Dynein axonemal heavy chain 8 promotes androgen receptor activity and associates with prostate cancer progression

Exploring Regorafenib Responsiveness and Uncovering Molecular Mechanisms in Recurrent Glioblastoma Tumors through Longitudinal In Vitro Sampling

Glioblastoma, a deadly brain tumor, shows limited response to standard therapies like temozolomide (TMZ). Recent findings from the REGOMA trial underscore a significant survival improvement offered by Regorafenib (REGO) in recurrent glioblastoma. Our study aimed to propose a 3D ex vivo drug response precision medicine approach to investigate recurrent glioblastoma sensitivity to REGO and elucidate the underlying molecular mechanisms involved in tumor resistance or responsiveness to treatment. Three-dimensional glioblastoma organoids (GB-EXPs) obtained from 18 patients' resected recurrent glioblastoma tumors were treated with TMZ and REGO. Drug responses were evaluated using NAD(P)H FLIM, stratifying tumors as responders (Resp) or non-responders (NRs). Whole-exome sequencing was performed on 16 tissue samples, and whole-transcriptome analysis on 13 GB-EXPs treated and untreated. We found 35% (n = 9) and 77% (n = 20) of tumors responded to TMZ and REGO, respectively, with no instances of TMZ-Resp being REGO-NRs. Exome analysis revealed a unique mutational profile in REGO-Resp tumors compared to NR tumors. Transcriptome analysis identified distinct expression patterns in Resp and NR tumors, impacting Rho GTPase and NOTCH signaling, known to be involved in drug response. In conclusion, recurrent glioblastoma tumors were more responsive to REGO compared to TMZ treatment. Importantly, our approach enables a comprehensive longitudinal exploration of the molecular changes induced by treatment, unveiling promising biomarkers indicative of drug response.

Comprehensive analysis indicated that NDE1 is a potential biomarker for pan-cancer and promotes bladder cancer progression

BACKGROUND

The nuclear distribution E homologue 1 (NDE1) is a crucial dynein binding partner. The NDE1 protein has the potential to disrupt the normal functioning of centrosomes, leading to a compromised ability to generate spindles and ensure precise separation of chromosomes during cell division. The potential consequences of this phenomenon include genomic instability, malignant transformation and the proliferation of neoplastic growths. However, studies examining the connection between NDE1 and cancer is still very rare.

METHODS

The expression level, prognostic impact, gene change, DNA methylation, protein interaction, mRNA m6A modification, ceRNA network, associated gene and function enrichment, and immune-related effects of NDE1 in pan-cancer were examined using a range of online analytic tools and the R software package. The CCK-8 test, transwell assay, scratch assay and colony formation assay were used to confirm the effects of NDE1 on the proliferation, invasion and metastasis of bladder cancer cells.

RESULTS

Numerous tumour types have elevated NDE1, which is linked to a bad prognosis. NDE1 is an excellent diagnostic tool for many different types of cancer. Numerous malignancies have been linked to genetic changes in NDE1. NDE1 was connected to TMB, MSI, several immunological checkpoint genes and immune cell infiltration. NDE1 is linked to a number of immunological subtypes. NDE1 could affect how well immunotherapy works to treat different types of cancer. NDE1 was mostly associated with cell cycle, chromosomal segregation, DNA replication and mitotic segregation, according to GO and KEGG analyses. NDE1 physically binds to PAFAH1B1 and DCTN1, respectively. The proliferation, invasion and metastasis of bladder cancer cells may be prevented by NDE1 knockdown. Furthermore, knockdown of NDE1 promoted the apoptosis of bladder cancer cells.

CONCLUSION

High expression of NDE1 is present in a variety of tumours, which is linked to a bad prognosis for cancer. Knockdown of NDE1 inhibited the proliferation, invasion and metastasis of bladder cancer cells, and promoted the apoptosis. For a number of malignancies, NDE1 may be a biomarker for immunotherapy and prognosis.

