Skip to main content
Download PDF

Clinical significance of Cyclin D1 by complete quantification detection in mantle cell lymphoma: positive indicator in prognosis

Diagnostic Pathology volume 19, Article number: 149 (2024) Cite this article

Abstract

Objectives

The positive expression of Cyclin D1 in immunohistochemical (IHC) staining serves as the cornerstone for diagnosing mantle cell lymphoma (MCL). However, existing literature does not conclusively establish whether the expression ratio and staining intensity significantly influence diagnostic outcomes or patient prognosis. In this retrospective study, the correlation between comprehensive Cyclin D1 quantification and the prognosis of MCL patients was studied.

Methods

The Cyclin D1 protein level was assessed in 120 formalin-fixed paraffin-embedded samples from MCL patients using the quantitative dot blot (QDB) analysis technique. R language software was employed for statistical analysis to determine the optimal threshold with statistical significance. Additionally, Kaplan-Meier method was utilized to evaluate the relationship between the absolute level of Cyclin D1 protein and overall survival (OS) of patients. Furthermore, the Chi-square test was applied to analyze the causes of single and multiple fractures, with a significance level of p < 0.05. Finally, the Log-rank test was used to compare two survival curves, where a significance level of p < 0.05 was considered statistically significant.

Results

At the optimized cutoff of 0.46 nmol/g, univariate analysis revealed a positive correlation between Cyclin D1 protein level and patient survival (OS). Specifically, in the subgroup with complete quantification of Cyclin D1 higher than the cutoff, the 5-year OS was 18%, whereas in the subgroup with complete quantification of Cyclin D1 lower than the cutoff, the 5-year OS was 4.8% (Log-rank test, P = 0.017). This indicates that patients with Cyclin D1 levels above the cutoff had significantly better overall survival compared to those below the cutoff. Additionally, in the Pearson distribution test, Ki-67 emerged as an independent prognostic factor for the complete quantification of Cyclin D1. Notably, Cyclin D1 complete quantification results remained unaffected by factors such as gender, age, LDH (Lactate Dehydrogenase) level, Ann Arbor stage(AAS), Ki-67, IPI(International prognostic index), MIPI(Mantle International prognostic index), and MIPI-c (MIPI Combined with Ki-67 Proliferation Index Chi-square test, p > 0.05).

Conclusions

Comprehensive Cyclin D1 quantification, especially above a threshold, significantly correlates with better overall survival in MCL. This highlights its prognostic importance in MCL management. Full quantification of CyclinD1 aids MCL prognosis, while QDB technology for biomarker quantification supports precise clinical prognostic stratification.

Introduction

Mantle cell lymphoma (MCL) is an uncommon subtype of non-Hodgkin lymphoma (NHL) originating from B cells, accounting for about 3–10% of NHL cases [1]. Its defining biological feature is the chromosomal translocation (11;14) (q13;q32), leading to the overexpression of Cyclin D1 [2]. This translocation results in a G1/S phase disorder, which forms the molecular basis of MCL. Most patients are diagnosed at an advanced disease stage (Ann Arbor stage III or IV), often with bone marrow or peripheral blood involvement [3]. The clinical heterogeneity of MCL presents a spectrum that includes both aggressive forms with slow progression, poor prognosis, and challenging treatment outcomes.

Cyclin D1 is a crucial positive regulator of the cell cycle and a recognized proto-oncogene [4]. It shows positive expression in various solid malignancies [5,6,7,8,9,10,11,12,13,14,15] and has a significant correlation with tumor prognosis [9, 10], playing a role in promoting tumor progression [11] and potentially serving as a therapeutic target in various cancers [12,13,14,15].

The Ehinger team [16] studied 231 cases of CD5-negative diffuse large B-cell lymphoma (DLBCL) and found a 4.3% positive expression rate of Cyclin D1 in the absence of chromosomal translocation. Lymphomas such as follicular center lymphoma (FCL), diffuse large cell lymphoma (DLCL), anaplastic large cell lymphoma (ALCL), and peripheral T-cell lymphoma (PTCL) exhibit nonspecific expression of Cyclin D1 [17], which has not been systematically studied in these lymphomas, leaving the mechanisms and prognostic implications unclear.

