The Ratio of Platelets to Lymphocytes Predicts the Prognosis of Metastatic Colorectal Cancer: A Review and Meta-Analysis

Wang, Jinming; Li, Jing; Wei, Sheng; Xu, Jie; Jiang, Xiaohui; Yang, Lei

doi:https://doi.org/10.1155/2021/9699499

Gastroenterology Research and Practice

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Abstract Introduction Materials and Methods Results Discussion Conclusion Abbreviations Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Review Article | Open Access

Volume 2021 | Article ID 9699499 | https://doi.org/10.1155/2021/9699499

The Ratio of Platelets to Lymphocytes Predicts the Prognosis of Metastatic Colorectal Cancer: A Review and Meta-Analysis

Jinming Wang,¹Jing Li,¹Sheng Wei,¹Jie Xu,¹Xiaohui Jiang,²and Lei Yang³

Academic Editor: Nicola Silvestris

Received02 Jun 2021

Accepted15 Oct 2021

Published02 Nov 2021

Abstract

Background. In recent years, the incidence of colorectal cancer (CRC) has continued to increase. Although the overall prognosis of CRC has improved with the continuous improvement of the level of treatment, the prognosis of metastatic colorectal cancer (mCRC) is still poor. The purpose of our study is to explore the prognostic value of platelet to lymphocyte ratio (PLR) in mCRC. Methods. The PubMed, Web of Science, and Embase (via OVID) were systematically searched to obtain all relevant research. We used hazard ratio (HR) with 95% confidence interval (CI) to assess the associations of PLR and overall survival (OS) and progression free survival (PFS). Results. A total of twelve studies containing 1452 patients were included in this meta-analysis. Pooled analysis showed that high levels of PLR were associated with poor OS (HR: 1.72, 95% CI: 1.27–2.33, and ) and PFS (HR: 1.64, 95% CI: 1.16–2.31, and ). Conclusion. Our analysis suggested that high levels of PLR pretreatment may be an effective predictive biomarker for the prognosis of mCRC patients.

1. Introduction

Colorectal cancer (CRC) is one of the most common malignant tumors in the world. The latest global cancer statistics in 2020 show that the incidence of CRC ranks third among malignant tumors, and the mortality rate ranks second [1, 2]. China accounted for 24% of newly diagnosed cancer patients and 30% of global cancer-related deaths in 2020 [3]. Despite the increasing popularity of cancer screening and the improvement of diagnostic technology, 20% of CRC patients have already developed distant metastasis at the time of diagnosis [4]. The prognosis of patients with metastatic colorectal cancer (mCRC) is still poor, and the median overall survival (OS) is about 30 months [5]. Appropriate prognostic markers are needed to predict the prognosis of patients.

In recent years, more and more studies have proved that inflammation plays an important role in different stages of tumor development, including tumor occurrence, progression, malignant transformation, invasion, and metastasis [6–10]. Peripheral blood before diagnosis or treatment can reflect the inflammation of the tumor to a certain extent. It has been proved that platelets, lymphocytes, C-reactive protein, and Glasgow prognostic score as indicators of inflammation and immune response play a prognostic role in different tumors [11, 12]. As one of the inflammatory indicators, platelet to lymphocyte ratio (PLR) also has a certain predictive effect on the prognosis of tumors, such as pancreatic cancer, lung cancer, liver cancer, ovarian cancer, and breast cancer ([13–16]; X. [17]; Y. [18]). Similarly, there are also studies on the prognosis of PLR in CRC, but the research results are inconsistent or even contradictory; the PLR cutoff values used in these studies are also inconsistent. The prognostic effect of PLR on mCRC has not been systematically studied; therefore, this study was conducted to better reveal the prognostic value of PLR for mCRC.