Genomic alterations in oral multiple primary cancers

Oral squamous cell carcinoma (OSCC) is the predominant type of oral cancer, while some patients may develop oral multiple primary cancers (MPCs) with unclear etiology. This study aimed to investigate the clinicopathological characteristics and genomic alterations of oral MPCs. Clinicopathological data from patients with oral single primary carcinoma (SPC, n = 202) and oral MPCs (n = 34) were collected and compared. Copy number alteration (CNA) analysis was conducted to identify chromosomal-instability differences among oral MPCs, recurrent OSCC cases, and OSCC patients with lymph node metastasis. Whole-exome sequencing was employed to identify potential unique gene mutations in oral MPCs patients. Additionally, CNA and phylogenetic tree analyses were used to gain preliminary insights into the molecular characteristics of different primary tumors within individual patients. Our findings revealed that, in contrast to oral SPC, females predominated the oral MPCs (70.59%), while smoking and alcohol use were not frequent in MPCs. Moreover, long-term survival outcomes were poorer in oral MPCs. From a CNA perspective, no significant differences were observed between oral MPCs patients and those with recurrence and lymph node metastasis. In addition to commonly mutated genes such as CASP8, TP53 and MUC16, in oral MPCs we also detected relatively rare mutations, such as HS3ST6 and RFPL4A. Furthermore, this study also demonstrated that most MPCs patients exhibited similarities in certain genomic regions within individuals, and distinct differences of the similarity degree were observed between synchronous and metachronous oral MPCs.

The 'omics of obesity in B-cell acute lymphoblastic leukemia

The obesity pandemic currently affects more than 70 million Americans and more than 650 million individuals worldwide. In addition to increasing susceptibility to pathogenic infections (eg, SARS-CoV-2), obesity promotes the development of many cancer subtypes and increases mortality rates in most cases. We and others have demonstrated that, in the context of B-cell acute lymphoblastic leukemia (B-ALL), adipocytes promote multidrug chemoresistance. Furthermore, others have demonstrated that B-ALL cells exposed to the adipocyte secretome alter their metabolic states to circumvent chemotherapy-mediated cytotoxicity. To better understand how adipocytes impact the function of human B-ALL cells, we used a multi-omic RNA-sequencing (single-cell and bulk transcriptomic) and mass spectroscopy (metabolomic and proteomic) approaches to define adipocyte-induced changes in normal and malignant B cells. These analyses revealed that the adipocyte secretome directly modulates programs in human B-ALL cells associated with metabolism, protection from oxidative stress, increased survival, B-cell development, and drivers of chemoresistance. Single-cell RNA sequencing analysis of mice on low- and high-fat diets revealed that obesity suppresses an immunologically active B-cell subpopulation and that the loss of this transcriptomic signature in patients with B-ALL is associated with poor survival outcomes. Analyses of sera and plasma samples from healthy donors and those with B-ALL revealed that obesity is associated with higher circulating levels of immunoglobulin-associated proteins, which support observations in obese mice of altered immunological homeostasis. In all, our multi-omics approach increases our understanding of pathways that may promote chemoresistance in human B-ALL and highlight a novel B-cell-specific signature in patients associated with survival outcomes.

Improving the Post-Operative Prediction of BCR-Free Survival Time with mRNA Variables and Machine Learning

Predicting the risk of, and time to biochemical recurrence (BCR) in prostate cancer patients post-operatively is critical in patient treatment decision pathways following surgical intervention. This study aimed to investigate the predictive potential of mRNA information to improve upon reference nomograms and clinical-only models, using a dataset of 187 patients that includes over 20,000 features. Several machine learning methodologies were implemented for the analysis of censored patient follow-up information with such high-dimensional genomic data. Our findings demonstrated the potential of inclusion of mRNA information for BCR-free survival prediction. A random survival forest pipeline was found to achieve high predictive performance with respect to discrimination, calibration, and net benefit. Two mRNA variables, namely ESM1 and DHAH8, were identified as consistently strong predictors with this dataset.

A Novel Four Mitochondrial Respiration-Related Signature for Predicting Biochemical Recurrence of Prostate Cancer

