BackgroundColorectal cancer (CRC) is the third most prevalent malignancy and the one of most lethal cancer. Metastatic CRC (mCRC) is the third most common cause of cancer deaths worldwide. DNA damage response (DDR) genes are closely associated with the tumorigenesis and development of CRC. In this study, we aimed to construct a DDR-related gene signature for predicting the prognosis of mCRC patients.MethodsThe gene expression and corresponding clinical information data of CRC/mCRC patients were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. A prognostic model was obtained and termed DDRScore by the multivariate Cox proportional hazards regression in the patients with mCRC. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were employed to validate the predictive ability of the prognostic model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were performed for patients between the high-DDRscore and low-DDRscore groups.ResultsWe constructed a prognostic model consisting of four DDR-related genes (EME2, MSH4, MLH3, and SPO11). Survival analysis showed that patients in the high-DDRscore group had a significantly worse OS than those in the low-DDRscore group. The area under the curve (AUC) value of the ROC curve of the predictive model is 0.763 in the training cohort GSE72970, 0.659 in the stage III/IV colorectal cancer (CRC) patients from The Cancer Genome Atlas (TCGA) data portal, and 0.639 in another validation cohort GSE39582, respectively. GSEA functional analysis revealed that the most significantly enriched pathways focused on nucleotide excision repair, base excision repair, homologous recombination, cytokine receptor interaction, chemokine signal pathway, cell adhesion molecules cams, ECM-receptor interaction, and focal adhesion.ConclusionThe DDRscore was identified as an independent prognostic and therapy response predictor, and the DDR-related genes may be potential diagnosis or prognosis biomarkers for mCRC patients.

Background: Pediatric gliomas (PGs) are highly aggressive and predominantly occur in young children. In pediatric gliomas, abnormal expression of Homeobox (HOX) family genes (HFGs) has been observed and is associated with the development and progression of the disease. Studies have found that overexpression or underexpression of certain HOX genes is linked to the occurrence and prognosis of gliomas. This aberrant expression may contribute to the dysregulation of important pathological processes such as cell proliferation, differentiation, and metastasis. This study aimed to propose a novel HOX-related signature to predict patients’ prognosis and immune infiltrate characteristics in PGs.Methods: The data of PGs obtained from publicly available databases were utilized to reveal the relationship among abnormal expression of HOX family genes (HFGs), prognosis, tumor immune infiltration, clinical features, and genomic features in PGs. The HFGs were utilized to identify heterogeneous subtypes using consensus clustering. Then random forest-supervised classification algorithm and nearest shrunken centroid algorithm were performed to develop a prognostic signature in the training set. Finally, the signature was validated in an internal testing set and an external independent cohort.Results: Firstly, we identified HFGs significantly differentially expressed in PGs compared to normal tissues. The individuals with PGs were then divided into two heterogeneous subtypes (HOX-SI and HOX-SII) based on HFGs expression profiles. HOX-SII showed higher total mutation counts, lower immune infiltration, and worse prognosis than HOX-SI. Then, we constructed a HOX-related gene signature (including HOXA6, HOXC4, HOXC5, HOXC6, and HOXA-AS3) based on the cluster for subtype prediction utilizing random forest supervised classification and nearest shrunken centroid algorithm. The signature was revealed to be an independent prognostic factor for patients with PGs by multivariable Cox regression analysis.Conclusion: Our study provides a novel method for the prognosis classification of PGs. The findings also suggest that the HOX-related signature is a new biomarker for the diagnosis and prognosis of patients with PGs, allowing for more accurate survival prediction.

IntroductionColorectal cancer (CRC) is one of the most prevalent cancers globally with a high mortality rate. Predicting prognosis using disease progression and cancer pathologic stage is insufficient, and a prognostic factor that can accurately evaluate patient prognosis needs to be developed. In this study, we aimed to infer a prognostic gene signature to identify a functional signature associated with the prognosis of CRC patients.MethodsFirst, we used univariate Cox regression, least absolute shrinkage and selection operator (lasso) regression, and multivariate Cox regression analyses to screen genes significantly associated with CRC patient prognosis, from colorectal cancer RNA sequencing data in The Cancer Genome Atlas (TCGA) database. We then calculated the risk score (RS) for each patient based on the expression of the nine candidate genes and developed a prognostic signature.ResultsBased on the optimal cut-off on the receiver operating characteristic (ROC) curve, patients were separated into high- and low-risk groups, and the difference in overall survival between the two groups was examined. Patients in the low-risk group had a better overall survival rate than those in the high-risk group. The results were validated using the GSE72970, GSE39582, and GSE17536 Gene Expression Omnibus (GEO) datasets, and the same conclusions were reached. ROC curve test of the RS signature also indicated that it had excellent accuracy. The RS signature was then compared with traditional clinical factors as a prognostic indicator, and we discovered that the RS signature had superior predictive ability.ConclusionThe RS signature developed in this study has excellent predictive power for the prognosis of patients with CRC and broad applicability as a prognostic indicator for patients.

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