2. Materials and Methods

2.1. Literature Search Strategy

The PubMed, Web of Science, and Embase (via OVID) were systematically searched to obtain all relevant research (up to March 2021). Search strategies included “PLR,” “platelet-lymphocyte ratio,” “platelet-to-lymphocyte ratio,” “platelet to lymphocyte ratio,” “platelet-lymphocyte,” “CRC,” “colorectal cancer,” “colon cancer,” “rectal cancer,” “prognosis,” “survival,” and “outcome.” There were no language restrictions in our study. Two reviewers screened the title and summary of each study individually. Once the relevant study was confirmed, the full text of the further evaluation will be available. This is a meta-analysis study and does not require ethical approval.

2.2. Inclusion and Exclusion Criteria

The including criteria of this meta-analysis were as follows: (1) all patients were pathologically diagnosed as mCRC, (2) the PLR was obtained from peripheral blood test before treatment, (3) the associations between PLR and OS and progression free survival (PFS) were investigated, (4) HR and 95% CI could be obtained by multivariate Cox regression analysis.

The excluding criteria of this meta-analysis were as follows:(1) irrelevant research, animal experiment, cell experiment, literature review, comment, letter, meta-analysis, or case report; (2) patients having other primary tumors; (3) insufficient prognosis data to estimate HR and 95% CI; (4) failed to provide cutoff value; and (5) if the same author reported patient results in multiple publications, only the most relevant results would be selected.

2.3. Data Extraction

Two researchers independently collected the data. The following data were extracted: publication details (first author’s surname, year of publication, and geographic area of study), demographic characteristics (number of patients, age, and gender), cancer and follow-up data (stage, treatment strategy, median/mean follow-up time, and survival analysis), PLR (assessment method and cutoff value), and cutoff value were used to determine the “high” PLR and the “low” PLR.

2.4. Quality Assessment

The Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the quality of the included studies, which includes 3 criteria, namely, selection (0-4 points), comparability (0-2 points), and results (0-3 points). is defined as high quality.

2.5. Statistical Analysis

We used Cochrane’s and statistic to assess the heterogeneity of the included trials. Significant heterogeneity was defined as and %, and then, a random-effects model was selected to pool the results of the study; and % were considered to be a value indicating homogeneity, so a fixed-effects model was subsequently applied.

Publication bias was assessed using the Begg’s test/Egger’s test and funnel plot [19]. We used the “trim and fill” method to assess the impact of publication bias on the overall effect [20]. All calculations were performed using STATA 14.0. was considered statistically significant.

3. Results

3.1. Description of the Included Studies

The literature retrieval process is shown in Figure 1. According to the above search strategy, a total of 547 articles were finally identified. After removing the duplicates, 251 studies were excluded. By reading the titles and abstracts, 227 of the remaining 296 studies were further excluded. And then, 69 full-text articles have gone downloaded to evaluate their eligibility, in which 57 were excluded because noneffective data could be collected (), cutoff value was not recorded (), and repeated data from same or similar population (). Ultimately, 12 articles including 1452 patients published between 2014 and 2021 were included in this meta-analysis. The characteristics of the included studies are summarized in Table 1 [21–32]. All included literatures are retrospective studies. In the included articles, eleven investigated the prognostic role of PLR for OS and six for PFS. Eight of the included studies come from Eastern countries, including six from China, one from Turkey, and one from Japan. Four studies were from Western countries, one study from UK, one from Spain, one from Canada, and one from US. The PLR value was obtained by dividing the platelet count pretreatment by the lymphocyte count pretreatment.

3.2. Meta-Analysis Results

There were 11 studies to explore the prognostic significance of PLR for OS in patients with CRC. There was a significant heterogeneity between the studies (%, ), so we chose the random-effects model to pool the results of the study. Pooled HR1.72 (95% CI: 1.27–2.33, , Figure 2) showed that patients with elevated PLR were expected to have lower OS after treatment.

