Predictor Of Prognosis In Colorectal Cancer Research Articles

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer.

Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction. We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models. Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis. The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

Construction of a 12-gene prognostic model for colorectal cancer based on heat shock protein-related genes

Some heat shock proteins (HSPs) have been shown to influence tumor prognosis, but their prognostic significance in colorectal cancer (CRC) remains unclear. This study explored the prognostic significance of HSP-related genes in CRC. Transcriptional data and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database, and a literature search was conducted to identify HSP-related genes. Using Least Absolute Selection and Shrinkage Operator (LASSO) regression and univariate/multivariate Cox regression analyses, 12 HSP-related genes demonstrating significant associations with CRC survival were successfully identified and employed to formulate a predictive risk score model. The efficacy and precision of this model were validated utilizing TCGA and Gene Expression Omnibus (GEO) datasets, demonstrating its reliability in CRC prognosis prediction. gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed significant disparities between high- and low-risk groups in chromatin remodeling biological functions and neutrophil extracellular trap formation pathways. Single sample gene set enrichment analysis (ssGSEA) further revealed differences in immune cell types and immune functional status between the two risk groups. Differential analysis showed higher expression of immune checkpoints within the low-risk group, while the high-risk group exhibited notably higher Tumor Immune Dysfunction and Exclusion (TIDE) scores. Additionally, we predicted the sensitivity of different prognosis risk patients to various drugs, providing potential drug choices for tailored treatment. Combined, our study successfully crafted a novel CRC prognostic model that can effectively predict patient survival, immune landscape, and treatment response, providing important support and guidance for CRC patient prognosis.

Identification and Validation of the Glycolysis and Immune-related Gene Signature for Prognosis in Colorectal Cancer.

Glycolysis has a role in regulating the tumor immune microenvironment. However, the functions and clinical role for facilitating the prognosis prediction of colorectal cancer (CRC) based on glycolysis and immune-related genes remain to be identified. Genes associated with glycolysis and immunity (GI) were identified from established databases (MSigDB and ImmPort). The TCGA (training cohort) and GSE39582 (validation cohort) datasets were used. Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were applied for model construction. The prognostic power of the GI signature was examined by multivariate Cox regression analysis. The correlations between the GI signature, immune cell infiltration and immune checkpoint blockade (ICB) genes were analyzed. To further validate the identified gene signature, quantitative RT-PCR was performed. Cell proliferation assays were conducted for CCK8 detection. A new GI model was constructed, and this signature may serve as an independent prognostic biomarker in CRC. The GI signature remained an effective tool for predicting prognosis among each clinical subgroup. This signature was related to immune cell infiltration of myeloid dendritic cells, cancer-associated fibroblasts (CAFs), CD4+ and CD8+ T cells and response to the ICB immunotherapy-related genes IDO1, BTLA, PD-L1 and PD-L2. In addition, our findings showed that PMM2, IL20RB, and NTF4 exhibited high expression levels in CRC. The upregulation of these genes resulted in the promotion of the proliferation of CRC cells. This novel prognostic signature contributed to CRC risk stratification and survival prediction based on glycolysis and immune status.

A pyroptosis-related signature in colorectal cancer: exploring its prognostic value and immunological characteristics.

