The kinesin KIF20A promotes progression to castration-resistant prostate cancer through autocrine activation of the androgen receptor

The Role and Clinical Relevance of Glycolysis-Associated Genes on Immune Infiltration in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) poses a significant challenge with dismal survival rates, necessitating a deeper understanding of its molecular mechanisms and the development of improved therapies. Metabolic reprogramming, particularly heightened glycolysis, plays a crucial role in HCC progression. Glycolysis-associated genes (GAGs) emerge as key players in HCC pathogenesis, influencing the tumor microenvironment and immune responses. This study aims to investigate the intricate interplay between GAGs and the immune landscape within HCC, offering valuable insights into potential prognostic markers and therapeutic targets to enhance treatment strategies and patient outcomes. Through the exploration of GAGs, we have identified two distinct molecular glycolytic subtypes in HCC patients, each exhibiting significant differences in both the immune microenvironment and prognosis. A risk model comprising five key GAGs was formulated and subsequently evaluated for their predictive accuracy. Our findings underscore the diverse tumor microenvironment and immune responses associated with the varying glycolytic subtypes observed in HCC. The identified key GAGs hold promise as prognostic indicators for evaluating HCC risk levels, predicting patient outcomes, and guiding clinical treatment decisions, particularly in the context of anticipating responses to immunotherapy drugs.

Molecular landscape for risk prediction and personalized therapeutics of castration-resistant prostate cancer: at a glance

Prostate cancer (PCa) is commonly occurred with high incidence in men worldwide, and many patients will be eventually suffered from the dilemma of castration-resistance with the time of disease progression. Castration-resistant PCa (CRPC) is an advanced subtype of PCa with heterogeneous carcinogenesis, resulting in poor prognosis and difficulties in therapy. Currently, disorders in androgen receptor (AR)-related signaling are widely acknowledged as the leading cause of CRPC development, and some non-AR-based strategies are also proposed for CRPC clinical analyses. The initiation of CRPC is a consequence of abnormal interaction and regulation among molecules and pathways at multi-biological levels. In this study, CRPC-associated genes, RNAs, proteins, and metabolites were manually collected and integrated by a comprehensive literature review, and they were functionally classified and compared based on the role during CRPC evolution, i.e., drivers, suppressors, and biomarkers, etc. Finally, translational perspectives for data-driven and artificial intelligence-powered CRPC systems biology analysis were discussed to highlight the significance of novel molecule-based approaches for CRPC precision medicine and holistic healthcare.

Cancer on motors: How kinesins drive prostate cancer progression?

Prostate cancer causes numerous male deaths annually. Although great progress has been made in the diagnosis and treatment of prostate cancer during the past several decades, much about this disease remains unknown, especially its pathobiology. The kinesin superfamily is a pivotal group of motor proteins, that contains a microtubule-based motor domain and features an adenosine triphosphatase activity and motility characteristics. Large-scale sequencing analyses based on clinical samples and animal models have shown that several members of the kinesin family are dysregulated in prostate cancer. Abnormal expression of kinesins could be linked to uncontrolled cell growth, inhibited apoptosis and increased metastasis ability. Additionally, kinesins may be implicated in chemotherapy resistance and escape immunologic cytotoxicity, which creates a barrier to cancer treatment. Here we cover the recent advances in understanding how kinesins may drive prostate cancer progression and how targeting their function may be a therapeutic strategy. A better understanding of kinesins in prostate cancer tumorigenesis may be pivotal for improving disease outcomes in prostate cancer patients.

Disrupting prostate cancer research: Challenge accepted; report from the 2023 Coffey-Holden Prostate Cancer Academy Meeting

INTRODUCTION

The 2023 Coffey-Holden Prostate Cancer Academy (CHPCA) Meeting, themed "Disrupting Prostate Cancer Research: Challenge Accepted," was convened at the University of California, Los Angeles, Luskin Conference Center, in Los Angeles, CA, from June 22 to 25, 2023.

METHODS

The 2023 marked the 10th Annual CHPCA Meeting, a discussion-oriented scientific think-tank conference convened annually by the Prostate Cancer Foundation, which centers on innovative and emerging research topics deemed pivotal for advancing critical unmet needs in prostate cancer research and clinical care. The 2023 CHPCA Meeting was attended by 81 academic investigators and included 40 talks across 8 sessions.

