Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis

Pleckstrin-2 Mediates the Activation of AKT in Prostate Cancer and Is Repressed by Androgen Receptor

Phosphoinositide 3-kinase (PI3K)-AKT and androgen receptor (AR) pathways are commonly activated in prostate cancers. Their reciprocal regulation makes advanced prostate cancers difficult to treat. The current study shows that pleckstrin-2 (PLEK2), a proto-oncoprotein involved in the activation and stabilization of AKT, connects these two pathways. Genetic evidence provided herein suggests that Plek2 deficiency largely reverted tumorigenesis in Pten prostate-specific knockout mice and that overexpression of PLEK2 promoted the proliferation and colony formation of prostate cancer cells in vitro. In addition, PLEK2 was negatively regulated by AR, AR transcriptionally repressed PLEK2 through binding to the PLEK2 promoter region, and overexpression of AR reduced PLEK2 expression, which inactivated AKT. Conversely, knockdown of AR in prostate cancer cells increased PLEK2 expression and activated the AKT pathway. This reciprocal inhibitory loop can be pharmacologically targeted using the PLEK2 inhibitor. PLEK2 inhibitor dose-dependently inhibited prostate cancer cell proliferation with the inactivation of AKT. Overall, the current study uncovered a crucial role of PLEK2 in prostate cancer proliferation and provided the rationale for targeting PLEK2 to treat prostate cancers.

Unraveling neoantigen-associated genes in bladder cancer: An in-depth analysis employing 101 machine learning algorithms

The therapeutic outcomes for bladder cancer (BLCA) remain suboptimal. Concurrently, there is a growing appreciation for the role of neoantigens in tumors. In this study, we explored the mechanisms underlying the involvement of neoantigen-associated genes in BLCA and their impact on prognosis. Our analysis incorporated both single-cell sequencing and bulk sequencing data sourced from publicly available databases. By employing a comprehensive set of 10 machine learning algorithms, we generated 101 algorithm combinations. The optimal combination, determined based on consistency indices, was utilized to construct a prognostic model comprising nine genes (CAPG, ACTA2, PDIA6, AKNA, PTMS, SNAP23, ID2, CD3G, SP140). Subsequently, we validated this model in an independent cohort, demonstrating its robust testing efficacy. Moreover, we explored the correlations between various clinical traits, model scores, and genes. Leveraging extensive public data resources, we conducted a drug sensitivity analysis to provide insights for targeted drug screening. Additionally, consensus clustering analysis and immune infiltration analysis were performed on bulk sequencing datasets and immunotherapy cohorts. These analyses yield valuable insights into the role of neoantigens in BLCA, guiding future research endeavors.

The predictive efficacy of programmed cell death in immunotherapy of melanoma: A comprehensive analysis of gene expression data for programmed cell death biomarker and therapeutic target discovery

In this study, genes linked to prognosis in skin cutaneous melanoma (SKCM) involved in programmed cell death (PCD) were identified and confirmed and prognostic models based on these genes were constructed. Acquisition and analysis of clinical data and RNA sequencing information from The Cancer Genome Atlas-SKCM (TCGA-SKCM) and Sangerbox databases, gene expression data for 477 tumor samples and 2 normal samples were successfully gathered. The patients were separated into two clusters based on consensus clustering of PCD-related genes, with Cluster A having greater tumor purity, ESTIMATE score, immune score, and matrix score, and Cluster B having a significantly distinct pattern of immune cell infiltration. The use of gene set enrichment analysis and weighted correlation network analysis showed significant associations between certain genes and factors such as tumor mutation burden, age, stage, grade, and tumor subtype. Finally, based on the 12 genes selected by Least Absolute Shrinkage and Selection Operator regression analysis (STAT3, IRF2, SLC7A11, ZEB1, LIPT1, PML, GCH1, GYS1, ABCC1, XBP1, TFAP2C, NOX4), a prognostic model of PGD-related genes was constructed. The effectiveness of the model's prognostic value was confirmed through survival analysis, time-dependent receiver operating characteristic curve, single-factor Cox regression analysis, and nomogram. We also verified the relationship between the GCH1 and MKI67 expression by wet experiment. This model has high prediction accuracy in SKCM patients and can provide a reference for clinical treatment.