To date, no studies have reported on the relationship between Cyclin D1 expression and prognosis in MCL patients. IHC can detect target proteins through localization and qualitative and semi-quantitative methods but does not achieve complete quantification. Traditional immunoblotting methods are also relatively quantitative. Recently, in light of the limitations of immunohistochemical staining, Yantai Zestern Biotechnique Co., LTD China developed a new immunoblotting technique for absolute quantification of protein biomarkers in formalin-fixed and FFPE- QDB [18]. This technique represents the first immunoassay method for absolute and complete quantification detection of protein biomarkers in FFPE tissues [19]. Based on the specific binding of antigens and antibodies, this method allows for the complete quantification of proteins and the analysis of many samples, thus facilitating the detection of protein expression levels across multiple parameters. QDB analysis uses 96-well plates with nitrocellulose membranes and requires only a small amount of sample cracking solution, saving significant amounts of working time and research resources, and effectively improving work efficiency. This method has been evaluated at both the cell and tissue levels, yielding good experimental results [20] and addressing the shortcomings of traditional immunoblotting methods by ensuring the objectivity of experimental results.

This study aims to detect Cyclin D1 protein in 120 FFPE samples from MCL patients using the QDB technique, analyze and evaluate the predictive value of Cyclin D1 protein for the prognosis of MCL patients, investigate its correlation with other clinical parameters, and explore its potential as a new molecular biomarker for prognostic assessment and clinical treatment decision-making in MCL.

Materials and methods

Patient selection

This retrospective study included 120 cases of MCL diagnosed by the pathology departments of Yantai Yuhuangding Hospital, Shandong Provincial Hospital, Shandong University Qilu Hospital, and the Affiliated Hospital of Southwest Medical University from 2008 to 2020.

Methods

All patients received treatment with Rituximab. The survival status of the patients was updated through telephone follow-ups. The last follow-up date was September 18, 2020. The overall survival period was calculated from the disease diagnosis date to either the date of death or the last follow-up date for the patient. The study received approval from the ethics committee of Yantai Yuhuangding Hospital (approval No.: Yantai Medical Ethics Review 2021-041).

QDB analysis

In this study, 0.5 µg of MCL FFPE lysate (adjusted to a concentration of 0.25 µg/µl) and 0.02 µg of BT474 lysate were added to the QDB plate. Simultaneously, serially diluted recombinant Cyclin D1 protein was added as the protein standard and dried at room temperature for 1 h. Primary antibodies were diluted in 4% non-fat milk blocking buffer to the desired concentrations (ranging from 1:500 to 1:5000) and incubated for 1 h at room temperature, with 100 µL per well, followed by overnight incubation at 4 °C. The QDB plate was rinsed with TBST (TBS + Tween) solution for 3 sets of 5-minute washes.

Next, the secondary antibody was diluted to the required concentration (ranging from 1:1000 to 1:50000) and incubated for 4 h at room temperature. The QDB plate was rinsed again, and ECL(enhanced chemiluminescence) substrates were added to the 96-well microplate evenly. The QDB plates were then inserted into the 96-well microplate for 2 min, removed, placed on the microplate, and the luminous intensity was detected using a microplate reader. Each sample was tested three times, and the average value of these three experiments was used for analysis.

The absolute level of Cyclin D1 in the BT474 cell lysate was detected using the QDB method with BT474 cell lysate serving as an internal control, and the results were recorded for further analysis.

Statistical analysis

The Survival and Survminer packages were used in R to analyze patient survival and prognosis in risk groups. Specifically, the “survminer” package version 4.1.2 in R was employed to calculate the optimal cut-off value. Cyclin D1 levels, measured by the QDB method, were dichotomized for OS analysis, and survival curves were generated accordingly.

To analyze the relationship between the complete quantification of Cyclin D1 and patient prognosis and survival, we employed SPSS software version 25.0. Kaplan-Meier analysis was used for univariate analysis of relevant clinicopathological parameters and prognosis. Univariate Cox proportional hazard models were fitted to estimate risk ratios (RR) and corresponding 95% confidence intervals (CI), and R packages were used to plot univariate survival curves for comparing survival differences.

