A Multigene Model for Predicting Tumor Responsiveness After Preoperative Chemoradiotherapy for Rectal Cancer

GNB4 Silencing Promotes Pyroptosis to Inhibit the Development of Glioma by Activating cGAS-STING Pathway

The induction of immunogenic cell death is a promising therapeutic option for gliomas. Pyroptosis is a type of programmed immunogenic cell death and its role in gliomas remains unclear. Differentially expressed genes (DEGs) were obtained from GSE4290 and GSE31262 datasets. Hub genes were screened from protein-protein interaction networks and assessed using principal component analysis and receiver operating characteristic curves. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to measure the mRNA expression of hub genes. Pyroptosis and pathway-related proteins were assessed using western blotting. Inflammatory factor levels were determined using enzyme-linked immunosorbent assay. The effect of guanine nucleotide-binding protein-4 (GNB4) on proliferation, migration, and invasion was evaluated using a cell viability test kit and wound-healing and transwell assays. In total, 202 DEGs were identified. Among them, F2R, GNG4, GNG3, PRKCB, and GNB4 were identified as hub genes in gliomas after comprehensive bioinformatics analysis. GNB4 was significantly upregulated in glioma cells compared to normal brain glial cells. Silencing GNB4 significantly inhibited proliferation, invasion, and migration of glioma cells. The expression of pyroptosis-related proteins increased after GNB4 silencing, with elevated levels of inflammatory factors. Pyroptosis inhibitors reversed the inhibitory effects of GNB4 silencing on cell proliferation, migration, and invasion. Additionally, GNB4 silencing activated the cGAS-STING pathway. Treatment with a cGAS-STING pathway inhibitor reversed the inhibition of proliferation, migration, and invasion while downregulating the expression of pyroptosis-related proteins. Silencing GNB4 promotes pyroptosis and thus inhibits the proliferation, migration, and invasion of glioma cells by activating the cGAS-STING pathway, which is a promising biomarker and therapeutic target for glioma.

The role of BCL2L13 in glioblastoma: turning a need into a target

Glioblastoma (GBM) is the most common aggressive central nervous system cancer. GBM has a high mortality rate, with a median survival time of 12-15 months after diagnosis. A poor prognosis and a shorter life expectancy may result from resistance to standard treatments such as radiation and chemotherapy. Temozolomide has been the mainstay treatment for GBM, but unfortunately, there are high rates of resistance with GBM bypassing apoptosis. A proposed mechanism for bypassing apoptosis is decreased ceramide levels, and previous research has shown that within GBM cells, B cell lymphoma 2-like 13 (BCL2L13) can inhibit ceramide synthase. This review aims to discuss the causes of resistance in GBM cells, followed by a brief description of BCL2L13 and an explanation of its mechanism of action. Further, lipids, specifically ceramide, will be discussed concerning cancer and GBM cells, focusing on ceramide synthase and its role in developing GBM. By gathering all current information on BCL2L13 and ceramide synthase, this review seeks to enable an understanding of these pieces of GBM in the hope of finding an effective treatment for this disease.

Validated Pretreatment Prediction Models for Response to Neoadjuvant Therapy in Patients with Rectal Cancer: A Systematic Review and Critical Appraisal

Pretreatment response prediction is crucial to select those patients with rectal cancer who will benefit from organ preservation strategies following (intensified) neoadjuvant therapy and to avoid unnecessary toxicity in those who will not. The combination of individual predictors in multivariable prediction models might improve predictive accuracy. The aim of this systematic review was to summarize and critically appraise validated pretreatment prediction models (other than radiomics-based models or image-based deep learning models) for response to neoadjuvant therapy in patients with rectal cancer and provide evidence-based recommendations for future research. MEDLINE via Ovid, Embase.com, and Scopus were searched for eligible studies published up to November 2022. A total of 5006 studies were screened and 16 were included for data extraction and risk of bias assessment using Prediction model Risk Of Bias Assessment Tool (PROBAST). All selected models were unique and grouped into five predictor categories: clinical, combined, genetics, metabolites, and pathology. Studies generally included patients with intermediate or advanced tumor stages who were treated with neoadjuvant chemoradiotherapy. Evaluated outcomes were pathological complete response and pathological tumor response. All studies were considered to have a high risk of bias and none of the models were externally validated in an independent study. Discriminative performances, estimated with the area under the curve (AUC), ranged per predictor category from 0.60 to 0.70 (clinical), 0.78 to 0.81 (combined), 0.66 to 0.91 (genetics), 0.54 to 0.80 (metabolites), and 0.71 to 0.91 (pathology). Model calibration outcomes were reported in five studies. Two collagen feature-based models showed the best predictive performance (AUCs 0.83-0.91 and good calibration). In conclusion, some pretreatment models for response prediction in rectal cancer show encouraging predictive potential but, given the high risk of bias in these studies, their value should be evaluated in future, well-designed studies.

