The identification of a common different gene expression signature in patients with colorectal cancer

Colorectal cancer (CRC) is one of the most common malignancies, giving rise to serious financial burden globally. This study was designed to explore the potential mechanisms implicated with CRC and identify some key biomarkers. CRC-associated gene expression dataset (GSE32323) was downloaded from GEO database. The differentially expressed genes (DEGs) were selected out based on the GEO2R tool. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to search the enriched pathways of these DEGs. Additionally, a protein-protein interaction (PPI) network was also constructed to visualize interactions between these DEGs. Quantitative Real-time PCR (qPCR) was further performed to valid the top5 up-regulated and top5 down-regulated genes in patients with CRC. Finally, the survival analysis of the top5 up-regulated and top5 down-regulated genes was conducted using GEPIA, aiming to clarify their potential effects on CRC. In this study, a total of 451 DEGs were captured (306 down-regulated genes and 145 up-regulated genes). Among these DEGs, the top5 up-regulated genes were DPEP1, KRT23, CLDN1, LGR5 and FOXQ1 while the top5 down-regulated genes were CLCA4, ZG16, SLC4A4, ADH1B and GCG. GO analysis revealed that these DEGs were mainly enriched in cell adhesion, cell proliferation, RNA polymerase II promoter and chemokine activity. KEGG analysis disclosed that the enriched pathway included mineral absorption, chemokine signaling pathway, transcriptional misregulation in cancer, pathways in cancer and PPAR signaling pathway. Survival analysis showed that the expression level of ZG16 may correlate with the prognosis of CRC patients. Furthermore, according to the connectivity degree of these DEGs, we selected out the top15 hub genes, namely MYC, CXCR1, TOP2A, CXCL12, SST, TIMP1, SPP1, PPBP, CDK1, THBS1, CXCL1, PYY, LPAR1, BMP2 and MMP3, which were expected to be promising therapeutic target in CRC. Collectively, our analysis unveiled potential biomarkers and candidate targets in CRC, which could be helpful to the diagnosis and treatment of CRC.

Identification of potential biomarkers with colorectal cancer based on bioinformatics analysis and machine learning

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Biomarker discovery is critical to improve CRC diagnosis, however, machine learning offers a new platform to study the etiology of CRC for this purpose. Therefore, the current study aimed to perform an integrated bioinformatics and machine learning analyses to explore novel biomarkers for CRC prognosis. In this study, we acquired gene expression microarray data from Gene Expression Omnibus (GEO) database. The microarray expressions GSE103512 dataset was downloaded and integrated. Subsequently, differentially expressed genes (DEGs) were identified and functionally analyzed via Gene Ontology (GO) and Kyoto Enrichment of Genes and Genomes (KEGG). Furthermore, protein protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software to identify hub genes; however, the hub genes were subjected to Support Vector Machine (SVM), Receiver operating characteristic curve (ROC) and survival analyses to explore their diagnostic values. Meanwhile, TCGA transcriptomics data in Gene Expression Profiling Interactive Analysis (GEPIA) database and the pathology data presented by in the human protein atlas (HPA) database were used to verify our transcriptomic analyses. A total of 105 DEGs were identified in this study. Functional enrichment analysis showed that these genes were significantly enriched in biological processes related to cancer progression. Thereafter, PPI network explored a total of 10 significant hub genes. The ROC curve was used to predict the potential application of biomarkers in CRC diagnosis, with an area under ROC curve (AUC) of these genes exceeding 0.92 suggesting that this risk classifier can discriminate between CRC patients and normal controls. Moreover, the prognostic values of these hub genes were confirmed by survival analyses using different CRC patient cohorts. Our results demonstrated that these 10 differentially expressed hub genes could be used as potential biomarkers for CRC diagnosis.

A novel identified pyroptosis-related prognostic signature of colorectal cancer

Colorectal cancer (CRC), one of the most common malignancies worldwide, leads to abundant cancer-related mortalities annually. Pyroptosis, a new kind of programmed cell death, plays a critical role in immune response and tumor progression. Our study aimed to identify a prognostic signature for CRC based on pyroptosis-related genes (PRGs). The difference in PRGs between CRC tissues and normal tissues deposited in the TCGA database was calculated by "limma" R package. The tumor microenvironment (TME) of CRC cases was accessed by the ESTIMATE algorithm. The prognostic PRGs were identified using Cox regression analysis. A least absolute shrinkage and selector operation (LASSO) algorithm was used to calculate the risk scores and construct a clinical predictive model of CRC. Gene Set Enrichment Analysis (GSEA) was performed for understanding the function annotation of the signature in the tumor microenvironment. We found that most PRGs were significantly dysregulated in CRC. Through the LASSO method, three key PRGs were selected to calculate the risk scores and construct the prognostic model for CRC. The risk score was an independent indicator of patient's prognosis. In addition, we classified the CRC patients into two clusters based on risk scores and discovered that CRC patients in cluster 2 underwent worse overall survival and owned higher expression levels of immune checkpoint genes in tumor tissues. In conclusion, our study identified a PRG-related prognostic signature for CRC, according to which we classified the CRC patients into two clusters with distinct prognosis and immunotherapy potential.