FAM72 family proteins as poor prognostic markers in clear cell renal carcinoma

Purpose

This study aimed to investigate the prognostic significance of the Family with Sequence Similarity 72 member (FAM72) gene family in clear cell renal carcinoma (ccRCC) using a bioinformatic approach.

Patients and methods

To investigate the association between FAM72 and ccRCC, we utilized various databases and analysis tools, including TCGA, GEPIA, Metscape, cBioPortal, and MethSurv. We conducted an analysis of FAM72 expression levels in ccRCC tissues compared to normal kidney tissues and performed univariate and multivariate Cox analysis to determine the relationship between FAM72 expression and patient prognosis. Furthermore, we carried out Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) to identify enriched biological processes associated with FAM72 expression. Additionally, we analyzed immune cell infiltration and the level of methylation in ccRCC patients. Our bioinformatic analysis revealed that FAM72 expression levels were significantly higher in ccRCC tissues than in normal kidney tissues. High expression of FAM72 was associated with poor prognosis in ccRCC patients and was found to be an independent prognostic factor for ccRCC. GO and GSEA analyses indicated that FAM72 was enriched in biological processes related to mitosis, cell cycle, and DNA metabolism. Moreover, we found a significant correlation between FAM72 and immune cell infiltration and the level of methylation in ccRCC patients.

Conclusion

Our findings suggest that FAM72 could serve as an unfavorable prognostic molecular marker for ccRCC. A comprehensive understanding of FAM72 could provide crucial insights into tumor progression and prognosis.

A Splicing Transcriptome-Wide Association Study Identifies Candidate Altered Splicing for Prostate Cancer Risk

Prostate cancer (PCa) represents a huge public health burden among men. Many susceptibility genetic factors for PCa still remain unknown. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for PCa risk by assessing 79,194 cases and 61,112 controls of European ancestry in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia. We identified 120 splicing introns of 97 genes showing an association with PCa risk at false discovery rate (FDR)-corrected threshold (FDR <0.05). Of them, 33 genes were enriched in PCa-related diseases and function categories. Fine-mapping analysis suggested that 21 splicing introns of 19 genes were likely causally associated with PCa risk. Thirty-five splicing introns of 34 novel genes were identified to be related to PCa susceptibility for the first time, and 11 of the genes were enriched in a cancer-related network. Our study identified novel loci and splicing introns associated with PCa risk, which can improve our understanding of the etiology of this common malignancy.

Fam72a functions as a cell-cycle-controlled gene during proliferation and antagonizes apoptosis through reprogramming PP2A substrates

The cell cycle is key to life. After decades of research, it is unclear whether any parts of this process have yet to be identified. Fam72a is a poorly characterized gene and is evolutionarily conserved across multicellular organisms. Here, we have found that Fam72a is a cell-cycle-regulated gene that is transcriptionally and post-transcriptionally regulated by FoxM1 and APC/C, respectively. Functionally, Fam72a directly binds to tubulin and both the Aα and B56 subunits of PP2A-B56 to modulate tubulin and Mcl1 phosphorylation, which in turn affects the progression of the cell cycle and signaling of apoptosis. Moreover, Fam72a is involved in early responses to chemotherapy, and it efficiently antagonizes various anticancer compounds such as CDK and Bcl2 inhibitors. Thus, Fam72a switches the tumor-suppressive PP2A to be oncogenic by reprogramming its substrates. These findings identify a regulatory axis of PP2A and a protein member in the cell cycle and tumorigenesis regulatory network in human cells.

PLEK2 promotes cancer stemness and tumorigenesis of head and neck squamous cell carcinoma via the c-Myc-mediated positive feedback loop

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the most frequent malignancies worldwide and is characterized by unfavorable prognosis, high lymph node metastasis and early recurrence. However, the molecular events regulating HNSCC tumorigenesis remain poorly understood. Therefore, uncovering the underlying mechanisms is urgently needed to identify novel and promising therapeutic targets for HNSCC. In this study, we aimed to explore the role of pleckstrin‐2 (PLEK2) in regulating HNSCC tumorigenesis.

