Abstract
Background:
Anemia is one of the most common manifestations in patients with cancer. Recently, multiple studies have shown a positive correlation between pretreatment anemia and tumor prognosis. Yet, the relationship between pretreatment anemia and the prognosis of soft tissue sarcomas (STS) is unclear.
Methods:
We searched the PubMed and EMBASE databases to identify relevant studies. Eligible studies were included according to the inclusion criteria to assess the relationship between pretreatment anemia and the prognosis of patients with STS. Prognostic significance was determined by studying hazard ratios (HR) and 95% confidence intervals (CIs).
Results:
A total of 12 studies are included. If there is significant heterogeneity, a random-effects model is used. Pooled data indicated that pretreatment anemia is related to poor overall survival (HR = 2.13; 95%CI = 1.52–2.98), disease-specific survival (HR = 1.53; 95%CI = 1.20–1.96), and disease-free survival (HR = 1.55; 95%CI = 1.10–2.17). The results of the subgroup analysis also support this conclusion.
Conclusion:
Our results suggest that pretreatment anemia may be a prognostic biomarker for STS.
Keywords: anemia, meta-analysis, prognosis, soft tissue sarcoma
1. Introduction
Soft tissue sarcoma (STS) mainly derived from embryonic mesoderm is a heterogeneous tumor that includes more than 50 subtypes.[1] STS is a rare tumor that accounts for about 1% of adult tumors. Different STS subtypes may have various degrees of biological behavior.[2] Primary tumors can be removed by surgery, however, metastatic or recurrent tumors usually require a systematic treatment.[3] Despite the development of new targeted therapies, approximately 50% of patients die due to recurrence or metastasis.[4] Therefore, the prognosis of STS is poor. To improve clinical treatment and prognosis attempts to find better biomarkers have been made in the mean time; results showed that many new markers such as MDM2, P53, and PD-1/PD-L1 may have a clinical significance.[5–8] Recently, hematological biomarkers are highly appreciated, due to their convenience and cost-effectiveness.[9,10] Hematological biomarkers such as the neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein have been introduced into the clinic to guide treatment. However, certain limitations remain. For example, the sampling size of STS tissues may cause changes in counting results, and the level of C-reactive protein does not always reflect the disease severity and treatment response. According to the clinical observation, anemia is one of the most common manifestations in patients with cancer, and ∼40% of cancer patients suffer from anemia.[11] Hemoglobin, a hematological marker for anemia, has been widely used in the diagnosis of anemia in the clinic, as well as the prognosis of a variety of tumors. Anemia is related to the general type and pathological stage of gastric cancer and lung cancer. The pathological stage of gastric cancer and lung cancer can be roughly estimated based on the occurrence and degree of anemia.[12,13] Recently, studies have reported a positive relationship between anemia and the prognosis of STS.[14,15] A well-designed meta-analysis is a powerful weapon for obtaining comprehensive conclusions. Compared with reviewing different studies one by one, meta-analysis has obvious advantages, less subjective influence by the author, and less bias in conclusions. Therefore, we have performed this systematic review with meta-analysis to assess its prognostic value.
2. Materials and methods
2.1. Search strategies
A systematic review and meta-analysis were carried out according to the guidelines of “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) statement. Literature searches were performed in PubMed and EMBASE by the authors using the search terms “hemoglobin,” “anemia,” “soft tissue sarcoma,” “survival,” and “prognosis.” We retrieved studies published before December 2020. After screening the abstracts, the articles deemed relevant were cross-referenced for additional manuscripts.
2.2. Inclusion and exclusion criteria
Inclusion criteria: (1) studies performed in humans; (2) the relationship between preoperative anemia or hemoglobin levels and prognosis of STS was studied using overall survival (OS), disease-specific survival (DSS), and/or disease-free survival (DFS); (3) studies that include the hazard ratio (HR) with a 95% confidence intervals (CI) or provides sufficient data to calculate it; (4) published in English; (5) full-text available; and (6) patient size >30.
Exclusion criteria: (1) animal research, case reports, letters, and comments; (2) subjects include patients with osteogenic tumors; (3) overlapping or duplicate studies; and (4) studies only published as abstract or not in English were excluded.
