Comprehensive analysis reveals CCDC60 as a potential biomarker correlated with prognosis and immune infiltration of head and neck squamous cell carcinoma

Integrated RNA expression and alternative polyadenylation analysis identified CPSF1-CCDC137 oncogenic axis in lung adenocarcinoma

This study aims to analyze the RNA expression and alternative polyadenylation (APA) events and identify APA tuned genes with prognostic significance in lung adenocarcinoma (LUAD). Genome-wide RNA expression profile and APA events were acquired in LUAD cancer and normal samples in GSE197346. Comparative analysis screened common deregulated genes and transcripts. All 11 and 19 transcripts were up and down expressed and polyadenylated in cancer samples, respectively. Clinical analysis found eight genes with prognostic significance, such as coiled-coil domain containing 137 (CCDC137). Role of CCDC137 in LUAD was first reported in this study. The cellular and animal experiments indicated that downregulated CCDC137 suppressed the malignant tumor phenotype and tumor growth in LUAD. Then, to identify APA regulators for elevated CCDC137, we analyzed the expression of 26 APA regulators in GSE197346 and The Cancer Genome Atlas (TCGA), and found 4 differential regulators: CPSF1, CELF2, NUDT21, and ELAVL1. At last, the correlation of eight genes with four differential APA regulators was analyzed, and CPSF1 showed a strong positive correlation with CCDC137. Based on the above results, we propose an oncogenic axis of CPSF1-CCDC137 in LUAD. This study first constructed a polyadenylation tuned RNA expression map in LUAD, and the proposed oncogenic axis of CPSF1-CCDC137 would shed light on the pathogenesis of LUAD.

A machine learning model identifies M3-like subtype in AML based on PML/RARα targets

The typical genomic feature of acute myeloid leukemia (AML) M3 subtype is the fusion event of PML/RARα, and ATRA/ATO-based combination therapy is current standard treatment regimen for M3 subtype. Here, a machine-learning model based on expressions of PML/RARα targets was developed to identify M3 patients by analyzing 1228 AML patients. Our model exhibited high accuracy. To enable more non-M3 AML patients to potentially benefit from ATRA/ATO therapy, M3-like patients were further identified. We found that M3-like patients had strong GMP features, including the expression patterns of M3 subtype marker genes, the proportion of myeloid progenitor cells, and deconvolution of AML constituent cell populations. M3-like patients exhibited distinct genomic features, low immune activity and better clinical survival. The initiative identification of patients similar to M3 subtype may help to identify more patients that would benefit from ATO/ATRA treatment and deepen our understanding of the molecular mechanism of AML pathogenesis.

A novel coiled-coil domain containing-related gene signature for predicting prognosis and treatment effect of breast cancer

PURPOSE

Breast cancer (BRCA) is a prevalent tumor worldwide. The association between the coiled-coil domain-containing (CCDC) protein family and different tumors has been established. However, the prognostic significance of this protein family in breast cancer remains uncertain.

METHODS

Gene expression and clinical data were obtained from the TCGA, METABRIC, and GEO databases. Prognosis genes were identified using univariate Cox and LASSO Cox regression, leading to the establishment of a prognostic signature. Subsequently, the risk model was conducted based on survival and clinical feature analyses, and a nomogram for prognosis prediction was developed. Furthermore, analyses of biological function, immune characteristics, and drug sensitivity were performed. Finally, single-cell sequencing data were utilized to uncover the expression patterns of genes in the risk model.

RESULTS

Five genes were identified and utilized for risk modeling. The model demonstrated excellent prognostic value as indicated by ROC and Kaplan-Meier analysis. The high-risk group exhibited shorter survival time and higher likelihood of recurrence. Functional annotation indicated a correlation between the risk score and immune pathways. Conversely, the low-risk group displayed a greater enrichment in immune pathways and exhibited more active immune microenvironment characteristics. Additionally, drug sensitivity analysis using both public and our sequencing data revealed that the risk model possessed a broad range of predictive values.

CONCLUSIONS

We have developed a gene signature and have verified that patients with low-risk are more likely to have better prognosis and respond positively to therapy. This finding offers a valuable point of reference for BRCA individualized treatment.