Methods

The expression pattern of PLEK2 and its clinical significance in HNSCC were determined by analyzing publicly assessable datasets and our own independent HNSCC cohort. In vitro and in vivo experiments, including cell proliferation, colony formation, Matrigel invasion, tumor sphere formation, ALDEFLUOR, Western blotting assays and xenograft mouse models, were used to investigate the role of PLEK2 in regulating the malignant behaviors of HNSCC cells. The underlying molecular mechanisms for the tumor‐promoting role of PLEK2 were elucidated using co‐immunoprecipitation, cycloheximide chase analysis, ubiquitination assays, chromatin immunoprecipitation‐quantitative polymerase chain reaction, luciferase reporter assays and rescue experiments.

Results

The expression levels of PLEK2 mRNA and protein were significantly increased in HNSCC tissues, and PLEK2 overexpression was strongly associated with poor overall survival and therapeutic resistance. Additionally, PLEK2 was important for maintaining the proliferation, invasion, epithelial‐mesenchymal transition, cancer stemness and tumorigenesis of HNSCC cells and could alter the cellular metabolism of the cancer cells. Mechanistically, PLEK2 interacted with c‐Myc and reduced the association of F‐box and WD repeat domain containing 7 (FBXW7) with c‐Myc, thereby avoiding ubiquitination and subsequent proteasome‐mediated degradation of c‐Myc. Moreover, the c‐Myc signaling activated by PLEK2 was important for sustaining the aggressive malignant phenotypes and tumorigenesis of HNSCC cells. c‐Myc also directly bounded to the PLEK2 promoter and activated its transcription, forming a positive feedback loop.

Conclusions

Collectively, these findings uncover a previously unknown molecular basis of PLEK2‐enhanced c‐Myc signaling in HNSCC, suggesting that PLEK2 may represent a promising therapeutic target for treating HNSCC.

Identification and Validation of a Novel Prognostic Gene Model for Colorectal Cancer

Aims

Colon cancer (CRC), with high morbidity and mortality, is a common and highly malignant cancer, which always has a bad prognosis. So it is urgent to employ a reasonable manner to assess the prognosis of patients. We developed and validated a gene model for predicting CRC risk.

Methods

The Gene Expression Omnibus (GEO) database was used to extract the gene expression profiles of CRC patients (N = 181) from GEO to identify genes that were differentially expressed between CRC patients and controls and then stable signature genes by firstly using both robust likelihood-based modeling with 1000 iterations and random survival forest variable hunting algorithms. Cluster analysis using the longest distance method was drawn out, and Kaplan–Meier (KM) survival analysis was used to compare the clusters. Meanwhile, the risk score was evaluated in three independent datasets including the GEO and Illumina HiSeq sequencing platforms. The corresponding risk index was calculated, and samples were clustered into high- and low-risk groups according to the median. And survival ROC analysis was used to evaluate the prognostic model. Finally, the Gene Set Enrichment Analysis (GSEA) was performed for further functional enrichment analyses.

Results

A 10-gene model was obtained, including 7 negative impact factors (SLC39A14, AACS, ERP29, LAMP3, TMEM106C, TMED2, and SLC25A3) and 3 positive ones (CNPY2, GRB10, and PBK), which related with several important oncogenic pathways (KRAS signaling, TNF-α signaling pathway, and WNT signaling pathway) and several cancer-related cellular processes (epithelial mesenchymal transition and cellular apoptosis). By using colon cancer datasets from The Cancer Genome Atlas (TCGA), the model was validated in KM survival analysis (P ≤ 0.001) and significant analysis with recurrence time (P = 0.0018).

Conclusions

This study firstly developed a stable and effective 10-gene model by using novel combined methods, and CRC patients might be able to use it as a prognostic marker for predicting their survival and monitoring their long-term treatment.

