGs. d. Protein-protein interaction (PPI) network of DEGs.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/49e665a557101b0507a96d63.png"},{"id":103258975,"identity":"27a51c2e-1fc6-4f90-bc3d-778c9acea331","added_by":"auto","created_at":"2026-02-23 17:34:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1953105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMachine learning and cross-analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. LASSO regression analysis for DEG selection. b. SVM-RFE algorithm for feature gene selection. c. Boruta algorithm for robust feature identification. d. XGBoost gene importance ranking. e. Random Forest (RF) error rate analysis. f. Consensus of machine learning-selected genes.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/ac346f06648703a754bbc605.png"},{"id":103506110,"identity":"a0de1c2b-fbb6-4bb0-a5ed-d1854c0ef20c","added_by":"auto","created_at":"2026-02-26 13:34:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":394512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC analysis and validation of expression levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. ROC curve analysis of NUDT6 and LINGO1 diagnostic performance. b. Differential expression of NUDT6 between the Treatment and Control groups. c. Differential expression of LINGO1 between the Treatment and Control groups.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/8489c6e90f22f1e425873c09.png"},{"id":103505597,"identity":"ffe553b0-e1ca-475d-b660-76b53059bb77","added_by":"auto","created_at":"2026-02-26 13:32:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1141722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram, calibration curve, and DCA based on the two key genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. Nomogram for predicting rectal cancer incidence post-nCRT. b. Calibration curve of the nomogram model. c. Decision curve analysis (DCA) of the nomogram.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/15ac34a523ef97da8eebd118.png"},{"id":103258977,"identity":"dcac66d3-0e86-47ce-8d3b-8151570f50a4","added_by":"auto","created_at":"2026-02-23 17:34:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2851649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA pathway enrichment analysis and immune infiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. GSEA enrichment plot of KEGG[43][44][45] pathways associated with NUDT6. b. GSEA enrichment plot of KEGG[43][44][45] pathways associated with LINGO1. c. Immune cell infiltration landscape in rectal cancer post-nCRT. d. Differential immune cell infiltration between Control and Treatment groups. e. Correlation of key genes with immune cell infiltration.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/de534be14b524eb81f5f7933.png"},{"id":103506113,"identity":"2d962a25-9171-4a07-aa28-c88f404fea56","added_by":"auto","created_at":"2026-02-26 13:34:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2556047,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003elncRNA-miRNA-mRNA regulatory network and targeted drug prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. lncRNA-miRNA-mRNA regulatory network of LINGO1. b. Drug-gene interaction predictions for LINGO1 and NUDT6.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/be9695d8b7fec9b3f14f9068.png"},{"id":104411911,"identity":"5b364e8f-54b2-496f-b5ad-008820de5a1d","added_by":"auto","created_at":"2026-03-11 12:58:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16131266,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/4c44f999-0dea-48e6-bb1d-64a1903d5edf.pdf"},{"id":103258966,"identity":"dfe9d6bd-7de7-4b2c-a0e7-c8a77a85ac11","added_by":"auto","created_at":"2026-02-23 17:34:46","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13936,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xls","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/7f05344c6180f06485cd818b.xls"},{"id":103505981,"identity":"a435e7b8-54bf-47c7-9746-d76754dc292d","added_by":"auto","created_at":"2026-02-26 13:33:43","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12886,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.xls","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/7a2f616644ffc33a52a8fee5.xls"},{"id":103258970,"identity":"1b50fb6d-e6c1-4957-9509-4bc31707f7e7","added_by":"auto","created_at":"2026-02-23 17:34:47","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10291,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.xls","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/46e4bf8d23dac9f902c1932c.xls"},{"id":103506088,"identity":"df629294-7e78-421e-860d-80c9adbe854c","added_by":"auto","created_at":"2026-02-26 13:34:03","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":30474,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4.xls","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/e095a56c14cef7e5a67f44c9.xls"},{"id":103505631,"identity":"6c50016b-2e46-4726-9f9a-d38e183b1258","added_by":"auto","created_at":"2026-02-26 13:32:16","extension":"xls","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":30372,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile5.xls","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/739500c7989fa64f728d1ece.xls"},{"id":103505503,"identity":"d7a9240b-9f6b-4e2e-bdf6-1df8099676a0","added_by":"auto","created_at":"2026-02-26 13:31:31","extension":"xls","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":30355,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile6.xls","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/7f9bf1a23577f05359e1a61d.xls"},{"id":103506323,"identity":"593174a9-f820-430c-bbcf-6b612b56af58","added_by":"auto","created_at":"2026-02-26 13:35:15","extension":"xls","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":31891,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile7.xls","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/08b5213ec672bb67bf4b9472.xls"},{"id":103258979,"identity":"3e0abecf-1d02-492f-8040-e72e1503015b","added_by":"auto","created_at":"2026-02-23 17:34:47","extension":"xls","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":32330,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile8.xls","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/cac05ec662b22efc558263d9.xls"},{"id":104397462,"identity":"dd2436d5-da64-44f8-b73b-80e694ca45b0","added_by":"auto","created_at":"2026-03-11 11:48:52","extension":"xls","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":13481,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile9.xls","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/9807419a21dd63f3618e4090.xls"},{"id":103258982,"identity":"2f2b2417-a278-42af-a065-8dd713d360e4","added_by":"auto","created_at":"2026-02-23 17:34:47","extension":"xls","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":12952,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile10.xls","url":"https://assets-eu.researchsquare.com/files/rs-8771996/v1/f1fc7908a91c6f1b17fafa45.