Research ArticleClinical Studies
Open Access

Integrin Expression in Localized Prostate Cancer: A TCGA and MSKCC Cohort-based Exploratory In Silico Analysis

MANUEL NEUBERGER, LISA FREY, KATJA NITSCHKE, FREDERIK WESSELS, NIKLAS WESTHOFF, FRANK WALDBILLIG, MALIN NIENTIEDT, FRIEDRICH HARTUNG, JOST VON HARDENBERG, MAURICE STEPHAN MICHEL, PHILIPP ERBEN, PHILIPP NUHN and THOMAS STEFAN WORST
Anticancer Research January 2023, 43 (1) 417-428; DOI: https://doi.org/10.21873/anticanres.16177

Abstract

Background/Aim: Diagnostic and prognostic biomarkers in localized prostate cancer (PC) are insufficient. Treatment stratification relies on prostate-specific antigen, clinical tumor staging and International Society of Urological Pathology (ISUP) grading, whereas molecular profiling remains unused. Integrins (ITG) have an important function in bidirectional signaling and are associated with progression, proliferation, perineural invasion, angiogenesis, metastasis, neuroendocrine differentiation, and a more aggressive disease phenotype in PC. However, ITG subunit expression in localized PC and their utility as prognostic biomarkers has not yet been analyzed. This study aimed to fill this gap and provide a comprehensive overview of ITG expression as well as ITG utility as biomarkers. Patients and Methods: The Cancer Genome Atlas (TCGA) and the Memorial Sloan Kettering Cancer Center (MSKCC) prostate adenocarcinoma cohorts were analyzed regarding ITG expression in correlation to ISUP, N- and American Joint Committee on Cancer (AJCC) stage and were correlated with disease-free survival (DFS). Statistical tests used included the Mann-Whitney U-test, logrank test and uni- and multivariable cox regression analyses. Results: After grouping for ISUP (1 and 2 vs. 3-5), N0 vs. N1 and AJCC stage (≤2 vs. ≥3), multiple ITGs showed significant expression differences. The most consistent results were observed for ITGα4, ITGαX, ITGα11, ITGβ2 and ITGα2. In multivariable cox regression, ITGα2, ITGα10, ITGαD, ITGαB2 (TCGA), ITGα11 and ITGβ4 (MSKCC) were independent predictors of DFS. Conclusion: The utility of ITGs as PC biomarkers was herein shown.

Key Words:

Prostate cancer (PC) is the second most common cancer in men and ranks fifth in cancer-related mortality worldwide (1). The introduction of prostate-specific antigen (PSA) screening led to a significant reduction of mortality over the last decades. Next to PSA, current PC screening and treatment decision/stratification is based on digital rectal examination (DRE) and prostate biopsy with or without magnetic resonance imaging (MRI) guidance (2). However, there are several limitations in detection and risk stratification. PSA is organ-, but not tumor-specific. This explains why PC detection biopsy is only positive in around 25% for patients with PSA 2-10 ng/ml (3). False-positive results above thresholds (e.g., in patients with benign prostatic hyperplasia or prostatitis) limit the accuracy. Another critique on the use of PSA screening in PC detection is overdiagnosis and subsequently overtreatment of patients, in which an insignificant PC would never surface during the patient lifespan (3). Additionally, biopsy undersampling may lead to underestimation of the true tumor grade and upgrading after radical prostatectomy (RP) (4-6). Once diagnosed, PC shows an enormous biological heterogeneity: Even though some patients die of metastatic disease within a few years, others can live for 10-20 years with an organ-confined disease (7). This might be due to genomic diversity-like differences in the transcriptome or copy-number alterations, which may lead to an increase in oncogenic pathways (7). Due to this heterogeneity and the above-mentioned sampling errors, PC prognosis is variable even among patients with the same Gleason score (GS) (6, 8). For improving PC screening as well as determining treatment response and a change in systemic therapy, better prognostic biomarkers are urgently needed. Integrins (ITGs) act as receptors, play important roles in cell-extracellular matrix (ECM) adhesion as well as in cell-cell and cell-ECM interaction. For interaction, they provide signal transduction in either direction (inside-out and outside-in) (9, 10). Integrin activated pathways have an impact on cell-cycle regulation (11) and, thereby, on proliferation, differentiation and cell death (10), as well as migration, gene expression and activation of growth factor receptors (12). These characteristics show the important role of ITGs in cancer in general (13). Additionally, in PC the tumor microenvironment is different compared to normal tissue microenvironment (12) indicating a stronger cell/ECM interaction (14). Without proper ECM anchoring adherent non-cancer cells cannot survive. In neoplastic cells this anchorage requirement is lost (10). Pickup et al. argue that biophysical and biochemical cues from the ECM, associated to the tumor, are critical for the malignancy since they influence each of the formerly established cancer hallmarks (e.g., induced angiogenesis, initiation of invasion, resistance to stressors inducing cell death) (15). Moreover, in PC different integrins were found to be associated with tumor progression, development of chemotherapy resistance (16), proliferation, perineural invasion, angiogenesis (17), metastases formation (18), neuroendocrine differentiation (19) and a more aggressive disease phenotype (11, 14, 20-24). Furthermore, it is supposed, that deregulation of integrin, and thereby cellular adhesion, facilitates the invasion of tumors and occurrence of metastases (14). ITGs are αβ heterodimers. In total, there are 24 known ITG heterodimers and 18 different α- and 8 different β-subunits (25). This raises not only the question on consistent differences in specific ITG subunits, but also which ITGs might be most promising for further research.

This analysis aimed to answer these questions in localized PC and provide a better understanding of the role of ITGs in the context of tumor biology.

Materials and Methods

mRNA expression data. The cBioPortal (https://www.cbioportal.org) database was screened for available PC cohorts with mRNA expression data. After screening all available cohorts for mRNA expression data for localized prostate cancer, the ITG data sets of The Cancer Genome Atlas (TCGA) Prostate Adenocarcinoma cohort (Firehose Legacy) consisting of data of 501 patients and the Memorial Sloan Kettering Cancer Center (MSKCC) Prostate Adenocarcinoma cohort (7) consisting of 240 samples were downloaded (download date: December 31, 2019) (26, 27). At the time the study was conducted, these cohorts were the only publicly available cohorts of patients with localized PC and mRNA expression data. Furthermore, the MSKCC cohort was the first public resource of integrated genomic profiling in PC. The well-known TCGA program has been collecting clinicopathologic data and molecular profiles in more than 30 different cancer types (28).