Identification of biomarkers for immunotherapy response in prostate cancer and potential drugs to alleviate immunosuppression

Background: Immunotherapy has a significant effect on the treatment of many tumor types. However, prostate cancers generally fail to show significant responses to immunotherapy owing to their immunosuppressive microenvironments. To sustain progress towards more effective immunotherapy for prostate cancer, comprehensive analyses of the genetic characteristics of the immune microenvironment and novel therapeutic strategies are required.

Methods: The transcriptome profiles of patients with prostate cancer were obtained from GEO and processed with the TIDE algorithm to predict their responses to immunotherapy. Next, the significant differentially expressed genes (DEGs) between the responder and non-responder groups were identified and used to compute the co-expression modules by WGCNA. Then, co-expression networks were constructed and survival analysis was applied to hub genes. Finally, drug candidates to alleviate immunosuppression were filtered in prostate cancer using GSEA based on hub genes.

Results: In total, we identified 2758 significant DEGs and constructed 16 co-expression modules, seven of which were significantly correlated with the immune response score. In total, 133 hub genes were identified, of which 13 were significantly associated with prostate cancer prognosis. Co-expression networks of hub genes were constructed with KMT2B at the center. Finally, six candidate drugs for prostate cancer immunotherapy were identified in PC3 and LNCaP cell lines.

Conclusions: We obtained datasets from multiple platforms, performed integrated bioinformatic analysis to identify 133 hub genes and 13 biomarkers of an immunotherapy response, and six candidate drugs were filtered to inhibit the immunosuppressive tumor microenvironment, to ultimately improve patient responses to immunotherapy in prostate cancer.

Emerging Roles of Pleckstrin-2 Beyond Cell Spreading

Pleckstrin-2 is a member of pleckstrin family with well-defined structural features that was first identified in 1999. Over the past 20 years, our understanding of PLEK2 biology has been limited to cell spreading. Recently, increasing evidences support that PLEK2 plays important roles in other cellular events beyond cell spreading, such as erythropoiesis, tumorigenesis and metastasis. It serves as a potential diagnostic and prognostic biomarker as well as an attractive target for the treatment of cancers. Herein, we summary the protein structure and molecular interactions of pleckstrin-2, with an emphasis on its regulatory roles in tumorigenesis.

TACR2 is associated with the immune microenvironment and inhibits migration and proliferation via the Wnt/β-catenin signaling pathway in prostate cancer

Background

The tachykinin receptor 2 (TACR2) is encoded by the tachykinin receptor correlation gene. Recent microarray analysis for prostate cancer suggests that TACR2 expression is associated with clinical phenotype and disease-free survival among patients with prostate cancer.

Results

TACR2 protein levels were lower in prostate cancer tissues than in adjacent normal prostate tissue. TACR2 expression significantly correlated with clinical stage, Gleason scores, and survival outcomes. TACR2 expression positively correlated with mast cells and negatively correlated with M2 macrophages. Overexpression of TACR2 promoted the migration and proliferation of prostate cancer cells by regulating the Wnt signaling pathway.

Conclusions

The TACR2-Wnt/β-catenin signaling pathway is critical in prostate cancer. TACR2 may affect tumor cells’ occurrence and development by changing the content of immune cells in the tumor microenvironment. These findings suggest that TACR2 may be a candidate molecular biomarker for prostate cancer therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02126-0.

Long non-coding RNAs correlate with genomic stability in prostate cancer: A clinical outcome and survival analysis

BACKGROUND

Long non-coding RNAs (lncRNAs) participate in the regulation of genomic stability. Understanding their biological functions can help us identify the mechanisms of the occurrence and progression of cancers and can provide theoretical guidance and the basis for treatment.

RESULTS

Based on the mutation hypothesis, we proposed a computational framework to identify genomic instability-related lncRNAs. Based on the differentially-expressed lncRNAs (DElncRNAs), we constructed a genomic instability-derived lncRNA signature (GILncSig) to calculate and stratify outcomes in patients with prostate cancer. It is an independent predictor of overall survival. The area under the curve = 0.805. This value may be more significant than the classic prognostic markers TP53 and Speckle-type POZ protein (SPOP) in terms of outcome prediction.