A novel 10-gene immune-related lncRNA signature model for the prognosis of colorectal cancer

BACKGROUND

The tumor immune microenvironment of colorectal cancer (CRC) affects tumor development, prognosis and immunotherapy strategies. Recently, immune-related lncRNA were shown to play vital roles in the tumor immune microenvironment. The objective of this study was to identify lncRNAs involved in the immune response, tumorigenesis and progression of CRC and to establish an immune-related lncRNA signature for predicting the prognosis of CRC.

METHODS

We used data retrieved from the cancer genome atlas (TCGA) dataset to construct a 10-gene immune-related lncRNA pair (IRLP) signature model using a method based on the ranking and comparison of paired gene expression in CRC. The clinical prognosis, immune checkpoints and lncRNA-protein networks were analyzed to evaluate the signature.

RESULTS

The signature was closely associated with overall survival of CRC patients (p < 0.001 in both of the training and validating cohorts) and the 3-year AUC values for the training and validating cohorts were 0.884 and 0.739, respectively. And, there were positive correlations between the signature and age (p = 0.048), clinical stage (p < 0.01), T stage (p < 0.01), N stage (p < 0.001) and M stage (p < 0.01). In addition, the signature model appeared to be highly relevant to some checkpoints, including CD160, TNFSF15, HHLA2, IDO2 and KIR3DL1. Further, molecular functional analysis and lncRNA-protein networks were applied to understand the molecular mechanisms underlying the carcinogenic effect and progression.

CONCLUSION

The 10-gene IRLP signature model is an independent prognostic factor for CRC patient and can be utilized for the development of immunotherapy.

Analysis of the impact of radiotherapy and surgical treatment regimens based on the SEER database on the survival outcomes of rectal cancer patients over 70 years

OBJECTIVE

This study evaluates the impact of different combinations of treatment regimens, such as additional radiation, chemotherapy, and surgical treatments, on the survival of elderly rectal cancer patients ≥ 70 years of age to support physicians' clinical decision-making.

METHODS

Data from a sample of elderly rectal cancer patients aged ≥ 70 years diagnosed from 2005-2015 from the US surveillance, epidemiology, and end results (SEER) database were retrospectively analyzed. The best cut-off point was selected using the x-tile software for the three continuity indices: age, tumor size, and number of regional lymph nodes. All patients were categorized into either the neoadjuvant radiotherapy and surgery group (R_S group), the surgical treatment group (S group), or the surgery and adjuvant radiotherapy group (S_R group). The propensity score allocation was used to match each included study subject in a 1:1 ratio, and the restricted mean survival time method (RMST) was used to predict the mean survival of rectal cancer patients within 5 and 10 years. The prognostic risk factors for rectal cancer patients were determined using univariate and multivariate Cox regression analyses, and nomograms were constructed. A subgroup stratification analysis of patients with different treatment combination regimens was performed using the Kaplan-Meier method, and log-rank tests were used for between-group comparisons. The model's predictive accuracy was assessed by receiver operating characteristic (ROC) curves, correction curves, and a clinical decision curve analysis (DCA).

RESULTS

A total of 7556 cases of sample data from 2005 to 2015 were included, which were categorized into 6639 patients (87.86%) in the S group, 408 patients (5.4%) in the R_S group, and 509 patients (6.74%) in the S_R group, according to the relevant order of radiotherapy and surgery. After propensity score matching (PSM), the primary clinical characteristics of the groups were balanced and comparable. The difference in the mean survival time before and after PSM was not statistically significant in both R_S and S groups (P value > 0.05), and the difference in the mean survival time after PSM was statistically substantial in S_R and S groups (P value < 0.05). In the multifactorial Cox analysis, the M1 stage and Nodes ≥ 9 were independent risk factors. An age between 70-75 was an independent protective factor for patients with rectal cancer in the R_S and S groups. The Marital_status, T4 stage, N2 stage, M1 stage, and Nodes ≥ 9 were independent risk factors for patients with rectal cancer in the S_R and S groups, and an age between 70-81 was an independent protective factor. The ROC curve area, the model C index, and the survival calibration curve suggested good agreement between the actual and predicted values of the model. The DCA for 3-year, 5-year, and 10-year survival periods indicated that the model had some potential for application.