There were 6 studies to explore the prognostic significance of PLR for PFS in patients with CRC. There was a significant heterogeneity between the studies (%, ), so we chose the random-effects model to pool the results of the study. Pooled HR1.64 (95% CI: 1.16–2.31, , Figure 3) showed that patients with elevated PLR were expected to have lower PFS after treatment.

3.3. Publication Bias

Due to the heterogeneity in studies assessing the prognostic significance of PLR for OS and PFS, we used publication bias estimation to assess the reliability of meta-analysis results for these two indicators. We constructed funnel plots (Figures 4(a) and 4(b)), and the Egger’s test indicated that publication bias was present, and , respectively. Next, we used trim and fill methods to evaluate the symmetry of the funnel chart, by supplementing unpublished research, and the final result shows that there is no obvious asymmetry in the funnel chart obtained by adding 6 and 2 studies to the OS and PFS analysis, respectively, indicating no publication bias (Figures 4(c) and 4(d)).

(a)

(b)

(c)

(d)

3.4. Sensitivity Analyses

One study was deleted each time to reveal the influence of the individual data. When excluding any studies, the combined HR and its 95% CIs of OS and PFS were obviously unaffected, showing the stability of this analysis (Figures 5(a) and 5(b)).

(a)

(b)

3.5. Subgroup Analysis

We further explained the source of heterogeneity through subgroup analysis; we conducted this subgroup analysis based on the geographic region, sample size, PLR cutoff value, and major treatment therapy. Our results showed that high PLR predicted poor OS for all subgroups (Table 2).

4. Discussion

Recent studies have shown a correlation between PLR and the clinical outcome of mCRC, but there is conflicting evidence of the effect of PLR on the prognosis of patients with mCRC. This meta-analysis combined the results of 1452 patients with mCRC from 12 individual studies; we reassessed the prognostic role of PLR in mCRC. The results of this study indicate that patients with higher PLR levels before treatment have worse OS and PFS. We conducted a subgroup analysis to assess the prognostic significance of PLR, and the results showed that the prediction of PLR for OS is meaningful in all subsets.

Chronic inflammation plays an important role in different stages of tumor development; the underlying infections and inflammatory responses are associated with 15-20% of all deaths from cancer worldwide. Triggers of chronic inflammation that increase the risk of developing cancer are many, including microbial infections (for example, hepatitis virus and liver cancer), autoimmune diseases (for example, inflammatory bowel disease and CRC), and inflammatory conditions of unknown cause (for example, prostatitis and prostate cancer) [33]. Cancer-related inflammatory markers include inflammatory cells and inflammatory mediators present in tumor tissues; these inflammatory cells and mediators promote the occurrence and progression of cancer and participate in the migration, invasion, and metastasis of cancer cells. Among them, inflammatory markers such as platelets, lymphocytes, C-reactive protein, and Glasgow prognostic have been used in the study of tumor prognosis. Serum indicators have important prognostic value in mCRC; Silvestris et al. reported that low basal lactate dehydrogenases (LDH) levels and low pretreatment fibrinogen (FBG) serum levels are associated with favorable PFS and OS in mCRC patients; meanwhile, they found that medical treatment may influence LDH levels which showed a possible correlation between LDH changes and clinical outcome [34].

Platelets have been widely recognized as a component of the tumor microenvironment; the activation of platelets can release a variety of factors that regulate the tumor microenvironment, such as vascular endothelial growth factor (VEGF), TGFβ1, fibroblast growth factor (FGF), and proinflammatory cytokines, which can affect tumor growth, tumor metastasis, tumor angiogenesis, tumor inflammation, and chemotherapy efficiency [35–37]. VEGF can induce the permeability of endothelial cells, promoting extravasation of cancer cells and promote angiogenesis at distant metastatic sites [38]. The synergistic effect of TGFβ1 and platelet activating factor promotes tumor metastasis and induces the EMT process of tumor cell [37, 39]. Moreover, platelets easily interact with circulating tumor cells to induce EMT to promote metastasis [40–42]. Lymphocytes are closely related to tumor immunity; the immune tolerance of CD4+ T cells and the inhibition of CD8+ T cell activation can promote tumor immune escape and further promote tumor progression [43]. We divide the number of platelets by the number of lymphocytes to get the PLR value; an elevated PLR usually indicates an increase in the number of platelets or a decrease in the number of lymphocytes, which can lead to tumor progression and is associated with a poor prognosis.