The heterogeneity of colorectal cancer (CRC) is the main cause of the disparity of drug sensitivity and the variability of prognosis. Pyroptosis is closely associated with the development and prognosis of various tumors, including CRC. Dividing CRC into distinct subgroups based on pyroptosis is a worthwhile topic for improving the precision treatment and prognosis prediction of CRC. We classified patients into two clusters using the consensus clustering based on the pyroptosis-related genes (PRGs). Next, the prognostic signature was developed with LASSO regression analysis using the screened genes from differentially expressed genes (DEGs) by univariate and multivariate Cox analyses. According to the pyroptosis-related score (PR score) calculated with the signature, patients belonged to two groups with distinct prognosis. Moreover, we assessed the immune profile to explore the relationship between the signature and immunological characteristics. Two single cell sequencing databases were adopted for further exploration of tumor immune microenvironment (TME). In addition, we applied our own cohort and Drugbank to explore the correlation of the signature and clinical therapies. We also studied the expression of key genes by immunohistochemistry. The signature performed well in predicting the prognosis of CRC as the high area under curve (AUC) value demonstrated. Patients with a higher PR score had poorer prognosis and higher expression of immune checkpoints but more abundant infiltration of immune cells. Combining with the indicator of therapeutic analysis, they might benefit more from immune checkpoint blockade (ICB) and neo-adjuvant chemoradiotherapy (nCRT). In conclusion, our study is based on genomics and transcriptomics to investigate the role of PRGs in CRC. We have established a prognostic signature and integrated single-cell data to study the relationship between the signature with the TME in CRC. Its clinical application in reliable prediction of prognosis and personalized treatment was validated by public and own sequencing cohort. It provided a new insight for the personalized treatment of CRC.

Intratumor APOL3 delineates a distinctive immunogenic ferroptosis subset with prognosis prediction in colorectal cancer.

With the essential role of lipid transporting signaling in cancer-related immunity, apolipoprotein L3 (APOL3), a member of the apolipoprotein L gene family, demonstrated significant modulation ability in immunity. However, the expression profile and critical role of APOL3 in colorectal cancer (CRC) remain unclear. This study aimed to investigate the prognostic significance of APOL3 expression and its biological predictive value in CRC. The study enrolled multiple cohorts, consisting of 911 tumor microarray specimens of CRC patients from Zhongshan Hospital, 412 transcriptional data from The Cancer Genome Atlas, and 30 single-cell RNA sequencing (scRNA-seq) from internal and external CRC patients. APOL3 mRNA expression was directly acquired from public datasets, and APOL3 protein expression was detected using immunohistochemistry. Finally, the associations of APOL3 expression with clinical outcomes, immune context, and genomic and ferroptotic features were analyzed. Low APOL3 expression predicted poor prognosis and inferior responsiveness to 5-fluorouracil-based adjuvant chemotherapy (ACT) and targeted therapy. APOL3 fosters an immune-active microenvironment characterized by the promotion of ferroptosis, downregulation of macrophages, and upregulation of CD8+ T cell infiltration. Moreover, the expression of APOL3 in CD8+ T cells is intrinsically linked to ferroptosis and immune activation in CRC. In summary, APOL3 serves as an independent prognosticator and predictive biomarker for immunogenic ferroptosis, ACT, and targeted therapy in CRC. Furthermore, the APOL3 signaling activator could be a novel agent alone or in combination with current therapeutic strategies for CRC.

Identification of necroptosis-related lncRNAs for prognosis prediction and screening of potential drugs in patients with colorectal cancer

BACKGROUND Tumor recurrence and metastasis lead to a poor prognosis in colorectal cancer (CRC). Necroptosis is closely related to the tumor microenvironment (TME) and affects tumor recurrence and metastasis. We aimed to stratify CRC patients according to necroptosis-related long noncoding RNAs (lncRNAs), which can be used to not only evaluate prognosis and improve precision medicine in clinical practice but also screen potential immunotherapy drugs. AIM To stratify CRC patients according to necroptosis-related lncRNAs (NRLs), which can be used to not only evaluate prognosis and improve precision medicine in clinical practice but also screen potential immunotherapy drugs. METHODS LncRNA expression profiles were collected from The Cancer Genome Atlas. NRLs were identified by coexpression analysis. Cox regression analysis identified a NRL signature. Then, the value of this signature was comprehensively and multidimensionally evaluated, and its reliability for CRC prognosis prediction was assessed with clinical CRC data and compared with that of six other lncRNA signatures. Gene set enrichment analysis, TME analysis and half-maximal inhibitory concentration (IC50) prediction were also performed according to the risk score (RS) of the signature. RESULTS An 8-lncRNA signature significantly associated with overall survival (OS) was constructed, and its reliability was validated with clinical CRC data. Most of the areas under the receiver operating characteristic curves (AUCs) values for 1-, 3- and 5-year OS for this signature were higher than those for the other six lncRNA signatures. OS, disease-specific survival and the progression-free interval were all significantly poorer in the high-risk group. The RS of the signature showed good concordance with the predicted prognosis, with AUCs for 1-, 3- and 5-year OS of 0.79, 0.81 and 0.77, respectively. Additionally, the calibration plots for this signature combined with clinical factors showed that this combination could effectively improve the ability to predict OS. The RS was correlated with tumor stage, lymph node metastasis and distant metastasis. Most of the enriched Kyoto Encyclopedia of Genes and Genomes and Gene Ontology terms were tumor metastasis-related pathways in the high-risk group; these patients showed greater infiltration of immunosuppressive cells, such as cancer-associated fibroblasts, hematopoietic stem cells and M2 macrophages, but less infiltration of infiltrating antitumor effector immune cells, such as cluster of differentiation 8+ T cells and regulatory T cells (Tregs). We explored additional potential immune checkpoint genes and potential immunotherapeutic and chemotherapeutic drugs with relatively low IC50 values. CONCLUSION We identified an NRL signature with strong fidelity that could stably predict prognosis and might be an indicator of the TME of CRC. Furthermore, additional potential immunotherapeutic and chemotherapeutic drugs were explored.