The correlation between Cyclin D1 complete quantification values and each clinicopathological parameter was assessed using the chi-square test. Additionally, multivariate Cox proportional hazards regression analysis was conducted to analyze the influence of clinicopathological parameters on patient prognosis.

Additionally, the correlation between the complete quantification value of Cyclin D1 and the IHC positive expression rate of Ki-67 in MCL patients was investigated using Pearson’s correlation coefficient. A p-value of less than 0.05 was considered statistically significant in all analyses.

Results

Patients characteristics

As of September 18, 2020, the follow-up duration for the study cohort was 116 months. The age of onset among patients ranged from 35 to 86 years, with a median age of 63 years. Among the patients with available follow-up data (n = 72), the median follow-up time was 48 months. The median survival time observed was 24 months, with a stable survival rate extending to 41 months.

Among these patients, 42 individuals succumbed to the disease, accounting for 58.3% (42/72) of the cohort, while 30 patients survived, representing 41.7% (30/72) of the cohort. The calculated 3-year OS rate was 31.9%, and the 5-year OS rate was 13.9%. These figures provide insight into the prognosis and survival outcomes observed in this cohort of patients with mantle cell lymphoma.(Fig. 1).

Fig. 1
figure 1

The median survival time for 72 MCL patients was 24 months (with corresponding 3-year and 5-year overall survival rates of 31.9% and 13.9%, respectively)

Full size image

Cyclin D1 complete quantification and optimal cut-off value

Cyclin D1 Quantification and Determination of Optimal Cut-off Value in MCL Samples: Cyclin D1 was quantified in 120 MCL samples. One case was excluded due to insufficient sample volume, leaving 119 samples for analysis. The quantification ranged from 0.081325936 nmol/g to 21.2259093 nmol/g, with an average of 1.9004718900794 nmol/g. Using the ‘survminer’ R package for statistical analysis, the optimal cut-off value for Cyclin D1 in OS analysis was determined to be 0.46 nmol/g, which showed statistical significance (as illustrated in Fig. 2). Patients were categorized into two subgroups based on this threshold: a Cyclin D1-high (> 0.46 nmol/g) subgroup and a Cyclin D1-low (≤ 0.46 nmol/g) subgroup.

Fig. 2
figure 2

Cyclin D1 absolute quantification via R software identified 0.46nmol/g as the optimal threshold for significantly predicting patient prognosis

Full size image

Correlation between complete quantification of Cyclin D1 and prognosis of patients

Based on the threshold classification data analyzed above, the analysis of the difference in prognosis and survival between the two subgroups was conducted using the R package. Among the 72 patients with follow-up results, the FFPE samples of one patient failed to pass the QDB test (n = 71). The Cyclin D1-h subgroup comprised 50 patients (70.4%, 50/71), whereas the Cyclin D1-l subgroup included 21 patients (29.6%, 21/71). Statistical analysis revealed a significant correlation between Cyclin D1 complete quantification and the survival prognosis of MCL patients (p = 0.017). This statistical significance is pertinent to patients’ OS prognosis.(Fig. 3).

Fig. 3
figure 3

Patients were stratified by Cyclin D1 levels (0.46nmol/g threshold), using “survminer” to assess prognosis. Cyclin D1-high patients (>0.46nmol/g) showed significantly better survival outcomes (p = 0.017), with longer median survival and higher survival rates than Cyclin D1-low (≤ 0.46nmol/g) patients

Full size image

Correlation between complete quantification of Cyclin D1 in MCL and various clinicopathological parameters

The relationship between complete quantification of Cyclin D1 and various clinicopathological parameters was analyzed using the chi-square test and exact Fisher test. However, no significant correlation was found between the absolute levels of Cyclin D1 protein and each clinicopathological parameter, as summarized in Table 1. The parameters assessed included age (P = 0.804), gender (P = 0.176), LDH level (P = 0.169), AAS (P = 0.72), IPI (P = 1.000), MIPI (P = 0.329), Ki67 (P = 0.729), and MIPI-c (P = 0.474). It is important to note that a p-value exceeding 0.05 was not considered statistically significant in this analysis.(Table 1).

