Comprehensive Analysis Identifying Wnt Ligands Gene Family for Biochemical Recurrence in Prostate Adenocarcinoma and Construction of a Nomogram

Comprehensive genomic characterization of programmed cell death-related genes to predict drug resistance and prognosis for patients with multiple myeloma

BACKGROUND

Multiple myeloma (MM) is a cancer that is difficult to be diagnosed and treated. This study aimed to identify programmed cell death (PCD)-related molecular subtypes of MM and to assess their impact on patients' prognosis, immune status, and drug sensitivity.

METHODS

We used the ConsensusClusterPlus method to classify molecular subtypes with prognostically relevant PCD genes from the MM patients screened. A prognostic model and a nomogram were established applying one-way COX regression analysis and LASSO Cox regression analysis. MM patients' sensitivity to chemotherapeutic agents was predicted for at-risk populations.

RESULTS

Six molecular subtypes were classified employing PCD-related genes, notably, three of them had a higher tendency for immune escape and two of them were correlated with a worse prognosis of MM. Furthermore, the C3 subtype had activated pathways such as oxidative phosphorylation and DNA repair, while the C2 and C4 subtypes had activated pathways related to apoptosis. The Risk score showed that the nomogram can correctly predict the OS for MM patients, in particular, patients in the high-risk group had low overall survival (OS). Pharmacovigilance analyses revealed that patients in the high-risk and low-risk groups had greater IC50 values for the drugs SB505124_1194 and AZD7762_1022, respectively.

CONCLUSIONS

A 12-gene Risk score model developed with PCD-related genes can accurately predict the survival for MM patients. Our study provided potential targets and strategies for individualized treatment of MM.

Prohibitin Links Cell Cycle, Motility and Invasion in Prostate Cancer Cells

Prohibitin (PHB) is a tumour suppressor gene with several different molecular activities. PHB overexpression leads to G1/S-phase cell cycle arrest, and PHB represses the androgen receptor (AR) in prostate cancer cells. PHB interacts with and represses members of the E2F family in a manner that may also be AR-linked, therefore making the AR:PHB:E2F interaction axis highly complex. PHB siRNA increased the growth and metastatic potential of LNCaP mouse xenografts in vivo. Conversely, PHB ectopic cDNA overexpression affected several hundred genes in LNCaP cells. Furthermore, gene ontology analysis showed that in addition to cell cycle regulation, several members of the WNT family were significantly downregulated (WNT7B, WNT9A and WNT10B), as well as pathways for cell adhesion. Online GEO data studies showed PHB expression to be decreased in clinical cases of metastatic prostate cancer, and to be correlated with higher WNT expression in metastasis. PHB overexpression reduced prostate cancer cell migration and motility in wound-healing assays, reduced cell invasion through a Matrigel layer and reduced cellular attachment. In LNCaP cells, WNT7B, WNT9A and WNT10B expression were also upregulated by androgen treatment and downregulated by androgen antagonism, indicating a role for AR in the control of these WNT genes. However, these WNTs were strongly cell cycle regulated. E2F1 cDNA ectopic expression and PHB siRNA (both cell cycle promoting effects) increased WNT7B, WNT9A and WNT10B expression, and these genes were also upregulated as cells were released from G1 to S phase synchronisation, indicating further cell cycle regulation. Therefore, the repressive effects of PHB may inhibit AR, E2F and WNT expression and its loss may increase metastatic potential in human prostate cancer.

Energy Metabolism-Related Gene Prognostic Index Predicts Biochemical Recurrence for Patients With Prostate Cancer Undergoing Radical Prostatectomy

Background

We aimed to construct and validate an energy metabolism-related gene prognostic index (EMRGPI) to predict biochemical recurrence (BCR) in patients undergoing radical prostatectomy.

Methods

We used Lasso and COX regression analysis to orchestrate the EMRGPI in the TCGA database, and the prognostic value of EMRGPI was further validated externally using the GSE46602. All analyses were conducted with R version 3.6.3 and its suitable packages.

Results

SDC1 and ADH1B were finally used to construct the risk formula. We classified the 430 tumor patients in the TCGA database into two groups, and patients in the high-risk group had a higher risk of BCR than those in the low-risk group (HR: 1.98, 95%CI: 1.18-3.32, p=0.01). Moreover, in the GSE46602, we confirmed that the BCR risk in the high-risk group was 3.86 times higher than that in the low-risk group (95%CI: 1.61-9.24, p=0.001). We found that patients in the high-risk group had significantly higher proportions of residual tumor, older age, and T stage. SDC1 and ADH1B were significantly expressed low in the normal tissues when compared to the tumor tissues, which were opposite at the protein level. The spearman analysis showed that EMRGPI was significantly associated with B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, dendritic cells, stromal score, immune score, and estimate score. In addition, the EMRGPI was positively associated with the 54 immune checkpoints, among which CD80, ADORA2A, CD160, and TNFRSF25 were significantly related to the BCR-free survival of PCa patients undergoing RP.

Conclusions

The EMRGPI established in this study might serve as an independent risk factor for PCa patients undergoing radical prostatectomy.

Identification of the prognostic value of a 2-gene signature of the WNT gene family in UCEC using bioinformatics and real-world data

Background

The WNT gene family plays an important role in the occurrence and development of malignant tumors, but its involvement has not been systematically analyzed in uterine corpus endometrial carcinoma (UCEC). This study aimed to evaluate the prognostic value of the WNT gene family in UCEC.

Methods

Pan-cancer transcriptome data of the UCSC Xena database and Genotype-Tissue Expression (GTEx) normal tissue data were downloaded to analyze the expression and prognosis of 19 WNT family genes in UCEC. A cohort from The Cancer Genome Atlas-Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) was used to analyze the expression of the WNT gene family in different immune subtypes and clinical subgroups. The STRING database was used to analyze the interaction of the WNT gene family and its biological function. Univariate Cox regression analysis and Lasso cox analysis were used to identify the genes associated with significant prognosis and to construct multi signature prognosis model. An immunohistochemical assay was used to verify the predictive ability of the model. Risk score and the related clinical features were used to construct a nomogram.

Results

The expression levels of WNT2, WNT3, WNT3A, WNT5A, WNT7A, and WNT10A were significantly different among different immune subtypes and correlated with TP53 mutation. According to the WNT family genes related to the prognosis of UCEC, UCEC was classified into two subtypes (C1, C2). The prognosis of subtype C1 was significantly better than that of subtype C2. A 2-gene signature (WNT2 and WNT10A) was constructed and the two significantly prognostic groups can be divided based on median Risk score. These results were verified using real-world data, and the nomogram constructed using clinical features and Risk score had good prognostic ability.

Conclusions

The 2-gene signature including WNT2 and WNT10A can be used to predict the prognosis of patients with UCEC, which is important for clinical decision-making and individualized therapy for patients with UCEC.