TransSurv: Transformer-Based Survival Analysis Model Integrating Histopathological Images and Genomic Data for Colorectal Cancer.

Survival analysis is a significant study in cancer prognosis, and the multi-modal data, including histopathological images, genomic data, and clinical information, provides unprecedented opportunities for its development. However, because of the high dimensionality and the heterogeneity of histopathological images and genomic data, acquiring effective predictive characters from these multi-modal data has always been a challenge for survival analysis. In this article, we propose a transformer-based survival analysis model (TransSurv) for colorectal cancer that can effectively integrate intra-modality and inter-modality features of histopathological images, genomic data, and clinical information. Specifically, to integrate the intra-modality relationship of image patches, we develop a multi-scale histopathological features fusion transformer (MS-Trans). Furthermore, we provide a cross-modal fusion transformer based on cross attention for multi-scale pathological representation and multi-omics representation, which includes RNA-seq expression and copy number alteration (CNA). At the output layer of the TransSurv, we adopt the Cox layer to integrate multi-modal fusion representation with clinical information for end-to-end survival analysis. The experimental results on the Cancer Genome Atlas (TCGA) colorectal cancer cohort demonstrate that the proposed TransSurv outperforms the existing methods and improves the prognosis prediction of colorectal cancer.

Clinicopathological Analysis of Complicated Colorectal Cancer: A Five-Year Retrospective Study from a Single Surgery Unit.

Patients with primary colorectal cancer can present with obstructions, tumor bleeding, or perforations, which represent acute complications. This paper aimed to analyze and compare the clinical and pathological profiles of two patient groups: one with colorectal cancer and a related complication and another without any specific complication. We performed a five-year retrospective study on colorectal cancer patients admitted to a surgery unit and comparatively explored the main clinical and pathological features of the tumors belonging to the two groups. A total of 250 patients with colorectal cancer were included in the analysis. Of these, 117 (46.8%) had presented a type of complication. The comparative analysis that examined several clinical and pathological parameters showed a statistically significant difference for unfavorable prognosis factors in the group with complications. This was evident for features such as vascular and perineural invasion, lymph node involvement, pathological primary tumor stage, and TNM stage. Colorectal cancers with a related complication belonged to a group of tumors with a more aggressive histopathologic profile and more advanced stages. Furthermore, the comparable incidence of cases in the two groups of patients warrants further efforts to be made in terms of early detection and prognosis prediction of colorectal cancer.

