Abstract

   Aberrant expression of protein kinase C (PKC) isozymes is a hallmark of
   cancer. The different members of the PKC family control cellular events
   associated with cancer development and progression. Whereas the
   classical/conventional PKCα isozyme has been linked to tumor
   suppression in most cancer types, here we demonstrate that this kinase
   is required for the mitogenic activity of aggressive human prostate
   cancer cells displaying aberrantly high PKCα expression. IHC analysis
   showed abnormal upregulation of PKCα in human primary prostate tumors.
   Interestingly, silencing PKCα expression from aggressive prostate
   cancer cells impairs cell-cycle progression, proliferation, and
   invasion, as well as their tumorigenic activity in a mouse xenograft
   model. Mechanistic analysis revealed that PKCα exerts a profound
   control of gene expression, particularly over genes and transcriptional
   networks associated with cell-cycle progression and E2F transcription
   factors. PKCα RNAi depletion from PC3 prostate cancer cells led to a
   reduction in the expression of proinflammatory cytokine and
   epithelial-to-mesenchymal transition (EMT) genes, as well as a
   prominent downregulation of the immune checkpoint ligand PD-L1. This
   PKCα-dependent gene expression profile was corroborated in silico using
   human prostate cancer databases. Our studies established PKCα as a
   multifunctional kinase that plays pleiotropic roles in prostate cancer,
   particularly by controlling genetic networks associated with tumor
   growth and progression. The identification of PKCα as a protumorigenic
   kinase in human prostate cancer provides strong rationale for the
   development of therapeutic approaches toward targeting PKCα or its
   effectors.

Significance:

   PKCα was found to be aberrantly expressed in human prostate cancer.
   Silencing the expression of this kinase from aggressive prostate cancer
   cell lines reduces their proliferative, tumorigenic, and invasive
   properties. In addition, our findings implicate PKCα as a major node
   for transcriptional regulation of tumorigenic, inflammatory, and EMT
   networks in prostate cancer, highlighting its potential relevance as a
   therapeutic target.

Introduction

   Prostate cancer is the second leading cause of cancer-related deaths
   among men in the United States, with approximately 268,500 new cases
   and approximately 34,600 projected deaths in the United States for 2022
   ([40]1). Although patients with prostate cancer generally respond
   favorably to available therapies, the disease eventually progresses to
   metastatic castration-resistant prostate cancer (mCRPC), which is hard
   to eradicate and is ultimately lethal ([41]2, 3). The initiation and
   progression of prostate cancer is driven by genetic and epigenetic
   changes leading to dysregulation of oncogenic signal transduction
   pathways mostly linked to the acquisition of proliferative and invasive
   traits. Hallmarks of prostate cancer include the functional
   inactivation and/or deletion of tumor suppressors (e.g., PTEN, NKX3.1),
   genomic rearrangements (e.g., TMPRSS2-ERG), altered growth factor
   signaling (e.g., IGF-1R, FGFR, ErbB receptors), and aberrant signaling
   (e.g., PI3K/Akt, Src; refs. [42]4–6).

   The protein kinase C (PKC family) has been recognized as a major player
   in the progression of multiple cancers, including prostate cancer.
   Members of the three PKC classes (“conventional/classical” cPKCs α, β,
   and γ; “novel” nPKCs δ, ε, η, and θ; and “atypical” aPKCs ζ and ι) have
   been widely implicated in fundamental cancer-driving events, such as
   cell proliferation, survival, motility, and invasion. cPKCs and nPKCs,
   the PKCs activated by the lipid second messenger diacylglycerol (DAG)
   and phorbol esters, have a remarkably high functional complexity.
   Indeed, these kinases could wield either tumor-promoting or
   tumor-suppressive activities depending on the cell type ([43]7–9).
   While PKC isozymes are rarely mutated in cancer, upregulation of
   prooncogenic PKCs and downregulation of tumor-suppressing PKCs are
   common events in epithelial tumors. This distorted pattern may result
   in rerouting DAG signals through abnormally overexpressed PKCs,
   ultimately boosting mitogenic and survival responses through oncogenic
   signaling pathways such as ERK, NFκB, PI3K, and STAT3 ([44]10).
   Aberrant PKC isozyme expression has been also linked to
   epithelial-to-mesenchymal transition (EMT), a dynamic process by which
   cancer cells acquire invasive properties ([45]11–14). In prostate
   cancer, PKCε expression is causally associated with disease initiation
   and progression, as previously established using transgenic mouse
   models. Conversely, overexpression of other DAG-regulated PKCs in the
   mouse prostate, namely PKCα and PKCδ, failed to confer a tumorigenic
   phenotype ([46]15–17). Consistent with these in vivo studies, PKCα and
   PKCδ mediate proapoptotic responses in androgen-dependent prostate
   cancer cellular models, such as LNCaP cells ([47]18–20). Indeed, there
   is a vast literature describing PKCα as a tumor-suppressive kinase in
   several cancers, such as lung, colon, endometrial, and skin cancer
   ([48]21–26). Intriguingly, PKCα has also been reported to be
   tumor-promoting kinase, for example in triple-negative breast cancer
   ([49]26–28). This dichotomy, together with the limited information
   available in models of prostate cancer, prompted us to investigate the
   role of DAG-regulated PKCα in this malignancy in further detail.

   Here, we show that PKCα is upregulated in primary prostate cancer, and
   its elevated levels in aggressive prostate cancer cell lines causally
   associate with proliferative, tumorigenic, and invasive behaviors. We
   found PKCα to be at the core of gene transcriptional networks
   associated with the progression of prostate cancer, playing a stringent
   control over mitogenic, invasive, inflammatory, and tumor evasion gene
   expression signatures.

Materials and Methods

Cell Lines and RNAi Interference

   Authenticated human prostate cancer cells were obtained from ATCC and
   cultured in RPMI medium supplemented with 10% FBS, 2 mmol/L glutamine,
   100 U/mL penicillin, and 100 μg/mL streptomycin). Cells are tested for
   Mycoplasma at least twice a year, and normally used at low passage
   (generally <10 passages).

   For the generation of stably depleted PKCα cell lines, we used a short
   hairpin RNA (shRNA) lentiviral approach. Cells were infected with
   MISSION shRNA lentiviruses (Sigma-Aldrich) designed for the human PRKCA
   gene (TRCN0000196730, TRCN0000233512), and followed by puromycin
   selection, as described previously ([50]29).