The biochemical recurrence (BCR) of patients with prostate cancer (PCa) after radical prostatectomy is high, and mitochondrial respiration is reported to be associated with the metabolism in PCa development. This study aimed to establish a mitochondrial respiratory gene-based risk model to predict the BCR of PCa. RNA sequencing data of PCa were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and mitochondrial respiratory-related genes (MRGs) were sourced via GeneCards. The differentially expressed mitochondrial respiratory and BCR-related genes (DE-MR-BCRGs) were acquired through overlapping BCR-related differentially expressed genes (BCR-DEGs) and differentially expressed MRGs (DE-MRGs) between PCa samples and controls. Further, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were performed to construct a DE-MRGs-based risk model. Then, a nomogram was established by analyzing the independent prognostic factor of five clinical features and risk scores. Moreover, Gene Set Enrichment Analysis (GSEA), tumor microenvironment, and drug susceptibility analyses were employed between high- and low-risk groups of PCa patients with BCR. Finally, qRT-PCR was utilized to validate the expression of prognostic genes. We identified 11 DE-MR-BCRGs by overlapping 132 DE-MRGs and 13 BCR-DEGs and constructed a risk model consisting of 4 genes (APOE, DNAH8, EME2, and KIF5A). Furthermore, we established an accurate nomogram, including a risk score and a Gleason score, for the BCR prediction of PCa patients. The GSEA result suggested the risk model was related to the PPAR signaling pathway, the cholesterol catabolic process, the organic hydroxy compound biosynthetic process, the small molecule catabolic process, and the steroid catabolic process. Simultaneously, we found six immune cell types relevant to the risk model: resting memory CD4+ T cells, monocytes, resting mast cells, activated memory CD4+ T cells, regulatory T cells (Tregs), and macrophages M2. Moreover, the risk model could affect the IC50 of 12 cancer drugs, including Lapatinib, Bicalutamide, and Embelin. Finally, qRT-PCR showed that APOE, EME2, and DNAH8 were highly expressed in PCa, while KIF5A was downregulated in PCa. Collectively, a mitochondrial respiratory gene-based nomogram including four genes and one clinical feature was established for BCR prediction in patients with PCa, which could provide novel strategies for further studies.

Role of motor proteins in human cancers

Motor proteins include several protein families (Kinesin, Dynein and Myosin) responsible for intracellular transport, intercellular communication, among other functions. In cancer cells, motor proteins along with microtubules (MT) and other tubulin and actin structures, are crucial for cell proliferation and invasion. The cBioPortal platform for Cancer Genomics database was queried for solid cancers in a combined cohort of 9204 patients with complete cancer genomics data. To assess the importance of motor proteins in cancer, copy number alterations (CNAs) and survival rates were analyzed in the combined dataset. Kinesin, Dynein, and Myosin families showed CNAs in 47%, 49%, and 57 % of patients, respectively, in at least one of their members. Survival analysis showed that CNAs in Kinesin and Dynein, families' genes in the same patients were significantly correlated to decreased overall survival. These results added more evidence to previous literature highlighting the importance of motor proteins as a target in cancer therapy. Kinesin inhibitors could act by several mechanisms such as inhibiting spindle assembly or centrosome separation during mitosis, leading to cell cycle arrest and eventually apoptosis. Dynein inhibitors modulate Dynein's activity and MT binding, inhibiting cell proliferation and invasion. Myosin inhibitors act by stabilizing MT, inducing cell cycle arrest and inhibiting invasiveness. Increasing the specificity of motor proteins targeting drugs could improve cancer therapy and patient survival.

A novel refined pyroptosis and inflammasome-related genes signature for predicting prognosis and immune microenvironment in pancreatic ductal adenocarcinoma

Pyroptosis is an inflammatory form of cell death, which plays a key role in the development of auto-inflammation and cancer. This study aimed to construct a pyroptosis and inflammasome-related genes for predicting prognosis of the pancreatic ductal adenocarcinoma (PDAC). This study was based primarily on the one-way analysis of variance, univariate Cox regression analysis, Least absolute shrinkage and selection operator (LASSO) Cox regression, a risk-prognostic signature, gene set variation analysis (GSVA), and immune microenvironment analysis, using PDAC data from The Cancer Genome Atlas and International Cancer Genome Consortium databases for the analysis of the role of 676 pyroptosis and inflammasome-related genes in PDAC retrieved from the Reactome and GeneCards databases. Lastly, we collected six paired PDAC and matched normal adjacent tissue samples to verify the expression of signature genes by quantitative real-time PCR (qRT-PCR). We identified 18 candidate pyroptosis and inflammasome-related genes that differed significantly between pathologic grades (stages) of PDAC patients. The univariate Cox and LASSO analyses pointed to six genes as the best variables for constructing a prognostic signature, including ACTA2, C1QTNF9, DNAH8, GATM, LBP, and NGF. The results of the risk prognostic model indicated that the AUCs at 1, 3, and 5 years were greater than 0.62. GSVA revealed that 'GLYCOLYSIS', 'P53 PATHWAY', 'KRAS SIGNALING UP', and 'INFLAMMATORY RESPONSE' hallmark gene sets were associated with the risk score. The high-risk group was associated with poor prognosis and was characterized by a lower infiltration of cells involved in anti-tumor immunity; whereas the low-risk group with higher T cells, NK cells, and macrophages showed relatively better survival and significantly higher upregulation of cytolytic scores and inflammation scores. Additionally, crucial pyroptosis and inflammasome-related genes were further validated by qRT-PCR. Our study revealed the prognostic role of the pyroptosis and inflammasome-related genes in PDAC for the first time. Simultaneously, the biological and prognostic heterogeneity of PDAC had been demonstrated, deepening our molecular understanding of this tumor.