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of NUDT6 and LINGO1 as key genes for predicting response to neoadjuvant chemoradiotherapy in rectal cancer patients","fulltext":[{"header":"1. Background","content":"\u003cp\u003eColorectal cancer (CRC), one of the most prevalent malignancies of the digestive system, represents a significant global health burden. According to recent epidemiological studies, CRC ranked as the third most lethal malignancy globally in 2022, with rectal adenocarcinoma (READ) comprising approximately 30% of CRC cases\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Colorectal cancer has a hidden onset, with early symptoms not being obvious, and most patients are diagnosed at an advanced stage. Currently, for locally advanced READ patients with T3-4 stage, lymph node negative or positive without distant metastasis, the standard treatment strategy is preoperative chemoradiotherapy (CRT) or combined total neoadjuvant chemotherapy (TNT) followed by total mesorectal excision\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Although the application of multidisciplinary comprehensive treatment has significantly reduced the local recurrence rate to 5\u0026ndash;9%\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, the lack of specific biomarkers during the treatment process makes it difficult to accurately monitor treatment effects. Some patients may face issues of overtreatment or undertreatment, which severely affect their quality of life and prognosis\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNeoadjuvant chemoradiotherapy (nCRT), a multimodal therapy administered prior to surgical resection, is a cornerstone in the management of multiple malignancies. In breast and lung cancers, nCRT demonstrates efficacy in tumor volume reduction, downstaging, and enhancement of both resection rates and survival outcomes. In the field of rectal cancer treatment, nCRT is of great significance for locally advanced patients. It reduces local recurrence and promotes tumor downstaging by inhibiting tumor cell growth, inducing apoptosis, and suppressing angiogenesis. It also increases the rate of anal preservation, greatly improving the quality of life for patients, with some achieving pathological complete response (pCR), leading to extended disease-free survival and overall survival\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. However, nCRT still faces many challenges in the treatment of rectal cancer. There is significant individual variability in patient responses to nCRT, with pCR rates only maintained between 15% and 20%, and there is a lack of effective biomarkers to accurately predict patient responses to nCRT\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. This makes it difficult for clinicians to develop personalized treatment plans, affecting treatment outcomes and patient prognosis \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Therefore, there is an urgent need to identify key genes related to the response of rectal cancer patients to neoadjuvant radiotherapy to provide a theoretical basis and new targets for precise treatment and prognosis improvement in rectal cancer.\u003c/p\u003e \u003cp\u003eThis study collected 26 rectal cancer samples from Sichuan Provincial People's Hospital, divided into a Treatment group with 14 cases receiving nCRT and a Control group with 12 cases not receiving it. Molecular information was obtained through high-throughput sequencing, followed by RNA extraction, sequencing quality control, DEGs identification, functional, and protein-protein interaction (PPI) analysis. Feature genes were screened using five machine learning algorithms (LASSO, SVM-RFE, Boruta, XGBoost, and RF), and key genes were identified for gene diagnostic assessment. This included constructing nomograms, GSEA, immune infiltration analysis, molecular regulation, and drug-key gene network construction. The aim is to identify key genes predicting the response to neoadjuvant chemoradiotherapy and analyze pathways, providing a basis and targets for precision treatment and prognosis improvement in rectal cancer.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample acquisition\u003c/h2\u003e \u003cp\u003eThe sample of this study was obtained from Sichuan Provincial People\u0026rsquo;s Hospital, comprising a total of 26 samples from rectal cancer patients. Among these, 14 samples were collected from patients who received neoadjuvant chemoradiotherapy (nCRT), referred to as the \"Treat\" group. The samples in this group include D2, D3, D6, D8, D9, D10, D12, D15, D17, D18, D21, D23, D22, and D25. The remaining 12 samples were from patients with rectal cancer who did not undergo nCRT, designated as the \"Control\" group, which includes D1, D4, D5, D7, D11, D13, D14, D16, D19, D20, D24, and D26. All samples were taken from rectal adenocarcinoma tumor tissues. The clinical indicators of the patients were recorded in detail, specifically including: height, age, Body Mass Index (BMI), gender, weight, preoperative tumor markers, distance from the anus (mm), involvement of the dentate line (1 for yes, 0 for no), size of the primary foci (long diameter, mm), preoperative magnetic resonance imaging/computed tomography (MRI/CT) results [(Extramural Vascular Invasion, EMVI) and (Mesorectal Fascia, MRF)], preoperative imaging staging (T, N, M), concomitant intestinal obstruction (1 for yes, 0 for no), tumor perforation (1 for yes, 0 for no), degree of differentiation (high intermediate 0, low 1), and postoperative routine pathology results (T, N, M, EMVI, neural fasciculus membrane invasion, and tumor emergence) (Additional files 1\u0026ndash;2). All samples were collected according to the protocol approved by the Medical Ethics Committee of the Sichuan Provincial People's Hospital (Ethics Approval Number: [Ethics ID: 2022\u0026thinsp;\u0026minus;\u0026thinsp;443]). Informed consent forms were in place for all patients in the study. After sample collection, high-throughput sequencing was performed on all rectal cancer tumor tissue samples to obtain comprehensive molecular information, providing data support for subsequent research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Extraction, sequencing, and quality control of RNA\u003c/h2\u003e \u003cp\u003eRectal adenocarcinoma tumor tissues from the treat and control groups were subjected to total RNA extraction. Extraction was conducted using TRIzol reagent (Invitrogen, CA, USA). The protocol was as described by the manufacturer. A NanoDrop ND-1000 spectrophotometer (NanoDrop, Wilmington, Delaware, USA) and a Bioanalyzer 2100 (Agilent, California, USA) were used for the assessment of the concentration and quality of the extracted RNA. Following this assessment, the purified RNA was fragmented and reverse transcribed in order to synthesise complementary DNA (cDNA). A 300 bp\u0026thinsp;\u0026plusmn;\u0026thinsp;50 bp insert size cDNA library was constructed using the NEBNext Ultra\u0026trade; RNA Library Preparation Kit for Illumina (New England Biolabs, USA). Finally, cDNA libraries were paired-end sequenced using Illumina Novaseq\u0026trade; 6000 platform (LC Bio Technology CO., Ltd., Hangzhou, China). The standard procedure for the appropriate sequencing mode was followed. In this study, fastq software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/OpenGene/fastp\u003c/span\u003e\u003cspan address=\"https://github.com/OpenGene/fastp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for quality control and data filtering of the raw data. The default parameters were used. The cleaned data were aligned to the human reference genome (GRCh38.p12) using HISAT2 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ccb.jhu.edu/software/\u003c/span\u003e\u003cspan address=\"https://ccb.jhu.edu/software/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) after removing low-quality RNA-Seq reads. Transcripts were then assembled and quantified using StringTie software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ccb.jhu.edu/software/\u003c/span\u003e\u003cspan address=\"https://ccb.jhu.edu/software/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) based on the reference human genome annotation file. The number of fragments per kilobase of transcript per million mapped reads (FPKM) was used to measure the relative quantification of transcripts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Identification of DEGs\u003c/h2\u003e \u003cp\u003eTo assess intergroup differences in the sequencing data of Treat and Control groups samples as well as within-sample correlations, and to aid in the exclusion of outlier samples. Pearson correlation analysis (|correlation (R)| \u0026gt; 0.3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was performed on the transcriptome sequencing data of the 2 groups of samples based on gene and FPKM comparisons using the R package \"psych\". The R package \"DESeq2\" (v 1.38.0)\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e was also used to identify DEGs between samples from the Treat and Control groups. DEGs were selected at adj. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2 (Fold Change)| \u0026gt; 0.5. Additionally \"ggplot2\" packages (v 3.4.4)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e and \"ComplexHeatmap\" packages (v 2.14.0)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e were used to create the Volcano maps and density heatmaps for DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional enrichment and protein-protein interaction (PPI) network of DEGs\u003c/h2\u003e \u003cp\u003eThe R package \"clusterProfiler\" (v 4.7.1.003)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e was utilized for Gene Ontology (GO) analysis of DEGs. The significance threshold was set at P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To explore the interactions with the corresponding proteins of DEGs, DEGs were entered into search tool for the STRING (retrieval of interacting genes/proteins, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the those with an interaction score\u0026thinsp;\u0026ge;\u0026thinsp;0.15 were selected to form a PPI network. This network was then visualised using Cytoscape (v 3.10.2)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Identification of characterized genes by means of machine learning\u003c/h2\u003e \u003cp\u003eTo obtain the characterized genes associated with chemoradiotherapy in rectal cancer, we employed five machine learning algorithms to analyze the DEGs in all sequencing data from the Treat and Control groups. The \"glmnet\" (v 4.1.4) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://glmnet.stanford.edu\u003c/span\u003e\u003cspan address=\"https://glmnet.stanford.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for the Least Absolute Shrinkage and Selection Operator (LASSO) analysis. The \"family\" parameter was set to \"binomia\", and the optimal lambda value was determined using 5-fold cross-validation to filter for filter LASSO genes. The support vector machine recursive feature elimination (SVM-RFE) was performed using the R package \"caret\" (v 6.0\u0026ndash;93) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=caret\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=caret\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Under the parameter \"method =svmRadial\", the optimal SVM-RFE genes were selected. The R package \"Boruta\" (v 8.0.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gitlab.com/mbq/Boruta/\u003c/span\u003e\u003cspan address=\"https://gitlab.com/mbq/Boruta/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to extract Boruta genes from the DEGs by creating shadow features. A XGBoost model based on the DEGs was constructed by the R package \"xgboost\" (v 1.7.8.1) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/dmlc/xgboost\u003c/span\u003e\u003cspan address=\"https://github.com/dmlc/xgboost\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the DEGs corresponding to the optimal model were selected as the XGBoost characterized genes. Random forest (RF) classification model based on the DEGs was built using the R package \"randomForest\" (v 4.7\u0026ndash;1.2)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The corresponding genes were obtained as RF genes by selecting the ntree value that minimised the out-of-bag error of the model in the range from 1 to 100 with a step size of 1. Finally, the intersection genes among the five sets of genes were obtained using the R package \"VennDiagram\" (v 1.7.3)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e and were defined as the characterized genes for further study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Assessment of the diagnostic value and expression levels of characterized genes\u003c/h2\u003e \u003cp\u003eCharacterized genes were assessed using the R package \"pROC\" (v 1.18.0)\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e in all samples from the Treat and Control groups. To assess the ability of these characterized genes to discriminate between samples from the treat and control groups, ROC curves were plotted. AUC values were calculated and genes with AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.8 were designated as candidate key genes. To further validate the differences in the expression of candidate key genes between the Treat and Control groups, Wilcoxon rank sum test was used to assess the significant differences in their expression levels (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Candidate key genes with significant differences between the 2 groups were defined as key genes. Box plots were then produced using the \"ggplot2\" package (v 3.4.4)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Developing and assessing a nomogram\u003c/h2\u003e \u003cp\u003eA nomogram model was developed using the \"rms\" package (v 6.8.1) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=rms\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=rms\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) based on key genes to determine the probability of developing rectal cancer after receiving chemoradiotherapy in all samples from the Treat and Control groups. To evaluate the accuracy of the nomogram, calibration curves were plotted using the R package \"ResourceSelection\" (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/psolymos/ResourceSelection\u003c/span\u003e\u003cspan address=\"https://github.com/psolymos/ResourceSelection\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Finally, decision curve analysis (DCA) of key genes and nomogram was conducted using the R package \"rms\" (v 6.8.1) (a net gain above 0 indicated good model predictions).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 GSEA\u003c/h2\u003e \u003cp\u003eGSEA was conducted using the R package \"clusterProfiler\" (v 4.7.1.003)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e to identify pathways associated with key genes. Furthermore, the Spearman correlation analysis was conducted using the \"psych\" package (v 2.4.3)\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e to assess the relationship between the key genes and all the other genes in the Treat and Control groups. The genes were then ranked in ascending order according to the correlation coefficients, and a list of related genes corresponding to each key genes was obtained. Subsequently, a reference gene set, c2.cp.kegg.v7.4.symbols.gmt, was obtained from the MSigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e for GSEA analysis. A |Normalized Enrichment Score (NES)| \u0026gt; 1 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance. The 5 pathways were ranked by P-value using \"enrichplot\" package (v 1.14.2)\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Immune infiltration analysis\u003c/h2\u003e \u003cp\u003eThe immune infiltration scores of 64 immune cell types\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e were calculated for each sample in the Treat and Control groups using the Xcell algorithm. In addition, Wilcoxon test was used to screen for immune infiltrating cells with significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) differences between the Treat and Control groups. Subsequently, Spearman correlation analysis of these differentially expressed immune cells and key genes was carried out using the \"psych\" R package (v 2.2.9)\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e and \"ggplot2\" (v 3.4.4)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e to visualize the analysis results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Molecular Regulatory Network Construction\u003c/h2\u003e \u003cp\u003eThe miRTarBase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/bioproject/\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/bioproject/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the miRWalk database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirwalk.umm.uni-heidelberg.de/\u003c/span\u003e\u003cspan address=\"http://mirwalk.umm.uni-heidelberg.