The Role of lncRNAs TAPIR-1 and -2 as Diagnostic Markers and Potential Therapeutic Targets in Prostate Cancer

Metabolomics-transcriptomics joint analysis: unveiling the dysregulated cell death network and developing a diagnostic model for high-grade neuroblastoma

High-grade neuroblastoma (HG-NB) exhibits a significantly diminished survival rate in comparison to low-grade neuroblastoma (LG-NB), primarily attributed to the mechanism of HG-NB is unclear and the lacking effective therapeutic targets and diagnostic model. Therefore, the current investigation aims to study the dysregulated network between HG-NB and LG-NB based on transcriptomics and metabolomics joint analysis. Meanwhile, a risk diagnostic model to distinguish HG-NB and LG-NB was also developed. Metabolomics analysis was conducted using plasma samples obtained from 48 HG-NB patients and 36 LG-NB patients. A total of 39 metabolites exhibited alterations, with 20 showing an increase and 19 displaying a decrease in HG-NB. Additionally, transcriptomics analysis was performed on NB tissue samples collected from 31 HG-NB patients and 20 LG-NB patients. Results showed that a significant alteration was observed in a total of 1,199 mRNAs in HG-NB, among which 893 were upregulated while the remaining 306 were downregulated. In particular, the joint analysis of both omics data revealed three aberrant pathways, namely the cAMP signaling pathway, PI3K-Akt signaling pathway, and TNF signaling pathway, which were found to be associated with cell death. Notably, a diagnostic model for HG-NB risk classification was developed based on the genes MGST1, SERPINE1, and ERBB3 with an area under the receiver operating characteristic curve of 0.915. In the validation set, the sensitivity and specificity were determined to be 75.0% and 80.0%, respectively.

Integrative multi-omics analysis unveils stemness-associated molecular subtypes in prostate cancer and pan-cancer: prognostic and therapeutic significance

BACKGROUND

Prostate cancer (PCA) is the fifth leading cause of cancer-related deaths worldwide, with limited treatment options in the advanced stages. The immunosuppressive tumor microenvironment (TME) of PCA results in lower sensitivity to immunotherapy. Although molecular subtyping is expected to offer important clues for precision treatment of PCA, there is currently a shortage of dependable and effective molecular typing methods available for clinical practice. Therefore, we aim to propose a novel stemness-based classification approach to guide personalized clinical treatments, including immunotherapy.

METHODS

An integrative multi-omics analysis of PCA was performed to evaluate stemness-level heterogeneities. Unsupervised hierarchical clustering was used to classify PCAs based on stemness signature genes. To make stemness-based patient classification more clinically applicable, a stemness subtype predictor was jointly developed by using four PCA datasets and 76 machine learning algorithms.

RESULTS

We identified stemness signatures of PCA comprising 18 signaling pathways, by which we classified PCA samples into three stemness subtypes via unsupervised hierarchical clustering: low stemness (LS), medium stemness (MS), and high stemness (HS) subtypes. HS patients are sensitive to androgen deprivation therapy, taxanes, and immunotherapy and have the highest stemness, malignancy, tumor mutation load (TMB) levels, worst prognosis, and immunosuppression. LS patients are sensitive to platinum-based chemotherapy but resistant to immunotherapy and have the lowest stemness, malignancy, and TMB levels, best prognosis, and the highest immune infiltration. MS patients represent an intermediate status of stemness, malignancy, and TMB levels with a moderate prognosis. We further demonstrated that these three stemness subtypes are conserved across pan-tumor. Additionally, the 9-gene stemness subtype predictor we developed has a comparable capability to 18 signaling pathways to make tumor diagnosis and to predict tumor recurrence, metastasis, progression, prognosis, and efficacy of different treatments.

CONCLUSIONS

The three stemness subtypes we identified have the potential to be a powerful tool for clinical tumor molecular classification in PCA and pan-cancer, and to guide the selection of immunotherapy or other sensitive treatments for tumor patients.