Identification of novel key genes associated with the metastasis of prostate cancer based on bioinformatics prediction and validation

Background

Metastatic prostate cancer (PCa) is a lethal tumor. However, the molecular mechanisms underlying PCa progression have not been fully elucidated.

Methods

Transcriptome expression profiling and clinical information on primary and metastatic PCa samples were obtained from TCGA. R software was used to screen the DEGs, and LASSO logistical regression method was utilized to identify the pivotal PCa metastasis-related DEGs. The transcriptional expression levels of the key genes were analyzed using the UALCAN database, and the corresponding protein expression were validated by Immunohistochemistry (IHC). Survival analysis of the key genes was performed using the GEPIA database. Wound healing assay and Transwell assay were conducted to determine whether knockdown of the key genes influence the migration and invasion abilities of PCa cells (22Rv1 and PC3). GSEA was performed to predict key genes-mediated signaling pathways for the development of PCa. Western blotting was used to evaluate the expression changes of E-cadherin, Twist1, and Vimentin in PCa cells with the key genes silencing. An in vivo mouse metastatic model for PCa was also generated to verify the important role of ISG15 and CST2 in PCa metastasis.

Results

A comparison between primary and metastatic PCa tissues was conducted, and 19 DEGs were screened. Among these, three key genes were identified that might be closely associated with PCa progression according to the LASSO logistical analysis, namely ISG15, DNAH8, and CST2. Further functional experiments revealed that knockdown of ISG15 and CST2 suppressed wound healing, migration, and invasion of PCa cells. To explore the molecular mechanism of ISG15 and CST2 in the development of PCa, GSEA was performed, and it was found that both genes play crucial roles in cell adhesion molecules, extracellular matrix-receptor interaction, and focal adhesion. Western blotting results exhibited that inhibiting ISG15 and CST2 led to increase the expression of E-cadherin and decrease the expression of Twist1 and Vimentin. Additionally, the metastatic in vivo study demonstrated that both PC3 and 22Rv1 cells expressing with luciferase-shISG15 and luciferase-shCST2 had significantly lower detectable bioluminescence than that in the control PCa cells.

Conclusion

ISG15 and CST2 may participate in PCa metastasis by regulating the epithelial-mesenchymal transition (EMT) signaling pathway. These findings may help to better understand the pathogenetic mechanisms governing PCa and provide promising therapeutic targets for metastatic PCa therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02258-3.

Identifying Driver Genes for Individual Patients through Inductive Matrix Completion

MOTIVATION

The driver genes play a key role in the evolutionary process of cancer. Effectively identifying these driver genes is crucial to cancer diagnosis and treatment. However, due to the high heterogeneity of cancers, it remains challenging to identify the driver genes for individual patients. Although some computational methods have been proposed to tackle this problem, they seldom consider the fact that the genes functionally similar to the well-established driver genes may likely play similar roles in cancer process, which potentially promotes the driver gene identification. Thus, here we developed a novel approach of IMCDriver to promote the driver gene identification both for cohorts and individual patients.

RESULTS

IMCDriver first considers the well-established driver genes as prior information, and adopts the using multi-omics data (e.g., somatic mutation, gene expression and protein-protein interaction) to compute the similarity between patients/genes. Then, IMCDriver prioritizes the personalized mutated genes according to their functional similarity to the well-established driver genes via Inductive Matrix Completion. Finally, IMCDriver identifies the highly rank-ordered genes as the personalized driver genes. The results on five cancer datasets from TCGA show that our IMCDriver outperforms other existing state-of-the-art methods both in the cohort and patient-specific driver gene identification. IMCDriver also reveals some novel driver genes that potentially drive cancer development. In addition, even for the driver genes rarely mutated among a population, IMCDriver can still identify them and prioritize them with high priorities.