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to predict the target miRNAs of the key genes in order to investigate the molecular regulatory mechanisms of the key genes. The R package \"VennDiagram\" (v 1.7.3)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e was then employed to intersect the miRNAs obtained from the 2 databases, aiming to identify the target miRNAs of key genes. Subsequently, miRNet (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://diana.e-ce.uth.gr/\u003c/span\u003e\u003cspan address=\"https://diana.e-ce.uth.gr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Starbase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/\u003c/span\u003e\u003cspan address=\"https://rnasysu.com/encori/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases were utilized to predict the target lncRNAs of the identified miRNAs. By intersecting the results of these 2 predictions, the target lncRNAs of key genes were determined. Finally, based on the identified target miRNAs, target lncRNAs, and key genes (mRNAs), a lncRNA-miRNA-mRNA regulatory network centered around key genes was constructed using the R package \"Cytoscape\" (v 3.10.2)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Establishment of drug-key genes network\u003c/h2\u003e \u003cp\u003eTo investigate potential therapeutic drugs linked to key genes, we used the Comparative Toxicogenomics Database (CTD, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ctdbase.org/\u003c/span\u003e\u003cspan address=\"http://ctdbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify target drugs for key genes. First, the names of the key genes were entered into the CTD database, and drugs with potential binding relationships with these key genes were identified with the help of database algorithms. After obtaining the prediction results, the binding ability between the predicted drugs and the key genes was ranked according to the binding affinity data provided by the CTD database. For each key gene, the top 20 relevant drugs with the highest binding ability scores were selected. Subsequently, drug-key gene network was constructed using the R package \"Cytoscape\" (v 3.10.2)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted in the R programming environment. The Wilcoxon test was specifically used to conduct comparisons among various groups. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant, unless otherwise specified.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 High-quality sequencing data\u003c/h2\u003e \u003cp\u003eIn this study, gene expression assays were conducted on 14 rectal cancer samples from the Treat group and 12 rectal cancer samples from the Control group. The results from the quality assessment of the sequencing data indicated that all samples in both groups demonstrated good quality characteristics. Specifically, the Q20 values for all samples were above 99.71%, and the Q30 values exceeded 97.58%, which met the established data quality standards (Additional file 3). Further analysis of the density distribution curves and gene expression comparisons revealed that the overall distribution of gene expression was quite similar between the 2 groups. This strongly supports the high quality of the sequencing data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-b). In addition, the distribution and reproducibility of the data between samples were investigated using correlation analysis. The analysis revealed that overall correlation coefficients among the samples exceeded 0.6, demonstrating that gene expression patterns within the groups and replicates were highly similar (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification and GO analysis of DEGs\u003c/h2\u003e \u003cp\u003eFirst, 35 DEGs were identified between the Treat and Control groups. Among them, 11 up-regulated genes/24 down-regulated genes were found in the Treatment group compared to the Control group. The difference in the distribution of DEGs was visualized by a volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) and further confirmed by a density heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), both of which showed significant differences. GO showed the biological processes enriched for 35 DEGs, including 279 biological processes (BP), 62 molecular functions (MF), and 19 cellular components (CC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, Additional files 4\u0026ndash;6). In the BP, DEGs were enriched in phosphatidylcholine metabolic process, axon ensheathment in the central nervous system, phosphatidylcholine biosynthetic process, response to auditory stimulus, and central nervous system myelination; CC was mainly enriched in the hinge region between urothelial plaques of apical plasma membrane, hyaluronan cable, nitric-oxide synthase complex, peroxisomal membrane, and microbody membrane; MF contained vitamin binding, phosphatidyl-N-methylethanolamine N-methyltransferase activity, acylglycerone-phosphate reductase activity, phosphatidylethanolamine N-methyltransferase activity. In addition, the established PPI network existed with 10 isolated proteins, retaining 25 proteins and 28 interaction pairs. For example, GPT2, ATP1A2, and RETNLB had close interactions with other genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 NUDT6 and LINGO1 were identified as characterized genes\u003c/h2\u003e \u003cp\u003eIn this study, 5 machine learning algorithms were applied to explore the characterized genes associated with nCRT in rectal cancer. First, DEGs were simplified by LASSO regression. When the lambda value was taken to be optimal, log (lambda. 1se) = -4.3395, and six LASSO genes were successfully acquired, namely, NUDT6, RBP4, PNPLA8, LINGO1, HLA-K, and HYAL1(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Then, the SVM-RFE method was adopted, and 14 SVM-RFE genes were selected, including NUDT6, NPW, GPT2, PEMT, RPL7P32, DHRS7B, MTARC1, HLA-K, HES5, LINGO1, PNPLA8, RBP4, SNX9, and MAL (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Meanwhile, 14 Boruta genes were identified by the Boruta algorithm, namely, NPW, NUDT6, RASD1, PNPLA8, GPT2, LINGO1, PEMT, HLA-K, DHRS7B, MTARC1, HES5, SNX9, RPL7P32, and MAL (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). In addition, the XGBoost algorithm was utilized to construct a model based on DEGs. It was found that the model was optimized when the first 6 DEGs were selected. The six XGBoost genes selected at this time were NUDT6, NPW, LINGO1, MTARC1, MUC17, and AQP5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Finally, the RF algorithm revealed the importance of genes with the help of the relationship between the error rate and the classification tree. Under the condition of ntree of 93, the top 10 DEGs in terms of importance were identified as RF genes, which were NUDT6, NPW, LINGO1, DHRS7B, GPT2, HLA-K, RPL7P32, MAL, HES5, and ATP1A2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Subsequently, the genes obtained from the above 5 machine learning algorithms were cross-analyzed, and it was finally determined that NUDT6 and LINGO1 could be used as characterized genes associated with rectal cancer nCRT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Assessment of the diagnostic value and expression levels of NUDT6 and LINGO1\u003c/h2\u003e \u003cp\u003eROC analysis showed that the AUC values of NUDT6 and LINGO1 exceeded 0.8 in all samples in the Treat and Control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The results showed that NUDT6 and LINGO1 demonstrated satisfactory ability in differentiating rectal cancer samples before and after receiving nCRT, suggesting that they had potential diagnostic value. Further validation of expression levels revealed a significant difference in the expression of NUDT6 and LINGO1 between the Treat and Control groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with both genes showing down-regulation in the Treat group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-c). Consequently, NUDT6 and LINGO1 were identified as key genes in this context.