The mRNA expression data are available as normalized mRNA expression z-scores. The respective normalization of mRNA expression data was performed before uploading data to cBioPortal. Of note, the method of normalization differs between the two cohorts: In the MSKCC cohort the mRNA expression is normalized to transcript expression in 29 normal prostate tissues (7). In the TCGA cohort the mRNA expression data is normalized relative to diploid samples or relative to all samples. To gain the most representative result, we used mRNA expression z-scores relative to diploid samples.

Clinical data. Clinical data of the respective cohorts (e.g., age, International Society of Urological Pathology (IUSP) grading, disease-free survival (DFS), etc.) were also downloaded from cBioPortal and merged with RNA expression data.

Exclusion criteria. RNA expression data of cell lines, samples obtained from metastases, patients with metastatic disease and patients without follow-up regarding DFS were excluded. Hereafter, the MSKCC cohort consisted of 104 patients. In the TCGA cohort, 489 patients were left for analysis after exclusion of patients without follow-up data for DFS. The methodology is presented as a flowchart in Figure 1.

Figure 1.
Figure 1.

Flowchart of the methodology followed, and statistical methods utilized for cohort comparisons. ISUP: International Society of Urological Pathology; AJCC: American Joint Committee on Cancer; MSKCC: Memorial Sloan Kettering Cancer Center prostate adenocarcinoma cohort (7); TCGA: The Cancer Genome Atlas prostate adenocarcinoma cohort (Firehose Legacy).

Statistical analysis. For comparison of the ITG expression the Mann-Whitney U-test was used. Furthermore, for each ITG a partition test was performed to determine the best cut-off regarding DFS in both cohorts. All patients with DFS <3 months were excluded from analyses and partition tests were performed with a minimum number of 20% of the respective cohort for partition test. Analyses to correlate each ITG subunit with DFS included Kaplan-Meier analysis and logrank test. Hereafter, univariable and multivariable cox regression analyses were performed including further possible predictors of DFS [age at diagnosis, ISUP grading, American Joint Committee on Cancer (AJCC) stage and N stage]. Frequencies and proportions were assessed for categorical variables, while means, medians, and interquartile ranges (IQR) were computed for continuous variables. All tests were two-sided with a statistical significance set at p<0.05. Analyses were conducted using the JMP®15 software (SAS Institute Inc., Cary, NC, USA) and GraphPad Prism9 (GraphPad Software, Inc., San Diego, CA, USA).

Results

A descriptive analysis of the cohort characteristics is shown in Table I. In a first step the expression of all ITGs in both cohorts as “ITG fingerprints” was analyzed. Results are shown in Figure 2.

View this table:
Table I.

Descriptive cohort characteristics.

Figure 2.
Figure 2.

ITG expression in localized PC. (A) The MSKCC cohort (n=104), normalized to normal samples. (B) The TCGA cohort (n=489), normalized to median tumor samples. ITG: Integrin; PC: prostate cancer; MSKCC: Memorial Sloan Kettering Cancer Center prostate adenocarcinoma cohort (7); TCGA: The Cancer Genome Atlas prostate adenocarcinoma cohort (Firehose Legacy).

ITG expression was analyzed in ISUP groups ISUP 1&2 and ISUP 3-5 (Figure 3). The comparison of ITG expression in these ISUP groups revealed significant differences in ITGα11 and ITGαX expression, which was significantly higher in the ISUP 3-5 group in both cohorts (p<0.01 and p<0.05 respectively), ITGβ6 expression was elevated in the MSKCC cohort in ISUP 1 and 2 (p<0.01). In the TCGA cohort, further significant differences in expression between ISUP 1 and 2 and 3-5 were found for ITGα2, ITGα4, ITGα5, ITGα6, ITGα7, ITGα9, ITGα10, ITGαD, ITGαE, ITGα2b, ITGβ2, ITGβ3 and ITGβ8 (all p-values <0.05). Results of group comparisons are shown in Figure 3C and Figure 3F.

Figure 3.
Figure 3.

ITG expression in the MSKCC (A-C) and TCGA (D-F) cohorts. Significant ITGs in grouped comparisons are shown in red for ISUP 1 and 2 and in blue for ISUP 3-5. Expression in ISUP 1 and 2 (n=73) (A) and ISUP 3-5 (n=29) (B). (C) Comparison of expression in ISUP 1 and 2 vs. ISUP 3-5 (n=102) revealed significant differences in ITGA11, ITGAX and ITGB6. Expression in ISUP 1 and 2 (n=191) (D) and ISUP 3-5 (n=298) (E). (F) Comparison of expression in ISUP 1 and 2 vs. ISUP 3-5 (n=489) reveal significant differences in ITGα2, ITGα4, ITGα5, ITGα6, ITGα7, ITGα9, ITGα10, ITGα11, ITGαD, ITGαE, ITGα2b, ITGαX, ITGβ2, ITGβ3 and ITGβ8 expression. ITG: Integrin; ISUP: International Society of Urological Pathology; MSKCC: Memorial Sloan Kettering Cancer Center prostate adenocarcinoma cohort (7); TCGA: The Cancer Genome Atlas prostate adenocarcinoma cohort (Firehose Legacy).

Additionally, the expression of all ITGs was analyzed in relation to N status. In the TCGA cohort significant differences were found for ITGα2 (p<0.01) with a significantly lower expression in the lymph node-positive group. All other significant expression results showed a higher expression in ITGα4, ITGαL, ITGβ2 (p<0.05), ITGαD, ITGβ7 (p<0.01), ITGα11 and ITGαX (p<0.01). Results are shown in Figure 4A-C.

Figure 4.
Figure 4.

ITG expression in the TCGA cohort in patients with: (A) N0-status (n=341), (B) N1-status (n=76) and (C) significant differences between N0- and N1-status patients (n=417). Significant ITGs are colored in red for N0 stage and in blue for N1 stage. ITG: Integrin; TCGA: The Cancer Genome Atlas Prostate Adenocarcinoma cohort (Firehose Legacy).

As a third variable ITG expression was analyzed in relation to AJCC stage. Patients were grouped in AJCC stage ≤2 and ≥3. Comparison of these two groups revealed no significant results in the MSKCC cohort (Figure 5A and B). In the TCGA cohort, significant differences in expression could be shown for ITGα2b, ITGβ2 (p<0.05), ITGα4, ITGα10 (p<0.01), ITGαX (p<0.01), ITGα2 and ITGα11 (p<0.01) (Figure 5C-E).