AVAILABILITY

Code available at https://github.com/NWPU-903PR/IMCDriver.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Long Non-Coding RNA Landscape in Prostate Cancer Molecular Subtypes: A Feature Selection Approach

Prostate cancer is one of the most common malignancies in men. It is characterized by a high molecular genomic heterogeneity and, thus, molecular subtypes, that, to date, have not been used in clinical practice. In the present paper, we aimed to better stratify prostate cancer patients through the selection of robust long non-coding RNAs. To fulfill the purpose of the study, a bioinformatic approach focused on feature selection applied to a TCGA dataset was used. In such a way, LINC00668 and long non-coding(lnc)-SAYSD1-1, able to discriminate ERG/not-ERG subtypes, were demonstrated to be positive prognostic biomarkers in ERG-positive patients. Furthermore, we performed a comparison between mutated prostate cancer, identified as "classified", and a group of patients with no peculiar genomic alteration, named "not-classified". Moreover, LINC00920 lncRNA overexpression has been linked to a better outcome of the hormone regimen. Through the feature selection approach, it was found that the overexpression of lnc-ZMAT3-3 is related to low-grade patients, and three lncRNAs: lnc-SNX10-87, lnc-AP1S2-2, and ADPGK-AS1 showed, through a co-expression analysis, significant correlation values with potentially druggable pathways. In conclusion, the data mining of publicly available data and robust bioinformatic analyses are able to explore the unknown biology of malignancies.

HIV-1 and Amyloid Beta Remodel Proteome of Brain Endothelial Extracellular Vesicles

Amyloid beta (Aβ) depositions are more abundant in HIV-infected brains. The blood-brain barrier, with its backbone created by endothelial cells, is assumed to be a core player in Aβ homeostasis and may contribute to Aβ accumulation in the brain. Exposure to HIV increases shedding of extracellular vesicles (EVs) from human brain endothelial cells and alters EV-Aβ levels. EVs carrying various cargo molecules, including a complex set of proteins, can profoundly affect the biology of surrounding neurovascular unit cells. In the current study, we sought to examine how exposure to HIV, alone or together with Aβ, affects the surface and total proteomic landscape of brain endothelial EVs. By using this unbiased approach, we gained an unprecedented, high-resolution insight into these changes. Our data suggest that HIV and Aβ profoundly remodel the proteome of brain endothelial EVs, altering the pathway networks and functional interactions among proteins. These events may contribute to the EV-mediated amyloid pathology in the HIV-infected brain and may be relevant to HIV-1-associated neurocognitive disorders.

Somatic mutation of DNAH genes implicated higher chemotherapy response rate in gastric adenocarcinoma patients

Background

The dynein axonemal heavy chain (DNAH) family of genes encode the dynein axonemal heavy chain, which is involved in cell motility. Genomic variations of DNAH family members have been frequently reported in diverse kinds of malignant tumors. In this study, we analyzed the genomic database to evaluate the mutation status of DNAH genes in gastric adenocarcinoma and further identified the significance of mutant DNAH genes as effective molecular biomarkers for predicting chemotherapy response in gastric cancer patients.

Methods

We analyzed the clinical and genomic data of gastric cancer patients published in The Cancer Genome Atlas (TCGA) project. Data on chemotherapy response, overall survival (OS) and chemotherapy-free survival were retrieved. Then, we verified the results via targeted sequencing of gastric cancer patients with similar clinical characteristics but different chemotherapeutic outcomes.

Results

In total, 132 gastric adenocarcinoma patients undergoing chemotherapy treatment from TCGA were included in our study. Somatic mutations in all 13 members of the DNAH family of genes were associated with different chemotherapy responses. Compared with patients with wild-type DNAH genes (n = 59), a significantly higher proportion of those with mutations in DNAH genes (n = 73) (55.9% vs 80.8%) responded to chemotherapy (P = 0.002). Moreover, DNAH mutations were correlated with significantly better OS (P = 0.027), chemotherapy-free survival (P = 0.027), fluoropyrimidine-free survival (P = 0.048) and platinum-free survival (P = 0.014). DNAH mutation status was an independent risk factor for OS (P = 0.015), chemotherapy-free survival (P = 0.015) and platinum-free survival (P = 0.011). We identified somatic mutations in 27 (42.2%) of the 64 stage III gastric adenocarcinoma patients receiving fluoropyrimidine-based chemotherapy by targeted exon sequencing with strict screening conditions. In our own cohort, a significantly higher proportion of patients (n = 32) with DNAH mutations than patients with wild-type DNAH genes (n = 32) had a good prognosis (OS > 48 months) (70.4% vs 35.1%) (P = 0.005).