Table 1 Relationship between Cyclin D1 and clinicopathological parameters in MCL
Full size table

Relationship between clinicopathological parameters and prognosis of patients

Following Kaplan-Meier analysis and log-rank test, the univariate Cox proportional hazards model was employed to fit OS data. This model was used to evaluate hazard ratios (HR) and corresponding 95% CI. This analysis revealed statistically significant correlations between Cyclin D1 complete quantification, age, Ki-67, MIPI, and AAS with patient prognosis and OS (P: 0.017, 0.040, 0.015, 0.048, 0.016, respectively). However, gender, MIPI-c, IPI, and LDH levels did not exhibit statistical significance in this context (P: 0.058, 0.066, 0.107, 0.347, respectively).(Table 2).

Table 2 Relationship between clinicopathological parameters and prognosis of patients
Full size table

Results of multivariate prognostic analysis

The Cox proportional hazards model was utilized to analyze the prognosis of patients, considering statistically significant single parameters and multivariate prognosis. The omnibus test yielded a p-value of 0.012. However, upon analysis, the effects of Cyclin D1, age, Ki-67, AAS, and MIPI on the survival time of patients were not statistically significant (P > 0.05). Consequently, no independent prognostic parameters affecting MCL were identified.(Table 3).

Table 3 Relationship between multivariate analysis and prognosis of MCL patients
Full size table

Correlation between complete quantification of Cyclin D1 and positive rate of Ki-67 in MCL

Both the Ki-67 index and Cyclin D1 are markers associated with cell proliferation. However, after conducting Spearman correlation analysis, it was found that the complete quantification of Cyclin D1 and the positive rate of Ki-67 index IHC staining were not correlated (p > 0.05). (Table 4)

Table 4 Correlation between complete quantitative of Cyclin D1 and positive rate of Ki-67 in MCL
Full size table

Disscussion

In this pioneering study, we utilized the QDB method for the first time to achieve a comprehensive quantification of Cyclin D1 in MCL FFPE samples. Our findings provide initial evidence that the absolute and exhaustive quantification of Cyclin D1 is intricately linked to patient prognosis, exhibiting a favorable correlation with survival duration and OS, albeit not as an autonomous determinant.

Given the intricate biological underpinnings of MCL onset and progression, coupled with the myriad factors influencing patient survival and prognosis, developing an optimal prognostic model remains a formidable challenge, particularly in light of MCL’s low incidence and inherent heterogeneity [21]. Prior investigations into prognostic factors in MCL patients have lamented suboptimal treatment outcomes [22]. While MCL characteristically expresses Cyclin D1 through IHC staining, this approach is inherently subjective and prone to variability [23]. To address these limitations, we adopted the QDB method [24], which not only circumvents the shortcomings of IHC but also uncovers a significant association between Cyclin D1 overexpression and MCL patient survival.

Using the R package, the optimal cut-off value of Cyclin D1 was determined to be 0.46 nmol/g. Patients were subsequently divided into two groups and subjected to Kaplan-Meier univariate survival analysis. This rigorous analysis underscores the pivotal role of Cyclin D1, age, MIPI, Ki-67, and AAS as statistically significant prognostic factors in MCL (P < 0.05). Furthermore, the R package facilitated the generation of prognostic survival curves for each factor, offering profound insights into their individual contributions to patient outcomes.(Fig. 4).

Fig. 4
figure 4

Univariate prognostic analysis of MCL patients showed that Ki-67, AAS, MIPI, and Age were significant for the prognosis of patients

Full size image

The median survival time of the Cyclin D1-h group (41 months) was significantly longer than that of the Cyclin D1-l group (12 months). At 30 months after diagnosis, the OS rate for patients in the Cyclin D1-h group was 46% (23/50), whereas it was approximately 28.6% (6/21) for patients in the Cyclin D1-l group. By the 60-month mark, the 5-year OS rate for the Cyclin D1-h group dropped to 18% (9/50), and for the Cyclin D1-l group, it was 4.8% (1/21). At 90 months, the OS rate for the Cyclin D1-h group was 4% (2/50), while it was 0% (0/21) for the Cyclin D1-l group. As of the follow-up date, one patient in the Cyclin D1-h group was still alive, with a survival time of 116 months (refer to Fig. 4). Cyclin D1 emerged as a significant prognostic factor for patients with MCL.