Figure 5.
Figure 5.

ITG expression in the MSKCC (A-B) and TCGA (C-E) cohort for AJCC stages ≤2 and ≥3. (A) Expression in stage ≤2 (n=63), (B) expression in stage ≥3 (n=40). Comparison of expression in stages ≤2 vs. ≥3 (n=103) revealed no significant differences. (C) Expression in stages ≤2 (n=186), (D) expression in stages ≥3 (n=297). (E) Comparison of expression in stages ≤2 and stages ≥3 (n=483) revealed significant differences in ITGα2, ITGα4, ITGα10, ITGα11, ITGα2b, ITGαX and ITGβ2 expression. Significant ITGs in group comparison are shown in red (≤2) and blue (≥3). ITG: Integrin; AJCC: American Joint Committee on Cancer; MSKCC: Memorial Sloan Kettering Cancer Center prostate adenocarcinoma cohort (7); TCGA: The Cancer Genome Atlas prostate adenocarcinoma cohort (Firehose Legacy).

In a last step, DFS was analyzed dependent upon the ITG expression. Kaplan-Meier curves and logrank test revealed a significantly longer survival for several ITGs. Results are shown in Figure 6A-M (TCGA cohort) and Figure 6N-P (MSKCC cohort).

Figure 6.
Figure 6.
Figure 6.

Kaplan-Meier analysis of disease-free survival regarding integrin expression showed significant differences in logrank test in the TCGA (A-M) and MSKCC (N-P) cohort. MSKCC: Memorial Sloan Kettering Cancer Center prostate adenocarcinoma cohort (7); TCGA: The Cancer Genome Atlas prostate adenocarcinoma cohort (Firehose Legacy).

Furthermore, significant ITGs, as depicted in Kaplan-Meier analyses and logrank test and further possible clinical predictors of DFS, were subsequently analyzed in uni- and multivariable cox regression analyses. Results are shown in Table II.

View this table:
Table II.

Results of uni- and multivariable cox regression analysis for DFS in the TCGA cohort and MSKCC cohorts.

Discussion

In the ERSPC trial, PSA-screening was associated with a significantly lower risk of metastatic disease and reduced mortality (29). However, other studies were not able to detect this effect (30, 31), thus contributing to the ongoing controversial discussion on the benefits and possible pitfalls of PSA screening. Both the PSA and other assessment tools (prostate biopsy, biopsy GS, DRE and imaging) are prone to errors. After diagnosis of localized PC, treatment options include active surveillance, local therapy, radical prostatectomy and radiation. Usually, decisions are based on GS. Epstein et al. showed that approximately 25% of GS 5-6 tumors on biopsy will be GS 7 or higher at RP (6). Furthermore, in patients with the same GS prognosis is variable (6). This indicates the need for additional and more accurate biomarkers to improve PC prediction as well as its management and therapy stratification following diagnosis. In this study, we aimed to provide further evidence for the utility of ITGs as predictive and prognostic biomarkers in localized PC.

The most consistent results were seen for ITGα11, ITGαX and ITGβ2 with higher expression in ISUP 3-5 (ITGα11 and ITGαX both cohorts, ITGβ2 in TCGA) and AJCC ≥3 as well as N1 in the TCGA cohort. Furthermore, a high ITGα11 expression could be associated with a shorter DFS in both cohorts. ITGαX and ITGβ2 were associated with a shorter DFS in the TCGA cohort. However, these integrins do not appear to have been thoroughly studied in prostate cancer to date.

In the TCGA cohort, ITGα2 showed very consistent results with lower expression in ISUP 3-5, AJCC ≥3, N1 and a significantly shorter DFS in the group with low expression. However, other studies showed exosome-mediate transfer of ITGα2 to promote migration and invasion in PC cells (32). Salemi et al., showed that an ITGα2β1 inhibition attenuates prostate cancer cell proliferation by cell-cycle arrest, promoting apoptosis and reducing epithelial-mesenchymal transition (11). Although these results seem contradictory, it is important to note that the studies examined different aspects and no absolute expression values are available. Therefore, the results are not directly comparable.

Further consistent results were seen for ITGα9, ITGβ3 and ITβ8 (all with lower expression in ISUP 3-5 and shorter DFS for low expression), ITGα10 and ITGα2b (both with higher expression in ISUP 3-5 and AJCC ≥3 and shorter DFS for high expression), ITGαD (higher expression in ISUP 3-5, N1 and shorter DFS for high expression) and ITGαE (higher expression in ISUP 3-5 and shorter DFS for high expression) in the TCGA cohort.

In a recent systematic review and meta-analysis including 14 studies Drivalos et al. showed a correlation of a low expression of ITGα6 and ITGβ1 with high GS and a “borderline trend” between advanced clinical stage and low ITGα6 expression (33). Whereas our analysis could not confirm the ITGβ1 results, we also found lower ITGα6 expression associated to higher ISUP grades. Another study found ITGα6β1 to promote cell survival and resistance to PI3K inhibitors in castration-resistant PC as ITGα6 is a direct transcriptional target of the androgen receptor (34). Taken together, this could generate the hypothesis, that higher ISUP grades might be less androgen dependent. Matching this, it is known that a higher ISUP grade is associated with faster castration resistance (35).

In localized PC ITGα3 and ITGα3β1 have been associated with worse disease outcomes by Pontes-Júnior et al. (36). The same working group revealed a correlation between negative ITGβ1 expression and biochemical recurrence and time to recurrence after RP (37). These results could not be confirmed in this analysis.

Further evidence exists for ITGαVβ6 and ITGαVβ3: ITGαVβ3 may promote a shift towards a neuroendocrine phenotype, but not αVβ6, since the latter is confined to adenocarcinomas of the prostate (38). Even though in our analyses no neuroendocrine phenotypes were included, high ITGβ3 expression was associated with shorter DFS. This might be associated with a molecular shift towards a neuroendocrine phenotype, which is associated with worse prognosis (39).

Lately ITGαV was suggested as a novel non-invasive marker for PC detection, since significantly lower levels were found in patients with PC compared to healthy controls (40). However, this analysis could not find significant ITGαV expression differences in patients with localized PC.