Multi-label transcriptional classification of colorectal cancer reflects tumor cell population heterogeneity

BackgroundTranscriptional classification has been used to stratify colorectal cancer (CRC) into molecular subtypes with distinct biological and clinical features. However, it is not clear whether such subtypes represent discrete, mutually exclusive entities or molecular/phenotypic states with potential overlap. Therefore, we focused on the CRC Intrinsic Subtype (CRIS) classifier and evaluated whether assigning multiple CRIS subtypes to the same sample provides additional clinically and biologically relevant information.MethodsA multi-label version of the CRIS classifier (multiCRIS) was applied to newly generated RNA-seq profiles from 606 CRC patient-derived xenografts (PDXs), together with human CRC bulk and single-cell RNA-seq datasets. Biological and clinical associations of single- and multi-label CRIS were compared. Finally, a machine learning-based multi-label CRIS predictor (ML2CRIS) was developed for single-sample classification.ResultsSurprisingly, about half of the CRC cases could be significantly assigned to more than one CRIS subtype. Single-cell RNA-seq analysis revealed that multiple CRIS membership can be a consequence of the concomitant presence of cells of different CRIS class or, less frequently, of cells with hybrid phenotype. Multi-label assignments were found to improve prediction of CRC prognosis and response to treatment. Finally, the ML2CRIS classifier was validated for retaining the same biological and clinical associations also in the context of single-sample classification.ConclusionsThese results show that CRIS subtypes retain their biological and clinical features even when concomitantly assigned to the same CRC sample. This approach could be potentially extended to other cancer types and classification systems.

Identification and validation of a novel stress granules-related prognostic model in colorectal cancer.

Aims: A growing body of evidence demonstrates that Stress granules (SGs), a non-membrane cytoplasmic compartments, are important to colorectal development and chemoresistance. However, the clinical and pathological significance of SGs in colorectal cancer (CRC) patients is unclear. The aim of this study is to propose a new prognostic model related to SGs for CRC on the basis of transcriptional expression. Main methods: Differentially expressed SGs-related genes (DESGGs) were identified in CRC patients from TCGA dataset by limma R package. The univariate and Multivariate Cox regression model was used to construct a SGs-related prognostic prediction gene signature (SGPPGS). The CIBERSORT algorithm was used to assess cellular immune components between the two different risk groups. The mRNA expression levels of the predictive signature from 3 partial response (PR) and 6 stable disease (SD) or progress disease (PD) after neoadjuvant therapy CRC patients' specimen were examined. Key findings: By screening and identification, SGPPGS comprised of four genes (CPT2, NRG1, GAP43, and CDKN2A) from DESGGs is established. Furthermore, we find that the risk score of SGPPGS is an independent prognostic factor to overall survival. Notably, the abundance of immune response inhibitory components in tumor tissues is upregulated in the group with a high-risk score of SGPPGS. Importantly, the risk score of SGPPGS is associated with the chemotherapy response in metastatic colorectal cancer. Significance: This study reveals the association between SGs related genes and CRC prognosis and provides a novel SGs related gene signature for CRC prognosis prediction.

Development and validation of cuproptosis-related lncRNA signatures for prognosis prediction in colorectal cancer

BackgroundCuproptosis, a novel form of programmed cell death, plays an essential role in various cancers. However, studies of the function of cuproptosis lncRNAs (CRLs) in colorectal cancer (CRC) remain limited. Thus, this study aims to identify the cuprotosis-related lncRNAs (CRLs) in CRC and to construct the potential prognostic CRLs signature model in CRC.MethodsFirst, we downloaded RNA-Seq data and clinical information of CRC patients from TCGA database and obtained the prognostic CRLs based on typical expression analysis of cuproptosis-related genes (CRGs) and univariate Cox regression. Then, we constructed a prognostic model using the Least Absolute Shrinkage and Selection Operator algorithm combined with multiple Cox regression methods (Lasso-Cox). Next, we generated Kaplan–Meier survival and receiver operating characteristic curves to estimate the performance of the prognostic model. In addition, we also analysed the relationships between risk signatures and immune infiltration, mutation, and drug sensitivity. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT -PCR) to verify the prognostic model.ResultLasso-Cox analysis revealed that four CRLs, SNHG16, LENG8-AS1, LINC0225, and RPARP-AS1, were related to CRC prognosis. Receiver operating characteristic (ROC) and Kaplan–Meier analysis curves indicated that this model performs well in prognostic predictions of CRC patients. The DCA results also showed that the model included four gene signatures was better than the traditional model. In addition, GO and KEGG analyses revealed that DE-CRLs are enriched in critical signalling pathway, such as chemical carcinogenesis-DNA adducts and basal cell carcinoma. Immune infiltration analysis revealed significant differences in immune infiltration cells between the high-risk and low-risk groups. Furthermore, significant differences in somatic mutations were noted between the high-risk and low-risk groups. Finally, we also validated the expression of four CRLs in FHCs cell lines and CRC cell lines using qRT-PCR.ConclusionThe signature composed of SNHG16, LENG8-AS1, LINC0225, and RPARP-AS1, which has better performance in predicting colorectal cancer prognosis and are promising biomarkers for prognosis prediction of CRC.