A harmonized resource of integrated prostate cancer clinical, -omic, and signature features

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or  not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

Systematic investigation of the mechanism of herbal medicines for the treatment of prostate cancer

Due to various unpleasant side effects and general ineffectiveness of current treatments for prostate cancer (PCa), more and more people with PCa try to look for complementary and alternative medicine such as herbal medicine. However, since herbal medicine has multi-components, multi-targets and multi-pathways features, its underlying molecular mechanism of action is not yet known and still needs to be systematically explored. Presently, a comprehensive approach consisting of bibliometric analysis, pharmacokinetic assessment, target prediction and network construction is firstly performed to obtain PCa-related herbal medicines and their corresponding candidate compounds and potential targets. Subsequently, a total of 20 overlapping genes between DEGs in PCa patients and the target genes of the PCa-related herbs, as well as five hub genes, i.e., CCNA2, CDK2, CTH, DPP4 and SRC were determined employing bioinformatics analysis. Further, the roles of these hub genes in PCa were also investigated through survival analysis and tumour immunity analysis. Moreover, to validate the reliability of the C-T interactions and to further explore the binding modes between ingredients and their targets, the molecular dynamics (MD) simulations were carried out. Finally, based on the modularization of the biological network, four signaling pathways, i.e., PI3K-Akt, MAPK, p53 and cell cycle were integrated to further analyze the therapeutic mechanism of PCa-related herbal medicine. All the results show the mechanism of action of herbal medicines on treating PCa from the molecular to systematic levels, providing a reference for the treatment of complex diseases using TCM.

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

Immune-related gene index predicts metastasis for prostate cancer patients undergoing radical radiotherapy

In this study, we established a novel immunologic gene prognostic index (IGPI) to predict metastasis and provided new insights into tumor immune microenvironment (TIME) for PCa patients receiving radical radiotherapy. GBP2 and IGF1 were independent factors associated with metastasis-free survival. IGPI score was calculated based on GBP2 and IGF1 and this score was an independent risk factor for PCa patients undergoing radical radiotherapy. Patients with higher IGPI score were at higher risk of metastasis and biochemical recurrence, which were externally validated in the TCGA database and other GEO datasets. IGPI score had demonstrated moderate diagnostic ability of radiation resistance (AUC: 0.889). This score increased with the augment of Gleason score and T stage, as well as biochemical recurrence. Using EPIC, ESTIMATE and immunophenoscore (IPS) algorithms, cancer associated fibroblasts (CAFs), macrophages, stromal score, and estimate score were significantly higher in patients with metastasis group compared to their counterpart. Besides, for CAFs, macrophages, stromal score, and estimate score, patients with higher scores were at higher risk of metastasis, and the HRs were 3.65, 4.01, 4.27, and 3.78, respectively. IGPI score was highly positively associated with stromal score (coefficient: 0.39), immune score (coefficient: 0.43), estimate score (coefficient: 0.45), CAFs (coefficient: 0.42) and macrophages (coefficient: 0.42), while showing the opposite relationship with tumor purity (coefficient: - 0.45). In conclusion, we found that IGPI based on GBP2 and IGF1 might serve as a biomarker predicting metastasis for PCa patients. Besides, the current data further highlight the importance of CAFs in the metastatic process of PCa.

The crucial role of LncRNA MIR210HG involved in the regulation of human cancer and other disease

Long noncoding RNAs (lncRNAs) have evoked considerable interest in recent years due to their critical functions in the regulation of disease processes. Abnormal expression of lncRNAs is found in multiple diseases, and lncRNAs have been exploited for diverse medical applications. The lncRNA MIR210HG is a recently discovered lncRNA that is widely dysregulated in human disease. MIR210HG was described to have biological functions with potential roles in disease development, including cell proliferation, invasion, migration, and energy metabolism. And MIR210HG dysregulation was confirmed to have promising clinical values in disease diagnosis, treatment, and prognosis. In this review, we systematically summarize the expression profiles, roles, underlying mechanisms, and clinical applications of MIR210HG in human disease.