There were some restrictions in our meta-analysis. First, the treatment method of each study was different, which may affect the relationship between PLR and OS or PFS. Second, the included studies were all retrospective studies, which may cause bias in the selection of patients. Third, because the sample size was small, including only six studies to assess the prognostic importance of PLR to PFS, which can be highly or underestimated, this also made us lack sufficient data to evaluate the association between PLR and disease free survival (DFS) and relapse free survival (RFS). Furthermore, due to the lack of appropriate data, the relationship between PLR and other important clinical parameters (such as age, sex, TNM staging, pathological type, tumor location, and neural invasion) has not been explored.

5. Conclusion

In conclusion, our meta-analysis showed that PLR is closely related with the survival outcome of mCRC patients. We can easily get the PLR value from routine blood tests, which is convenient for assessing the prognosis of mCRC patients and guiding individualized treatment. More studies are still needed to make up for the deficiencies of this analysis to improve the clinical utility of PLR.

Abbreviations

CRC:	Colorectal cancer
mCRC:	Metastatic colorectal cancer
OS:	Overall survival
PFS:	Progression free survival
DFS:	Disease free survival
RFS:	Relapse free survival
PLR:	Platelet to lymphocyte ratio
CI:	Confidence interval
HR:	Hazard ratio
NOS:	Newcastle-Ottawa Scale.

Data Availability

The data used in this meta-analysis can be obtained from the corresponding authors upon request.

Conflicts of Interest

The authors have declared no conflicts of interest.

Authors’ Contributions

Lei Yang and Xiaohui Jiang designed the study, Sheng Wei and Jie Xu acquired the studies and recorded the data, Jing Li checked the results and revised the draft, Jinming Wang and Sheng Wei contributed to data analysis, and Jinming Wang and Jing Li drafted the paper. Jinming Wang and Jing Li contributed equally to this work.

Acknowledgments

The present study was supported by the Health Committee of Nantong (grant No. QA2020018), the Clinical Medicine Special Program from Nantong University (grant No. 2019JY016), and the Nantong University Pre-Research Project (grant No. 17ZY36).