Development and validation of an RBP gene signature for prognosis prediction in colorectal cancer based on WGCNA

BackgroundRNA binding proteins (RBPs) have been implicated in oncogenesis and progression in various cancers. However, the potential value of RBPs as prognostic indicators and therapeutic targets in colorectal cancer (CRC) requires further investigation.MethodsFour thousand eighty two RBPs were collected from literature. The weighted gene co-expression network analysis (WGCNA) was performed to identify prognosis-related RBP gene modules based on the data attained from the TCGA cohorts. LASSO algorithm was conducted to establish a prognostic risk model, and the validity of the proposed model was confirmed by an independent GEO dataset. Functional enrichment analysis was performed to reveal the potential biological functions and pathways of the signature and to estimate tumor immune infiltration. Potential therapeutic compounds were inferred utilizing CMap database. Expressions of hub genes were further verified through the Human Protein Atlas (HPA) database and RT-qPCR.ResultsOne thousand seven hundred thirty four RBPs were differently expressed in CRC samples and 4 gene modules remarkably linked to the prognosis were identified, based on which a 12-gene signature was established for prognosis prediction. Multivariate Cox analysis suggested this signature was an independent predicting factor of overall survival (P < 0.001; HR:3.682; CI:2.377–5.705) and ROC curves indicated it has an effective predictive performance (1-year AUC: 0.653; 3-year AUC:0.673; 5-year AUC: 0.777). GSEA indicated that high risk score was correlated with several cancer-related pathways, including cytokine-cytokine receptor cross talk, ECM receptor cross talk, HEDGEHOG signaling cascade and JAK/STAT signaling cascade. ssGSEA analysis exhibited a significant correlation between immune status and the risk signature. Noscapine and clofazimine were screened as potential drugs for CRC patients with high-risk scores. TDRD5 and GPC1 were identified as hub genes and their expression were validated in 15 pairs of surgically resected CRC tissues.ConclusionOur research provides a depth insight of RBPs’ role in CRC and the proposed signature are helpful to the personalized treatment and prognostic judgement.

An immune, stroma, and epithelial-mesenchymal transition-related signature for predicting recurrence and chemotherapy benefit in stage II-III colorectal cancer.

Debates exist on the treatment decision of the stage II/III colorectal cancer (CRC) due to the insufficiency of the current TNM stage-based risk stratification system. Epithelial-mesenchymal transition (EMT) and tumor microenvironment (TME) have both been linked to CRC progression in recent studies. We propose to improve the prognosis prediction of CRC by integrating TME and EMT. In total, 2382 CRC patients from seven datasets and one in-house cohort were collected, and 1640 stage II/III CRC patients with complete survival information and gene expression profiles were retained and divided into a training cohort and three independent validation cohorts. Integrated analysis of 398 immune, stroma, and epithelial-mesenchymal transition (ISE)-related genes identified an ISE signature independently associated with the recurrence of CRC. The underlying biological mechanism of the ISE signature and its influence on adjuvant chemotherapy was further explored. We constructed a 26-gene signature which was significantly associated with poor outcome in Training cohort (p <0.001, HR [95%CI]=4.42 [3.25-6.01]) and three independent validation cohorts (Validation cohort-1: p < 0.01, HR [95%CI]=1.70 [1.15-2.51]; Validation cohort-2: p < 0.001, HR [95% CI]=2.30 [1.67-3.16]; Validation cohort-3: p < 0.01, HR [95% CI]=2.42 [1.25-4.70]). After adjusting for known clinicopathological factors, multivariate cox analysis confirmed the ISE signature's independent prognostic value. Subgroup analysis found that stage III patients with low ISE score might benefit from adjuvant chemotherapy (p < 0.001, HR [95%CI]=0.15 [0.04-0.55]). Hypergeometric test and enrichment analysis revealed that low-risk group was enriched in thr immune pathway while high-risk group was associated with the EMT pathway and CMS4 subtype. We proposed an ISE signature for robustly predicting the recurrence of stage II/III CRC and help treatment decision by identifying patients who will not benefit from current standard treatment.