Tumor suppressor circPDE4D inhibits the progression of colorectal cancer and regulates oxaliplatin chemoresistance

Colorectal cancer (CRC) is one of the most common cancers worldwide, and it frequently develops resistance to chemotherapy. It was discovered that circular RNAs, which function as microRNA sponges, are involved in the pathogenesis of many cancers. This study aimed to investigate the biological functions of a circRNA derived from phosphodiesterase 4D (circPDE4D, hsa_circ_0072568) and its potential mechanism in oxaliplatin-resistant CRC. CircPDE4D expression were validated in human CRC cell lines and tissues. CircPDE4D siRNAs (si-circPDE4D) and LV003-circPDE4D plasmid were applied to investigate the function of circPDE4D. A quantitative real-time polymerase chain reaction was used to detect the levels of circPDE4D, its predicted sponge miRNAs, and their target genes. Cell proliferation was assessed by MTS（3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium） assay. Cell migration and invasion capacity were evaluated by transwell assay. Terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling was used to stain apoptotic cells. The results showed that circPDE4D expression was downregulated in CRC cells and tissues. Transfection with si-circPDE4D promoted cell proliferation, migration, and invasion, and inhibited apoptosis in DLD1 cells. Transfection with LV003-circPDE4D showed the opposite effect. Besides, circPDE4D presented higher expression in HCT116/L cells than that in HCT116 cells. Si-circPDE4D or lv003-circPDE4D transfection increased or decreased cell proliferationin in both two cells. Moreover, si-circPDE4D transfection inhibited cell apoptosis, while LV003-circPDE4D induced apoptosis in HCT116/L cells. LV003-CircPDE4D reduced hsa-miR-569 expression while increasing SPI1 expression in HCT116/L. CircPDE4D could inhibit tumorigenesis and progression of both CRC and oxaliplatin-resistant CRC, providing insight for the development of therapeutic strategies.

Clinical Significance of NKD Inhibitor of WNT Signaling Pathway 1 (NKD1) in Glioblastoma

Introduction. As the most malignant type of gliomas, glioblastoma is characterized with disappointing prognosis. Here, we aimed to investigate expression and function of NKD inhibitor of Wnt signaling pathway 1 (NKD1), an antagonist of Wnt-beta-catenin signaling pathways, in glioblastoma. Methods. The mRNA level of NKD1 was firstly retrieved from TCGA glioma dataset to evaluate its correlation with clinical characteristics and its value in prognosis prediction. Then, its protein expression level in glioblastoma was tested by immunohistochemistry staining in a retrospectively cohort collected from our medical center (n = 66). Univariate and multivariate survival analyses were conducted to assess its effect on glioma prognosis. Two glioblastoma cell lines, U87 and U251, were used to further investigate the tumor-related role of NKD1 through overexpression strategy in combination with cell proliferation assays. Immune cell enrichment in glioblastoma and its correlation with NKD1 level was finally assessed using bioinformatics analyses. Results. NKD1 shows a lower expression level in glioblastoma compared to that in the normal brain or other glioma subtypes, which is independently correlated to a worse prognosis in both the TCGA cohort and our retrospective cohort. Overexpressing NKD1 in glioblastoma cell lines can significantly attenuate cell proliferation. In addition, expression of NKD1 in glioblastoma is negatively correlated to the T cell infiltration, indicating it may have crosstalk with the tumor immune microenvironment. Conclusions. NKD1 inhibits glioblastoma progression and its downregulated expression indicates a poor prognosis.

A meta-learning approach to improving radiation response prediction in cancers

PURPOSE

Predicting the efficacy of radiotherapy in individual patients has drawn widespread attention, but the limited sample size remains a bottleneck for utilizing high-dimensional multi-omics data to guide personalized radiotherapy. We hypothesize the recently developed meta-learning framework could address this limitation.