   For transient silencing of PKCα, we used three different siRNA duplexes
   (#1 and #2 as in ref. [51]12, #3 from Dharmacon, catalog no.
   J-003523-18-0005). As non-target control (NTC), we used D-001810-02-05
   ON-TARGETplus nontargeting siRNA. siRNA duplexes were transfected using
   Lipofectamine RNAi Max (Invitrogen), as described previously ([52]30,
   31).

Western Blots

   Western blots were done essentially as described previously ([53]31).
   Briefly, cells were harvested in lysis buffer containing 50 mmol/L
   Tris-HCl, pH 6.8, 10% glycerol, and 2% β-mercaptoethanol. Cell lysates
   were subjected to SDS-PAGE and transferred to polyvinylidene difluoride
   membranes (Millipore Corporation). After blocking with 5% milk or 5%
   BSA in TBS/0.1% Tween for 1 hour, membranes were incubated overnight
   with the following primary antibodies: anti-PKCα anti-PKCδ, anti-PKCε
   (Cell Signaling Technology, catalog nos. 2056, #2058, and #2083,
   respectively), anti-phospho-Rb (Cell Signaling Technology, catalog no.
   2181), anti-caspase-3 (Cell Signaling Technology, catalog no. 9662),
   anti-caspase-9 (Cell Signaling Technology, catalog no. 9508),
   anti-PARP1 (Cell Signaling Technology, catalog no. 9532), anti-vimentin
   (Cell Signaling Technology, catalog no. 5741), anti-Zeb1 (Cell
   Signaling Technology, catalog no. 3396), anti-AXL (Cell Signaling
   Technology, catalog no. 8661), anti-E-cadherin (R&D, catalog no.
   AF748), anti-PD-L1 (Cell Signaling Technology, catalog no. 13684),
   anti-vinculin (Sigma-Aldrich, catalog no. V9131), or β-actin
   (Sigma-Aldrich, catalog no. A5441). Membranes were then incubated for 1
   hour with either anti-mouse or anti-rabbit secondary antibodies
   conjugated to horseradish peroxidase (Bio-Rad Laboratories). Bands were
   visualized and subjected to densitometric analysis using an Odyssey Fc
   system (LI-COR Biotechnology).

Cell Proliferation and Viability Assay

   Cell number was determined using a Bio-Rad TC20 automated cell counter.
   Cell viability was assessed using an
   3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT)
   colorimetric assay in 96-well plates. Absorbance at 570 nm was
   determined in a plate-reader spectrophotometer (EMax Plus Microplate
   Reader, Molecular Devices Inc.).

Invasion Assay

   A Boyden chamber assay was used, as described previously ([54]17, 29).
   Briefly, cells were trypsinized, suspended in 0.1% BSA/RPMI, and seeded
   (2.5 × 10^4 cells/well) in the upper compartment of a Boyden chamber
   (NeuroProbe). A 12-μm-pore Matrigel-coated polycarbonate membrane was
   used to separate the upper and lower compartments. In the lower
   chamber, RPMI medium containing 10% FBS was used. After an incubation
   period of 16 hours at 37°C, membranes were recovered and cells on the
   upper side of the membrane (nonmigrating) were wiped off the surface.
   Migrating cells on the lower side of the membrane were fixed and
   stained with the Hema 3 Staining kit (Thermo Fisher Scientific).
   Migrating cells in each well were counted in five random fields by
   contrast microscopy using an Eclipse E200 Nikon microscopy (4X
   magnification) and the ImageJ/Fiji software.

Tumorigenesis in Nude Mice

   Male athymic nude mice (Foxn1^nu/Foxn1^nu) were purchased from Harlan
   Laboratories. Animals were maintained in a temperature-controlled room
   located at the University of Pennsylvania School of Veterinary Medicine
   (Philadelphia, PA) and fed ad libitum. All animal studies were carried
   out in strict accordance with the University of Pennsylvania
   Institutional Animal Care and Use Committee guidelines.

   PC3 cells (1 × 10^5) were injected subcutaneously into the flanks of
   6-week-old male athymic nude mice (Foxn1^nu/Foxn1^nu). Tumor formation
   was monitored for 37 days. Tumor volume was determined with caliper
   measurements and calculated using the formula L1 × L2 × H × 0.5238,
   where L1 is the long diameter, L2 is the short diameter, and H is the
   height of the tumor.

RNA Isolation and Real-time Quantitative PCR

   Total RNA was extracted from subconfluent plates using the RNeasy kit
   as directed by the manufacturer (Qiagen), as described previously
   ([55]31). Briefly, 1 μg of RNA per sample was reverse transcribed using
   the TaqMan reverse transcription reagent kit and random hexamers as
   primers (Applied Biosystems). Primers for individual genes were
   purchased from Applied Biosystems. PCR amplifications were performed
   using an ABI PRISM 7300 Detection System in a total volume of 20 μL
   containing Taqman Universal PCR Master Mix (Applied Biosystems),
   commercial target primers (300 nmol/L), the fluorescent probe (200
   nmol/L), and 1 μL of cDNA. PCR product formation was continuously
   monitored using the Sequence Detection System software version 1.7
   (Applied Biosystems). The FAM signal was normalized to endogenous UBC
   (housekeeping gene).

RNA Sequencing Data Analysis

   RNA was isolated from triplicate subconfluent plates using the RNeasy
   kit. RNA concentration and integrity were measured on an Agilent 2100
   Bioanalyzer (Agilent Technologies). Only RNA samples with RNA integrity
   values (RIN) over 8.0 were considered for subsequent analysis. One of
   the triplicate samples in the PKCα2 siRNA group behaved as an outlier
   based on unsupervised analysis of the transcriptomic data, and
   therefore this group remained as duplicate samples. RNA samples were
   processed for directional RNA sequencing (RNA-seq) library construction
   and sequencing at the Next-Generation Sequencing (NGS) Core facility of
   the Perelman School of Medicine, University of Pennsylvania
   (Philadelphia, PA). We performed 100 nt singled-end sequencing using an
   Illumina HiSeq4000 platform and obtained approximately 20 million reads
   per sample. The short-sequenced reads were mapped to the human
   reference genome (hg19) with the splice junction aligner Rsubread
   R/Bioconductor package (v2.6.3). We employed featureCounts function to
   calculate the gene expression abundance using the aligned BAM files. To
   identify differentially expressed genes (log2 fold change > ±1, FDR <
   0.001) between the three different siRNA duplexes (α1, α2, or α3) with
   the NTC siRNA and parental cells, we employed the edgeR R/Bioconductor
   package based on the normalized log2 based count per million values.