ITGs have also been associated with other cancer entities: For example, increased ITGβ1 expression could be associated with decreased survival in invasive breast cancer (41) and increased ITGα6β4, ITGαVβ5, ITGα3, ITGα5, ITGβ1 and ITGβ3 expression with decreased OS in non-small cell lung cancer (NSCLC) (42-45). In esophageal adenocarcinoma ITGαV expression was correlated with shortened OS (46) and in colon carcinoma a correlation between ITGαVβ3 expression and a shorter relapse-free and OS as well as presence of liver metastases could be shown (47). As mentioned above, our analysis also revealed a high ITGβ3 with a shorter DFS. This matches the mentioned findings in NSCLC and colon carcinoma. ITGβ3 might be strongly associated to worse prognosis across multiple cancer entities and should be further characterized in future studies.

These findings and the fact that commercially available ITG antibodies already exist in non-cancer treatment [Natalizumab, ITGα4β1/α4β7 antibody, used for multiple sclerosis and Crohn’s disease (48); Vedolizumab, ITGα4β7 antibody, used in adult inflammatory bowel disease (49)], the interest for integrins as drug targets has risen (45). Recent endeavors have led to the development of integrin antagonists (14) and anti-angiogenic cancer therapy based on ITGαVβ3 antibodies (50). These showed a decrease in metastasis and angiogenesis as well as improved survival in mice (51). However, these drugs have not yet been tested in clinical trials.

Despite all the evidence on expression of single ITGs and their correlation with progression and survival, molecular pathways by which integrins contribute to cancer progression and metastasis still need to be investigated since much more mechanistic insight is required to determine which integrin blocking strategies may be applied to which types and stages of cancer. However, not only expression in tumor tissue or metastases but also the mechanisms, by which PC may communicate, must be considered. Recent studies isolated extracellular vesicles (EV) from blood of patients with PC and showed that ITGαVβ3 is transferred to recipient cells using these EV (52). Additionally, it must be considered that not only gene expression, but also epigenetic and epitranscriptomic modification of ITGs might be a promising mechanism to develop diagnostic or therapeutic strategies.

Limitations. The explorative character of this study must be emphasized. This approach led to a large amount of data, that need to be further characterized in future studies. For this reason, an additional analysis of known and possible ITG heterodimers was not performed, as this would lead to a huge and confusing amount of data. At the same time their explorative character is an unbiased approach: All ITG subunits were analyzed and interpreted with the same thoroughness. Another possible limitation is the relatively small number of patients in the MSKCC cohort. Additionally, information on N status was only available in the TCGA cohort and existed for only 417 of the 489 patients included.

Conclusion

In conclusion this study – for the first time – provided a comprehensive overview and presents evidence for the utility of ITG subunit expression as biomarkers in localized PC. ITGα2, ITGα4, ITGα11, ITGαX, ITGβ2 showed consistent results in both cohorts. After further validation, they might be used for treatment stratification and prognosis – either alone or in combination with other biomarkers. Additionally, the identified ITGs might be interesting for the development of liquid biopsy-based analyses, and functional experiments.

Acknowledgements

The results shown here are, in part, based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga and in part upon data generated by the MSKCC cohort from Taylor et al. (7). Data was obtained using the cBioPortal (26, 27). K.N., P.N. and T.S.W received research funding from the Hector Stiftung II, Weinheim, Germany. J.H. and T.S.W. received research funding from the Southwest German Society of Urology (Südwestdeutsche Gesellschaft für Urologie), Stuttgart, Germany.

Footnotes

  • Authors’ Contributions

    Conceptualization: T.S.W. and M.Ne.; Data curation: L.F. and M.Ne.; Formal analysis: M.Ne. and L.F.; Funding acquisition: K.N., P.N., J.H. and T.S.W.; Investigation: M.Ne, T.S.W. and F.We.; Methodology: M.Ne. and T.S.W.; Project administration: M.Ne. and T.S.W.; Supervision: T.S.W. and P.N.; Validation: T.S.W., K.N. and F.We.; Visualization: M.Ne., F.We., F.Wa.; Writing – original draft: M.Ne. and T.S.W.; Writing – review and editing: M.Ne., K.N., F.We., N.W., F.H., F.Wa., M.Ni., M.S.M., P.E., J.H. and T.S.W.

  • Data Availability Statement

    The data supporting the reported results can be found at cBioPortal under <https://www.cbioportal.org/study/summary?id=prad_tcga (TCGA cohort)> and <https://www.cbioportal.org/study/summary?id=prad_mskcc> (MSKCC cohort).

  • Conflicts of Interest

    The Authors declare no conflicts of interest. Funding by the Hector Stiftung II and the Southwest German Society of Urology had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

  • Received October 20, 2022.
  • Revision received November 5, 2022.
  • Accepted November 14, 2022.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).