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5 Construction and evaluation of a nomogram model with superior predictive power based on 2 key genes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA nomogram was created based on 2 genes, NUDT6 and LINGO1, to predict the incidence of rectal cancer after receiving nCRT (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The calibration curve showed that the difference between the actual risk of developing rectal cancer after receiving nCRT and the predicted risk of the nomogram was small (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). In addition, DCA showed that the nomogram had a high clinical benefit and was superior in predictive efficacy to NUDT6 or LINGO1 alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Crucial functional pathways and differential immune microenvironment\u003c/h2\u003e \u003cp\u003eGSEA revealed that NUDT6 was significantly enriched in 84 KEGG pathways, particularly in the ribosome, oxidative phosphorylation, Huntington's disease pathways, focal adhesion, and Extracellular Matrix (ECM)-receptor interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, Additional file 7). Similarly, LINGO1 was also enriched in 86 KEGG pathways, with notable enrichment in the proteasome, systemic lupus erythematosus, cell cycle, cytokine receptor interaction, and DNA replication pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, Additional file 8). These findings offer valuable insights into the potential mechanisms by which nCRT may affect the pathogenesis of rectal cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe tumor microenvironment plays a crucial role in the diagnosis, survival, and treatment response of malignant tumors. To investigate how key genes affect the progression of rectal cancer following nCRT, we analyzed the relationship between the expression of these key genes and immune cell infiltration in the tumor The heatmap showed the distribution of immune scores for 64 immune cell types in the Treat and Control groups, with varying degrees of infiltration for each immune cell type (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). A comparison of immune cell infiltration between the Treatment and Control groups revealed significant differences in the levels of erythrocytes, megakaryocyte-erythroid progenitors (MEPs), myocytes, and T helper cell 1 (Th1 cells) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). In the Treat group, Erythrocytes, MEP, and Th1 cells were significantly lower than in the Control group, while Myocytes were significantly higher than in the Control group. Additionally, the relationship between immune cells and key genes (NUDT6 and LINGO1) was further investigated. The findings revealed a significant positive correlation between LINGO1 and erythrocytes (R\u0026thinsp;=\u0026thinsp;0.63, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). NUDT6 showed a significant positive correlation with megakaryocyte-erythroid progenitors (MEP) (R\u0026thinsp;=\u0026thinsp;0.50, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Th1 cells (R\u0026thinsp;=\u0026thinsp;0.50, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, NUDT6 exhibited a significant negative correlation with myocytes (R = -0.55, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7 The lncRNA-miRNA-mRNA network and drug prediction based on key genes\u003c/h2\u003e \u003cp\u003eA sum of 3 target miRNAs and 38 target lncRNAs of LINGO1 were predicted, and a lncRNA-miRNA-mRNA regulatory network with 3 miRNAs, 1 mRNA, 38 lncRNAs, and 42 interaction pairs was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). In addition, drugs targeting NUDT6 and LINGO1 were predicted based on the CTD database, including 75 drugs targeting LINGO1 and 66 drugs targeting NUDT6. Notably, the drug with the highest correlation with LINGO1 was bisphenol A, while valproic acid had the strongest correlation with NUDT6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb, Additional files 9\u0026ndash;10). It was hypothesized that these bisphenol A and valproic acid drugs could be potential drugs for nCRT-targeted therapy in rectal cancer patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eRectal cancer is a common malignant tumor of the colorectal region, posing significant challenges in clinical management due to its complex pathophysiology and variable treatment responses\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Standard treatment methods typically involve neoadjuvant chemoradiotherapy (nCRT), aimed at reducing tumor size and improving surgical outcomes\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. However, the efficacy of nCRT can vary greatly among patients, necessitating the identification of reliable biomarkers to predict treatment responses and guide therapeutic strategies\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Understanding the molecular basis of cancer, particularly in the context of nCRT, is crucial for developing targeted interventions and improving patient prognosis\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study collected samples from 26 rectal cancer patients (Treat group and Control group) and utilized transcriptome sequencing technology and bioinformatics methods to explore key genes and their enriched pathways associated with neoadjuvant chemoradiotherapy in rectal cancer. Ultimately, NUDT6 and LINGO1 were identified as key genes that can predict nCRT association in rectal cancer patients. Further analysis examined the biological pathways involving these key genes and their correlation with immune cells, exploring the potential mechanisms by which nCRT affects the onset of rectal cancer and predicting potential therapeutic drugs, providing new reference points for the precise diagnosis and personalized treatment of rectal cancer.\u003c/p\u003e \u003cp\u003eNUDT6(Nudix-type motif 6) has been identified as a fibroblast growth factor 2 (FGF2) antisense gene, playing a significant role in regulating FGF2 expression. NUDT6 is implicated in various cellular processes, including cell proliferation and differentiation. Studies have demonstrated that NUDT6 can influence the stability and expression of FGF2 mRNA, thereby modulating its biological activity\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In the context of rectal cancer, NUDT6's role in cell proliferation is particularly relevant. Enhanced expression of NUDT6 has been associated with increased cell proliferation, which may contribute to tumor growth and resistance to therapy. Furthermore, NUDT6 has been shown to interact with key signaling pathways such as the extracellular signal-regulated kinase (ERK) and p38MAPK pathways, which are crucial for cell survival and proliferation\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. These interactions suggest that NUDT6 could be a potential target for therapeutic intervention, particularly in enhancing the efficacy of chemoradiotherapy by modulating its expression or function. Additionally, NUDT6's involvement in the regulation of FGF2 indicates that it could play a role in the tumor microenvironment, influencing processes such as angiogenesis and immune cell infiltration, which are critical for tumor progression and response to treatment\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLINGO1, or Leucine-rich repeat and Ig domain-containing Nogo receptor-interacting protein 1, is a transmembrane protein that plays a crucial role in the central nervous system, particularly in neuronal survival, axonal regeneration, and oligodendrocyte maturation. It has been implicated in various neurological disorders, including Parkinson's disease and essential tremor, due to its regulatory effects on neuronal pathways\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. In the context of rectal cancer, LINGO1's role in modulating immune responses and cellular signaling pathways is of particular interest. Studies have shown that LINGO1 can influence the activity of large conductance Ca\u003csup\u003e2+\u003c/sup\u003e-activated K\u003csup\u003e+\u003c/sup\u003e (BK) channels, which are involved in regulating cell proliferation and apoptosis\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. This regulatory function suggests that LINGO1 could impact tumor growth and response to therapy by modulating these pathways. Additionally, LINGO1's involvement in the immune system, particularly in the regulation of T-cell responses, highlights its potential role in the tumor microenvironment and immune evasion mechanisms\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Given these multifaceted roles, targeting LINGO1 could provide a novel approach to enhancing the efficacy of chemoradiotherapy in rectal cancer by modulating immune responses and cellular signaling pathways.