   For functional enrichment analyses (FEA), we used ClueGo Cytoscape's
   plug-in ([56]http://www.cytoscape.org/) and the InnateDB resource
   ([57]http://www.innatedb.com/) based on the list of dysregulated
   transcripts. Gene set enrichment analysis (GSEA) of transcription
   factor binding sites (TFBS) was performed with the R/Bioconductor
   package clusterProfiler based on the TFBS signature obtained from
   MSigDB (c3.tft.v7.4.entrez.gmt). RNA-seq expression profiles of 497
   primary prostate adenocarcinomas from The Cancer Genome Atlas Prostate
   Adenocarcinoma (TCGA-PRAD) project were downloaded from the UCSD-Xena
   resource ([58]https://xena.ucsc.edu/). Prostate carcinomas were divided
   into low (n = 75) or high (n = 134) PRKCA expression levels according
   to the StepMiner one-step algorithm for further analysis of
   PKCα-modulated genes. Data integration and visualization of
   differentially expressed transcripts were done with R/Bioconductor and
   the MultiExperiment Viewer software (MeV v4.9).

Correlation Analyses

   To explore associations between PRKCA mRNA levels and EMT phenotype of
   primary prostate carcinomas, EMTome resource
   ([59]http://www.emtome.org/) was employed to extract three relevant EMT
   gene expression signatures identified in invasive prostate carcinomas
   ([60]32–34). PKCα mRNA expression levels, the EMT gene expression
   signatures, and their derived EMT scores from TCGA-PRAD dataset were
   directly retrieved and visualized using the UCSC Xena resource
   ([61]https://xenabrowser.net/). PKCα mRNA levels and EMT scores for
   each of the EMT signatures across all primary invasive prostate
   carcinomas were used for correlation analysis with R software. EMT
   scores were computed as an average weighted sum of the gene expression
   levels that constitute each EMT signature.

   In a separate association analysis between PKCα expression and androgen
   receptor (AR), we carried out a meta-analysis of correlation
   coefficients obtained from eight independent prostate cancer datasets
   using the DerSimonian-Laird (DSL) random-effect method with correlation
   coefficients as effect sizes. Briefly, PRKCA and AR mRNA profiles from
   primary prostate carcinomas were retrieved from Gene Expression Omnibus
   ([62]GSE41967 and [63]GSE70768), CancerTool ([64]GSE3325, [65]GSE3933,
   [66]GSE21032, [67]GSE35988, and MSKCC) and UCSC Xena (GDC-TCGA-PRAD)
   resources. Pearson correlation coefficients were independently computed
   from each study in R and subsequently integrated into a meta-analysis
   using metacor R package.

Cytokine Measurements

   PC3 cells were transfected with siRNA duplexes for PKCα or NTC. After
   48 hours, medium was replaced, and conditioned medium (CM) was
   collected after 12 hours (IL8/CXCL8) or 24 hours (GRO). Cytokines in
   the CM were determined using the Quantikine ELISA kit for human IL8
   (R&D Systems) and the human GRO ELISA kit (Invitrogen), following the
   protocols provided by the manufacturers.

Tissue Microarray and IHC

   For each case of prostatic adenocarcinoma, one slide most
   representative of the overall Gleason score and stage was identified.
   The corresponding paraffin-embedded tissue block was used to construct
   a tissue microarray (TMA) using 1 mm coring needle (Beecher Tissue
   Arrayer MTA-1). All 88 cases were represented at least in triplicate
   resulting in production of four TMA blocks that contained a total of
   284 cores. Non-neoplastic liver, kidney, spleen, and tonsil of patients
   operated for diseases other than cancer was also included in the same
   blocks as controls. For staining, we used an anti-PKCα antibody (Abcam,
   ab32376, clone Y124, a validated antibody for IHC in human specimens)
   at a 1:1,000 dilution. Staining was performed on a Leica Bond-IIITM
   instrument using the Bond Polymer Refine Detection System (Leica
   Microsystems DS9800). Heat-induced epitope retrieval was performed for
   20 minutes with ER2 solution (Leica Microsystems AR9640). All the
   experiments were performed at room temperature. For calculation of the
   H score, the intensity of staining was graded on a scale of 0 to 3,
   with 0 being no staining and 3 being strongest staining. The percentage
   of tumor cells staining was calculated over multiple high-power fields
   in the region over the entire tumor. The staining was normalized to
   compare different tumors by multiplying the intensity of staining with
   the percentage of tumor cells staining.

Statistical Analysis

   For most experiments, statistical analysis (Student t test, ANOVA) was
   performed using GraphPad Prism software built-in analysis tools. A P
   value < 0.05 was considered statistically significant.

Data Availability Statement

   Raw data for this study were generated at the NGS Core facility of the
   Perelman School of Medicine, University of Pennsylvania (Philadelphia,
   PA). Derived data supporting the findings of this study are available
   in [68]Supplementary Table S1. Other data generated in this study are
   available within the article and its Supplementary Data.

Results

Upregulation of PKCα in Human Prostate Cancer

   The aberrant PKC signaling observed in multiple cancer types foresees
   important roles for this pathway in disease progression ([69]7, 8), as
   we previously established for PKCε in prostate cancer ([70]15–17).
   PKCα, a DAG-responsive PKC with dual effects either as a tumor promoter
   or suppressor ([71]21–28), has been poorly studied in prostate cancer.
   Examination of PKCα expression in established prostate cancer cell
   models showed a prominent upregulation in PC3, PC3-ML, and DU145
   prostate cancer cell lines compared with less aggressive LNCaP cells
   and its sublines (C4, C4-2) or 22RV1 cells ([72]Fig. 1A).

FIGURE 1.

   [73]FIGURE 1
   [74]Open in a new tab

   PKCα upregulation in aggressive prostate cancer cells. A, Left, PKCα
   expression in prostate cancer cells, as determined by Western blot
   analysis. Right, Densitometric analysis. Results are expressed as mean
   ± SEM (n = 4). B, Representative IHC staining of PKCα in prostate
   cancer specimens. Left, Prostate cancer with low PKCα expression.
   Right, Prostate cancer with high PKCα expression. C, Enhanced view of
   PKCα staining in a prostate cancer specimen. D,H score is plotted for
   normal and cancer areas stainings with an anti-PKCα antibody. The mean
   and P value are indicated.