References

  1. ↵
    1. Bray F,
    2. Ferlay J,
    3. Soerjomataram I,
    4. Siegel RL,
    5. Torre LA and
    6. Jemal A
    : Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6): 394-424, 2018. PMID: 30207593. DOI: 10.3322/caac.21492
    OpenUrlCrossRefPubMed
  2. ↵
    1. Mottet N,
    2. van den Bergh RCN,
    3. Briers E,
    4. Van den Broeck T,
    5. Cumberbatch MG,
    6. De Santis M,
    7. Fanti S,
    8. Fossati N,
    9. Gandaglia G,
    10. Gillessen S,
    11. Grivas N,
    12. Grummet J,
    13. Henry AM,
    14. van der Kwast TH,
    15. Lam TB,
    16. Lardas M,
    17. Liew M,
    18. Mason MD,
    19. Moris L,
    20. Oprea-Lager DE,
    21. van der Poel HG,
    22. Rouvière O,
    23. Schoots IG,
    24. Tilki D,
    25. Wiegel T,
    26. Willemse PM and
    27. Cornford P
    : EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer-2020 update. Part 1: Screening, diagnosis, and local treatment with curative intent. Eur Urol 79(2): 243-262, 2021. PMID: 33172724. DOI: 10.1016/j.eururo.2020.09.042
    OpenUrlCrossRefPubMed
  3. ↵
    1. Filella X and
    2. Foj L
    : Prostate cancer detection and prognosis: from prostate specific antigen (PSA) to exosomal biomarkers. Int J Mol Sci 17(11): 1784, 2016. PMID: 27792187. DOI: 10.3390/ijms17111784
    OpenUrlCrossRefPubMed
  4. ↵
    1. Klein EA,
    2. Cooperberg MR,
    3. Magi-Galluzzi C,
    4. Simko JP,
    5. Falzarano SM,
    6. Maddala T,
    7. Chan JM,
    8. Li J,
    9. Cowan JE,
    10. Tsiatis AC,
    11. Cherbavaz DB,
    12. Pelham RJ,
    13. Tenggara-Hunter I,
    14. Baehner FL,
    15. Knezevic D,
    16. Febbo PG,
    17. Shak S,
    18. Kattan MW,
    19. Lee M and
    20. Carroll PR
    : A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. Eur Urol 66(3): 550-560, 2014. PMID: 24836057. DOI: 10.1016/j.eururo.2014.05.004
    OpenUrlCrossRefPubMed
    1. Müntener M,
    2. Epstein JI,
    3. Hernandez DJ,
    4. Gonzalgo ML,
    5. Mangold L,
    6. Humphreys E,
    7. Walsh PC,
    8. Partin AW and
    9. Nielsen ME
    : Prognostic significance of Gleason score discrepancies between needle biopsy and radical prostatectomy. Eur Urol 53(4): 767-75; discussion 775-6, 2008. PMID: 18060681. DOI: 10.1016/j.eururo.2007.11.016
    OpenUrlCrossRefPubMed
  6. ↵
    1. Epstein JI,
    2. Feng Z,
    3. Trock BJ and
    4. Pierorazio PM
    : Upgrading and downgrading of prostate cancer from biopsy to radical prostatectomy: incidence and predictive factors using the modified Gleason grading system and factoring in tertiary grades. Eur Urol 61(5): 1019-1024, 2012. PMID: 22336380. DOI: 10.1016/j.eururo.2012.01.050
    OpenUrlCrossRefPubMed
  7. ↵
    1. Taylor BS,
    2. Schultz N,
    3. Hieronymus H,
    4. Gopalan A,
    5. Xiao Y,
    6. Carver BS,
    7. Arora VK,
    8. Kaushik P,
    9. Cerami E,
    10. Reva B,
    11. Antipin Y,
    12. Mitsiades N,
    13. Landers T,
    14. Dolgalev I,
    15. Major JE,
    16. Wilson M,
    17. Socci ND,
    18. Lash AE,
    19. Heguy A,
    20. Eastham JA,
    21. Scher HI,
    22. Reuter VE,
    23. Scardino PT,
    24. Sander C,
    25. Sawyers CL and
    26. Gerald WL
    : Integrative genomic profiling of human prostate cancer. Cancer Cell 18(1): 11-22, 2010. PMID: 20579941. DOI: 10.1016/j.ccr.2010.05.026
    OpenUrlCrossRefPubMed
  8. ↵
    1. Wang T,
    2. Dong L,
    3. Sun J,
    4. Shao J,
    5. Zhang J,
    6. Chen S,
    7. Wang C,
    8. Wu G and
    9. Wang X
    : miR-145-5p: a potential biomarker in predicting gleason upgrading of prostate biopsy samples scored 3+3=6. Cancer Manag Res 13: 9095-9106, 2021. PMID: 34916852. DOI: 10.2147/CMAR.S336671
    OpenUrlCrossRefPubMed
  9. ↵
    1. Evans EA and
    2. Calderwood DA
    : Forces and bond dynamics in cell adhesion. Science 316(5828): 1148-1153, 2007. PMID: 17525329. DOI: 10.1126/science.1137592
    OpenUrlAbstract/FREE Full Text
  10. ↵
    1. Giancotti FG and
    2. Ruoslahti E
    : Integrin signaling. Science 285(5430): 1028-1032, 1999. PMID: 10446041. DOI: 10.1126/science.285.5430.1028
    OpenUrlAbstract/FREE Full Text
  11. ↵
    1. Salemi Z,
    2. Azizi R,
    3. Fallahian F and
    4. Aghaei M
    : Integrin α2β1 inhibition attenuates prostate cancer cell proliferation by cell cycle arrest, promoting apoptosis and reducing epithelial-mesenchymal transition. J Cell Physiol 236(7): 4954-4965, 2021. PMID: 33305380. DOI: 10.1002/jcp.30202
    OpenUrlCrossRefPubMed
  12. ↵
    1. Goel HL,
    2. Li J,
    3. Kogan S and
    4. Languino LR
    : Integrins in prostate cancer progression. Endocr Relat Cancer 15(3): 657-664, 2008. PMID: 18524948. DOI: 10.1677/ERC-08-0019
    OpenUrlAbstract/FREE Full Text
  13. ↵
    1. Valdembri D and
    2. Serini G
    : The roles of integrins in cancer. Fac Rev 10: 45, 2021. PMID: 34131655. DOI: 10.12703/r/10-45
    OpenUrlCrossRefPubMed
  14. ↵
    1. Philippe CL
    : Therapeutic value of an integrin antagonist in prostate cancer. Curr Drug Targets 17(3): 321-327, 2016. PMID: 26648077. DOI: 10.2174/1389450117666151209115324
    OpenUrlCrossRefPubMed
  15. ↵
    1. Pickup MW,
    2. Mouw JK and
    3. Weaver VM
