CACNA1C is a prognostic predictor for patients with ovarian cancer

Chang, Xiaohan; Dong, Yuanhua

doi:10.1186/s13048-021-00830-z

Cited by 19 publications

(12 citation statements)

References 53 publications

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“…VSTM2L could induce adverse survival outcomes in rectal cancer [42]. CACNA1C, as a voltage-gated calcium channel, is up-regulated in brain tumors, leukemia, breast cancer, and other tumors [43] and plays as an oncogene in OC tumors [44]. GDF3 is widely accepted as a pluripotency marker and expressed in several cancer types such as breast carcinoma [45] melanoma [46].…”

Section: Discussionmentioning

confidence: 99%

A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

Chen

Wang

et al. 2022

J Ovarian Res

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Background Ovarian cancer (OC) is the deadliest tumor in the female reproductive tract. And increased resistance to platinum-based chemotherapy represents the major obstacle in the treatment of OC currently. Robust and accurate gene expression models are crucial tools in distinguishing platinum therapy response and evaluating the prognosis of OC patients. Methods In this study, 230 samples from The Cancer Genome Atlas (TCGA) OV dataset were subjected to mRNA expression profiling, single nucleotide polymorphism (SNP), and copy number variation (CNV) analysis comprehensively to screen out the differentially expressed genes (DEGs). An SVM classifier and a prognostic model were constructed using the Random Forest algorithm and LASSO Cox regression model respectively via R. The Gene Expression Omnibus (GEO) database was applied as the validation set. Results Forty-eight differentially expressed genes (DEGs) were figured out through integrated analysis of gene expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) data. A 10-gene classifier was constructed which could discriminate platinum-sensitive samples precisely with an AUC of 0.971 in the training set and of 0.926 in the GEO dataset (GSE638855). In addition, 8 optimal genes were further selected to construct the prognostic risk model whose predictions were consistent with the actual survival outcomes in the training cohort (p = 9.613e-05) and validated in GSE638855 (p = 0.04862). PNLDC1, SLC5A1, and SYNM were then identified as hub genes that were associated with both platinum response status and prognosis, which was further validated by the Fudan University Shanghai cancer center (FUSCC) cohort. Conclusion These findings reveal a specific risk model that could serve as effective biomarkers to identify patients’ platinum response status and predict survival outcomes for OC patients. PNLDC1, SLC5A1, and SYNM are the hub genes that may serve as potential biomarkers in OC treatment.

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Section: Discussionmentioning

confidence: 99%

A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

Chen

Wang

et al. 2022

J Ovarian Res

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“…Hailong Pei et al found that RAC2 boosts the proliferation of quiescent cells by promoting JUNB expression via the MAL-SRF pathway 48 . Xiaohan Chang et al found that CACNA1C is a prognostic marker for patients with ovarian cancer, which showed a lower expression in ovarian cancer tissues 49 . Julian Tabasaran et al found that PAX6 is frequently expressed in ependymal tumors 50 .…”

Section: Discussionmentioning

confidence: 99%

Identification of key molecules and signaling pathways in GOLPH3 mediated colorectal cancer cells

Wang¹,

Wang²,

Wang

et al. 2022

Preprint

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GOLPH3 is a peripheral membrane protein that is significantly associated with the development of cancer. However, the signaling pathways and mechanisms of GOLPH3 mediated cancer are still unclear. Here, our objective is to identify the essential molecules and signaling pathways by analyzing the RNA-seq data. The GSE196875 was produced by the Illumina NovaSeq 6000 (Homo sapiens). The KEGG and GO analyses indicated that axon guidance, signaling pathways regulating pluripotency of stem cells, and circadian entrainment are the major affected processes during the development of prostate cancer with GOLPH3 overexpression. Moreover, the ten interacting molecules were figured out including BMP2, RAC2, CACNA1C, PAX6, COL4A1, SPARC, GRIN1, CAV3, COL5A1, COL4A2. Thus, our study provides novel knowledge on the GOLPH3 affected cancer cells.

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“…By analyzing the correlation of ten single-nucleotide polymorphisms (SNPs), S P Gampenrieder et al found that EDN1 can be concerned a predictive biomarker for bevacizumab treatment in patients with metastatic breast cancer 40 . The study by Xiaohan Chang found that CACNA1C has lower expression in ovarian cancer tissues and CACNA1C could be a prognostic biomarker for patients with ovarian cancer 41 . S-Y Ma et al reported that KCNMA1 inhibits the apoptosis of ovarian cancer cells, which serves as a biomarker for patients with ovarian cancer 42 .…”

Section: Discussionmentioning

confidence: 99%

Identification of transcriptional landscapes and functions of the inhibition of ARHGEF2 in hepatocellular carcinoma cells

Zhang

Chung

2022

Preprint

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The RHO guanine exchange factor ARHGEF2 has exchange activity toward RHOA, which is essential for the development of cancers such as liver cancer. However, the potential functions and mechanisms of ARHGEF2 in the progression of liver cancer are largely unknown. In this study, we identified the transcriptional landscapes of hepatocellular carcinoma cells treated with ARHGEF2 shRNAs. The gene enrichment assays such as KEGG and GO were used to further analyze the potential signaling pathways. Moreover, the PPI network and Reactome map were used to further identify the biological processes. The results showed that Alzheimer's disease disease (AD) and Cushing syndrome (CS) are the major signaling pathways involved in the ARHGEF2-shRNAs treated hepatocellular carcinoma cells. We identified the top ten interactive genes including ICAM1, APOE, LDLR, NAT10, HSPA1A, EDN1, CACNA1C, KCNMA1, SNAI1, and ELN. Our study may provide novel mechanisms for the treatment of liver cancer by inhibiting ARHGEF2.

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CACNA1C is a prognostic predictor for patients with ovarian cancer

Cited by 19 publications

References 53 publications

A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

Identification of key molecules and signaling pathways in GOLPH3 mediated colorectal cancer cells

Identification of transcriptional landscapes and functions of the inhibition of ARHGEF2 in hepatocellular carcinoma cells

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