   Next, we took advantage of a TMA comprising prostate cancer specimens
   from 88 patients and evaluated PKCα staining by IHC using an anti-PKCα
   antibody previously validated in PKCα wild-type versus knockout mice
   ([75]25), followed by a quantitative analysis reported as H score. We
   observed heterogenous PKCα staining across different specimens, with
   cases displaying very high staining and others with weak or no
   staining. Representative examples of TMA micrographs are shown in
   [76]Fig. 1B. A detailed analysis revealed low or no intensity staining
   in nontumor areas, whereas PKCα staining was primarily detected in
   those areas defined as adenocarcinoma. An enhanced view is shown in
   [77]Fig. 1C. Quantitative analysis of PKCα immunostaining in tumor
   areas relative to normal areas is depicted in [78]Fig. 1D.

PKCα Mediates Human Prostate Cancer Cell Growth

   To begin elucidating the functional consequences of PKCα upregulation
   in aggressive cellular models of prostate cancers, we first analyzed
   the effect of PKCα RNAi silencing on proliferation. Three different
   siRNA duplexes were used to knockdown PKCα in PC3 and DU145 cell lines.
   These duplexes caused >80% PKCα depletion in all cases without
   affecting the expression of PKCδ and PKCε, the other DAG-responsive
   PKCs expressed in these cells ([79]Fig. 2A). PKCα depletion lasted for
   at least >120 hours posttransfection ([80]Supplementary Fig. S1).
   Silencing PKCα conferred slower growth rate properties to both PC3
   ([81]Fig. 2B, top) and DU145 cells ([82]Fig. 2B, bottom) when compared
   with parental cells or cells subjected to NTC RNAi. Cell-cycle
   distribution analysis using FACS revealed an accumulation of
   PKCα-depleted PC3 and DU145 cells in G[0]–G[1], with a subsequent
   reduction in S-phase ([83]Fig. 2C). 5-Ethynyl-2′-deoxyuridine (EdU)
   incorporation assays showed a significant decrease in the fraction of
   cells undergoing DNA synthesis in PKCα-depleted PC3 or DU145 cells
   relative to control cells ([84]Fig. 2D). Consistent with the inhibition
   in G[1] to S transition, a prominent Rb dephosphorylation was observed
   in PKCα knockdown cells ([85]Supplementary Fig. S2A). We were unable to
   detect any significant changes in the expression of apoptotic markers,
   namely cleaved PARP1, cleaved caspase-3, and cleaved caspase-9, in PC3
   or DU145 cells subjected to PKCα RNAi depletion ([86]Fig. 2E). There
   was no significant sub-G[0]–G[1] population in the FACS analysis in
   PKCα-depleted cells, consistent with the absence of apoptotic cells
   ([87]Supplementary Fig. S2B). In addition, there was no additive growth
   inhibitory effect between PKCα RNAi and treatment with either the dual
   Cdk4/6 inhibitor palbociclib/PD 0332991 ([88]Supplementary Fig. S3A) or
   the Cdk4 inhibitor NSC 625987 ([89]Supplementary Fig. S3B). Altogether,
   these results strongly support the requirement of PKCα for mitogenesis
   in aggressive prostate cancer cells.

FIGURE 2.

   [90]FIGURE 2
   [91]Open in a new tab

   Silencing PKCα from aggressive prostate cancer cells induces cell
   growth arrest. PC3 or DU145 cells were transfected with three different
   PKCα (α1, α2, or α3) or NTC siRNA duplexes. Twenty-four hours later,
   cells were serum starved for 24 hours and experiments carried out at
   the indicated times. P, parental. A, Expression of PKC isozymes was
   determined by Western blot analysis at day 3 after transfection.
   Representative Western blots are shown. B, Effect of PKCα RNAi
   depletion on cell number. Left, Representative micrographs 1–5 days
   after transfection of siRNA duplexes. Right, Cell number was expressed
   as mean ± SD (n = 3). An additional experiment gave similar results. C,
   Percentage of cells in S-phase 48 hours after transfection with siRNA
   duplexes, as determined by flow cytometry in EdU-labeled cells. Results
   are expressed as mean ± SE (n = 3). D, Cell-cycle distribution was
   determined by flow cytometry 48 hours after transfection with siRNA
   duplexes. Left, Representative flow cytometry charts. Right, Cell-cycle
   distribution in a representative experiment. E, Expression of apoptosis
   markers (cleaved PARP1, cleaved caspase 3, and cleaved caspase 9). A
   representative experiment is shown. *, P < 0.05; **, P < 0.01; ****, P
   < 0.0001 versus NTC.

   Next, to examine whether PKCα is involved in in vivo prostate cancer
   tumor growth, we generated stably depleted PKCα PC3 cell lines using
   shRNA lentiviruses. Like the siRNA-mediated PKCα transient approach,
   stable knockdown was highly specific for PKCα without significant
   changes in the expression of other DAG-regulated PKCs ([92]Fig. 3A).
   Upon subcutaneous inoculation of parental PC3 cells into nude mice, a
   tumor growth response was readily observed. Notably, PKCα-depleted PC3
   cells displayed a significant reduction in tumor growth compared with
   parental PC3 cells or cells subjected to NTC RNAi, as determined by
   measurements of tumor volume ([93]Fig. 3B and [94]C) and weight
   ([95]Fig. 3D). PKCα-depleted tumors exhibit reduced Ki67 staining, a
   marker for mitotic index, as well as diminished phospho-Erk staining
   compared to tumors from parental or NTC PC3 cells. We detected
   TUNEL-positive areas in PKCα-depleted tumors ([96]Fig. 3E), suggesting
   that PKCα may confer to some degree an anti-apoptotic response in an in
   vivo setting.

FIGURE 3.

   [97]FIGURE 3
   [98]Open in a new tab

   Silencing PKCα reduces PC3 cell tumorigenic activity in nude mice. PC3
   cells were subjected to stable PKCα depletion using two different shRNA
   lentiviruses, followed by puromycin selection. Cells were inoculated
   subcutaneously into nude mice, and tumor formation was followed for the
   time indicated in the figure. A, Expression of PKC isozymes in the
   inoculated cell lines, as determined by Western blot analysis. B,
   Pictures of tumors isolated from all mouse cohorts. C, Time-course
   analysis of tumor formation Results are expressed as mean ± SD (n = 8).
   D, Tumor weight was determined after sacrificing mice at day 37
   postinoculation with the different PC3 cell lines. Results are
   expressed as mean ± SD (n = 8). E, Representative IHC images for Ki67,
   phospho-ERK, and TUNEL. NTC, non-target control shRNA lentivirus. ****,
   P < 0.0001 versus NTC.