References

J. Ferlay, M. Colombet, I. Soerjomataram et al., “Cancer statistics for the year 2020: an overview,” International Journal of Cancer, vol. 149, no. 4, pp. 778–789, 2021.
View at: Publisher Site | Google Scholar
H. Sung, J. Ferlay, R. L. Siegel et al., “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: a Cancer Journal for Clinicians, vol. 71, no. 3, pp. 209–249, 2021.
View at: Publisher Site | Google Scholar
W. Cao, H. D. Chen, Y. W. Yu, N. Li, and W. Q. Chen, “Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020,” Chinese Medical Journal, vol. 134, no. 7, pp. 783–791, 2021.
View at: Publisher Site | Google Scholar
A. B. Benson, A. P. Venook, M. M. Al-Hawary et al., “Colon cancer, version 2.2021, NCCN clinical practice guidelines in oncology,” Journal of the National Comprehensive Cancer Network, vol. 19, no. 3, pp. 329–359, 2021.
View at: Publisher Site | Google Scholar
E. Van Cutsem, A. Cervantes, R. Adam et al., “ESMO consensus guidelines for the management of patients with metastatic colorectal cancer,” Annals of Oncology, vol. 27, no. 8, pp. 1386–1422, 2016.
View at: Publisher Site | Google Scholar
M. T. Chow, A. Moller, and M. J. Smyth, “Inflammation and immune surveillance in cancer,” Seminars in Cancer Biology, vol. 22, no. 1, pp. 23–32, 2012.
View at: Publisher Site | Google Scholar
S. P. Hussain and C. C. Harris, “Inflammation and cancer: an ancient link with novel potentials,” International Journal of Cancer, vol. 121, no. 11, pp. 2373–2380, 2007.
View at: Publisher Site | Google Scholar
A. Korniluk, O. Koper, H. Kemona, and V. Dymicka-Piekarska, “From inflammation to cancer,” Irish Journal of Medical Science, vol. 186, no. 1, pp. 57–62, 2017.
View at: Publisher Site | Google Scholar
B. Z. Qian, “Inflammation fires up cancer metastasis,” Seminars in Cancer Biology, vol. 47, pp. 170–176, 2017.
View at: Publisher Site | Google Scholar
N. Singh, D. Baby, J. P. Rajguru, P. B. Patil, S. S. Thakkannavar, and V. B. Pujari, “Inflammation and cancer,” Annals of African Medicine, vol. 18, no. 3, pp. 121–126, 2019.
View at: Publisher Site | Google Scholar
D. C. McMillan, “Systemic inflammation, nutritional status and survival in patients with cancer,” Current Opinion in Clinical Nutrition and Metabolic Care, vol. 12, no. 3, pp. 223–226, 2009.
View at: Publisher Site | Google Scholar
D. G. Watt, C. S. Roxburgh, M. White, J. Z. Chan, P. G. Horgan, and D. C. McMillan, “A survey of attitudes towards the clinical application of systemic inflammation based prognostic scores in cancer,” Mediators of Inflammation, vol. 2015, Article ID 842070, 2015.
View at: Publisher Site | Google Scholar
N. Ding, Z. Pang, H. Shen, Y. Ni, J. Du, and Q. Liu, “The prognostic value of PLR in lung cancer, a meta-analysis based on results from a large consecutive cohort,” Scientific Reports, vol. 6, no. 1, p. 34823, 2016.
View at: Publisher Site | Google Scholar
C. H. Koh, N. Bhoo-Pathy, K. L. Ng et al., “Utility of pre-treatment neutrophil-lymphocyte ratio and platelet-lymphocyte ratio as prognostic factors in breast cancer,” British Journal of Cancer, vol. 113, no. 1, pp. 150–158, 2015.
View at: Publisher Site | Google Scholar
Z. Zhao, X. Zhao, J. Lu, J. Xue, P. Liu, and H. Mao, “Prognostic roles of neutrophil to lymphocyte ratio and platelet to lymphocyte ratio in ovarian cancer: a meta-analysis of retrospective studies,” Archives of Gynecology and Obstetrics, vol. 297, no. 4, pp. 849–857, 2018.
View at: Publisher Site | Google Scholar
J. Zheng, J. Cai, H. Li et al., “Neutrophil to lymphocyte ratio and platelet to lymphocyte ratio as prognostic predictors for hepatocellular carcinoma patients with various treatments: a meta-analysis and systematic review,” Cellular Physiology and Biochemistry, vol. 44, no. 3, pp. 967–981, 2017.