METHODS AND MATERIALS

By combining gene expression, DNA methylation, and clinical data of 806 patients who had received radiotherapy from The Cancer Genome Atlas (TCGA), we applied the Model-Agnostic Meta-Learning (MAML) framework to tasks consisting of pan-cancer data, to obtain the best initial parameters of a neural network for a specific cancer with smaller number of samples. The performance of meta-learning framework was compared with four traditional machine learning methods based on two training schemes, and tested on Cancer Cell Line Encyclopedia (CCLE) and Chinese Glioma Genome Atlas (CGGA) datasets. Moreover, biological significance of the models was investigated by survival analysis and feature interpretation.

RESULTS

The mean AUC (Area under the ROC Curve) [95% confidence interval] of our models across nine cancer types was 0.702 [0.691-0.713], which improved by 0.166 on average over other the four machine learning methods on two training schemes. Our models performed significantly better (p < 0.05) in seven cancer types and performed comparable to the other predictors in the rest of two cancer types. The more pan-cancer samples were used to transfer meta-knowledge, the greater the performance improved (p < 0.05). The predicted response scores that our models generated were negatively correlated with cell radiosensitivity index in four cancer types (p < 0.05), while not statistically significant in the other three cancer types. Moreover, the predicted response scores were shown to be prognostic factors in seven cancer types and eight potential radiosensitivity-related genes were identified.

CONCLUSIONS

For the first time, we established the meta-learning approach to improving individual radiation response prediction by transferring common knowledge from pan-cancer data with MAML framework. The results demonstrated the superiority, generalizability, and biological significance of our approach.

Construction and characterization of rectal cancer-related lncRNA-mRNA ceRNA network reveals prognostic biomarkers in rectal cancer

Rectal cancer is an important cause of cancer‐related deaths worldwide. In this study, the differentially expressed (DE) lncRNAs/mRNAs were first identified and the correlation level between DE lncRNAs and mRNAs were calculated. The results showed that genes of highly correlated lncRNA‐mRNA pairs presented strong prognosis effects, such as GPM6A, METTL24, SCN7A, HAND2‐AS1 and PDZRN4. Then, the rectal cancer‐related lncRNA‐mRNA network was constructed based on the ceRNA theory. Topological analysis of the network revealed that the network was maintained by hub nodes and a hub subnetwork was constructed, including the hub lncRNA MIR143HG and MBNL1‐SA1. Further analysis indicated that the hub subnetwork was highly related to cancer pathways, such as ‘Focal adhesion’ and ‘Wnt signalling pathway’. Hub subnetwork also had significant prognosis capability. A closed lncRNA‐mRNA module was identified by bilateral network clustering. Genes in modules also showed high prognosis effects. Finally, a core lncRNA‐TF crosstalk network was identified to uncover the crosstalk and regulatory mechanisms of lncRNAs and TFs by integrating ceRNA crosstalks and TF binding affinities. Some core genes, such as MEIS1, GLI3 and HAND2‐AS1 were considered as the key regulators in tumourigenesis. Based on the authors’ comprehensive analysis, all these lncRNA‐mRNA crosstalks provided promising clues for biological prognosis of rectal cancer.

A 41-Gene Pair Signature for Predicting the Pathological Response of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiation

Background and Purpose: Pathological response status is a standard reference for the early evaluation of the effect of neoadjuvant chemoradiation (nCRT) on locally advanced rectal cancer (LARC) patients. Various patients respond differently to nCRT, but identifying the pathological response of LARC to nCRT remains a challenge. Therefore, we aimed to identify a signature that can predict the response of LARC to nCRT. Material and Methods: The gene expression profiles of 111 LARC patients receiving fluorouracil-based nCRT were used to obtain gene pairs with within-sample relative expression orderings related to pathological response. These reversal gene pairs were ranked according to the mean decrease Gini index provided by the random forest algorithm to obtain the signature. This signature was verified in two public cohorts of 46 and 42 samples, and a cohort of 33 samples measured at our laboratory. In addition, the signature was used to predict disease-free survival benefits in a series of colorectal cancer datasets. Results: A 41-gene pair signature (41-GPS) was identified in the training cohort with an accuracy of 84.68% and an area under the receiver operating characteristic curve (AUC) of 0.94. In the two public test cohorts, the accuracy was 93.37 and 73.81%, with AUCs of 0.97 and 0.86, respectively. In our dataset, the AUC was 0.80. The results of the survival analysis show that 41-GPS plays an effective role in identifying patients who will respond to nCRT and have a better prognosis. Conclusion: The signature consisting of 41 gene pairs can robustly predict the clinical pathological response of LARC patients to nCRT.