Mesenteric location of lymph node metastasis for colorectal cancer.

The number of lymph node metastasis (LNM) is a strong prognostic factor in the treatment of colorectal cancer (CRC). However, the impact of the mesentery location on LNM remains unclear. We assessed the impact LNM location on the recurrence of stage III CRC. Subjects with CRC and pathologically positive LNM were enrolled retrospectively. We defined three groups: LNM adjacent to the tumour (group A), metastases with horizontal or vertical spread (group B), and metastases with both horizontal and vertical spread (group C). Recurrence-free survival (RFS) was the primary outcome measure used for the study. A total of 241 (Group A: 121, B: 90, and C: 30) patients were recruited for the study. Multivariate analysis by Cox regression model indicated LNM location to be an independent predisposing risk factor for recurrence [group B: Hazard ratio (HR) 2.01, 95% Confidential interval (CI) 1.12-3.60, P=0.019; group C: HR 3.00, 95% CI 1.34-6.72, P=0.008]. Addition of mesentery spread to the N classification was significant risk factor for recurrence (mN2a: HR 2.01, 95% CI 1.07-3.78, P=0.029; mN2b: HR 3.96, 95% CI 2.12-7.40, P < 0.01). Comparison of Harrell's C-index values was conducted, and the modified N staging risk was 0.6377, whereas the TNM N stage classification was 0.5869. Mesentery location of LNM was a risk factor and consideration of it might be beneficial for accurate prediction of CRC prognosis.

Early detection and prognosis prediction for colorectal cancer by circulating tumour DNA methylation haplotypes: A multicentre cohort study.

Early detection and prognosis prediction of colorectal cancer (CRC) can significantly reduce CRC-related mortality. Recently, circulating tumour DNA (ctDNA) methylation has shown good application foreground in the early detection and prognosis prediction of multiple tumours. This multicentre cohort study evaluated ctDNA methylation haplotype patterns based on archived plasma samples (collected between 2010 and 2018) from 1138 individuals at two medical centres: Fudan University Shanghai Cancer Center (Shanghai, China) and Southern Medical University Nanfang Hospital (Guangzhou, Guangdong, China), including 366 healthy individuals, 182 patients with advanced adenoma (AA), and 590 patients with CRC. Samples were processed using the ColonES assay, a targeted bisulfite sequencing method that detects ctDNA methylation haplotype patterns in 191 genomic regions. Among these 1138 samples, 748 were used to develop a classification model, and 390 served as a blinded cohort for independent validation. The study is registered at https://register.clinicaltrials.gov with the unique identifier NCT03737591. The model obtained from unblinded samples discriminated patients with CRC or AA from normal controls with high accuracy. In the blinded validation set, the ColonES assay achieved sensitivity values of 79.0% (95% confidence interval (CI), 66%-88%) in AA patients and 86.6% (95% CI, 81%-91%) in CRC patients with a specificity of 88.1% (95% CI, 81%-93%) in healthy individuals. The model area under the curve (AUC) for the blinded validation set was 0.903 for AA samples and 0.937 for CRC samples. Additionally, the prognosis of patients with high preoperative ctDNA methylation levels was worse than that of patients with low ctDNA methylation levels (p=0.001 for relapse-free survival and p=0.004 for overall survival). We successfully developed and validated an accurate, noninvasive detection method based on ctDNA methylation haplotype patterns that may enable early detection and prognosis prediction for CRC. The Grant of National Natural Science Foundation of China (No.81871958), National Natural Science Foundation of China (No. 82203215), Shanghai Science and Technology Committee (No. 19140902100), Scientific Research Fund of Fudan University (No.IDF159052), Shanghai Municipal Health Commission (SHWJRS 2021-99), and Shanghai Sailing Program (22YF1408800).