Therapeutic siRNA: State-of-the-Art and Future Perspectives

The highly specific induction of RNA interference-mediated gene knockdown, based on the direct application of small interfering RNAs (siRNAs), opens novel avenues towards innovative therapies. Two decades after the discovery of the RNA interference mechanism, the first siRNA drugs received approval for clinical use by the US Food and Drug Administration and the European Medicines Agency between 2018 and 2022. These are mainly based on an siRNA conjugation with a targeting moiety for liver hepatocytes, N-acetylgalactosamine, and cover the treatment of acute hepatic porphyria, transthyretin-mediated amyloidosis, hypercholesterolemia, and primary hyperoxaluria type 1. Still, the development of siRNA therapeutics faces several challenges and issues, including the definition of optimal siRNAs in terms of target, sequence, and chemical modifications, siRNA delivery to its intended site of action, and the absence of unspecific off-target effects. Further siRNA drugs are in clinical studies, based on different delivery systems and covering a wide range of different pathologies including metabolic diseases, hematology, infectious diseases, oncology, ocular diseases, and others. This article reviews the knowledge on siRNA design and chemical modification, as well as issues related to siRNA delivery that may be addressed using different delivery systems. Details on the mode of action and clinical status of the various siRNA therapeutics are provided, before giving an outlook on issues regarding the future of siRNA drugs and on their potential as one emerging standard modality in pharmacotherapy. Notably, this may also cover otherwise un-druggable diseases, the definition of non-coding RNAs as targets, and novel concepts of personalized and combination treatment regimens.

LncRNAs as Theragnostic Biomarkers for Predicting Radioresistance in Cancer: A Systematic Review and Meta-Analysis

Background

Radioresistance is the major obstacle after cancer radiotherapy. The dysregulation of long non-coding RNAs (lncRNAs) was closely related the radioresistance response. This meta-analysis was aimed to interpret the relationship between lncRNAs and radiotherapy responses in different cancers.

Method

The studies were selected from databases including PubMed, ISI Web of Science, Embase, Google Scholar, PMC, and CNKI (China National Knowledge Infrastructure). The publication time was limited to before March 20, 2021. The hazard ratios (HRs) and 95% confidence interval were calculated with random-effects models. Subgroup analyses, sensitivity analyses, and publication bias were also conducted.

Result

Twenty-seven lncRNAs in 14 cancer types were investigated, in which 23 lncRNAs were upregulated and four lncRNAs were downregulated. Dysregulation of these lncRNAs were found to be related to radioresistance response. The pooled HR and 95% confidence interval for the combined up-regulated lncRNAs was 1.73 (95% CI=1.50-2.00; P< 0.01) and down-regulated lncRNAs was 2.09 (95% CI= 1.60-2.72; P< 0.01). The HR values of the subgroup analysis for glioma (HR= 2.22, 95% CI= 1.79-2.74; p< 0.01), non-small cell lung cancer (HR=1.48, 95% CI=1.18-1.85; P<0.01), nasopharyngeal carcinoma (HR=4.26; 95% CI= 1.58-11.46; P< 0.01), and breast cancer (HR=1.29; 95% CI= 1.08-1.54; P< 0.01) were obtained. Moreover, the expression of lncRNAs was significantly related to overall survival of patients no matter if the sample size was >50 or not. In addition, the HR values of the subgroup analysis for lncRNA H19 (HR=2.68; 95% CI= 1.92-3.74; P <0.01), lncRNA FAM201A (HR=2.15; 95% CI= 1.15-3.99; P <0.01), and lncRNA HOTAIR (HR=1.22; 95% CI= 0.98-1.54; P =0.08) were also obtained.

Conclusion

LncRNAs can induce cancer radioresistance by regulating cell death-related signaling pathways. Results indicated that lncRNAs, especially lncRNA H19, FAM201A, and HOTAIR, could be considered as a predictive theragnostic biomarker to evaluate radiotherapy response.

Developing an immune-related gene prognostic index associated with progression and providing new insights into the tumor immune microenvironment of prostate cancer

We developed an immune-related gene prognostic index (IGPI) associated with progression and provided new insights into the tumor immune microenvironment (TIME) for prostate cancer (PCA) patients undergoing radical prostatectomy. All analyses were conducted with R software (version 3.6.3) and its suitable packages. Meta analysis was performed through STATA 16.0. TUBB3, WDR62, and PPARGC1A were finally identified to establish the IGPI score. IGPI score increased with the augment of Gleason score and T stage, as well as biochemical recurrence (BCR) and prostate specific antigen (PSA). Patients with higher IGPI score were at higher risk of progress (HR: 2.88; 95%CI: 95%CI: 1.80-4.61). Gene set enrichment analysis indicated that patients in high-risk group was positively associated with mismatch repair, cell cycle, DNA replication, base excision repair, nucleotide excision repair, homologous recombination, and pyrimidine metabolism. We observed that patients in the high-risk group had significantly higher tumor mutation burden score and microsatellite instability score than those in the low-risk group. For analysis of immune checkpoint, ADORA2A, CD80, TNFRSF4, TNFRSF18, and TNFRSF25 were differentially expressed between no progress and progress groups, and were significantly associated with progress free survival. We observed positive correlations between IGPI score, and lymphoid immune cells, macrophages M2 and immune score, while negative association between IGPI score, and dendritic cells, fibroblasts, stromal score, and microenvironment score. In conclusion, the IGPI score constructed in this study might serve as an independent risk factor associated with PCA progression. ADORA2A, CD80, TNFRSF4, TNFRSF18, and TNFRSF25 might be the potential targets in the treatment of PCA.