A Cox proportional hazards regression multivariate analysis indicated that no independent factors significantly affected MCL prognosis. Cyclin D1, along with other factors, were all identified as risk factors for MCL occurrence. Notably, Cyclin D1 did not independently influence prognosis (p > 0.05), aligning with existing literature [25].

This study revealed that the complete quantification of Cyclin D1 was not correlated with patients’ age, MIPI, AAS, Ki-67, MIPI-c, sex, or LDH level (chi-square test, p > 0.05, see Table 1). Therefore, the absolute level of Cyclin D1 in MCL remained unaffected by other clinicopathological factors.

Both Ki-67 and Cyclin D1 serve as markers of cell proliferation and are associated with MCL prognosis. However, bivariate Pearson analysis demonstrated their independence from each other in the context of MCL (r = -0.048, p = 0.621, p > 0.05). The t (11; 14) (q13; q32) chromosomal translocation leading to Cyclin D1 overexpression promotes abnormal cell proliferation and is considered an initial factor in MCL. Meanwhile, Ki-67 reflects tumor cell proliferative activity independently.

The complete quantification of Cyclin D1 may emerge as a novel biomarker for evaluating MCL prognosis, OS, and survival rates. However, this study focused solely on patients who received rituximab treatment and had follow-up results (n = 72). Therefore, the applicability of the determined threshold requires confirmation in future studies. Larger-scale studies are advocated to validate the application of this biomarker in routine clinical practice.

Conclusions

In conclusion, we advocate for incorporating Cyclin D1 into MCL prognostic assessments, underscoring its autonomy from other risk factors and positive survival correlation. The pivotal prognostic threshold of 0.46 nmol/g for MCL underscores its clinical significance. Looking ahead, QDB is an emerging technology for the fully quantitative detection of biomarkers. In the past, our research group has conducted in-depth studies on important biomarkers of breast cancer using this technology and achieved certain research results. This article represents the first attempt to apply QDB technology in clinical lymphoma research, focusing solely on the fully quantitative study of CyclinD1 protein, which has significant diagnostic value in mantle cell lymphoma (MCL). Other important markers such as P53, Ki67, and even T-lymphocytes in the tumor microenvironment will be included in subsequent research. We hope that this work can provide assistance in the precise diagnosis, prognostic stratification, and selection of treatment strategies for patients with mantle cell lymphoma.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Cyclin D1:

G1/S-specific Cyclin-D1

IHC:

Immunohistochemical

MCL:

Mantle cell lymphoma

QDB:

Quantitative dot blot

OS:

Overall survival

LDH:

Lactate Dehydrogenase

AAS:

Ann Arbor stage

IPI:

International prognostic index

MIPI:

Mantle International prognostic index

MIPI-c:

MIPI Combined with Ki-67 Proliferation Index

NHL:

Non-Hodgkin lymphoma

DLBCL:

Diffuse large B-cell lymphoma

FCL:

Follicular center lymphoma

DLCL:

Diffuse large cell lymphoma

ALCL:

Anaplastic large cell lymphoma

PTCL:

Peripheral T-cell lymphoma

FFPE:

Formalin-fixed and paraffin-embedded tissues

TBST:

TBS + Tween

ECL:

Enhanced chemiluminescence

RR:

Risk ratios

CI:

Confidence intervals

HR:

Hazard ratios

References

  1. Sabattini E, Bacci F, Sagramoso C, Pileri SA. WHO classification of tumours of haematopoietic and lymphoid tissues in 2008: an overview. Pathologica. 2010;102(3):83–87.

  2. Alba N, Sílvia Beà, Pedro J, Campo E. Molecular pathogenesis of mantle cell lymphoma[J]. Hematol Oncol Clin North Am. 2020;34(5):795–807.

  3. Lenz G, Dreyling M, Hiddemann W. Mantle cell lymphoma: established therapeutic options and future directions[J]. Ann Hematol. 2004;83(2):71–7.

    Article  CAS  PubMed  Google Scholar 

  4. Baoming Ren W, Li Y, Yang, et al. The impact of Cyclin D1 overexpression on the prognosis of bladder cancer: a meta-analysis[J]. World J Surg Oncol. 2014;12:55.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Lin Y, Cheng A, Solanki M, Su W, Zaki M, Tirado CA. Amplification of Cyclin D1 in Urothelial Carcinoma[J]. J Assoc Genet Technol. 2022;48(1):4–9.