RESULTS

The central topic areas covered at the meeting included: targeting transcription factor neo-enhancesomes in cancer, AR as a pro-differentiation and oncogenic transcription factor, why few are cured with androgen deprivation therapy and how to change dogma to cure metastatic prostate cancer without castration, reducing prostate cancer morbidity and mortality with genetics, opportunities for radiation to enhance therapeutic benefit in oligometastatic prostate cancer, novel immunotherapeutic approaches, and the new era of artificial intelligence-driven precision medicine.

DISCUSSION

This article provides an overview of the scientific presentations delivered at the 2023 CHPCA Meeting, such that this knowledge can help in facilitating the advancement of prostate cancer research worldwide.

In vitro combination effects of plant-derived quercetin with synthetic bicalutamide on prostate cancer and normal cell lines: in silico comparison

Prostate cancer is the second most frequent and the fifth greatest cause of death in men. Although diet has been connected to the prevalence of cancer in addition to other factors, the relation between cancer and prevention is weak. Treatment options are at risk due to cell resistance. To identify new combinations, we tried plant-derived quercetin with bicalutamide on cell lines. To determine the cytotoxicity and apoptotic potential of plant-derived quercetin and its combination, MTT [3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyl tetrazolium bromide] and dual stain assays were performed. In silico protein-ligand interaction was performed to support the in vitro findings. A thin layer, column, and high-performance chromatography were used to purify quercetin along with an authentic sample. In the cytotoxic study, quercetin was minimized by 80% similar to bicalutamide and a combination of quercetin and bicalutamide by 50% when compared to controls by 2%. Quercetin and bicalutamide showed a similar binding affinity for androgen receptors (9.7 and 9.8), hub genes (10.8 and 10.0), and a few other PCa-related genes (9.4 and 9.1). We propose to conclude that the combination of quercetin plus bicalutamide can be used for chemotherapy if additional in vivo studies are conducted. The intake of foods high in polyphenolic compounds can help to prevent prostate cancer. Examination of quercetin on several cell lines will provide a definite conclusion to combat cancers.

Ginsenoside 20(S)-Rg3 reduces KIF20A expression and promotes CDC25A proteasomal degradation in epithelial ovarian cancer

Background

Ginsenoside 20(S)-Rg3 shows promising tumor-suppressive effects in ovarian cancer via inhibiting NF-κB signaling. This study aimed to explore the downstream tumor suppressive mechanisms of ginsenoside Rg3 via this signaling pathway.

Materials and methods

A systematical screening was applied to examine the expression profile of 41 kinesin family member genes in ovarian cancer. The regulatory effect of ginsenoside Rg3 on KIF20A expression was studied. In addition, we explored interacting proteins of KIF20A and their molecular regulations in ovarian cancer. RNA-seq data from The Cancer Genome Atlas (TCGA) was used for bioinformatic analysis. Epithelial ovarian cancer cell lines SKOV3 and A2780 were used as in vitro and in vivo cell models. Commercial human ovarian cancer tissue arrays were used for immunohistochemistry staining.

Results

KIF20A is a biomarker of poor prognosis among the kinesin genes. It promotes ovarian cancer cell growth in vitro and in vivo. Ginsenoside Rg3 can suppress the transcription of KIF20A. GST pull-down and co-immunoprecipitation (IP) assays confirmed that KIF20A physically interacts with BTRC (β-TrCP1), a substrate recognition subunit for SCFβ-TrCP E3 ubiquitin ligase. In vitro ubiquitination and cycloheximide (CHX) chase assays showed that via interacting with BTRC, KIF20A reduces BTRC-mediated CDC25A poly-ubiquitination and enhances its stability. Ginsenoside Rg3 treatment partly abrogates KIF20A overexpression-induced CDC25A upregulation.

Conclusion

This study revealed a novel anti-tumor mechanism of ginsenoside Rg3. It can inhibit KIF20A transcription and promote CDC25A proteasomal degradation in epithelial ovarian cancer.

