Discovering Breast Cancer Biomarkers Candidates through mRNA Expression Analysis Based on The Cancer Genome Atlas Database

Deciphering the influence of AP1M2 in modulating hepatocellular carcinoma growth and Mobility through JNK/ErK signaling pathway control

BACKGROUND

Hepatocellular Carcinoma (HCC) is the most common digestive system malignancy, with unclear pathogenesis and low survival rates. AP1M2 is associated with tumor progression, but its role and molecular mechanisms in HCC remain poorly understood and require further investigation.

METHODS

We utilized the Gene Expression Omnibus (GEO) and Expression Analysis Interactive Hub (XENA) databases to assess AP1M2 mRNA expression levels in HCC patients. Additionally, we employed the Cancer Genome Atlas (TCGA) database to identify pathways associated with both AP1M2 and HCC development. To evaluate the effect of AP1M2 on HCC cell proliferation and migration, we employed various techniques including EdU, CCK-8, Colony formation assay, and Transwell assays. Furthermore, Western blot analysis was conducted to examine the signaling pathways influenced by AP1M2.

RESULTS

AP1M2 expression was significantly increased at the mRNA level in HCC tissues(P<0.001). Importantly, overall survival (OS) analysis confirmed the association between higher AP1M2 expression and a poorer prognosis in HCC patients compared to those with lower AP1M2 expression (P<0.019).Multivariate Cox regression analysis showed that AP1M2 was an independent prognostic factor and a valid predictor for HCC patients. Furthermore, GSEA results indicated differential enrichment of lipid, metal metabolism, and coagulation processes in HCC samples demonstrating a high AP1M2 expression phenotype. In vitro experiments supported these findings by demonstrating that AP1M2 promotes HCC cell proliferation and migration, while activating the JNK/ERK pathway.

CONCLUSION

Our findings indicate that AP1M2 expression may serve as a potential molecular marker indicating a poor prognosis for HCC patients. Furthermore, we have demonstrated that AP1M2 significantly influences HCC cell proliferation and migration, with the JNK/ERK signaling pathway playing a key role in AP1M2-mediated regulation in the context of HCC.

The MexTAg collaborative cross: host genetics affects asbestos related disease latency, but has little influence once tumours develop

Objectives: This study combines two innovative mouse models in a major gene discovery project to assess the influence of host genetics on asbestos related disease (ARD). Conventional genetics studies provided evidence that some susceptibility to mesothelioma is genetic. However, the identification of host modifier genes, the roles they may play, and whether they contribute to disease susceptibility remain unknown. Here we report a study designed to rapidly identify genes associated with mesothelioma susceptibility by combining the Collaborative Cross (CC) resource with the well-characterised MexTAg mesothelioma mouse model. Methods: The CC is a powerful mouse resource that harnesses over 90% of common genetic variation in the mouse species, allowing rapid identification of genes mediating complex traits. MexTAg mice rapidly, uniformly, and predictably develop mesothelioma, but only after asbestos exposure. To assess the influence of host genetics on ARD, we crossed 72 genetically distinct CC mouse strains with MexTAg mice and exposed the resulting CC-MexTAg (CCMT) progeny to asbestos and monitored them for traits including overall survival, the time to ARD onset (latency), the time between ARD onset and euthanasia (disease progression) and ascites volume. We identified phenotype-specific modifier genes associated with these traits and we validated the role of human orthologues in asbestos-induced carcinogenesis using human mesothelioma datasets. Results: We generated 72 genetically distinct CCMT strains and exposed their progeny (2,562 in total) to asbestos. Reflecting the genetic diversity of the CC, there was considerable variation in overall survival and disease latency. Surprisingly, however, there was no variation in disease progression, demonstrating that host genetic factors do have a significant influence during disease latency but have a limited role once disease is established. Quantitative trait loci (QTL) affecting ARD survival/latency were identified on chromosomes 6, 12 and X. Of the 97-protein coding candidate modifier genes that spanned these QTL, eight genes (CPED1, ORS1, NDUFA1, HS1BP3, IL13RA1, LSM8, TES and TSPAN12) were found to significantly affect outcome in both CCMT and human mesothelioma datasets. Conclusion: Host genetic factors affect susceptibility to development of asbestos associated disease. However, following mesothelioma establishment, genetic variation in molecular or immunological mechanisms did not affect disease progression. Identification of multiple candidate modifier genes and their human homologues with known associations in other advanced stage or metastatic cancers highlights the complexity of ARD and may provide a pathway to identify novel therapeutic targets.

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome

BACKGROUND

Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information.

RESULTS

The model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study.

CONCLUSIONS

Our proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet .

tRNA-Uridine Aminocarboxypropyltransferase DTW Domain Containing 2 Suppresses Colon Adenocarcinoma Progression

Background

DTW Domain Containing 2 (DTWD2) is a newly identified transfer RNA-uridine aminocarboxypropyltransferase. Dysregulated expression of DTWD1 has been reported in several malignancies, nevertheless, the role of DTWD2 in cancers remains completely unknown. Here, we aimed to initially investigate the expression and role of DTWD2 in colon adenocarcinoma.

Methods

We first evaluated the transcription and mRNA levels of DTWD2 using data from The Cancer Genome Atlas. Besides, we tested its mRNA and protein expression in our enrolled retrospective cohort. Univariate and multivariate analyses were conducted to assess its prognostic value. Cellular experiments and xenografts were also performed to validate the role of DTWD2 in colon cancer progression.

Results

DTWD2 was downregulated in colon adenocarcinoma and associated with poor prognosis. Lymph node metastasis, distant metastasis, and advanced tumor stage are all characterized by lower DTWD2 levels. Furthermore, Cox regression analysis demonstrated that DTWD2 is a novel independent prognostic factor for colon cancer patients. Finally, cellular and xenograft data demonstrated that silencing DTWD2 significantly enhanced colon cancer growth.

Conclusion

Low expression of DTWD2 may be a potential molecular marker for poor prognosis in colon cancer.

A novel signature based on cancer-associated fibroblast genes to predict prognosis, immune feature, and therapeutic response in breast cancer

Breast cancer (BC) ranks first in the incidence of tumors in women and remains the most prevalent malignancy in women worldwide. Cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME) profoundly influence the progression, recurrence, and therapeutic resistance in BC. Here, we intended to establish a risk signature based on screened CAF-associated genes in BC (BCCGs) for patient stratification. Initially, BCCGs were screened by a combination of several CAF gene sets. The identified BCGGs were found to differ significantly in the overall survival (OS) of BC patients. Accordingly, we constructed a prognostic prediction signature of 5 BCCGs, which were independent prognostic factors associated with BC based on univariate and multivariate Cox regression. The risk model divided patients into low- and high-risk groups, accompanied by different OS, clinical features, and immune infiltration characteristics. Receiver operating characteristic (ROC) curves and a nomogram further validated the predictive performance of the prognostic model. Notably, 21 anticancer agents targeting these BCCGs possessed better sensitivity in BC patients. Meanwhile, the elevated expression of the majority of immune checkpoint genes suggested that the high-risk group may benefit more from immune checkpoint inhibitors (ICIs) therapy. Taken together, our well-established model is a robust instrument to precisely and comprehensively predict the prognosis, immune features, and drug sensitivity in BC patients, for combating BC.