Androgen receptor phosphorylated at Ser815: The expression and function in the prostate and tumor-derived cells

Androgen is beneficial for the prostate with normal functions but creates a risk for prostate cancer progression. How androgen receptor (AR) mediates these various androgen actions remains elusive. AR conserves a phosphorylation motif within its ligand-binding domain throughout species. Here, we have found AR phosphorylated at Ser815 (P-AR) is expressed in normal tissues of both human and mouse prostates. P-AR begins expression in association with prostatic development and castration decreases its expression levels in the mouse prostate. Functional analysis of AR in prostate cancer PC-3 cells showed ligand-induced AR nuclear translocation and transactivation were disturbed by its phosphorylation at Ser815. Moreover, P-AR suppressed oncogenic AKT signaling suggesting a suppressive function for prostate cancer development. In fact, AR phosphorylation levels progressively decrease in human prostates as cancer worsens. These findings showed androgen might utilize P-AR to self-antagonize oncogenic signals and cancer progression believed to be regulated by non-phosphorylated AR (NonP-AR). By differing its target genes and signal regulations from those of NonP-AR, P-AR co-expression with NonP-AR may be the molecular basis for androgen to balance its actions and to control disease developments.

The noncoding RNA LINC00152 conveys contradicting effects in different glioblastoma cells

Glioblastoma multiforme (GBM) is an extremely aggressive brain tumor, characterized by its high genetic heterogeneity. In search of novel putative therapeutic RNA targets we investigated the role of the oncogenic long noncoding RNA LINC00152 (CYTOR, and STAiR18) in A172 glioblastoma cells. Here, we are the first to describe, that LINC00152 unexpectedly acts in a tumor suppressive manner in this cell line. SiRNA-based knockdown of LINC00152 enhanced malignant tumor behaviors including proliferation, cell cycle entry, migration, and invasion, contradicting previous studies using U87-MG and LN229 glioblastoma cells. Furthermore, LINC00152 knockdown had no influence on survival of A172 glioblastoma cells. In a genome wide transcription analysis of A172 and U87-MG glioblastoma cells, we identified 70 LINC00152 target genes involved in locomotion, cell migration, and motility in A172 cells, whereas in U87-MG cells only 40 target genes were detected. The LINC00152-regulated genes found in A172 differed from those identified in U87-MG glioblastoma cells, none of them being regulated in both cell lines. These findings underline the strong genetic heterogeneity of glioblastoma and point to a potential, yet unknown risk addressing LINC00152 lncRNA as a prospective therapeutic target in GBM.

Tyrosine-Modification of Polypropylenimine (PPI) and Polyethylenimine (PEI) Strongly Improves Efficacy of siRNA-Mediated Gene Knockdown

The delivery of small interfering RNAs (siRNA) is an efficient method for gene silencing through the induction of RNA interference (RNAi). It critically relies, however, on efficient vehicles for siRNA formulation, for transfection in vitro as well as for their potential use in vivo. While polyethylenimines (PEIs) are among the most studied cationic polymers for nucleic acid delivery including small RNA molecules, polypropylenimines (PPIs) have been explored to a lesser extent. Previous studies have shown the benefit of the modification of small PEIs by tyrosine grafting which are featured in this paper. Additionally, we have now extended this approach towards PPIs, presenting tyrosine-modified PPIs (named PPI-Y) for the first time. In this study, we describe the marked improvement of PPI upon its tyrosine modification, leading to enhanced siRNA complexation, complex stability, siRNA delivery, knockdown efficacy and biocompatibility. Results of PPI-Y/siRNA complexes are also compared with data based on tyrosine-modified linear or branched PEIs (LPxY or PxY). Taken together, this establishes tyrosine-modified PPIs or PEIs as particularly promising polymeric systems for siRNA formulation and delivery.