    : The extracellular matrix modulates the hallmarks of cancer. EMBO Rep 15(12): 1243-1253, 2014. PMID: 25381661. DOI: 10.15252/embr.201439246
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Gasca J,
    2. Flores ML,
    3. Jiménez-Guerrero R,
    4. Sáez ME,
    5. Barragán I,
    6. Ruíz-Borrego M,
    7. Tortolero M,
    8. Romero F,
    9. Sáez C and
    10. Japón MA
    : EDIL3 promotes epithelial-mesenchymal transition and paclitaxel resistance through its interaction with integrin αVβ3 in cancer cells. Cell Death Discov 6: 86, 2020. PMID: 33014430. DOI: 10.1038/s41420-020-00322-x
    OpenUrlCrossRefPubMed
  17. ↵
    1. Krishn SR,
    2. Salem I,
    3. Quaglia F,
    4. Naranjo NM,
    5. Agarwal E,
    6. Liu Q,
    7. Sarker S,
    8. Kopenhaver J,
    9. McCue PA,
    10. Weinreb PH,
    11. Violette SM,
    12. Altieri DC and
    13. Languino LR
    : The αvβ6 integrin in cancer cell-derived small extracellular vesicles enhances angiogenesis. J Extracell Vesicles 9(1): 1763594, 2020. PMID: 32595914. DOI: 10.1080/20013078.2020.1763594
    OpenUrlCrossRefPubMed
  18. ↵
    1. Connell B,
    2. Kopach P,
    3. Ren W,
    4. Joshi R,
    5. Naber S,
    6. Zhou M and
    7. Mathew P
    : Aberrant integrin αv and α5 expression in prostate adenocarcinomas and bone-metastases is consistent with a bone-colonizing phenotype. Transl Androl Urol 9(4): 1630-1638, 2020. PMID: 32944524. DOI: 10.21037/tau-19-763
    OpenUrlCrossRefPubMed
  19. ↵
    1. Quaglia F,
    2. Krishn SR,
    3. Daaboul GG,
    4. Sarker S,
    5. Pippa R,
    6. Domingo-Domenech J,
    7. Kumar G,
    8. Fortina P,
    9. McCue P,
    10. Kelly WK,
    11. Beltran H,
    12. Liu Q and
    13. Languino LR
    : Small extracellular vesicles modulated by αVβ3 integrin induce neuroendocrine differentiation in recipient cancer cells. J Extracell Vesicles 9(1): 1761072, 2020. PMID: 32922691. DOI: 10.1080/20013078.2020.1761072
    OpenUrlCrossRefPubMed
  20. ↵
    1. Putz E,
    2. Witter K,
    3. Offner S,
    4. Stosiek P,
    5. Zippelius A,
    6. Johnson J,
    7. Zahn R,
    8. Riethmüller G and
    9. Pantel K
    : Phenotypic characteristics of cell lines derived from disseminated cancer cells in bone marrow of patients with solid epithelial tumors: establishment of working models for human micrometastases. Cancer Res 59(1): 241-248, 1999. PMID: 9892213.
    OpenUrlAbstract/FREE Full Text
    1. De S,
    2. Chen J,
    3. Narizhneva NV,
    4. Heston W,
    5. Brainard J,
    6. Sage EH and
    7. Byzova TV
    : Molecular pathway for cancer metastasis to bone. J Biol Chem 278(40): 39044-39050, 2003. PMID: 12885781. DOI: 10.1074/jbc.M304494200
    OpenUrlAbstract/FREE Full Text
    1. Goc A,
    2. Liu J,
    3. Byzova TV and
    4. Somanath PR
    : Akt1 mediates prostate cancer cell microinvasion and chemotaxis to metastatic stimuli via integrin β3 affinity modulation. Br J Cancer 107(4): 713-723, 2012. PMID: 22767145. DOI: 10.1038/bjc.2012.295
    OpenUrlCrossRefPubMed
    1. Romanov VI and
    2. Goligorsky MS
    : RGD-recognizing integrins mediate interactions of human prostate carcinoma cells with endothelial cells in vitro. Prostate 39(2): 108-118, 1999. PMID: 10221566. DOI: 10.1002/(sici)1097-0045(19990501)39:2<108::aid-pros5>3.0.co;2-9
    OpenUrlCrossRefPubMed
  24. ↵
    1. Sroka IC,
    2. Anderson TA,
    3. McDaniel KM,
    4. Nagle RB,
    5. Gretzer MB and
    6. Cress AE
    : The laminin binding integrin alpha6beta1 in prostate cancer perineural invasion. J Cell Physiol 224(2): 283-288, 2010. PMID: 20432448. DOI: 10.1002/jcp.22149
    OpenUrlCrossRefPubMed
  25. ↵
    1. Alberts B,
    2. Johnson A and
    3. Lewis J
    : Integrins. In: Molecular Biology of the Cell. 4th edition. New York, NY, USA, Garland Science, 2002.
  26. ↵
    1. Cerami E,
    2. Gao J,
    3. Dogrusoz U,
    4. Gross BE,
    5. Sumer SO,
    6. Aksoy BA,
    7. Jacobsen A,
    8. Byrne CJ,
    9. Heuer ML,
    10. Larsson E,
    11. Antipin Y,
    12. Reva B,
    13. Goldberg AP,
    14. Sander C and
    15. Schultz N
    : The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2(5): 401-404, 2012. PMID: 22588877. DOI: 10.1158/2159-8290.CD-12-0095
    OpenUrlAbstract/FREE Full Text
  27. ↵
    1. Gao J,
    2. Aksoy BA,
    3. Dogrusoz U,
    4. Dresdner G,
    5. Gross B,
    6. Sumer SO,
    7. Sun Y,
    8. Jacobsen A,
    9. Sinha R,
    10. Larsson E,
    11. Cerami E,
    12. Sander C and
    13. Schultz N
    : Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6(269): pl1, 2013. PMID: 23550210. DOI: 10.1126/scisignal.2004088
    OpenUrlAbstract/FREE Full Text
  28. ↵
    1. Liu J,
    2. Lichtenberg T,
    3. Hoadley KA,
    4. Poisson LM,
    5. Lazar AJ,
    6. Cherniack AD,
    7. Kovatich AJ,
    8. Benz CC,
    9. Levine DA,
    10. Lee AV,
    11. Omberg L,
    12. Wolf DM,
    13. Shriver CD,
    14. Thorsson V, Cancer Genome Atlas Research Network and
    15. Hu H
    : An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173(2): 400-416.e11, 2018. PMID: 29625055. DOI: 10.1016/j.cell.2018.02.052
    OpenUrlCrossRefPubMed
  29. ↵
    1. Schröder FH,
    2. Hugosson J,
    3. Roobol MJ,
    4. Tammela TL,
    5. Zappa M,
    6. Nelen V,
    7. Kwiatkowski M,