  6. Roland S, George NT, Diana R, et al. Cyclin D1/ Cyclin D1status in metastasizing bladder cancer: a prognosticator and predictor of chemotherapeutic response[J]. Mod Pathol. 2014;27(1):87–95.

    Article  Google Scholar 

  7. Zhongxiao Lin H, Sheng C, You, et al. Inhibition of the Cyclin D1 promoter in response to sonic hedgehog signaling pathway transduction is mediated by Gli1[J]. Exp Ther Med. 2017;13(1):307–14.

    Article  PubMed  Google Scholar 

  8. Xi J, Sun Y, Zhang M, et al. GLS1 promotes proliferation in hepatocellular carcinoma cells via AKT/GSK3β/Cyclin D1 pathway[J]. Exp Cell Res. 2019;381(1):1–9.

    Article  CAS  PubMed  Google Scholar 

  9. Ahlin C, Lundgren C, Embretsén-Varro E, Jirström K, Blomqvist C, Fjällskog M -L. High expression of Cyclin D1 is associated to high proliferation rate and increased risk of mortality in women with ER-positive but not in ER-negative breast cancers[J]. Breast cancer Res Treat. 2017;164(3):667–78.

  10. FrancescaIda M, Francesca DA. Cyclin D1 in Cancer: a molecular connection for cell cycle control, Adhesion and Invasion in Tumor and Stroma[J]. Cells. 2020;9(12):2648.

  11. Pandey A, Bahl C, Sharma S, Singh N, Behera D. Functional role of Cyclin D1polymorphism (G870A) in modifying susceptibility and overall survival of north Indian lung cancer patients[J]. Tumori. 2018;104(3):179–87.

  12. Sajjad K, Jaudah AA, Hasan MA, et al. Cyclin D1 as a therapeutic target of renal cell carcinoma- a combined transcriptomics, tissue microarray and molecular docking study from the Kingdom of Saudi Arabia[J]. BMC Cancer. 2016;16(2):741.

    Google Scholar 

  13. Quan Liang Q, GuoYing Y. Cyclin D1 is a new target gene of tumor suppressor miR-520e in breast cancer[J]. Open Med(Wars. 2019;14:913–9.

    Article  PubMed  Google Scholar 

  14. Ortiz BA, Garcia D, Vicente Y, Palka M, Bellas C, Martin P. Prognostic significance of Cyclin D1 protein expression and gene amplification in invasive breast carcinoma[J]. PloS One. 2017;12(11):e0188068.

  15. Swarnalatha Y, Vidhya VG, Murugan A. Isochamanetin is a selective inhibitor for Cyclin D1 in SKOV3 cell Lines[J]. Nutr Cancer. 2019;71(4):657–67.

    Article  CAS  PubMed  Google Scholar 

  16. Ehinger M, Linderoth J, Christensson B, et al. A subset of CD5-diffuse large B-cell lymphomas expresses nuclear Cyclin D1 with aberrations at the Cyclin D1 locus[J]. Am J Clin Pathol. 2008;129(4):630–8.

    Article  PubMed  Google Scholar 

  17. Aguilera NS, Bijwaard KE, Duncan B, et al. Differential expression of Cyclin D1 in mantle cell lymphoma and other non-hodgkin’s lymphomas[J]. Am J Pathol. 1998;153(6):1969–76.

    Article  CAS  PubMed  Google Scholar 

  18. Tian G, Tang F, Yang C, Zhang W, Bergquist J, Wang B, et al. Quantitative dot blot analysis (QDB), aversatile high throughput immunoblot method. [J] Oncotarget. 2017;8:58553–62.

  19. Yu G, Zhang W, Zhang Y, et al. Developing a routine lab test for absolute quantification of HER2 in FFPE breast cancer tissues using quantitative dot blot (QDB) method[J]. Sci Rep. 2020;10(1):12502.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hao J, Lyu Y, Zou J, Zhang Y, Xie S, Jing L, et al. Improving prognosis of surrogate assay for breast Cancer patients by Absolute quantitation of Ki67 protein levels using quantitative dot blot (QDB) Method[J]. Front Oncol. 2021,11:737781.