Automation, live-cell imaging, and endpoint cell viability for prostate cancer drug screens

Androgen deprivation therapy (ADT) is the standard of care for high risk and advanced prostate cancer; however, disease progression from androgen-dependent prostate cancer (ADPC) to lethal and incurable castration-resistant prostate cancer (CRPC) and (in a substantial minority of cases) neuroendocrine prostate cancer (NEPC) is common. Identifying effective targeted therapies is challenging because of acquired resistance to established treatments and the vast heterogeneity of advanced prostate cancer (PC). To streamline the identification of potentially active prostate cancer therapeutics, we have developed an adaptable semi-automated protocol which optimizes cell growth and leverages automation to enhance robustness, reproducibility, and throughput while integrating live-cell imaging and endpoint viability assays to assess drug efficacy in vitro. In this study, culture conditions for 72-hr drug screens in 96-well plates were established for a large, representative panel of human prostate cell lines including: BPH-1 and RWPE-1 (non-tumorigenic), LNCaP and VCaP (ADPC), C4-2B and 22Rv1 (CRPC), DU 145 and PC3 (androgen receptor-null CRPC), and NCI-H660 (NEPC). The cell growth and 72-hr confluence for each cell line was optimized for real-time imaging and endpoint viability assays prior to screening for novel or repurposed drugs as proof of protocol validity. We demonstrated effectiveness and reliability of this pipeline through validation of the established finding that the first-in-class BET and CBP/p300 dual inhibitor EP-31670 is an effective compound in reducing ADPC and CRPC cell growth. In addition, we found that insulin-like growth factor-1 receptor (IGF-1R) inhibitor linsitinib is a potential pharmacological agent against highly lethal and drug-resistant NEPC NCI-H660 cells. This protocol can be employed across other cancer types and represents an adaptable strategy to optimize assay-specific cell growth conditions and simultaneously assess drug efficacy across multiple cell lines.

Prognostic model of hepatocellular carcinoma based on cancer grade

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. With highly invasive biological characteristics and a lack of obvious clinical manifestations, HCC usually has a poor prognosis and ranks fourth in cancer mortality. The aetiology and exact molecular mechanism of primary HCC are still unclear.

AIM

To select the characteristic genes that are significantly associated with the prognosis of HCC patients and construct a prognosis model of this malignancy.

METHODS

By comparing the gene expression levels of patients with different cancer grades of HCC, we screened out differentially expressed genes associated with tumour grade. By protein-protein interaction (PPI) network analysis, we obtained the top 2 PPI networks and hub genes from these differentially expressed genes. By using least absolute shrinkage and selection operator Cox regression, 13 prognostic genes were selected for feature extraction, and a prognostic risk model of HCC was established.

RESULTS

The model had significant prognostic ability in HCC. We also analysed the biological functions of these prognostic genes.

CONCLUSION

By comparing the gene profiles of patients with different stages of HCC, We have constructed a prognosis model consisting of 13 genes that have important prognostic value. This model has good application value and can be explained clinically.

Multi-omics analysis and validation of the tumor microenvironment of hepatocellular carcinoma under RNA modification patterns

BACKGROUND

Multiple types of RNA modifications are associated with the prognosis of hepatocellular carcinoma (HCC) patients. However, the overall mediating effect of RNA modifications on the tumor microenvironment (TME) and the prognosis of patients with HCC is unclear.

METHODS

Thoroughly analyze the TME, biological processes, immune infiltration and patient prognosis based on RNA modification patterns and gene patterns. Construct a prognostic model (RNA modification score, RNAM-S) to predict the overall survival (OS) in HCC patients. Analyze the immune status, cancer stem cell (CSC), mutations and drug sensitivity of HCC patients in both the high and low RNAM-S groups. Verify the expression levels of the four characteristic genes of the prognostic RNAM-S using in vitro cell experiments.

RESULTS

Two modification patterns and two gene patterns were identified in this study. Both the high-expression modification pattern and the gene pattern exhibited worse OS. A prognostic RNAM-S model was constructed based on four featured genes (KIF20A, NR1I2, NR2F1 and PLOD2). Cellular experiments suggested significant dysregulation of the expression levels of these four genes. In addition, validation of the RNAM-S model using each data set showed good predictive performance of the model. The two groups of HCC patients (high and low RNAM-S groups) exhibited significant differences in immune status, CSC, mutation and drug sensitivity.

CONCLUSION

The findings of the study demonstrate the clinical value of RNA modifications, which provide new insights into the individualized treatment for patients with HCC.