View at: Publisher Site | Google Scholar
X. Zhou, Y. du, Z. Huang et al., “Prognostic value of PLR in various cancers: a meta-analysis,” PLoS One, vol. 9, no. 6, article e101119, 2014.
View at: Publisher Site | Google Scholar
Y. Zhou, S. Cheng, A. H. Fathy, H. Qian, and Y. Zhao, “Prognostic value of platelet-to-lymphocyte ratio in pancreatic cancer: a comprehensive meta-analysis of 17 cohort studies,” Oncotargets and Therapy, vol. Volume 11, pp. 1899–1908, 2018.
View at: Publisher Site | Google Scholar
M. Egger, G. Davey Smith, M. Schneider, and C. Minder, “Bias in meta-analysis detected by a simple, graphical test,” BMJ, vol. 315, no. 7109, pp. 629–634, 1997.
View at: Publisher Site | Google Scholar
S. Duval and R. Tweedie, “Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis,” Biometrics, vol. 56, no. 2, pp. 455–463, 2000.
View at: Publisher Site | Google Scholar
G. Bulut and Z. N. Ozdemir, “Prognostic significance of neutrophil-lymphocyte ratio and platelet-lymphocyte ratio in metastatic colorectal cancer,” Journal of Gastrointestinal Cancer, vol. 1, no. 1, pp. 1–6, 2021.
View at: Publisher Site | Google Scholar
J. Chang, G. Lin, M. Ye et al., “Decreased mean platelet volume predicts poor prognosis in metastatic colorectal cancer patients treated with first-line chemotherapy: results from mCRC biomarker study,” BMC Cancer, vol. 19, no. 1, p. 15, 2019.
View at: Publisher Site | Google Scholar
M. Cruz-Ramos, L. del Puerto-Nevado, B. Zheng et al., “Prognostic significance of neutrophil-to lymphocyte ratio and platelet-to lymphocyte ratio in older patients with metastatic colorectal cancer,” Journal of Geriatric Oncology, vol. 10, no. 5, pp. 742–748, 2019.
View at: Publisher Site | Google Scholar
D. J. Erstad, M. S. Taylor, M. Qadan et al., “Platelet and neutrophil to lymphocyte ratios predict survival in patients with resectable colorectal liver metastases,” American Journal of Surgery, vol. 220, no. 6, pp. 1579–1585, 2020.
View at: Publisher Site | Google Scholar
J. Jiang, T. Ma, W. Xi et al., “Pre-treatment inflammatory biomarkers predict early treatment response and favorable survival in patients with metastatic colorectal cancer who underwent first line cetuximab plus chemotherapy,” Cancer Management and Research, vol. Volume 11, pp. 8657–8668, 2019.
View at: Publisher Site | Google Scholar
Z. M. Li, Y. F. Peng, C. Z. Du, and J. Gu, “Colon cancer with unresectable synchronous metastases: the AAAP scoring system for predicting the outcome after primary tumour resection,” Colorectal Disease, vol. 18, no. 3, pp. 255–263, 2016.
View at: Publisher Site | Google Scholar
A. Matsuda, T. Yamada, S. Matsumoto et al., “Prognostic role of the platelet-to-lymphocyte ratio for patients with metastatic colorectal cancer treated with aflibercept,” In Vivo, vol. 34, no. 5, pp. 2667–2673, 2020.
View at: Publisher Site | Google Scholar
J. Mercier and I. A. Voutsadakis, “Comparison of hematologic and other prognostic markers in metastatic colorectal cancer,” Journal of Gastrointestinal Cancer, vol. 50, no. 3, pp. 493–506, 2019.
View at: Publisher Site | Google Scholar
K. Neofytou, E. C. Smyth, A. Giakoustidis, A. Z. Khan, D. Cunningham, and S. Mudan, “Elevated platelet to lymphocyte ratio predicts poor prognosis after hepatectomy for liver-only colorectal metastases, and it is superior to neutrophil to lymphocyte ratio as an adverse prognostic factor,” Medical Oncology, vol. 31, no. 10, p. 239, 2014.
View at: Publisher Site | Google Scholar