    8. Lujan M,
    9. Määttänen L,
    10. Lilja H,
    11. Denis LJ,
    12. Recker F,
    13. Paez A,
    14. Bangma CH,
    15. Carlsson S,
    16. Puliti D,
    17. Villers A,
    18. Rebillard X,
    19. Hakama M,
    20. Stenman UH,
    21. Kujala P,
    22. Taari K,
    23. Aus G,
    24. Huber A,
    25. van der Kwast TH,
    26. van Schaik RH,
    27. de Koning HJ,
    28. Moss SM,
    29. Auvinen A and ERSPC Investigators
    : Screening and prostate cancer mortality: results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up. Lancet 384(9959): 2027-2035, 2014. PMID: 25108889. DOI: 10.1016/S0140-6736(14)60525-0
    OpenUrlCrossRefPubMed
  30. ↵
    1. Martin RM,
    2. Donovan JL,
    3. Turner EL,
    4. Metcalfe C,
    5. Young GJ,
    6. Walsh EI,
    7. Lane JA,
    8. Noble S,
    9. Oliver SE,
    10. Evans S,
    11. Sterne JAC,
    12. Holding P,
    13. Ben-Shlomo Y,
    14. Brindle P,
    15. Williams NJ,
    16. Hill EM,
    17. Ng SY,
    18. Toole J,
    19. Tazewell MK,
    20. Hughes LJ,
    21. Davies CF,
    22. Thorn JC,
    23. Down E,
    24. Davey Smith G,
    25. Neal DE,
    26. Hamdy FC and CAP Trial Group
    : Effect of a low-intensity PSA-based screening intervention on prostate cancer mortality: The CAP randomized clinical trial. JAMA 319(9): 883-895, 2018. PMID: 29509864. DOI: 10.1001/jama.2018.0154
    OpenUrlCrossRefPubMed
  31. ↵
    1. Andriole GL,
    2. Crawford ED,
    3. Grubb RL 3rd.,
    4. Buys SS,
    5. Chia D,
    6. Church TR,
    7. Fouad MN,
    8. Isaacs C,
    9. Kvale PA,
    10. Reding DJ,
    11. Weissfeld JL,
    12. Yokochi LA,
    13. O’Brien B,
    14. Ragard LR,
    15. Clapp JD,
    16. Rathmell JM,
    17. Riley TL,
    18. Hsing AW,
    19. Izmirlian G,
    20. Pinsky PF,
    21. Kramer BS,
    22. Miller AB,
    23. Gohagan JK,
    24. Prorok PC and PLCO Project Team
    : Prostate cancer screening in the randomized Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial: mortality results after 13 years of follow-up. J Natl Cancer Inst 104(2): 125-132, 2012. PMID: 22228146. DOI: 10.1093/jnci/djr500
    OpenUrlCrossRefPubMed
  32. ↵
    1. Gaballa R,
    2. Ali HEA,
    3. Mahmoud MO,
    4. Rhim JS,
    5. Ali HI,
    6. Salem HF,
    7. Saleem M,
    8. Kandeil MA,
    9. Ambs S and
    10. Abd Elmageed ZY
    : Exosomes-mediated transfer of Itga2 promotes migration and invasion of prostate cancer cells by inducing epithelial-mesenchymal transition. Cancers (Basel) 12(8): 2300, 2020. PMID: 32824235. DOI: 10.3390/cancers12082300
    OpenUrlCrossRefPubMed
  33. ↵
    1. Drivalos A,
    2. Emmanouil G,
    3. Gavriatopoulou M,
    4. Terpos E,
    5. Sergentanis TN and
    6. Psaltopoulou T
    : Integrin expression in correlation to clinicopathological features and prognosis of prostate cancer: A systematic review and meta-analysis. Urol Oncol 39(4): 221-232, 2021. PMID: 33558138. DOI: 10.1016/j.urolonc.2020.12.024
    OpenUrlCrossRefPubMed
  34. ↵
    1. Nollet EA,
    2. Cardo-Vila M,
    3. Ganguly SS,
    4. Tran JD,
    5. Schulz VV,
    6. Cress A,
    7. Corey E and
    8. Miranti CK
    : Androgen receptor-induced integrin α6β1 and Bnip3 promote survival and resistance to PI3K inhibitors in castration-resistant prostate cancer. Oncogene 39(31): 5390-5404, 2020. PMID: 32565538. DOI: 10.1038/s41388-020-1370-9
    OpenUrlCrossRefPubMed
  35. ↵
    1. Lin TT,
    2. Chen YH,
    3. Wu YP,
    4. Chen SZ,
    5. Li XD,
    6. Lin YZ,
    7. Chen SH,
    8. Zheng QS,
    9. Wei Y,
    10. Xu N and
    11. Xue XY
    : Risk factors for progression to castration-resistant prostate cancer in metastatic prostate cancer patients. J Cancer 10(22): 5608-5613, 2019. PMID: 31632505. DOI: 10.7150/jca.30731
    OpenUrlCrossRefPubMed
  36. ↵
    1. Pontes-Júnior J,
    2. Reis ST,
    3. de Oliveira LC,
    4. Sant’anna AC,
    5. Dall’oglio MF,
    6. Antunes AA,
    7. Ribeiro-Filho LA,
    8. Carvalho PA,
    9. Cury J,
    10. Srougi M and
    11. Leite KR
    : Association between integrin expression and prognosis in localized prostate cancer. Prostate 70(11): 1189-1195, 2010. PMID: 20564421. DOI: 10.1002/pros.21153
    OpenUrlCrossRefPubMed
  37. ↵
    1. Pontes-Júnior J,
    2. Reis ST,
    3. Bernardes FS,
    4. Oliveira LC,
    5. Barros ÉA,
    6. Dall’Oglio MF,
    7. Timosczuk LM,
    8. Ribeiro-Filho LA,
    9. Srougi M and
    10. Leite KR
    : Correlation between beta1 integrin expression and prognosis in clinically localized prostate cancer. Int Braz J Urol 39(3): 335-42; discussion 343, 2013. PMID: 23849566. DOI: 10.1590/S1677-5538.IBJU.2013.03.06
    OpenUrlCrossRefPubMed
  38. ↵
    1. Quaglia F,
    2. Krishn SR,
    3. Wang Y,
    4. Goodrich DW,
    5. McCue P,
    6. Kossenkov AV,
    7. Mandigo AC,
    8. Knudsen KE,
    9. Weinreb PH,
    10. Corey E,
    11. Kelly WK and
    12. Languino LR
    : Differential expression of αVβ3 and αVβ6 integrins in prostate cancer progression. PLoS One 16(1): e0244985, 2021. PMID: 33481853. DOI: 10.1371/journal.pone.0244985
    OpenUrlCrossRefPubMed
  39. ↵
    1. Yao J,
    2. Liu Y,
    3. Liang X,
    4. Shao J,
    5. Zhang Y,
    6. Yang J and
    7. Zheng M
