SMARCA4 promotes benign skin malignant transformation into melanoma through Adherens junction signal transduction

Leveraging various extracellular matrix levels to assess prognosis and sensitivity to immunotherapy in patients with ovarian cancer

Background

Ovarian cancer (OC) is the fifth leading cause of cancer-related deaths among women. Late diagnosis and heterogeneous treatment result in a poor prognosis for patients with OC. Therefore, we aimed to develop new biomarkers to predict accurate prognoses and provide references for individualized treatment strategies.

Methods

We constructed a co-expression network applying the "WGCNA" package and identified the extracellular matrix-associated gene modules. We figured out the best model and generated the extracellular matrix score (ECMS). The ECMS' ability to predict accurate OC patients' prognoses and responses to immunotherapy was evaluated.

Results

The ECMS was an independent prognostic factor in the training [hazard ratio (HR) = 3.132 (2.068-4.744), p< 0.001] and testing sets [HR = 5.514 (2.084-14.586), p< 0.001]. The receiver operating characteristic curve (ROC) analysis showed that the AUC values for 1, 3, and 5 years were 0.528, 0.594, and 0.67 for the training set, respectively, and 0.571, 0.635, and 0.684 for the testing set, respectively. It was found that the high ECMS group had shorter overall survival than the low ECMS group [HR = 2 (1.53-2.61), p< 0.001 in the training set; HR = 1.62 (1.06-2.47), p = 0.021 in the testing set; HR = 1.39 (1.05-1.86), p = 0.022 in the training set]. The ROC values of the ECMS model for predicting immune response were 0.566 (training set) and 0.572 (testing set). The response rate to immunotherapy was higher in patients with low ECMS.

Conclusion

We created an ECMS model to predict the prognosis and immunotherapeutic benefits in OC patients and provided references for individualized treatment of OC patients.

SWI/SNF complexes and cancers

Epigenetics refers to the study of genetic changes that can affect gene expression without altering the underlying DNA sequence, including DNA methylation, histone modification, chromatin remodelling, X chromosome inactivation and non-coding RNA regulation. Of these, DNA methylation, histone modification and chromatin remodelling constitute the three classical modes of epigenetic regulation. These three mechanisms alter gene transcription by adjusting chromatin accessibility, thereby affecting cell and tissue phenotypes in the absence of DNA sequence changes. In the presence of ATP hydrolases, chromatin remodelling alters the structure of chromatin and thus changes the transcription level of DNA-guided RNA. To date, four types of ATP-dependent chromatin remodelling complexes have been identified in humans, namely SWI/SNF, ISWI, INO80 and NURD/MI2/CHD. SWI/SNF mutations are prevalent in a wide variety of cancerous tissues and cancer-derived cell lines as discovered by next-generation sequencing technologies.. SWI/SNF can bind to nucleosomes and use the energy of ATP to disrupt DNA and histone interactions, sliding or ejecting histones, altering nucleosome structure, and changing transcriptional and regulatory mechanisms. Furthermore, mutations in the SWI/SNF complex have been observed in approximately 20% of all cancers. Together, these findings suggest that mutations targeting the SWI/SNF complex may have a positive impact on tumorigenesis and cancer progression.

RTP4 is a novel prognosis-related hub gene in cutaneous melanoma

OBJECTIVE

Melanoma accounts for 80% of skin cancer deaths. The pathogenesis of melanoma is regulated by gene networks. Thus, we aimed here to identify gene networks and hub genes associated with melanoma and to further identify their underlying mechanisms.

METHODS

GTEx (normal skin) and TCGA (melanoma tumor) RNA-seq datasets were employed for this purpose. We conducted weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes associated with melanoma. Log-rank analysis and multivariate Cox model analysis were performed to identify prognosis genes, which were validated using two independent melanoma datasets. We also evaluated the correlation between prognostic gene and immune cell infiltration.

RESULTS

The blue module was the most relevant for melanoma and was thus considered the key module. Intersecting genes were identified between this module and differentially expressed genes (DEGs). Finally, 72 genes were identified and verified as hub genes using the Oncomine database. Log-rank analysis and multivariate Cox model analysis identified 13 genes that were associated with the prognosis of the metastatic melanoma group, and RTP4 was validated as a prognostic gene using two independent melanoma datasets. RTP4 was not previously associated with melanoma. When we evaluated the correlation between prognostic gene and immune cell infiltration, we discovered that RTP4 was associated with immune cell infiltration. Further, RTP4 was significantly associated with genes encoding components of immune checkpoints (PDCD1, TIM-3, and LAG3).

CONCLUSIONS

RTP4 is a novel prognosis-related hub gene in cutaneous melanoma. The novel gene RTP4 identified here will facilitate the exploration of the molecular mechanism of the pathogenesis and progression of melanoma and the discovery of potential new target for drug therapy.