J. Peng, H. Li, Q. Ou et al., “Preoperative lymphocyte-to-monocyte ratio represents a superior predictor compared with neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios for colorectal liver-only metastases survival,” Oncotargets and Therapy, vol. Volume 10, pp. 3789–3799, 2017.
View at: Publisher Site | Google Scholar
Y. Wu, C. Li, J. Zhao et al., “Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios predict chemotherapy outcomes and prognosis in patients with colorectal cancer and synchronous liver metastasis,” World Journal of Surgical Oncology, vol. 14, no. 1, p. 289, 2016.
View at: Publisher Site | Google Scholar
Q. Yan, Z. Ertao, Z. Zhimei et al., “Systemic immune-inflammation index (SII): a more promising inflammation-based prognostic marker for patients with synchronic colorectal peritoneal carcinomatosis,” Journal of Cancer, vol. 11, no. 18, pp. 5264–5272, 2020.
View at: Publisher Site | Google Scholar
A. Mantovani, P. Allavena, A. Sica, and F. Balkwill, “Cancer-related inflammation,” Nature, vol. 454, no. 7203, pp. 436–444, 2008.
View at: Publisher Site | Google Scholar
N. Silvestris, M. Scartozzi, G. Graziano et al., “Basal and bevacizumab-based therapy-induced changes of lactate dehydrogenases and fibrinogen levels and clinical outcome of previously untreated metastatic colorectal cancer patients: a multicentric retrospective analysis,” Expert Opinion on Biological Therapy, vol. 15, no. 2, pp. 155–162, 2015.
View at: Publisher Site | Google Scholar
A. K. Olsson and J. Cedervall, “The pro-inflammatory role of platelets in cancer,” Platelets, vol. 29, no. 6, pp. 569–573, 2018.
View at: Publisher Site | Google Scholar
S. Sabrkhany, M. J. E. Kuijpers, A. W. Griffioen, and M. G. A. Oude Egbrink, “Platelets: the holy grail in cancer blood biomarker research?” Angiogenesis, vol. 22, no. 1, pp. 1-2, 2019.
View at: Publisher Site | Google Scholar
Q. Wang, Z. Li, L. Sun et al., “Platelets enhance the ability of bone-marrow mesenchymal stem cells to promote cancer metastasis,” Oncotargets and Therapy, vol. Volume 11, pp. 8251–8263, 2018.
View at: Publisher Site | Google Scholar
M. Z. Wojtukiewicz, E. Sierko, D. Hempel, S. C. Tucker, and K. V. Honn, “Platelets and cancer angiogenesis nexus,” Cancer Metastasis Reviews, vol. 36, no. 2, pp. 249–262, 2017.
View at: Publisher Site | Google Scholar
M. Labelle, S. Begum, and R. O. Hynes, “Direct signaling between platelets and cancer cells induces an epithelial-mesenchymal-like transition and promotes metastasis,” Cancer Cell, vol. 20, no. 5, pp. 576–590, 2011.
View at: Publisher Site | Google Scholar
H. Kassassir, K. Karolczak, K. M. Siewiera, D. W. Wojkowska, M. Braun, and C. W. Watala, “Time-dependent interactions of blood platelets and cancer cells, accompanied by extramedullary hematopoiesis, lead to increased platelet activation and reactivity in a mouse orthotopic model of breast cancer - implications for pulmonary and liver metastasis,” Aging, vol. 12, no. 6, pp. 5091–5120, 2020.
View at: Publisher Site | Google Scholar
D. G. Menter, S. Kopetz, E. Hawk et al., “Platelet "first responders" in wound response, cancer, and metastasis,” Cancer Metastasis Reviews, vol. 36, no. 2, pp. 199–213, 2017.
View at: Publisher Site | Google Scholar
N. Sol and T. Wurdinger, “Platelet RNA signatures for the detection of cancer,” Cancer Metastasis Reviews, vol. 36, no. 2, pp. 263–272, 2017.
View at: Publisher Site | Google Scholar
X. Yang, W. Wu, Y. Pan, Q. Zhou, J. Xu, and S. Han, “Immune-related genes in tumor-specific CD4(+) and CD8(+) T cells in colon cancer,” BMC Cancer, vol. 20, no. 1, p. 585, 2020.
View at: Publisher Site | Google Scholar

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Copyright © 2021 Jinming Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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