Construction and validation of a novel algorithm based on oncosis-related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis.

Colorectal cancer (CRC) has high morbidity and mortality, particularly if diagnosed at an advanced stage. Although there have been several studies on CRC, few have investigated the relationship between oncosis and CRC. Thus, the purpose of the present study was to identify oncosis-related long noncoding RNAs (lncRNAs) and to establish a clinical prognostic model. Original data were acquired from The Cancer Genome Atlas database and PubMed. Differentially expressed oncosis-related lncRNAs (DEorlncRNAs) were identified and were subsequently formed into pairs. Next, a series of tests and analyses, including both univariate and multivariate analyses, as well as Lasso and Cox regression analyses, were performed to establish a receiver operating characteristic curve. A cut-off point was subsequently used to divide the samples into groups labelled as high- or low-risk. Thus, a model was established and evaluated in several dimensions. Six pairs of DEorlncRNAs associated with prognosis according to the algorithm were screened out and the CRC cases were divided into high- and low-risk groups. Significant differences between patients in the different risk groups were observed for several traits, including survival outcomes, clinical pathology characteristics, immune cell infiltration status and drug sensitivity. In addition, PCR and flow cytometry were performed to further verify the model. In summary, a new risk model algorithm based on six pairs of DEorlncRNAs in CRC, which does not require specific data regarding the level of gene expression, was established and validated. This algorithm may be used to predict patient prognosis, immune cell infiltration and drug sensitivity.

Exploration and validation of a combined immune and metabolism gene signature for prognosis prediction of colorectal cancer.

Colorectal cancer (CRC) is still one of the most frequently diagnosed malignancy around the world. The complex etiology and high heterogeneity of CRC necessitates the identification of new reliable signature to identify different tumor prognosis, which may help more precise understanding of the molecular properties of CRC and identify the appropriate treatment for CRC patients. In this study, we aimed to identify a combined immune and metabolism gene signature for prognosis prediction of CRC from large volume of CRC transcriptional data. Gene expression profiling and clinical data of HCC samples was retrieved from the from public datasets. IRGs and MRGs were identified from differential expression analysis. Univariate and multivariate Cox regression analysis were applied to establish the prognostic metabolism-immune status-related signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curves were generated for diagnostic efficacy estimation. Real-time polymerase chain reaction (RT-PCR), Western blot and immunohistochemistry (IHC) was conducted to verified the expression of key genes in CRC cells and tissues. A gene signature comprising four genes (including two IRGs and two MRGs) were identified and verified, with superior predictive performance in discriminating the overall survival (OS) of high-risk and low-risk compared to existing signatures. A prognostic nomogram based on the four-gene signature exhibited a best predictive performance, which enabled the prognosis prediction of CRC patients. The hub gene ESM1 related to CRC were selected via the machine learning and prognostic analysis. RT-PCR, Western blot and IHC indicated that ESM1 was high expressed in tumor than normal with superior predictive performance of CRC survival. A novel combined MRGs and IRGs-related prognostic signature that could stratify CRC patients into low-and high- risk groups of unfavorable outcomes for survival, was identified and verified. This might help, to some extent, to individualized treatment and prognosis assessment of CRC patients. Similarly, the mining of key genes provides a new perspective to explore the molecular mechanisms and targeted therapies of CRC.

A novel Anoikis and immune-related genes marked prognostic signature for colorectal cancer.