Validation of Gene Expression-Based Predictive Biomarkers for Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

BACKGROUND

Neoadjuvant chemoradiotherapy (nCRT) followed by surgery is widely used for patients with locally advanced rectal cancer. However, response to nCRT varies substantially among patients, highlighting the need for predictive biomarkers that can distinguish non-responsive from responsive patients before nCRT. This study aimed to build novel multi-gene assays for predicting nCRT response, and to validate our signature and previously-reported signatures in multiple independent cohorts.

METHODS

Three microarray datasets of pre-therapeutic biopsies containing a total of 61 non-responders and 53 responders were used as the discovery cohorts to screen for genes that were consistently associated with nCRT response. The predictive values of signatures were tested in a meta-analysis using six independent datasets as the validation cohorts, consisted of a total of 176 non-responders and 99 responders.

RESULTS

We identified four genes, including BRCA1, GPR110, TNIK, and WDR4 in the discovery cohorts. Although our 4-gene signature and nine published signatures were evaluated, they were unable to predict nCRT response in the validation cohorts.

CONCLUSIONS

Although this is one of the largest studies addressing the validity of gene expression-based classifiers using pre-treatment biopsies from patients with rectal cancer, our findings do not support their clinically meaningful values to be predictive of nCRT response.

Improvement in the Assessment of Response to Preoperative Chemoradiotherapy for Rectal Cancer Using Magnetic Resonance Imaging and a Multigene Biomarker

The response to preoperative chemoradiotherapy (PCRT) is correlated with oncologic outcomes in patients with locally advanced rectal cancer. Accurate prediction of PCRT response before surgery can provide crucial information to aid clinicians in further treatment planning. This study aimed to develop an evaluation tool incorporating a genetic biomarker and magnetic resonance imaging (MRI) to improve the assessment of response in post-CRT patients with locally advanced rectal cancer. A total of 198 patients who underwent PCRT followed by surgical resection for locally advanced rectal cancer between 2010 and 2016 were included in this study. Each patient's response prediction index (RPI) score, a multigene biomarker developed in our previous study, and magnetic resonance tumor regression grade (mrTRG) score were added to create a new predictive value for pathologic response after PCRT, called the combined radiation prediction value (cRPV). Based on the new value, 121 and 77 patients were predicted to be good and poor responders, respectively, showing significantly different cRPV values (p = 0.001). With an overall predictive accuracy of 84.8%, cRPV was superior to mrTRG and RPI for the prediction of pathologic chemoradiotherapy response (mrTRG, 69.2%; RPI, 77.3%). In multivariate analysis, cRPV was found to be the sole predictor of tumor response (odds ratio, 32.211; 95% confidence interval, 14.408-72.011; p = 0.001). With its good predictive value for final pathologic regression, cRPV may be a valuable tool for assessing the response to PCRT before surgery.

Serum Apolipoprotein A-I Predicts Response of Rectal Cancer to Neoadjuvant Chemoradiotherapy

Background

Serum lipids have been reported as prognosticators for malignancies, including rectal cancer (RC). Yet, their value in predicting the response of RC to neoadjuvant chemoradiotherapy (NACRT) remains unknown. This study aimed to assess the predictive abilities of serum lipids for a bad response, and to build a serum lipid-based prediction model.

Methods

In total, 751 patients diagnosed with stage cII–III RC and treated with NACRT plus surgery from January 2007 to August 2018 were retrospectively reviewed and randomly divided into two data sets, in a ratio of 1:1. Receiver operating characteristics (ROC) analysis was conducted in the development set to select possible predictors of bad NACRT response from pathoclinical factors, including serum lipids. Multivariate logistic regression was conducted to further determine independent predictors, which were then used to develop a prediction index (PI). Finally, the PI was verified in the validation set, through ROC analysis and chi-squared test.

Results

Five independent predictors were identified: tumor length ≥4 cm, cT4 stage, carcinoembryonic antigen ≥5.0 ng/mL, irradiation with three-dimensional conformal radiotherapy technique, and apolipoprotein A-I ≤1.20 g/L. Each of them was assigned a number of points. In the validation set, the area under the curve of PI appeared as 0.642 (95% confidence interval 0.586–0.697). The sensitivity, specificity, positive and negative predictive values, and concordance were 72.3%, 52.3%, 63.8%, 61.9%, and 63.0%, respectively.

Conclusion

Serum apolipoprotein A-I was found to correlate negatively with the RC response to NACRT. It could serve as a biomarker for guiding individualized treatment and a potential target for improving sensitivity to chemoradiation.