PKCα Expression Correlates with EMT Markers and is Required for Prostate
Cancer Cell Invasion

   Recent studies linked PKC isozymes to EMT in a variety of tumor types
   ([99]11–14, 35). We used the Cancer Cell Line Encyclopedia (CCLE) to
   investigate whether there is an association between the expression of
   PKCα and markers of the mesenchymal phenotype in prostate cancer cell
   lines. This analysis revealed a striking positive correlation between
   the expression of PKCα (PRKCA gene) and mesenchymal markers vimentin,
   ZEB1, ZEB2, and AXL in prostate cancer cell lines ([100]Fig. 4A). A
   similar positive trend between PKCα and EMT markers was detected in
   human prostate cancer specimens upon inquire of TCGA-PRAD dataset
   ([101]Fig. 4B). To further explore the association between PRKCA and
   EMT phenotype, EMTome resource ([102]http://www.emtome.org/) was
   employed to extract three relevant EMT gene expression signatures
   identified in prostate carcinomas ([103]32–34). A significant positive
   correlation between PRKCA and prostate cancer EMT signatures was
   observed in all cases ([104]Fig. 4C). Proof of principle for the
   association between PKCα expression and EMT was established by Western
   blot analysis using LNCaP cell lines and 22RV1 as an “epithelial” model
   and PC3/PC3-ML/DU145 cells as “mesenchymal” models. PC3, PC3-ML, and
   DU145 cell lines, which aberrantly express PKCα, displayed high
   expression of mesenchymal markers vimentin and AXL as well as
   E-cadherin downregulation, whereas the opposite was true for
   LNCaP/22RV1 cell lines ([105]Fig. 4D).

FIGURE 4.

   [106]FIGURE 4
   [107]Open in a new tab

   PKCα association with prostate cancer cell line EMT and invasiveness.
   A, Correlations between PKCα (PRKCA) and EMT markers from the CCLE
   database. ●, DU145; ▲, PC3; ■, NCI H660; ●, VCaP; ▲, 22RV1; ■, LNCaP.
   B, Correlations between PKCα (PRKCA) and EMT markers from TCGA-PRAD
   human dataset (498 patients). D, Representative Western blot analysis
   for PKCα and EMT markers in prostate cancer cell lines. E, Cell
   invasion was determined in PC3 and DU145 cells using a Boyden chamber
   with Matrigel, 48 hours after transfection with siRNA duplexes for PKCα
   (α1, α2) or NTC. Experiments were done in the absence or presence of 5%
   FBS in the lower chamber. Left, Representative invasion experiment.
   Right, Quantification of Boyden chamber migration using 5% FBS. Results
   are normalized to invasion in parental cells (no siRNA, dotted lines),
   and expressed as mean ± SEM of three independent experiments. *, P <
   0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 versus NTC.

   Because the mesenchymal phenotype of cancer cells is linked to highly
   invasive traits, we next investigated the effect of knocking down PKCα
   on prostate cancer cell invasion. Assessment of PC3 and DU145 invasion
   through Matrigel using a Boyden chamber assay showed a major reduction
   in transwell migration in PKCα knockdown PC3 and DU145 cells relative
   to their corresponding parental cells or cells transfected with NTC
   siRNA ([108]Fig. 4E). Similar results were observed using stably PKCα
   depleted PC3 and DU145 prostate cancer cells ([109]Supplementary Fig.
   S4).

   Next, we explored a potential relationship between PKCα and AR
   expression by analyzing eight prostate cancer datasets (see Materials
   and Methods for details). Although a general pattern could not be
   defined, a trend for negative correlation was observed in some cases,
   reaching statistically significance in four datasets. No negative
   correlation between PRKCA and AR expression could be found in
   TCGA-PRAD. Nonetheless, a meta-analysis of the eight datasets showed a
   statistically significant negative correlation between PRKCA and AR
   ([110]Supplementary Fig. S5).

Characterization of the PKCα Transcriptome in Prostate Cancer Cells

   Previous studies from our laboratory established key roles for
   DAG-regulated PKCs in the control of gene expression ([111]17, 31, 36,
   37). To begin elucidating the molecular changes associated with PKCα
   proliferative and invasive activities in prostate cancer, we carried
   out a whole transcriptome analysis in PC3 cells. We used five cohorts
   (parental, NTC siRNA, PKCα siRNA #1, #2, and #3). RNA was extracted 48
   hours after transfection of the corresponding siRNA duplexes and
   subjected to RNA-seq. A comparison of transcriptome profiles was
   conducted to identify differentially regulated genes using the edgeR
   test (FDR < 0.001). A 2-fold change relative to parental cells was used
   as a cutoff. Cluster dendrogram revealed significant overlapping
   between parental and NTC cells, as well as between the three different
   PKCα knockdown cell lines ([112]Fig. 5A). Using the described stringent
   cutoffs, we identified 848 upregulated genes and 602 downregulated
   genes overlapped by all three PKCα RNAi sequences relative to NTC (P <
   0.05; [113]Fig. 5B and [114]C). A complete list of PKCα-regulated genes
   is shown in [115]Supplementary Table S1. As expected, the PKCα gene
   (PRKCA) was a top downregulated gene (>99% depletion), whereas
   expression of other PKC genes remained unchanged. The AR gene was not
   among the PKCα-regulated genes (see [116]Supplementary Table S1).
   Validation of the RNA-seq analysis was done by Q-PCR using specific
   probes for the three top downregulated genes (ESM1, BCL2A1, and PRKCA)
   and the three top upregulated genes (DMBT1, GLYATL2, and ABCB11;
   [117]Fig. 5D).

FIGURE 5.

   [118]FIGURE 5
   [119]Open in a new tab

   PKCα regulates gene expression in prostate cancer cells. A, Dendrogram
   for deregulated genes in PC3 cells subjected to PKCα silencing using
   three different siRNA duplexes (α1, α2, or α3), and comparison with NTC
   siRNA and parental cells. B, Number of genes regulated by PKCα, as
   determined by RNA-seq, which represents the overlapped signature with
   α1, α2, and α3 siRNA duplexes. C, Heatmap for PKCα-regulated genes. D,
   Q-PCR validation the top three upregulated and downregulated genes.