Nucleotide-free structures of KIF20A illuminate atypical mechanochemistry in this kinesin-6

KIF20A is a critical kinesin for cell division and a promising anti-cancer drug target. The mechanisms underlying its cellular roles remain elusive. Interestingly, unusual coupling between the nucleotide- and microtubule-binding sites of this kinesin-6 has been reported, but little is known about how its divergent sequence leads to atypical motility properties. We present here the first high-resolution structure of its motor domain that delineates the highly unusual structural features of this motor, including a long L6 insertion that integrates into the core of the motor domain and that drastically affects allostery and ATPase activity. Together with the high-resolution cryo-electron microscopy microtubule-bound KIF20A structure that reveals the microtubule-binding interface, we dissect the peculiarities of the KIF20A sequence that influence its mechanochemistry, leading to low motility compared to other kinesins. Structural and functional insights from the KIF20A pre-power stroke conformation highlight the role of extended insertions in shaping the motor's mechanochemical cycle. Essential for force production and processivity is the length of the neck linker in kinesins. We highlight here the role of the sequence preceding the neck linker in controlling its backward docking and show that a neck linker four times longer than that in kinesin-1 is required for the activity of this motor.

Pan-cancer analysis of kinesin family members with potential implications in prognosis and immunological role in human cancer

Background

Kinesin is a molecular motor for transporting "goods" within cells and plays a key role in many types of tumors. The multi-angle study of kinesin at the pan-cancer level is conducive to understanding its role in tumorigenesis and development and clinical treatment potential.

Methods

We evaluated the expression of KIF genes, performed differential analysis by using the R package limma, and explored the pan-cancer prognosis of KIF genes by univariate Cox regression analysis. To evaluate the pan-cancer role of KIF genes as a whole, we defined the KIFscore with the help of gene set variation analysis (GSVA) and explored the KIFscores across normal tissues, tumor cell lines, and 33 tumor types in TCGA. Next, we used spearman correlation analysis to extensively study the correlation between the KIFscore and tumor prognosis and be-tween the KIFscore and clinical indicators. We also identified the relationship between the KIFscore and genomic variation and immune molecular signatures by multiplatform analysis. Finally, we identified the key genes in clear cell renal cell carcinoma (ccRCC) through machine learning algorithms and verified the candidate genes by CCK8, wound healing assay, Transwell assay, and flow cytometry.

Results

In most cancers, KIFscores are high and they act as a risk factor for cancer. The KIFscore was significantly associated with copy number variation (CNV), tumor mutation burden (TMB), immune subtypes, DNA repair deficiency, and tumor stemness indexes. Moreover, in almost all cancer species, the KIFscore was positively correlated with T cell CD4+ TH2, the common lymphoid pro-genitor, and the T cell follicular helper. In addition, it was negatively correlated with CXCL16, CCL14, TNFSF13, and TNFRSF14 and positively correlated with ULBP1, MICB, and CD276. Machine learning helped us to identify four hub-genes in ccRCC. The suitable gene, KIF14, is highly expressed in ccRCC and promotes tumor cell proliferation, migration, and invasion.

Conclusion

Our study shows that the KIF genes play an important pan-cancer role and may become a potential new target for a variety of tumor treatments in the future. Furthermore, KIF14, a key molecule in the KIF genes, can provide a new idea for the ccRCC treatment.

Coordinated reprogramming of renal cancer transcriptome, metabolome and secretome associates with immune tumor infiltration

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer. The molecules (proteins, metabolites) secreted by tumors affect their extracellular milieu to support cancer progression. If secreted in amounts detectable in plasma, these molecules can also serve as useful, minimal invasive biomarkers. The knowledge of ccRCC tumor microenvironment is fragmentary. In particular, the links between ccRCC transcriptome and the composition of extracellular milieu are weakly understood. In this study, we hypothesized that ccRCC transcriptome is reprogrammed to support alterations in tumor microenvironment. Therefore, we comprehensively analyzed ccRCC extracellular proteomes and metabolomes as well as transcriptomes of ccRCC cells to find molecules contributing to renal tumor microenvironment.

METHODS

Proteomic and metabolomics analysis of conditioned media isolated from normal kidney cells as well as five ccRCC cell lines was performed using mass spectrometry, with the following ELISA validation. Transcriptomic analysis was done using microarray analysis and validated using real-time PCR. Independent transcriptomic and proteomic datasets of ccRCC tumors were used for the analysis of gene and protein expression as well as the level of the immune infiltration.