BCL2L13: physiological and pathological meanings

BCL2L13 is a BCL2-like protein. It has been discovered for two decades, now on the way to be a hotspot of research with its physiological and pathological meanings found in recent years. Start with the pro-apoptotic activity, there have been reported consecutively that BCL2L13 could also induce mitochondrial fragmentation, inhibit cell death and promote mitophagy. Similar to BNIP3, BCL2L13 cannot be indiscriminately categorized into pro- or anti-apoptotic proteins. It anchors in the mitochondrial outer membrane, and expresses in various cells and tissues. This article reviews for the first time that BCL2L13 functions in physiological processes, such as growth and development and energy metabolism, and its dysregulation participating in pathological processes, including cancer, bacterial infection, cardiovascular diseases and degenerative diseases, suggesting its important roles in these events.

An In Silico Analysis Identified FZD9 as a Potential Prognostic Biomarker in Triple-Negative Breast Cancer Patients

Objective

Breast cancer (BC) is the main cause of cancer-related deaths in women across the world. It can be classified into different subtypes, including triple-negative (TN), which is characterized by the absence of hormone receptors for estrogen and progesterone and the lack of the human epidermal growth factor receptor 2. These tumors have high heterogeneity, acquire therapeutic resistance, and have no established target-driven treatment yet. The identification of differentially expressed genes in TN breast tumors and the in silico validation of their prognostic role in these tumors.

Materials and Methods

We employed a microarray dataset and, by using the GEO2R tool, we identified a list of differentially expressed genes. The in silico validation was conducted using several online platforms including the KM Plotter, cBioPortal, bc-GenExMiner, Prognoscan, and Roc Plotter.

Results

We observed that FZD9 was among the top differentially expressed genes in a cohort of patients with different TNBC subtypes. The FZD9 expression was significantly different in TN breast tumors than in non-TN (nTN) breast tumors (p<0.0001), and the basal TN subtype showed the highest levels (p<0.0001). In addition, the FZD9 levels were significantly inversely and positively proportional (p<0.0001) to estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 clinical parameters. The high levels of FZD9 were associated with worse overall survival (p=0.007), relapse-free survival (p=5.8e-05), and worse survival in patients who received chemotherapy (p=3.2e-05; 0.007).

Conclusion

Our cumulative results demonstrated that FZD9 plays an important role in TNBC and may be a potential prognostic biomarker. Nevertheless, further in vitro and in vivo assays are necessary to confirm our findings and to strengthen the evidences about the mechanisms by which FZD9 functions in these tumors.

γ-H2AX as a potential indicator of radiosensitivity in colorectal cancer cells

Preoperative radiotherapy improves local disease control and disease-free survival in patients with advanced rectal cancer; however, a reliable predictive biomarker for the effectiveness of irradiation has yet to be elucidated. Phosphorylation of H2A histone family member X (H2AX) to γ-H2AX is induced by DNA double-strand breaks and is associated with the development of colorectal cancer (CRC). The current study aimed to clarify the relationship between γ-H2AX expression and CRC radiosensitivity in vitro and in vivo. H2AX levels were analyzed in datasets obtained from cohort studies and γ-H2AX expression was investigated by performing immunohistochemistry and western blotting using clinical CRC samples from patients without any preoperative therapy. In addition, the CRC cell lines WiDr and DLD-1 were subjected to irradiation and/or small interfering RNA-H2AX, after which the protein levels of γ-H2AX were examined in samples obtained from patients undergoing preoperative chemoradiotherapy. To quantify the observable effect of treatment on cancer cells, outcomes were graded as follows: 1, mild; 2, moderate; and 3, marked, with defined signatures of cellular response. Datasets obtained from cohort studies demonstrated that H2AX mRNA levels were significantly upregulated and associated with distal metastasis and microsatellite instability in CRC tissues, in contrast to that of normal tissues. In addition, γ-H2AX was overexpressed in clinical samples. In vitro, following irradiation, γ-H2AX expression levels increased and cell viability decreased in a time-dependent manner. Combined irradiation and γ-H2AX knockdown reduced the viability of each cell line when compared with irradiation or γ-H2AX knockdown alone. Furthermore, among clinical CRC samples from patients undergoing preoperative chemoradiotherapy, levels of γ-H2AX in the grade 1 group were significantly higher than those in grade 2 or grade 3. In conclusion, γ-H2AX may serve as a novel predictive marker and target for preoperative radiotherapy effectiveness in patients with CRC.