    : Neuroendocrine carcinoma as an independent prognostic factor for patients with prostate cancer: a population-based study. Front Endocrinol (Lausanne) 12: 778758, 2021. PMID: 34956090. DOI: 10.3389/fendo.2021.778758
    OpenUrlCrossRefPubMed
  40. ↵
    1. Zemskova MY,
    2. Marinets MV,
    3. Sivkov AV,
    4. Pavlova JV,
    5. Shibaev AN and
    6. Sorokin KS
    : Integrin Alpha V in urine: a novel noninvasive marker for prostate cancer detection. Front Oncol 10: 610647, 2021. PMID: 33791193. DOI: 10.3389/fonc.2020.610647
    OpenUrlCrossRefPubMed
  41. ↵
    1. Yao ES,
    2. Zhang H,
    3. Chen YY,
    4. Lee B,
    5. Chew K,
    6. Moore D and
    7. Park C
    : Increased beta1 integrin is associated with decreased survival in invasive breast cancer. Cancer Res 67(2): 659-664, 2007. PMID: 17234776. DOI: 10.1158/0008-5472.CAN-06-2768
    OpenUrlAbstract/FREE Full Text
  42. ↵
    1. Stewart RL,
    2. West D,
    3. Wang C,
    4. Weiss HL,
    5. Gal T,
    6. Durbin EB,
    7. O’Connor W,
    8. Chen M and
    9. O’Connor KL
    : Elevated integrin α6β4 expression is associated with venous invasion and decreased overall survival in non-small cell lung cancer. Hum Pathol 54: 174-183, 2016. PMID: 27107458. DOI: 10.1016/j.humpath.2016.04.003
    OpenUrlCrossRefPubMed
    1. Bai SY,
    2. Xu N,
    3. Chen C,
    4. Song YL,
    5. Hu J and
    6. Bai CX
    : Integrin αvβ5 as a biomarker for the assessment of non-small cell lung cancer metastasis and overall survival. Clin Respir J 9(4): 457-467, 2015. PMID: 24815623. DOI: 10.1111/crj.12163
    OpenUrlCrossRefPubMed
    1. Dingemans AM,
    2. van den Boogaart V,
    3. Vosse BA,
    4. van Suylen RJ,
    5. Griffioen AW and
    6. Thijssen VL
    : Integrin expression profiling identifies integrin alpha5 and beta1 as prognostic factors in early stage non-small cell lung cancer. Mol Cancer 9: 152, 2010. PMID: 20565758. DOI: 10.1186/1476-4598-9-152
    OpenUrlCrossRefPubMed
  45. ↵
    1. Aksorn N and
    2. Chanvorachote P
    : Integrin as a molecular target for anti-cancer approaches in lung cancer. Anticancer Res 39(2): 541-548, 2019. PMID: 30711928. DOI: 10.21873/anticanres.13146
    OpenUrlAbstract/FREE Full Text
  46. ↵
    1. Loeser H,
    2. Scholz M,
    3. Fuchs H,
    4. Essakly A,
    5. Damanakis AI,
    6. Zander T,
    7. Büttner R,
    8. Schröder W,
    9. Bruns C,
    10. Quaas A and
    11. Gebauer F
    : Integrin alpha V (ITGAV) expression in esophageal adenocarcinoma is associated with shortened overall-survival. Sci Rep 10(1): 18411, 2020. PMID: 33110104. DOI: 10.1038/s41598-020-75085-7
    OpenUrlCrossRefPubMed
  47. ↵
    1. Vonlaufen A,
    2. Wiedle G,
    3. Borisch B,
    4. Birrer S,
    5. Luder P and
    6. Imhof BA
    : Integrin alpha(v)beta(3) expression in colon carcinoma correlates with survival. Mod Pathol 14(11): 1126-1132, 2001. PMID: 11706074. DOI: 10.1038/modpathol.3880447
    OpenUrlCrossRefPubMed
  48. ↵
    1. Ley K,
    2. Rivera-Nieves J,
    3. Sandborn WJ and
    4. Shattil S
    : Integrin-based therapeutics: biological basis, clinical use and new drugs. Nat Rev Drug Discov 15(3): 173-183, 2016. PMID: 26822833. DOI: 10.1038/nrd.2015.10
    OpenUrlCrossRefPubMed
  49. ↵
    1. Shah P and
    2. McDonald D
    : Vedolizumab: an emerging treatment option for pediatric inflammatory bowel disease. J Pediatr Pharmacol Ther 26(8): 795-801, 2021. PMID: 34790068. DOI: 10.5863/1551-6776-26.8.795
    OpenUrlCrossRefPubMed
  50. ↵
    1. Cai W and
    2. Chen X
    : Anti-angiogenic cancer therapy based on integrin alphavbeta3 antagonism. Anticancer Agents Med Chem 6(5): 407-428, 2006. PMID: 17017851. DOI: 10.2174/187152006778226530
    OpenUrlCrossRefPubMed
  51. ↵
    1. Reinmuth N,
    2. Liu W,
    3. Ahmad SA,
    4. Fan F,
    5. Stoeltzing O,
    6. Parikh AA,
    7. Bucana CD,
    8. Gallick GE,
    9. Nickols MA,
    10. Westlin WF and
    11. Ellis LM
    : Alphavbeta3 integrin antagonist S247 decreases colon cancer metastasis and angiogenesis and improves survival in mice. Cancer Res 63(9): 2079-2087, 2003. PMID: 12727823.
    OpenUrlAbstract/FREE Full Text
  52. ↵
    1. Krishn SR,
    2. Singh A,
    3. Bowler N,
    4. Duffy AN,
    5. Friedman A,
    6. Fedele C,
    7. Kurtoglu S,
    8. Tripathi SK,
    9. Wang K,
    10. Hawkins A,
    11. Sayeed A,
    12. Goswami CP,
    13. Thakur ML,
    14. Iozzo RV,
    15. Peiper SC,
    16. Kelly WK and
    17. Languino LR
    : Prostate cancer sheds the αvβ3 integrin in vivo through exosomes. Matrix Biol 77: 41-57, 2019. PMID: 30098419. DOI: 10.1016/j.matbio.2018.08.004
    OpenUrlCrossRefPubMed
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools
Reprints and Permissions
Integrin Expression in Localized Prostate Cancer: A TCGA and MSKCC Cohort-based Exploratory In Silico Analysis
MANUEL NEUBERGER, LISA FREY, KATJA NITSCHKE, FREDERIK WESSELS, NIKLAS WESTHOFF, FRANK WALDBILLIG, MALIN NIENTIEDT, FRIEDRICH HARTUNG, JOST VON HARDENBERG, MAURICE STEPHAN MICHEL, PHILIPP ERBEN, PHILIPP NUHN, THOMAS STEFAN WORST
Anticancer Research Jan 2023, 43 (1) 417-428; DOI: 10.21873/anticanres.16177
Twitter logo Facebook logo Mendeley logo

Jump to section

Keywords