CONCLUSIONS

The results of the study showed no significant difference in the overall survival (OS) between elderly patients who received neoadjuvant radiotherapy and surgery and those who received surgery alone; elderly patients who received surgery and adjuvant radiotherapy had some survival benefits compared with those who received surgery alone, though the benefit of adjuvant radiotherapy was not significant. Therefore, radiotherapy for rectal cancer patients older than 70 years old should be based on individual differences in condition, and a precise treatment plan should be developed.

Mathematical modeling the gene mechanism of colorectal cancer and the effect of radiation exposure

Cancer is the result of continuous accumulation of gene mutations in normal cells. The number of mutations is different in different types of cancer and even in different patients with the same type of cancer. Therefore, studying all possible numbers of gene mutations in malignant cells is of great value for the understanding of tumorigenesis and the treatment of cancer. To this end, we applied a stochastic mathematical model considering the clonal expansion of any premalignant cells with different mutations to analyze the number of gene mutations in colorectal cancer. The age-specific colorectal cancer incidence rates from the Surveillance, Epidemiology and End Results (SEER) registry in the United States and the Life Span Study (LSS) in Nagasaki and Hiroshima, Japan are chosen to test the reasonableness of the model. Our fitting results indicate that the transformation from normal cells to malignant cells may undergo two to five driver mutations for colorectal cancer patients without radiation-exposed environment, two to four driver mutations for colorectal cancer patients with low level radiation-exposure, and two to three driver mutations for colorectal cancer patients with high level radiation-exposure. Furthermore, the net growth rate of the mutated cells with radiation-exposure was is higher than that of the mutated cells without radiation-exposure for the models with two to five driver mutations. These results suggest that radiation environment may affect the clonal expansion of cells and significantly affect the development of tumors.

Dynamic gene regulatory network reconstruction and analysis based on clinical transcriptomic data of colorectal cancer

Inferring dynamic regulatory networks that rewire at different stages is a reasonable way to understand the mechanisms underlying cancer development. In this study, we reconstruct the stage-specific gene regulatory networks (GRNs) for colorectal cancer to understand dynamic changes of gene regulations along different disease stages. We combined multiple sets of clinical transcriptomic data of colorectal cancer patients and employed a supervised approach to select initial gene set for network construction. We then developed a dynamical system-based optimization method to infer dynamic GRNs by incorporating mutual information-based network sparsification and a dynamic cascade technique into an ordinary differential equations model. Dynamic GRNs at four different stages of colorectal cancer were reconstructed and analyzed. Several important genes were revealed based on the rewiring of the reconstructed GRNs. Our study demonstrated that reconstructing dynamic GRNs based on clinical transcriptomic profiling allows us to detect the dynamic trend of gene regulation as well as reveal critical genes for cancer development which may be important candidates of master regulators for further experimental test.

Colon histology slide classification with deep-learning framework using individual and fused features

Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (ⅰ) Image collection, resizing, and pre-processing; (ⅱ) Deep-Features (DF) extraction with a chosen scheme; (ⅲ) Binary classification with a 5-fold cross-validation; and (ⅳ) Verification of the clinical significance. This work classifies the considered image database using the follwing: (ⅰ) Individual DF, (ⅱ) Fused DF, and (ⅲ) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively.

Intestinal Stomas in Abdominal Surgery: Etiological Circumstances, Indications, High Gravity Factors and Complications

This is a retrospective study of 264 intestinal stomas performed in the surgical unit of the Emergency Clinical Hospital "Bagdasar Arseni", Bucharest, within a 7-year period (2015-2021) aiming to evaluate their evolutive complications, risk factors, management and prevention strategies. Material and method. Colostomies: 218 (82.57%) cases, ileostomies 46 (17.43%) cases. Temporary stomas (103 cases or 39.02%) were isolated stomas of discharge in 45 cases and associated with other colorectal procedures in 58 cases. Postoperative complications included general systemic complications in 60 (22.72%) cases and local complications specific to stomas in 84 (31.81%) cases and common to abdominal surgery in 94 (35.60%) cases, which were solved by reoperation in 51 cases, with a reintervention rate of 19.31%. Stoma closures were performed in 34 (33.0%) of the 103 patients with temporary stomas. Of these, 26 (25.24%) patients died in the early postoperative period (< 30 days), the remaining 60 patients refused reintervention or were lost to follow-up. Conclusions. Faecal diversion still represents a therapeutic option for a wide range of benign or malignant digestive or extra-digestive abdominal diseases performed in emergency or scheduled surgeries, mostly for colorectal cancer and its complications.