   By means of pathway enrichment analysis using REACTOME and Kyoto
   Encyclopedia of Genes and Genomes (KEGG), we were able to identify
   prominent enrichment of pathways associated with cell-cycle
   progression, DNA replication, and regulation of the extracellular
   matrix (ECM; [120]Fig. 6A). Gene Ontology (GO) analysis provided
   similar enrichment in gene sets associated with mitogenesis and ECM
   function, as well as processes related to cell migration and immune
   responses ([121]Fig. 6B). Complete lists of pathway enrichment analysis
   and GO enrichment analysis regulated by PKCα can be found in
   [122]Supplementary Table S2. Further analysis using GSEA for
   quantitative determination of activated versus suppressed functional
   enrichments, clearly revealed a major suppression of pathways
   associated with cell proliferation in PKCα-silenced cells, as depicted
   in [123]Fig. 6C and [124]D. Top suppressed pathways include “chromosome
   segregation,” “mitotic cell-cycle process,” “DNA replication,” “mitotic
   cell-cycle phase transition,” and “DNA replication.”

FIGURE 6.

   [125]FIGURE 6
   [126]Open in a new tab

   FEAs for PKCα regulates gene expression in prostate cancer cells. A,
   FEA of pathways identified as affected by PKCα silencing in PC3 cells.
   B, FEA of GO biological processes affected by PKCα silencing in PC3
   cells. C, GSEA of the activated and suppressed bioprocesses in PKCα
   silencing in PC3 cells. D, GSEA of the activated and suppressed KEGG
   pathways in PKCα silencing in PC3 cells. E, GSEA of the activated and
   suppressed TFBS upon PKCα silencing in PC3 cells based on the TFBS
   Molecular Signatures Database (MsigDB).

   An analysis of TFBS enrichment in PKCα-regulated genes was performed
   using the R/Bioconductor package clusterProfiler and the TFBS
   signatures available at the Molecular Signatures Database. Results
   depicted in [127]Fig. 6E showed a striking enrichment for E2F TFBSs in
   the promoters of genes which are downregulated when PKCα is silenced.
   We also noticed an enrichment for Oct-1 TFBSs in the promoters of genes
   upregulated upon PKCα silencing. A complete list of transcriptional
   binding sites regulated by PKCα is presented in [128]Supplementary
   Table S3. Notably, this E2F signature is suppressed in PKCα knockdown
   cells, a finding that fits with the expected G[1] arrest and enhanced
   E2F activity resulting from Rb dephosphorylation upon PKCα silencing.
   These results undoubtedly place PKCα as a major hub for the control of
   transcriptional pathways associated with prostate cancer cell
   proliferation.

Differential Expression of EMT Genes in Human Prostate Tumors Based on PKCα
Expression

   To further ascertain the biological relevance of the PKCα gene
   signature identified by RNA-seq in PC3 cells, we next analyzed
   TCGA-PRAD data collection. Briefly, a group of 498 patients with
   primary prostate adenocarcinomas were classified into “Low PKCα” or
   “High PKCα” mRNA expression levels according to the StepMiner one-step
   algorithm, establishing 134 tumors and 75 tumors in each category,
   respectively ([129]Fig. 7A). Differentially expressed genes were
   scrutinized with the MultiExperiment Viewer software (MeV v4.9),
   revealing 116 genes in the “High PKCα” group which are downregulated in
   PKCα silenced PC3 cells, and 196 genes in the “Low PKCα” group which
   are upregulated in PKCα-depleted PC3 cells ([130]Fig. 7B). Hence, a
   significant overlap exists between PKCα-regulated genes in the RNA-seq
   analysis and a human prostate cancer database.

FIGURE 7.

   FIGURE 7
   [131]Open in a new tab

   PKCα controls the expression of EMT markers in human prostate cancer.
   A, Classification of primary prostate adenocarcinomas (TCGA-PRAD, n =
   498) into “High PKCa” or “Low PKCa” expression levels according to the
   StepMiner one-step algorithm. B, Heatmap showing the differential gene
   expression from RNA-seq analysis in comparison with the same genes in
   “High PKCa” or “Low PKCa” human prostate tumors. C, Heatmaps for the
   expression of EMT-TFs and mesenchymal markers. Left, Expression upon
   PKCα RNAi silencing in PC3 cells, as determined by RNA-seq. Right,
   Expression in “High PKCa” and “Low PKCa” human prostate tumors from
   TCGA-PRAD. The epithelial marker E-cadherin (CDH1) is also shown. D,
   Violin plots indicating P values for the comparison of EMT-TF and
   mesenchymal marker expression in “High PKCa” and “Low PKCa” human
   prostate tumors from TCGA-PRAD.

   Next, we carried out an analysis for selected genes associated with
   epithelial and mesenchymal phenotypes. Interestingly, RNA-seq analysis
   showed significant downregulation of ZEB1, ZEB2, SNAI1, and SNAI2, as
   also determined in the correlation analysis presented in [132]Fig. 4B.
   Therefore, PKCα upregulation is associated with the expression of these
   EMT transcription factors. No changes were observed in TWIST1, and
   TWIST2 was not detected in the RNA-seq profiling. The RNA-seq in PC3
   cells also revealed prominent downregulation of AXL and vimentin (VIM)
   upon PKCα silencing ([133]Fig. 7C, left). These results display a
   remarkable overlap with expression data from TCGA-PRAD, which shows a
   major upregulation of these EMT markers in “High PKCα” human prostate
   adenocarcinomas relative to the “Low-PKCα” patient group ([134]Fig. 7C,
   right). A significant upregulation of CDH1 was detected upon PKCα
   silencing in the RNA-seq profile; however, this was not observed in the
   human TCGA-PRAD database. Violin plots depicted in [135]Fig. 7D
   indicate the expression of mesenchymal markers in “High PKCα” versus
   “Low-PKCα” groups with the corresponding P values.

PKCα Controls the Expression of Proinflammatory Cytokines and PD-L1 in Human
Prostate Cancer

   A focused analysis of cytokine expression from the RNA-seq data
   revealed prominent downregulation of proinflammatory cytokines in
   PKCα-depleted PC3 cells, including IL1β, IL6, IL8 (CXCL8), CXCL1
   (GRO1), CXCL3 (GRO3), and CXCL6 ([136]Fig. 8A, left). This is
   remarkable because most of these cytokines have important
   tumor-promoting roles in prostate cancer and have been associated with
   high-grade disease ([137]38–40). Interestingly, “High PKCα” human
   prostate tumors displayed elevated expression of these cytokines
   relative to “Low PKCα” tumors, as depicted in heatmaps ([138]Fig. 8A,
   right) and violin plots ([139]Fig. 8B). Proof of principle for the
   PKCα-dependent production of cytokines (IL8 and GRO) was determined by
   ELISA in conditioned medium ([140]Supplementary Fig. S6).

FIGURE 8.