\u003c/p\u003e \u003cp\u003eIn our study, we identified NUDT6 and LINGO1 as critical genes associated with the response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer. The enrichment analysis revealed that NUDT6 and LINGO1 are involved in several key pathways that could elucidate their roles in rectal cancer progression and treatment response. NUDT6, a member of the Nudix hydrolase family, is downregulated by green tea catechins, which suppress its proliferative activity in colorectal cancer cells\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Additionally, NUDT6 has been implicated in angiogenesis regulation, as knockdown of PROX1, a transcription factor, leads to upregulation of angiogenic factors including NUDT6, suggesting its role in tumor vascularization\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLINGO1, on the other hand, has been identified as a prognostic gene in various cancers. In glioblastoma, LINGO1 was found to be upregulated and associated with poor prognosis, indicating its potential role in tumor aggressiveness\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Furthermore, LINGO1 has been explored as a target for chimeric antigen receptor (CAR) T cell therapy in Ewing sarcoma, highlighting its relevance as a tumor-specific antigen\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. The cell surface proteome analysis of Ewing sarcoma revealed LINGO1 as a unique therapeutic target, emphasizing its potential in targeted cancer therapies\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe involvement of these genes in multiple cancer-related pathways, including those regulating cell proliferation, angiogenesis, and immune response, underscores their significance in rectal cancer. The good diagnostic performance of NUDT6 and LINGO1, with AUC values exceeding 0.8, further validates their potential as biomarkers for assessing nCRT response. The construction of a nomogram model based on these genes offers a robust tool for predicting rectal cancer recurrence post-nCRT, providing a valuable asset for personalized treatment strategies.\u003c/p\u003e \u003cp\u003eIn summary, our findings not only highlight the importance of NUDT6 and LINGO1 in rectal cancer but also open new avenues for therapeutic interventions targeting these pathways. The integration of gene expression analysis with machine learning and immune profiling presents a comprehensive approach to understanding and combating rectal cancer.\u003c/p\u003e \u003cp\u003eIn our study, the immune cell infiltration analysis revealed significant differences between the treatment group (Treat) and the control group (Control), particularly in the levels of erythrocytes, megakaryocyte-erythroid progenitors (MEP), myocytes, and T helper 1 (Th1) cells. The observed decrease in erythrocytes and MEP in the treatment group aligns with previous research indicating that neoadjuvant chemoradiotherapy (nCRT) can alter the tumor microenvironment, potentially reducing the overall immune cell infiltration\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Additionally, the significant reduction in Th1 cells in the treatment group suggests a suppression of the immune response, which is crucial for anti-tumor immunity. Th1 cells are known for their role in producing interferon-gamma (IFN-γ), which activates macrophages and enhances the cytotoxic activities of natural killer (NK) cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConversely, the increase in myocytes in the treatment group may indicate a tissue remodeling process post-nCRT, which has been observed in other studies where radiochemotherapy induced changes in the tumor stroma and surrounding tissues\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. The correlation of LINGO1 with erythrocytes and NUDT6 with MEP and Th1 cells further underscores the potential regulatory roles these genes play in modulating the immune microenvironment in response to nCRT. For instance, the positive correlation of NUDT6 with MEP and Th1 cells suggests that this gene might be involved in promoting the differentiation and proliferation of these immune cells, thereby influencing the overall immune landscape\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese findings are significant as they highlight the impact of nCRT on the immune cell composition within the tumor microenvironment, which could have implications for the efficacy of immunotherapies. The modulation of immune cell infiltration by nCRT indicates a potential window for combining immunotherapy with nCRT to enhance anti-tumor immune responses. Moreover, understanding the molecular mechanisms by which NUDT6 and LINGO1 influence immune cell dynamics could provide new therapeutic targets to improve treatment outcomes for rectal cancer patients undergoing nCRT\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003e This study identifies NUDT6 and LINGO1 as novel biomarkers for predicting the efficacy of neoadjuvant chemoradiotherapy (nCRT) in rectal cancer for the first time through transcriptome analysis and clinically applicable nomograms. These findings not only reveal the association between key signaling pathways and immune regulation but also provide potential intervention targets for precise tumor treatment strategies. However, the study has certain limitations: a single-center small sample size (n\u0026thinsp;=\u0026thinsp;26), insufficient adjustment for clinical covariates, and the research on drug-gene interaction mechanisms is still in its early stages. To advance subsequent research, it is recommended to focus on the following tasks: (1) validating the universality of the biomarkers using multi-center large sample cohorts; (2) elucidating the molecular mechanisms of NUDT6/LINGO1 in nCRT resistance/sensitivity through functional experiments such as CRISPR screening or patient-derived organoids; (3) conducting translational medicine research to integrate biomarkers with emerging therapies like immunotherapy in prospective clinical trials. By addressing these key issues, it is expected that the exploratory findings of this study can be transformed into clinically valuable diagnostic and therapeutic tools.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Medical Ethics Committee of the Sichuan Provincial People's Hospital (Ethics Approval Number: [Ethics ID: 2022\u0026thinsp;\u0026minus;\u0026thinsp;443], Approval Date: November 28, 2022). Written informed consent was obtained from individual or guardian participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eSupported by Sichuan Science and Technology Program (No.2022YFS0223 to Zhou Yang).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe authors\u0026rsquo; contributions are as follows\u0026mdash;Zhou Yang and Qian Wang contributed to the umbrella review design. All authors conducted the literature search, extracted the data, and performed the analysis. Qian Wang wrote the first draft of the manuscript and Zhou Yang is the guarantor of the article. All authors wrote and reviewed the manuscript, and all authors read and approved the final manuscript. Qian Wang and Guangpeng Shen contributed equally to this work.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the NCBI Sequence Read Archive (SRA) repository, [http://www.ncbi.nlm.nih.gov/bioproject/1423203, Accession: PRJNA1423203].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, R. L. et al. Cancer statistics, 2022. CA: A Cancer. \u003cem\u003eJ. Clin.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 7\u0026ndash;33 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeng, H. et al. Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer. \u003cem\u003eCancer Cell.