RESULTS

Renal cancer secretome contained 85 proteins detectable in human plasma, consistently altered in all five tested ccRCC cell lines. The top upregulated extracellular proteins included SPARC, STC2, SERPINE1, TGFBI, while downregulated included transferrin and DPP7. The most affected extracellular metabolites were increased 4-hydroxy-proline, succinic acid, cysteine, lactic acid and downregulated glutamine. These changes were associated with altered expression of genes encoding the secreted proteins (SPARC, SERPINE1, STC2, DPP7), membrane transporters (SLC16A4, SLC6A20, ABCA12), and genes involved in protein trafficking and secretion (KIF20A, ANXA3, MIA2, PCSK5, SLC9A3R1, SYTL3, and WNTA7). Analogous expression changes were found in ccRCC tumors. The expression of SPARC predicted the infiltration of ccRCC tumors with endothelial cells. Analysis of the expression of the 85 secretome genes in > 12,000 tumors revealed that SPARC is a PanCancer indicator of cancer-associated fibroblasts' infiltration.

CONCLUSIONS

Transcriptomic reprogramming of ccRCC supports the changes in an extracellular milieu which are associated with immune infiltration. The proteins identified in our study represent valuable cancer biomarkers detectable in plasma.

Vesicle-mediated transport-related genes are prognostic predictors and are associated with tumor immunity in lung adenocarcinoma

Background

Globally, lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths. It is a progressive disorder that arises from multiple genetic and environmental factors. Dysregulated expression of vesicle-mediated transport-related genes (VMTRGs) have been reported in several cancers. However, the prognostic significance of VMTRGs in LUAD has yet to be established.

Methods

The VMTRG profiling data for 482 LUAD patients and 59 normal controls were downloaded from The Cancer Genome Altas (TCGA). Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct and optimize the risk model. Several GEO datasets were used to validate the risk model. The roles of these genes were investigated via the Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses. Differences in immune cell infiltrations between risk groups were evaluated using five algorithms. "pRRophetic" was used to investigate anti-cancer drug sensitivities in two groups. Expression of these five genes in LUAD samples and adjacent normal tissues were evaluated by qRT-PCR. Colony formation and wound healing assays were performed to assess the significance of CNIH1 and AP3S1 in LUAD cells.

Results

We identified 85 prognosis-associated VMTRGs that could be constructed a risk model for LUAD patients, indicating their potential importance in LUAD development. The risk model including the five VMTRGs (CNIH1, KIF20A, GALNT2, GRIA1, and AP3S1) was associated with clinical outcomes. Tumor stage and risk score were found to be independent prognostic factors for LUAD patients. The five VMTRGs were also correlated with activation of the Notch and p53 signaling pathways. The risk model was significantly associated with immune responses and with high-level expression of immune checkpoints. High-risk group patients were more sensitive to several chemotherapeutic drugs and Lapatinib. Furthermore, CNIH1 and AP3S1 promoted LUAD cell growth and migration in vitro.

Conclusion

We constructed a VMTRG-based risk model for effective prediction of prognostic outcomes for LUAD patients. The risk model was associated with immune infiltration levels. These five hub genes are potential targets for immune therapy combined with chemotherapy in LUAD.

Role of estrogen receptor signaling pathway-related genes in diffuse large B-cell lymphoma and identification of key targets via integrated bioinformatics analysis and experimental validation

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignancy. Epidemiologically, the incidence of DLBCL is higher in men, and the female sex is a favorable prognostic factor, which can be explained by estrogen. This study aimed to explore the potential targets of the estrogen receptor (ER) signaling pathway and provide a meaningful way to treat DLBCL patients. Datasets were obtained from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). Representative gene sets estrogen receptor pathways, and growth regulatory pathways were identified based on Gene Set Enrichment Analysis (GSEA) analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for function and pathway analysis. STRING and Cytoscape were used to construct the interaction network, and the MCODE plug-in performed the module analysis. GEPIA, TCGA, and LOGpc databases were used for expression and predictive analysis. The Human Protein Atlas (HPA) database was used to analyze the protein expression levels, cBioPortal was used to explore genetic alterations, and ROC analysis and prognostic assessment were used to predict the diagnostic value of genes. Finally, BJAB cells were treated with ER inhibitor fulvestrant and specific shRNA, and the expression of hub genes was verified by RT-qPCR. We identified 81 overlapping DEGs and CDC6, CDC20, KIF20A, STIL, and TOP2A as novel biomarkers affecting the prognosis of DLBCL. In addition, the STAT and KRAS pathways are considered potential growth regulatory pathways. These results hold promise for new avenues for the treatment of DLBCL patients.