   [141]FIGURE 8
   [142]Open in a new tab

   Association of PKCα with proinflammatory and/or tumorigenic cytokines
   and PD-L1 expression in human prostate cancer. A, Heatmaps for the
   cytokines and/or chemokines regulated by PKCα. Left, Expression upon
   PKCα RNAi silencing in PC3 cells, as determined by RNA-seq. Right,
   Expression in “High PKCa” and “Low PKCa” human prostate tumors from
   TCGA-PRAD. NTC, non-target control. B, Violin plots indicating P values
   for the comparison of cytokine expression in “High PKCa” and “Low PKCa”
   human prostate tumors from TCGA-PRAD. C, Representative Western blot
   analysis for PD-L1 expression in prostate cells. D, Inhibition of PD-L1
   expression in PC3 cells by PKCα RNAi silencing, as determined by
   RNA-seq. E, Heatmaps for PD-L1. Top, Expression upon PKCα RNAi
   silencing in PC3 cells, as determined by RNA-seq. Bottom, Expression in
   “High PKCa” and “Low PKCa” human prostate tumors from TCGA-PRAD. F,
   Violin plots indicating P value for PD-L1 expression in “High PKCa” and
   “Low PKCa” human prostate tumors from TCGA-PRAD. G, PD-L1 protein
   expression was determined by Western blot analysis in PC3 cells, 48
   hours after transfection with siRNA duplexes for PKCα (α1, α2, α3) or
   NTC.

   Another important finding from the RNA-seq analysis was the
   identification of the immune checkpoint PD-L1/CD274 as a PKCα-regulated
   gene. In agreement with other studies ([143]41, 42), we observed PD-L1
   upregulation in PC3 and DU145 cell lines compared to LNCaP cell lines
   ([144]Fig. 8C). Notably, our RNA-seq analysis showed downregulated
   PD-L1 mRNA levels in PKCα-silenced PC3 cells, ranging between 60% and
   80% ([145]Fig. 8D and [146]E, top). Moreover, analysis in TCGA-PRAD
   revealed significantly elevated PD-L1 levels in “High PKCα” versus “Low
   PKCα” human prostate adenocarcinomas ([147]Fig. 8E, bottom; violin plot
   in [148]Fig. 8F). We verified the causal association between PD-L1 and
   PKCα expression at the protein level, with a clear PD-L1 downregulation
   in PC3 cells upon PKCα RNAi-mediated silencing, as determined by
   Western blot analysis ([149]Fig. 8G). In conclusion, high PKCα levels
   associate with upregulation of proinflammatory and/or tumorigenic
   cytokines as well as with immune checkpoint PD-L1 in human prostate
   cancer.

Discussion

   Our study established PKCα as a protumorigenic kinase in human prostate
   cancer. PKCα is prominently upregulated in aggressive cellular models
   of prostate cancer, and is required for their growth in culture and as
   xenografts in mice. Using a human prostate cancer TMA, we found PKCα to
   be highly expressed in a significant fraction of tumors. We hypothesize
   that abnormally expressed PKCα reroutes DAG inputs by boosting
   oncogenic PKCα effector pathways, favoring prostate cancer cells to
   become addicted to this pro-oncogenic axis. The tumorigenic role of
   PKCα in prostate cancer emphasizes its remarkable functional complexity
   and epitomizes an example of a DAG/phorbol ester-regulated kinase
   having dual effects. Indeed, PKCα has been shown to be downregulated
   and to act as a tumor suppressor kinase in many cancer types. The
   dichotomous roles of PKCα likely reflect unique links with effector
   pathways and transcriptional programs depending on expression levels
   and context, as thoroughly reviewed recently ([150]26), possibly
   exposing distinctive functional PKCα interactions with oncogenic and
   tumor-suppressing signals.

   Our findings, together with preceding studies, underscore precise roles
   for PKC isozymes in different stages of prostate cancer progression. We
   previously generated prostate-specific transgenic mouse models for PKC
   isozyme overexpression under the control of the probasin (PB) promoter.
   PB-PKCε mice develop a dysplastic phenotype characteristic of prostatic
   intraepithelial neoplasia (PIN; ref. [151]15), underlining a role for
   PKCε in tumor initiation, as we also postulated in lung cancer models
   ([152]37). PIN lesions in PB-PKCε mice exhibit early signs of
   deregulated oncogenic signaling, including hyperactivation of Akt, ERK,
   STAT3, and NFκB ([153]15–17). However, PB-PKCα mice did not develop any
   obvious phenotype, indicating its lack of involvement in tumor
   initiation ([154]15). On the other hand, the PKCα requirement for
   proliferation strongly supports its involvement in the expanding phase
   of tumor growth. In this regard, our results show that silencing PKCα
   expression from PC3 or DU145 cells leads to the accumulation of cells
   in G[0]–G[1] and a discernible Rb hypophosphorylation. While the
   mechanistic aspects of this growth delay remain to be determined,
   reduced growth upon PKCα inhibition has been linked to p21^cip1
   induction in other cancer types ([155]26, 43–45). The weakened
   phospho-ERK signal in PKCα-deficient PC3 xenografts fits with the
   established role of the MEK/ERK cascade in the control of
   transcriptional events leading to DNA synthesis and mitogenesis
   ([156]46). Moreover, the enrichment in E2F transcription binding sites
   in PKCα-regulated genes is a clear indication of the impact that this
   kinase has on transcriptional events associated with proliferative
   programs. On the other hand, in scenarios of G[1] arrest driven by PKCα
   (i.e., tumor suppression), this kinase drives a transcriptional program
   of cell-cycle exit, as extensively described by Black and co-workers in
   models of intestinal epithelial cells ([157]26, 47). In that model,
   PKCα suppresses the expression of inhibitor of DNA binding (Id)
   transcription factors through an ERK-dependent mechanism ([158]23, 26).
   Because Id transcriptions factors have been associated to mitogenesis
   and oncogenicity, and they are heavily regulated by phosphorylation
   ([159]48), it would be interesting to determine whether aberrant PKCα
   signaling contributes to Id activation and/or upregulation, which has
   been reported in hormone refractory prostate cancer ([160]49, 50). Of
   note, our RNA-seq analysis reveals a major downregulation of Id1 upon
   PKCα silencing in PC3 cells (see [161]Supplementary Table S1).
   Disentangling this enthralling dichotomy represents a significant
   challenge in PKCα signaling.

