Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

Noncoding RNA-associated competing endogenous RNA networks in trastuzumab-induced cardiotoxicity

Trastuzumab-induced cardiotoxicity (TIC) is a common and serious disease with abnormal cardiac function. Accumulating evidence has indicated certain non-coding RNAs (ncRNAs), functioning as competing endogenous RNAs (ceRNAs), impacting the progression of cardiovascular diseases. Nonetheless, the specific involvement of ncRNA-mediated ceRNA regulatory mechanisms in TIC remains elusive. The present research aims to comprehensively investigate changes in the expressions of all ncRNA using whole-transcriptome RNA sequencing. The sequencing analysis unveiled significant dysregulation, identifying a total of 43 circular RNAs (circRNAs), 270 long noncoding RNAs (lncRNAs), 12 microRNAs (miRNAs), and 4131 mRNAs in trastuzumab-treated mouse hearts. Subsequently, circRNA-based ceRNA networks consisting of 82 nodes and 91 edges, as well as lncRNA-based ceRNA networks comprising 111 nodes and 112 edges, were constructed. Using the CytoNCA plugin, pivotal genes-miR-31-5p and miR-644-5p-were identified within these networks, exhibiting potential relevance in TIC treatment. Additionally, KEGG and GO analyses were conducted to explore the functional pathways associated with the genes within the ceRNA networks. The outcomes of the predicted ceRNAs and bioinformatics analyses elucidated the plausible involvement of ncRNAs in TIC pathogenesis. This insight contributes to a better understanding of underlying mechanisms and aids in identifying promising targets for effective prevention and treatment strategies.

Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method

PURPOSE

This study aims to assess the diagnostic value of ultrasound habitat sub-region radiomics feature parameters using a fully connected neural networks (FCNN) combination method L2,1-norm in relation to breast cancer Ki-67 status.

METHODS

Ultrasound images from 528 cases of female breast cancer at the Affiliated Hospital of Xiangnan University and 232 cases of female breast cancer at the Affiliated Rehabilitation Hospital of Xiangnan University were selected for this study. We utilized deep learning methods to automatically outline the gross tumor volume and perform habitat clustering. Subsequently, habitat sub-regions were extracted to identify radiomics features and underwent feature engineering using the L1,2-norm. A prediction model for the Ki-67 status of breast cancer patients was then developed using a FCNN. The model's performance was evaluated using accuracy, area under the curve (AUC), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), Recall, and F1. In addition, calibration curves and clinical decision curves were plotted for the test set to visually assess the predictive accuracy and clinical benefit of the models.

RESULT

Based on the feature engineering using the L1,2-norm, a total of 9 core features were identified. The predictive model, constructed by the FCNN model based on these 9 features, achieved the following scores: ACC 0.856, AUC 0.915, Spe 0.843, PPV 0.920, NPV 0.747, Recall 0.974, and F1 0.890. Furthermore, calibration curves and clinical decision curves of the validation set demonstrated a high level of confidence in the model's performance and its clinical benefit.

CONCLUSION

Habitat clustering of ultrasound images of breast cancer is effectively supported by the combined implementation of the L1,2-norm and FCNN algorithms, allowing for the accurate classification of the Ki-67 status in breast cancer patients.

Upregulation of POC1A in lung adenocarcinoma promotes tumour progression and predicts poor prognosis

Lung adenocarcinoma (LUAD) is characterized by a high incidence rate and mortality. Recently, POC1 centriolar protein A (POC1A) has emerged as a potential biomarker for various cancers, contributing to cancer onset and development. However, the association between POC1A and LUAD remains unexplored. We extracted The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) data sets to analyse the differential expression of POC1A and its relationship with clinical stage. Additionally, we performed diagnostic receiver operator characteristic (ROC) curve analysis and Kaplan-Meier (KM) survival analysis to assess the diagnostic and prognostic value of POC1A in LUAD. Furthermore, we investigated the correlation between POC1A expression and immune infiltration, tumour mutation burden (TMB), immune checkpoint expression and drug sensitivity. Finally, we verified POC1A expression using real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC). Cell experiments were conducted to validate the effect of POC1A expression on the proliferation, migration and invasion of lung cancer cells. POC1A exhibited overexpression in most tumour tissues, and its overexpression in LUAD was significantly correlated with late-stage presentation and poor prognosis. The high POC1A expression group showed lower levels of immune infiltration but higher levels of immune checkpoint expression and TMB. Moreover, the high POC1A expression group demonstrated sensitivity to multiple drugs. In vitro experiments confirmed that POC1A knockdown led to decreased proliferation, migration, and invasion of lung cancer cells. Our findings suggest that POC1A may contribute to tumour development by modulating the cell cycle and immune cell infiltration. It also represents a potential therapeutic target and marker for the diagnosis and prognosis of LUAD.