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 124\u0026ndash;138e6 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosset, J-F. et al. Chemotherapy with preoperative radiotherapy in rectal cancer. \u003cem\u003eN. Engl. J. Med.\u003c/em\u003e \u003cb\u003e355\u003c/b\u003e, 1114\u0026ndash;1123 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeller, D. S. et al. The multidisciplinary management of rectal cancer. \u003cem\u003eNat. Rev. Gastroenterol. Hepatol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 414\u0026ndash;429 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen, L., Han, Z. \u0026amp; Du, Y. Identification of gene biomarkers and immune cell infiltration characteristics in rectal cancer. Journal of Gastrointestinal Oncology; 12. Epub ahead of print June 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/jgo-21-255\u003c/span\u003e\u003cspan address=\"10.21037/jgo-21-255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, K. et al. Development and validation of a novel hypoxia score for predicting prognosis and immune microenvironment in rectal cancer. \u003cem\u003eFront. Surg.\u003c/em\u003e ; \u003cb\u003e9\u003c/b\u003e. Epub ahead of print 25 April 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fsurg.2022.881554\u003c/span\u003e\u003cspan address=\"10.3389/fsurg.2022.881554\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y. et al. Neoadjuvant chemoradiotherapy combined with immunotherapy for locally advanced rectal cancer: A new era for anal preservation. \u003cem\u003eFront. Immunol.\u003c/em\u003e ; \u003cb\u003e13\u003c/b\u003e. Epub ahead of print 8 December 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2022.1067036\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.1067036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGambacorta, M. A. et al. Timing to achieve the highest rate of pCR after preoperative radiochemotherapy in rectal cancer: A pooled analysis of 3085 patients from 7 randomized trials. \u003cem\u003eRadiother. Oncol.\u003c/em\u003e \u003cb\u003e154\u003c/b\u003e, 154\u0026ndash;160 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmejc, M. \u0026amp; Potisek, M. Prognostic significance of tumor regression in locally advanced rectal cancer after preoperative radiochemotherapy. \u003cem\u003eRadiol. Oncol.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e, 30\u0026ndash;35 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, H. et al. Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy in patients with rectal cancer. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 7802 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, P. et al. Identification of hub genes and potential molecular mechanisms related to radiotherapy sensitivity in rectal cancer based on multiple datasets. \u003cem\u003eJ. Translational Med.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 176 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove, M. I., Huber, W. \u0026amp; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 550 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIto, K. \u0026amp; Murphy, D. Application of ggplot2 to pharmacometric graphics. \u003cem\u003eCPT: Pharmacometrics Syst. Pharmacol.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 79 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu, Z., Eils, R. \u0026amp; Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 2847\u0026ndash;2849 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. \u003cem\u003eInnov.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 100141 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. \u003cem\u003eGenome Res.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 2498\u0026ndash;2504 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim, J. et al. Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis. \u003cem\u003eBMC Complement. Med. Ther.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 409 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, H. \u0026amp; Boutros, P. C. VennDiagram: A package for the generation of highly-customizable venn and euler diagrams in R. \u003cem\u003eBMC Bioinform.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 35 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobin, X. et al. pROC: An open-source package for R and S\u0026thinsp;+\u0026thinsp;to analyze and compare ROC curves. \u003cem\u003eBMC Bioinform.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 77 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhan, Z-Q. et al. Gastroesophageal reflux disease with 6 neurodegenerative and psychiatric disorders: Genetic correlations, causality, and potential molecular mechanisms. \u003cem\u003eJ. Psychiatr. Res.\u003c/em\u003e \u003cb\u003e172\u003c/b\u003e, 244\u0026ndash;253 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, L., Hua, J. \u0026amp; He, X. Bioinformatics analysis identifies a key gene HLA_DPA1 in severe influenza-associated immune infiltration. \u003cem\u003eBMC Genom.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 257 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, C. et al. Genome-wide mutation profiling and related risk signature for prognosis of papillary renal cell carcinoma. \u003cem\u003eAnnals Translational Med.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 427\u0026ndash;427 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, K. et al. Bioinformatics analysis identifies immune-related gene signatures and subtypes in diabetic nephropathy. Front Endocrinol; 13. Epub ahead of print 7 December 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2022.1048139\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2022.1048139\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaidmamatov, O. et al. Translation and adaptation of the adult developmental coordination disorder/dyspraxia checklist (ADC) into asian uzbekistan. \u003cem\u003eSports\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 135 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei, F-Z. et al. Development and validation of a nomogram and a comprehensive prognostic analysis of an LncRNA-associated competitive endogenous RNA network based on immune-related genes for locally advanced rectal cancer with neoadjuvant therapy. Front Oncol; 11. Epub ahead of print 19 July 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2021.697948\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2021.697948\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShu, P. et al. An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence. \u003cem\u003eFront. Oncol.\u003c/em\u003e ; \u003cb\u003e14\u003c/b\u003e. Epub ahead of print 9 February 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2024.1294440\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2024.1294440\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi, D. et al. Somatic mutations and immune alternation in rectal cancer following neoadjuvant chemoradiotherapy. \u003cem\u003eCancer Immunol. Res.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 1401\u0026ndash;1416 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSukhthankar, M. et al. A potential proliferative gene, NUDT6, is down-regulated by green tea catechins at the posttranscriptional level. \u003cem\u003eJ. Nutr. Biochem.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 98\u0026ndash;106 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaguma-Nibasheka, M., MacFarlane, L. A. \u0026amp; Murphy, P. R. Regulation of fibroblast growth factor-2 expression and cell cycle progression by an endogenous antisense RNA. \u003cem\u003eGenes\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 505\u0026ndash;520 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinter, H. et al. Targeting long non-coding RNA NUDT6 enhances smooth muscle cell survival and limits vascular disease progression. \u003cem\u003eMol. Ther.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 1775\u0026ndash;1790 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStefansson, H. et al. Variant in the sequence of the LINGO1 gene confers risk of essential tremor. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 277\u0026ndash;279 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDudem, S. et al. LINGO1 is a regulatory subunit of large conductance, Ca2+-activated potassium channels. Proceedings of the National Academy of Sciences. ; 117: 2194\u0026ndash;2200. (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVilari\u0026ntilde;o-G\u0026uuml;ell, C. et al. LINGO1 and LINGO2 variants are associated with essential tremor and parkinson disease. \u003cem\u003eNeurogenetics\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 401\u0026ndash;408 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRudzińska, M. et al. Transcription factor prospero homeobox 1 (PROX1) as a potential angiogenic regulator of follicular thyroid cancer dissemination. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 5619 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, S., Xu, Y. \u0026amp; Zhang, S. LINGO1, C7orf31 and VEGFA are prognostic genes of primary glioblastoma: Analysis of gene expression microarray. neo. ; 65: 532\u0026ndash;541. (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorales, E. A. et al. Restricting CAR T cell trafficking expands targetable antigen space. Epub ahead of print 11 February 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2024.02.08.579002\u003c/span\u003e\u003cspan address=\"10.1101/2024.02.08.579002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTown, J. et al. Exploring the surfaceome of ewing sarcoma identifies a new and unique therapeutic target. Proceedings of the National Academy of Sciences. ; 113: 3603\u0026ndash;3608. (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJarosch, A. et al. Neoadjuvant radiochemotherapy decreases the total amount of tumor infiltrating lymphocytes, but increases the number of CD8+/granzyme B+ (GrzB) cytotoxic T-cells in rectal cancer. \u003cem\u003eOncoimmunology\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, e1393133 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026auml;ster, S. et al. High frequency of CD8 positive lymphocyte infiltration correlates with lack of lymph node involvement in early rectal cancer. Disease Markers. ; 2014: 792183. (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWagner, F. et al. Neoadjuvant radiochemotherapy significantly alters the phenotype of plasmacytoid dendritic cells and 6-sulfo LacNAc+ monocytes in rectal cancer. Front Immunol; 10. Epub ahead of print 29 March 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2019.00602\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2019.00602\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, L. et al. Identification of four immune subtypes in locally advanced rectal cancer treated with neoadjuvant chemotherapy for predicting the efficacy of subsequent immune checkpoint blockade. \u003cem\u003eFront. Immunol.\u003c/em\u003e ; \u003cb\u003e13\u003c/b\u003e. Epub ahead of print 27 September 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2022.955187\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.955187\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoukourakis, I. M. et al. Immune response and immune checkpoint molecules in patients with rectal cancer undergoing neoadjuvant chemoradiotherapy: A review. \u003cem\u003eCurr. Issues. Mol. Biol.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e, 4495\u0026ndash;4517 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M. et al. KEGG: Biological systems database as a model of the real world. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, D672\u0026ndash;D677 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M. \u0026amp; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 27\u0026ndash;30 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M. Toward understanding the origin and evolution of cellular organisms. \u003cem\u003eProtein Sci.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 1947\u0026ndash;1951 (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rectal cancer, Neoadjuvant chemoradiotherapy, High-throughput sequencing, Machine learning algorithms, Immune-related","lastPublishedDoi":"10.21203/rs.3.rs-8771996/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8771996/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRectal cancer is a highly prevalent cancer worldwide and a common cause of cancer death. Neoadjuvant chemoradiotherapy (nCRT) is the first choice for advanced rectal cancer. In this study, we used bioinformatics approaches to explore key genes affected by nCRT in rectal cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 26 samples of rectal cancer patients were collected and divided into the Treatment group (14 patients who received nCRT) and the Control group (12 patients who did not receive nCRT). Key genes were selected by high-throughput sequencing, differential expression analysis, machine learning algorithms, receiver operating characteristic (ROC) curves, and gene expression analysis. Gene Set Enrichment Analysis (GSEA) was used to trace the enrichment pathways of these key genes. Additionally, the relationship between immune cells and these key genes was explored. A nomogram and molecular regulatory network were constructed based on the selected key genes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe quality of sequencing data was high for all samples. Overall, 35 differentially expressed genes (DEGs) were discovered. Among them, NUDT6 and LINGO1 had excellent predictive values (both with area under the curve (AUC)\u0026thinsp;\u0026gt;\u0026thinsp;0.8) and were identified as key genes. A nomogram with good diagnostic performance was constructed. NUDT6 was significantly enriched in the ribosome and oxidative phosphorylation pathways, and was significantly positively correlated with Megakaryocyte-Erythroid Progenitor (MEP) and T helper cell 1 (Th1 cells), and significantly negatively correlated with Myocyte cells. LINGO1 was significantly enriched in the proteasome pathway and significantly positively correlated with Erythrocytes. Additionally, drug prediction analyses indicated that valproic acid was most highly associated with NUDT6, while bisphenol A is most closely linked to LINGO1.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn this study, the NUDT6 and LINGO1 genes were identified as key genes related to nCRT in rectal cancer. These genes might significantly influence the sensitivity of rectal cancer to nCRT, and the findings could provide valuable insights for developing personalized treatment strategies.\u003c/p\u003e","manuscriptTitle":"Identification of NUDT6 and LINGO1 as key genes for predicting response to neoadjuvant chemoradiotherapy in rectal cancer patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 17:34:41","doi":"10.21203/rs.3.rs-8771996/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-31T12:48:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T02:47:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210675880233107436910933347097124765558","date":"2026-03-31T02:18:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T10:21:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177034564267034254928747862841130487838","date":"2026-03-08T10:47:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-16T23:30:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T23:27:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-16T15:59:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-13T05:20:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-13T05:16:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9375c646-8101-4634-abab-97f8fdc1c202","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":63035668,"name":"Health sciences/Biomarkers"},{"id":63035669,"name":"Biological sciences/Cancer"},{"id":63035670,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":63035671,"name":"Biological sciences/Genetics"},{"id":63035672,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-31T12:56:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 17:34:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8771996","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8771996","identity":"rs-8771996","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}