   Intriguingly, PKCα expression levels do not unequivocally associate
   with AR responsiveness status. While cell lines displaying aberrant
   PKCα expression (PC3, PC3-ML, DU145) are AR negative, LNCaP variants
   that lose androgen responsiveness display low PKCα levels. Notably,
   PKCα-depleted PC3 cells did not reveal changes in AR levels, an
   indication that PKCα per se does not regulate AR expression. Moreover,
   analysis of PKCα and AR expression from multiple databases did not show
   an obvious association, despite a trend for negative correlation in
   some datasets and a statistically significant negative correlation
   between PRKCA and AR in our meta-analysis. It would be interesting to
   determine the molecular basis behind the PKCα upregulation in AR
   negative cells, which presumably involves reprogrammed transcriptional
   mechanisms that occur in mCRPC.

   Our bioinformatics analysis using the CCLE database and the TCGA-PRAD
   tumor database revealed positive correlations between PKCα and the
   expression of EMT markers. Notably, “High PKCα” tumors express high
   levels of mesenchymal phenotype markers, including EMT transcription
   factors. A causal association seems to be in place based on the noted
   downregulation of these markers upon PKCα silencing, as shown in our
   RNA-seq analysis. It remains to be determined whether PKCα is causally
   related to the transition from the epithelial to mesenchymal state in
   prostate cancer. Our previous studies in lung cancer cells revealed
   that silencing the expression of individual PKC isozymes, including
   PKCα, did not prevent the acquisition of the mesenchymal phenotype in
   response to TGFβ ([162]51). Still, a partial reversal of the
   mesenchymal phenotype upon PKCα inhibition is seen in breast and lung
   cancer models ([163]12, 52). A likely possibility is that PKCα
   upregulation is secondary to the acquisition of a mesenchymal state
   rather than contributing to the EMT transformation process. Elevated
   PKCα signaling may aid in the maintenance of the mesenchymal phenotype
   by upregulating EMT transcription factors ([164]14, 35) and upholding
   invasive capacity, as revealed in our Boyden chamber analysis using
   PKCα-silenced cells. Along this line, our previous study in lung cancer
   models established PKCα as a determining factor for the production of
   metalloproteases required for ECM degradation ([165]31). PKCα may also
   control fundamental cellular processes associated with cell migration,
   a conclusion supported by the enrichment in PKCα-regulated genes
   associated with cell motility and ECM function. PKCα may regulate basic
   mechanisms driving cell motility, such as the coordinated processes
   involved in actin cytoskeleton assembly and disassembly. In fact, PKC
   signaling regulates Rho GTPases involved in the formation of
   promotility actin-rich peripheral protrusions (e.g., lamellipodia,
   ruffles) and stress fibers ([166]29, 30, 49, 53–55). Dissecting PKCα
   downstream effectors is key to untangle the molecular basis of these
   regulatory processes.

   Finally and unexpectedly, our study identified PKCα as a cancer cell
   intrinsic factor responsible for PD-L1 upregulation in aggressive
   prostate cancer cells. It is well known that oncogenes and loss of
   tumor suppressor genes induce PD-L1 expression in cancer cells, often
   involving PKC effector pathways such as MEK/ERK ([167]56). Our finding
   may have significant impact on tumor immune evasion, because elevated
   PKCα levels in prostate cancer cells may potentially contribute to the
   abrogation of T-cell antitumor responses. Our RNA-seq analysis also
   established PKCα as a key signaling node for proinflammatory cytokine
   expression. Many of the identified cytokines, including IL6, IL8/CXCL8,
   and IL1β, are known to stimulate cell-autonomous protumorigenic
   mechanisms and shape the local microenvironment to support growth,
   survival, and invasion of primary tumors ([168]38–40). An additional
   inference relates to the ability of PD-L1 to transduce intrinsic
   signals independent of T-cell PD-1 ligation. Indeed, PD-L1 contributes
   to the protumoral activities of cancer cells by increasing
   proliferation and suppressing apoptotic responses. In addition,
   overexpressed PD-L1 promotes EMT, and augments migratory and invasive
   properties of cancer cells ([169]57), Thus, our observation raises the
   question whether a PKCα/PD-L1 link contributes to proliferative and
   invasive activities of prostate cancer cells in addition to promoting
   an inflammatory and/or immunosuppressive landscape.

   In summary, the identification of PKCα as a “multifunctional” kinase
   underlines its relevance in the control of myriad of events associated
   with prostate cancer. Considering the reported antitumorigenic activity
   of PKC inhibitors, including those targeting cPKCs ([170]10), PKCα may
   represent an attractive therapeutic target for aggressive prostate
   cancer. Overall, the extraordinary functional complexity of PKCα
   signaling merits a dedicated mechanistic analysis in a cancer
   type–specific manner, determining their eligibility for a rationale
   pharmacological approach targeting this kinase or its effectors.

Supplementary Material

   Supplementary Figure 1

   Time-dependent expression of PKCalpha in prostate cancer cells after
   RNAi silencing with three different siRNA duplexes.
   [171]Click here for additional data file.^ (2.6MB, pdf)
   Supplementary Figure 2

   Cell cycle analysis upon PKCalpha RNAi silencing. Representative Rb
   phosphorylation as well as cell cycle distribution analysis by FACS in
   PKCalpha silenced PC3 cells are shown.
   [172]Click here for additional data file.^ (677KB, pdf)
   Supplementary Figure 3

   Effect of treatment with Cdk4/6 and Cdk4 inhibitors in combination with
   PKCα RNAi silencing on prostate cancer cell viability.
   [173]Click here for additional data file.^ (695.9KB, pdf)
   Supplementary Figure 4

   Effect of PKCalpha depletion using shRNA lentiviruses on PC3 cell
   invasion, as determined with a Boyden chamber.
   [174]Click here for additional data file.^ (665.7KB, pdf)
   Supplementary Figure 5

   Relationship between PRKCA and AR expression in prostate cancer.
   Correlation graphs for individual datasets as well as a meta-analysis
   are shown.
   [175]Click here for additional data file.^ (930.3KB, pdf)
   Supplementary Figure 6

   Reduced production of cytokines in PC3 cells subjected to PKCalpha RNAi
   depletion. IL-8 and GRO were measured in conditioned medium of PC3
   cells by ELISA.
   [176]Click here for additional data file.^ (602.2KB, pdf)
   Supplementary Table 1

   Differentially expressed genes in PKCalpha silenced PC3 cells.
   [177]Click here for additional data file.^ (400KB, xlsx)
   Supplementary Table 2

   Pathway and GO enrichment analysis.
   [178]Click here for additional data file.^ (146.5KB, xlsx)
   Supplementary Table 3

   Gene Set Enrichment analysis (GSEA) of transcription factor binding
   sites (TFBS).
   [179]Click here for additional data file.^ (10.8KB, xlsx)

Acknowledgments