Comprehensive analysis of clinical prognosis and biological significance of CNIH4 in cervical cancer

BACKGROUND

Cornichon homolog 4 (CNIH4) belongs to the CNIH family. It functions as an oncogene in many tumors. However, CNIH4's significance in the immune landscape and its predictive potential in cervical cancer (CESC) is unexplored.

METHODS

CNIH4 levels and its effect on the survival of patients with CESC were evaluated using data retrieved from The Cancer Genome Atlas (TCGA). The oncogenic effect of CNIH4 in CESC was determined using small interfering RNA-mediated transfected cell lines and tumorigenesis experiments in animal models.

RESULTS

Higher expression of CNIH4 was found in advanced tumor and pathological stages, as well as lymph node metastasis. CNIH4 expression correlated positively with the infiltration of macrophages M2 and resting dendritic cells into the affected tissue. Additionally, functional enrichment of RNA-sequencing of CNIH4-knocked down CESC cell lines showed the association of CNIH4 to the PI3K-Akt signaling pathway. Single-sample gene set enrichment analysis highlighted several immune pathways that were elevated in the CESC samples with enhanced levels of CNIH4, including Type-I and Type-II IFN-response pathways. The impact of CNIH4 on drug sensitivity was further assessed using the GDSC database. As CNIH4 is linked to the immune landscape in CESC, this study determined a four-gene risk prediction signature utilizing CNIH4-related immunomodulators. The risk score quantified from the prediction signature was an independent predictive indicator in CESC. Receiver operating characteristic curve analysis verified the good predictive ability of the four-gene signature in TCGA-CESC cohort. Thus, the CNIH4-related model showed potential as an auxiliary TNM staging system tool.

CONCLUSION

CNIH4 may be an effective predictive biomarker for patients with cervical cancer, thus providing new ideas and research directions for CESC.

CNIH4 governs cervical cancer progression through reducing ferroptosis

Cervical cancer is one of the most leading causes of cancer death worldwide, and ferroptosis is implicated in the progression of cervical cancer. Cornichon family AMPA receptor auxiliary protein 4 (CNIH4) is involved in the progression of various human cancers; however, its function in cervical cancer remains unclear. The present study aims to investigate the role and mechanism of CNIH4 in cervical cancer using gain- and loss-of-function studies in vitro. SiHa and CaSki cells were infected with lentiviral vectors to manipulate the expression of CNIH4 in vitro, and cell viability, migration, invasion as well as ferroptosis were evaluated. Transcriptome sequencing analysis was performed to further validate the mechanism through which CNIH4 regulated the progression of cervical cancer. The expression of CNIH4 was upregulated in human cervical cancer tissues and cells, and strongly correlated with the decreases in overall survival and disease free survival rates of cervical cancer patients. CNIH4 silence inhibited, while CNIH4 overexpression facilitated the survival of human cervical cancer cells. Mechanistically, CNIH4 elevated solute carrier family 7 member 11 (SLC7A11)-mediated cystine import, and subsequently increased intracellular glutathione synthesis and glutathione peroxidase 4 activity, thereby inhibiting ferroptosis of human cervical cancer cells. SLC7A11 silence significantly abolished CNIH4-mediated inhibition of ferroptosis in cervical cancer cells in vitro. Our study for the first time reveals that CNIH4 inhibits ferroptosis of human cervical cancer cells through upregulating SLC7A11, defining CNIH4 as an attractive therapeutic and prognostic target for cervical cancer.