  21. Yuandong Zhu, Xu X, Zheng X, Zheng Z. Nomogram incorporating clinicopathological parameters to predict the survival of patients with mantle cell lymphoma[J]. J Investig Meg. 2019;67(2):331–7.

  22. Wu M, Li Y, Huang H, Xu W, Wang Y, Huang H, et al. Initial treatment patterns and survival outcomes of mantle cell lymphoma patients managed at Chinese academic centers in the rituximab era: a real-world Study[J]. Front Oncol. 2022;11:770988.

  23. Allen MG. Diagnostic immunohistochemistry: what can go wrong and how to prevent it. Arch Pathol Lab Med. 2016;140(9):893–8.

  24. Hao J, Zhang W, Lyu Y, Zou J, Zhang Y, Lyu J, et al. Combined use of Cyclin D1 and Ki67 for prognosis of Luminal-Like breast Cancer Patients[J]. Front Oncol. 2021;11:737794.

  25. Sergio C, Maurilio P, Andrés JM, Ferreri. Mantle cell lymphoma[J]. Crit Rev Oncol Hematol. 2020;153:103038.

    Article  Google Scholar 

Download references

Acknowledgements

This project received financial support from the Shandong Natural Science Foundation under the general project (No. ZR2022MH297) and from the Yantai Science and Technology Plan under project (No. 2021MSGY043). We express our gratitude to Xingwang Sun from the Department of Pathology, Southwest Medical University; Cuijuan Zhang from the Department of Pathology, Cheeloo College of Medicine, Shandong University; and Xiaoyan Lin from the Department of Pathology, Shandong Provincial Hospital affiliated with Shandong First Medical University, for their invaluable assistance in providing relevant case information. We also extend our appreciation to the patients’ relatives and families for granting us permission to share their medical history and clinical course.

Funding

This project is supported by the general project ZR2022MH297 of Shandong Natural Science Foundation and the project 2021MSGY043 of Yantai Science and Technology Plan.

Author information

Author notes

  1. Yan Yang and Liling Song contributed equally to this work.

Authors and Affiliations

  1. Departments of Pathology, Affiliated Yantai Yuhuangding Hospital, Qingdao University, 20 Yuhuangding East Road, Yantai, 264000, China

    Yan Yang, Ying Yin, Yuan Gao, Yunjun Wang, Shishou Wu, Jun Wang, Yu Pan, Xiaolong Sui, Lei Jiang & Guohua Yu

  2. The 2nd Medical College of Binzhou Medical University, 346 Guanhai Road, Yantai, China

    Liling Song

  3. Yantai Quanticision Diagnostics, Inc, No. 39, Keji Avenue, Yantai, 264003, China

    Yunyun Zhang

Authors
  1. Yan Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Liling Song
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ying Yin
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yuan Gao
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yunjun Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Shishou Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Jun Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Yu Pan
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Xiaolong Sui
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Lei Jiang
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Yunyun Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Guohua Yu
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

※Y. Yang and L. Song made qual contribution to this research.*Corresponding author: Y. Zhang (404269654@qq.com) and G. Yu (guohuayu@qdu.edu.cn).This project received financial support from the Shandong Natural Science Foundation under the general project (No. ZR2022MH297) and from the Yantai Science and Technology Plan under project (No. 2021MSGY043). We express our gratitude to Xingwang Sun from the Department of Pathology, Southwest Medical University; Cuijuan Zhang from the Department of Pathology, Cheeloo College of Medicine, Shandong University; and Xiaoyan Lin from the Department of Pathology, Shandong Provincial Hospital affiliated with Shandong First Medical University, for their invaluable assistance in providing relevant case information. We also extend our appreciation to the patients’ relatives and families for granting us permission to share their medical history and clinical course.

Corresponding authors

Correspondence to Yunyun Zhang or Guohua Yu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Song, L., Yin, Y. et al. Clinical significance of Cyclin D1 by complete quantification detection in mantle cell lymphoma: positive indicator in prognosis. Diagn Pathol 19, 149 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13000-024-01577-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13000-024-01577-z

Keywords

Diagnostic Pathology

ISSN: 1746-1596

Contact us