Conclusions

Dynein axonemal heavy chain gene mutations contribute positively to chemotherapy sensitivity in gastric cancer patients.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-1867-6) contains supplementary material, which is available to authorized users.

The association between methylation patterns of DNAH17 and clinicopathological factors in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a malignancy with poor prognosis. Complex genetic and epigenetic alterations are the two primary causes of HCC. The aim of the study was mainly to explore the correlation between the methylation status of DNAH17 and HCC.

Methods

We evaluated the methylation levels of DNAH17 in 163 HCC samples and their paired normal tissue using Sequenom EpiTYPER assays and performed the TaqMan copy number assay to assess the copy number status of DNAH17 in HCC samples.

Results

The mean methylation levels were significantly decreased in the tumor tissues compared to the paired normal tissues in both selected regions of DNAH17 (amplicon 1:58.7% vs 84.5%, P < 0.0001; amplicon 2:69.9% vs 84.5%, P = 0.0060). Contrarily，both RNA‐seq and immunohistochemistry indicated the expression of DNAH17 was increased in tumor tissues (P < 0.05). DNMT inhibitor decitabine treatment could increase the expression of DNAH17 in HCC cell lines. DNAH17 gene amplification always companied with hypomethylation status. Moreover, hypomethylation status was associated with several clinical characteristics, such as male patients, higher AFP values, higher age of onset, fibrous capsules, tumor necrosis, liver cirrhosis, and tumor thrombus (P < 0.05). Receiver operator characteristic (ROC) curve analysis demonstrated the methylation levels of DNAH17 could efficiently predict the existence of the fibrous capsule (AUC = 0.695) and tumor thrombus (AUC = 0.806).

Conclusions

These findings suggested that aberrant methylation of DNAH17 was associated with comprehensive HCC clinicopathological factors and could be a promising biomarker for tumor thrombosis in HCC patients.

Exploration of the diagnostic value and molecular mechanism of miR‑1 in prostate cancer: A study based on meta‑analyses and bioinformatics

Prostate cancer (PCa) remains a principal issue to be addressed in male cancer-associated mortality. Therefore, the present study aimed to examine the clinical value and associated molecular mechanism of microRNA (miR)-1 in PCa. A meta-analysis was conducted to evaluate the diagnosis of miR-1 in PCa via Gene Expression Omnibus and ArrayExpress datasets, The Cancer Genome Atlas miR-1 expression data and published literature. It was identified that expression of miR-1 was significantly downregulated in PCa. Decreased miR-1 expression possessed moderate diagnostic value, with area under the curve, sensitivity, specificity and odds ratio values at 0.73, 0.77, 0.57 and 4.60, respectively. Using bioinformatics methods, it was revealed that a number of pathways, including the ‘androgen receptor signaling pathway’, ‘androgen receptor activity’, ‘transcription factor binding’ and ‘protein processing in the endoplasmic reticulum’, were important in PCa. A total of seven hub genes, including phosphoribosylaminoimidazole carboxylase and phosphoribosylaminoimidazolesuccin ocarboxamide synthase (PAICS), cadherin 1 (CDH1), SRC proto-oncogene, non-receptor tyrosine kinase, twist family bHLH transcription factor 1 (TWIST1), ZW10 interacting kinetochore protein (ZWINT), PCNA clamp associated factor (KIAA0101) and androgen receptor, among which, five (PAICS, CDH1, TWIST1, ZWINT and KIAA0101) were significantly upregulated and negatively correlated with miR-1, were identified as key miR-1 target genes in PCa. Additionally, it was investigated whether miR-1 and its hub genes were associated with clinical features, including age, tumor status, residual tumor, lymph node metastasis, pathological T stage and prostate specific antigen level. Collectively the results suggest that miR-1 may be involved in the progression of PCa, and consequently be a promising diagnostic marker. The ‘androgen receptor signaling pathway’, ‘androgen receptor activity’, ‘transcription factor binding’ and ‘protein processing in the endoplasmic reticulum’ may be crucial interactive pathways in PCa. Furthermore, PAICS, CDH1, TWIST1, ZWINT and KIAA0101 may serve as crucial miR-1 target genes in PCa.