Identification of candidate mediators of chemoresponse in breast cancer through therapy-driven selection of somatic variants

Purpose

More than a third of primary breast cancer patients are treated with cytotoxic chemotherapy, typically without guidance from predictive markers. Increased use of neoadjuvant chemotherapy provides opportunities for identification of molecules associated with treatment response, by comparing matched tumour samples before and after therapy. Our hypothesis was that somatic variants of increased prevalence after therapy promote resistance, while variants with reduced prevalence cause sensitivity.

Methods

We performed systematic analyses of matched pairs of cancer exomes from primary oestrogen receptor-positive/HER2-negative breast cancers (n = 6) treated with neoadjuvant epirubicin/cyclophosphamide. We identified candidate genes as mediators of chemotherapy response by consistent subclonal changes in somatic variant prevalence through therapy, predicted variant impact on gene function, and enrichment of specific functional pathways. Influence of candidate genes on breast cancer outcome was tested using publicly available breast cancer expression data (n = 1903).

Results

We identified 14 genes as the strongest candidate mediators of chemoresponse: TCHH, MUC17, ARAP2, FLG2, ABL1, CENPF, COL6A3, DMBT1, ITGA7, PLXNA1, S100PBP, SYNE1, ZFHX4, and CACNA1C. Genes contained somatic variants showing prevalence changes in up to 4 patients, with up to 3 being predicted as damaging. Genes coding for extra-cellular matrix components or related signalling pathways were significantly over-represented among variants showing prevalence changes. Expression of 5 genes (TCHH, ABL1, CENPF, S100PBP, and ZFHX4) was significantly associated with patient survival.

Conclusions

Genomic analysis of paired pre- and post-therapy samples resulting from neoadjuvant therapy provides a powerful method for identification of mediators of response. Genes we identified should be assessed as predictive markers or targets in chemo-sensitization.

Electronic supplementary material

The online version of this article (10.1007/s10549-020-05836-7) contains supplementary material, which is available to authorized users.

A Four Gene-Based Risk Score System Associated with Chemoradiotherapy Response and Tumor Recurrence in Rectal Cancer by Co-Expression Network Analysis

Aim

Resistance to neoadjuvant chemoradiotherapy (NCRT) and tumor recurrence presents a major clinical problem in locally advanced rectal cancer (LARC) patients. This study aimed to explore a genetic risk score related to NCRT response and tumor recurrence in rectal cancer after NCRT.

Materials and Methods

Weighted gene co-expression network analysis was employed to identify hub genes associated with NCRT response from the GSE93375 dataset. Prognostic hub genes were determined using Cox regression analysis and associated with disease-free survival (DFS). A risk score system was constructed and the prognostic significance of the risk score was validated in our patient cohort. A predictive nomogram for DFS was developed and validated internally.

Results

The Tan module had the highest correlations with NCRT response. Ten hub genes (COL15A1, THBS2, ITGB1, MMP2, CD34, SPARC, NOTCH3, PDGFRB, DCN, and SERPINH1) were associated with NCRT response. Immunostaining expression of four genes (NOTCH3, SPARC, DCN, and ITGB1) was found to be significantly associated with both NCRT response and DFS in our patient cohort and was selected to build a prognostic risk score for DFS as follows: risk score= (0.6188×Exp _NOTCH3 ) + (0.6511×Exp _SPARC ) + (-0.2976×Exp _DCN ) + (1.0035×Exp _ITGB1 ). Using this risk score, patients could be separated into high- and low-risk groups for tumor recurrence. A nomogram that incorporated the risk score, ypTNM stage, and tumor regression grade (TRG) was constructed and utilized to predict DFS in LARC patients.

Conclusion

The four-gene expression-based risk score system presented here could be potentially used for predicting tumor recurrence in LARC patients after NCRT.

Assessment for Risk Status of Colorectal Cancer Patients: A Novel Prediction Model Based on Immune-Related Genes

Heterogeneity in patients with colorectal cancer (CRC) leads to different strategies in clinical decision making. Identifying distinctive subgroups in patients contributes to develop more individualized treatments. This study constructed a novel prediction model for the prognosis of CRC patients based on the value of risk score combining the expression status of immune-related genes and coefficients. In this study, we built an interactive network of prognosis-related immune genes and transcription factors and adopted several methods to verify the accuracy of model. Moreover, we assessed the correlation between risk score and immune infiltration. The results suggested that the model was well fit and the risk score could be an independent predictive factor for CRC patients. This model has high application value in the clinic.