CONCLUSIONS

In summary, we conducted a computation approach and resource for mining genome instability-related lncRNAs. It may turn out to be highly significant for genomic instability and customized decision-making for patients with prostate cancer. It also may lead to effective methods and resources to study the molecular mechanism of genomic instability-related lncRNAs.

Genome instability-related long non-coding RNA in clear renal cell carcinoma determined using computational biology

Background

There is evidence that long non-coding RNA (lncRNA) is related to genetic stability. However, the complex biological functions of these lncRNAs are unclear.

Method

TCGA - KIRC lncRNAs expression matrix and somatic mutation information data were obtained from TCGA database. “GSVA” package was applied to evaluate the genomic related pathway in each samples. GO and KEGG analysis were performed to show the biological function of lncRNAs-mRNAs. “Survival” package was applied to determine the prognostic significance of lncRNAs. Multivariate Cox proportional hazard regression analysis was applied to conduct lncRNA prognosis model.

Results

In the present study, we applied computational biology to identify genome-related long noncoding RNA and identified 26 novel genomic instability-associated lncRNAs in clear cell renal cell carcinoma. We identified a genome instability-derived six lncRNA-based gene signature that significantly divided clear renal cell samples into high- and low-risk groups. We validated it in test cohorts. To further elucidate the role of the six lncRNAs in the model’s genome stability, we performed a gene set variation analysis (GSVA) on the matrix. We performed Pearson correlation analysis between the GSVA scores of genomic stability-related pathways and lncRNA. It was determined that LINC00460 and LINC01234 could be used as critical factors in this study. They may influence the genome stability of clear cell carcinoma by participating in mediating critical targets in the base excision repair pathway, the DNA replication pathway, homologous recombination, mismatch repair pathway, and the P53 signaling pathway.

Conclusion subsections

These data suggest that LINC00460 and LINC01234 are crucial for the stability of the clear cell renal cell carcinoma genome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08356-9.

Identification of a Prognostic Signature Model with Tumor Microenvironment for predicting Disease-free Survival after Radical Prostatectomy

Background: The tumor microenvironment (TME) and immune checkpoint inhibitors have been shown to promote active immune responses through different mechanisms. We attempted to identify the important prognostic genes and prognostic characteristics related to TME in prostate cancer (PCa).

Methods: The gene transcriptome profiles and clinical information of PCa patients were obtained from The Cancer Genome Atlas (TCGA) database, and the immune and stromal scores were calculated by the ESTIMATE algorithm. We evaluated the prognostic value of the risk score (RS) model based on univariate Cox analysis and least absolute shrinkage and selection operation (LASSO) Cox regression analysis and established a nomogram to predict disease-free survival (DFS) in PCa patients. The GSE70768 dataset was utilized for external validation. Twenty-two subsets of tumor-infiltrating immune cells were analyzed using the CIBERSORT algorithm.

Results: In this study, the patients with higher immune/stromal scores were associated with a worse DFS, higher Gleason score, and higher pathological T stage. Based on the immune and stromal scores, 515 differentially expressed genes (DEGs) were identified. The univariate Cox and LASSO Cox regression models were employed to select 18 DEGs from 515 DEGs and construct an RS model. The DFS of the high-RS group was significantly lower than that of the low-RS group (P<0.001). The AUCs for the 1-year, 3-year and 5-year DFS rates in the RS model were 0.890, 0.877 and 0.841, respectively. A nomogram of DFS was established based on the RS and Gleason score, and the AUCs for the 1-year, 3-year and 5-year DFS rates in the nomogram were 0.907, 0.893, and 0.872, respectively. These results were further validated in the GSE70768 dataset. In addition, the proportion of Tregs was determined to be higher in high-RS patients (P<0.05), and the expression levels of five immune checkpoints (CTLA-4, PD-1, LAG-3, TIM-3 and TIGIT) were observed to be higher in high-RS patients (P<0.05).

Conclusions: Our study established and validated an 18-gene prognostic signature model associated with TME, which might serve as a prognosis stratification tool to predict DFS in PCa patients after radical prostatectomy.