Colorectal cancer (CRC) is second most commonly diagnosed cancer with high morbidity and mortality. The heterogeneity of CRC makes clinical treatment tremendously challenging. Here, we aimed to comprehensively analyze the prognosis of CRC patients based on ANOIKIS- and immune-related genes. ANOIKIS-related genes were identified by differentially analysis of high anoikis score group (ANOIKIS_high group) and low anoikis score group (ANOIKIS_low group) divided by the cutoff value of anoikis score. Immune-related genes were screened by differentially analysis of high immune score group (ImmuneScore_high group) and low immune score group (ImmuneScore_low group) classified by the cutoff value of ImmuneScore. Prognostic ANOIKIS- and immune-related genes were identified by univariate Cox regression analysis. Multivariate Cox regression analysis were used for prognostic model construction. Ferroptosis expression profiles, the infiltration of immune cells, and the somatic mutation status were analyzed and compared. Univariate and multivariate Cox-regression analyses were performed to identify independent prognostic factors for CRC patient. Nomogram that contained the independent prognostic factors was established to predict 1-, 3-, and 5-year OS probability of CRC patients. Three ANOIKIS- and immune-related signatures were applied to construct a prognostic model, which divided the CRC patients into high-risk and low-risk groups. The patients with high-risk scores had obviously shorter OSs than those with low-risk scores. The time dependent ROC curve indicated that the risk score model had a stable performance to predict survival rates. Notably, the age, pathologic T, and risk score could be used independent indicators for CRC prognosis prediction. A nomogram containing the independent prognostic factors showed that the nomogram accurately predicted 1-, 3-, and 5-year survival rates of CRC patients. In our research, a novel prognostic model was developed based on ANOIKIS- and immune-related genes in CRC, which could be used for prognostic prediction of CRC patients.

Preoperative carcinoembryonic antigen to body mass index ratio contributes to prognosis prediction in colorectal cancer.

Both carcinoembryonic antigen (CEA) level and body mass index (BMI) are traditional prognostic markers in colorectal cancer (CRC); however, to the best of our knowledge, the value of the CEA to BMI ratio (CBR) has never been addressed. In the present study, 191 patients with CRC treated using radical resection were retrospectively included, and the significance of the CBR in predicting disease-free survival (DFS) or overall survival (OS) rates was calculated. The prognostic efficacy of the CBR in predicting OS was compared with individual CEA and BMI values. The survival differences of the subgroups were calculated by Kaplan-Meier analysis, and corresponding risk factors were then estimated by a Cox proportional hazards model. As a result, 29.84% (57/191) of the patients were assigned to the high CBR group (cut-off, ≥0.28); the CBR had a sensitivity of 56.50 and 68.90%, and a specificity of 80.60 and 80.10% for DFS and OS, respectively. Patients with a high CBR more commonly underwent laparotomy and exhibited advanced T stages, the presence of tumor deposits and advanced Tumor-Node-Metastasis stages (stage II or III). The CBR was more efficient than the CEA or BMI alone in predicting OS. In addition, patients with a high CBR presented with a significantly worse outcome than patients with a low CBR. Finally, the CBR was an independent risk factor for both DFS and OS. In conclusion, the CBR was a more robust prognostic factor in CRC, and patients with a relatively high CBR exhibited poorer survival.

Emerging role of non-coding RNAs in glucose metabolic reprogramming and chemoresistance in colorectal cancer.

Metabolic reprogramming plays a critical role in colorectal cancer (CRC). It contributes to CRC by shaping metabolic phenotypes and causing uncontrolled proliferation of CRC cells. Glucose metabolic reprogramming is common in carcinogenesis and cancer progression. Growing evidence has implicated the modifying effects of non-coding RNAs (ncRNAs) in glucose metabolic reprogramming and chemoresistance in CRC. In this review, we have summarized currently published studies investigating the role of ncRNAs in glucose metabolic alterations and chemoresistance in CRC. Elucidating the interplay between ncRNAs and glucose metabolic reprogramming provides insight into exploring novel biomarkers for the diagnosis and prognosis prediction of CRC.