2.3. Data extraction and quality assessment
Two investigators independently reviewed each eligible study and extracted the following data: first author, published year, country, number of patients, treatment, sarcoma stage, cutoff value, and prognostic value. The quality of each study was assessed using the Newcastle–Ottawa scale, and studies with a score greater than 6 are considered high-quality studies.[16] Subsequently, the correspondent author checked all the data and resolved the discrepancies through discussion.
2.4. Statistical analysis
The meta-analysis was conducted by Stata software 15.0 (Stata Corp. LLC, College Station, TX, USA). The HR and the corresponding 95% CI were calculated to investigate the relationship between preoperative anemia and STS. Heterogeneity between studies was assessed by Cochran Q test and I2 statistic, and random-effects models were used when significant heterogeneity existed.[17,18] Besides, we use subgroup analysis and sensitivity analysis to explore sources of heterogeneity. The publication bias was evaluated by funnel plots and Begg test.[19] A P value of less than .05 is considered statistically significant.
3. Results
3.1. Study selection and characteristics
Our search process is shown in PRISMA 2009 Flow Diagram. Initially, 341 potential kinds of literature are retrieved from PubMed and EMBASE. By removing duplicates, reading title and abstract, 317 articles that do not meet the inclusion criteria were further excluded. Then, 12 articles were excluded by reading the full text. Finally, 12 articles were included in our meta-research.[14,20–29]
The main characteristics of the 12 included studies are shown in Table 1. These studies were published between 2002 and 2020 with a total of 2445 patients. The median sample size of the study is 121, ranging from 47 to 403. Regarding the ethics of included studies, 8 are Caucasian, 3 are Asian, and 1 is mixed ethnic. Four studies are unable to get cutoff values, and 2 studies are treated with drugs. All studies are retrospective.
Table 1.
Baseline characteristics of studies included in the meta-analysis.
| References | Year | Country | Sample size | Treatment | Stage | Cutoff value | Outcome |
| Wang et al[14] | 2016 | USA | 54 | Mixed | Mixed | 10 | OS |
| Iqbal et al[27] | 2016 | India | 110 | Mixed | Metastatic | 10 | OS/DFS |
| Panotopoulos et al[21] | 2015 | Austria | 85 | Surgery | Mixed | 13.1 | OS/DSS |
| Szkandera et al[22] | 2014 | Austria | 367 | Surgery | Nonmetastatic | 13/12∗ | OS/DSS |
| Kasper et al[29] | 2013 | Multi-country | 343 | Drug | Metastatic | NA | OS/DFS |
| Willegger et al[24] | 2017 | Austria | 132 | Surgery | Mixed | NA | OS/DSS/DFS |
| Stefanovski et al[28] | 2012 | Italy | 376 | Mixed | Mixed | 10 | OS |
| Nakamura et al[23] | 2017 | UK | 376 | Surgery | Nonmetastatic | 13/12∗ | DSS |
| Nakamura et al[20] | 2017 | Japan | 47 | Mixed | Metastatic | 13/12∗ | DSS |
| Maretty-Kongstad et al[25] | 2017 | Denmark | 403 | Mixed | Nonmetastatic | NA | DSS |
| de Nonneville et al[26] | 2019 | France | 72 | Drug | Metastatic | 12 | OS/DFS |
| Mahyudin et al[41] | 2020 | Indonesia | 80 | Surgery | Nonmetastatic | NA | OS |
DFS = disease-free survival, DSS = disease-specific survival, NA = not available, OS = overall survival.
13 mg/L in male and 12 mg/L in female.
3.2. The prognostic value of pre-treatment anemia for OS/DSS/DFS
A total of 9 studies explored the relationship between pre-treatment anemia and the OS of patients with STS.[14,21,22,24,26–29,41] The pooled data shows that pre-treatment anemia is related to a poor OS (HR: 2.13, 95%CI: 1.52–2.98, P < .00001). A random-effect model was used due to the discovery of significant heterogeneity (I2 = 75.9%; Fig. 1).
Figure 1.
Forest plots of the prognostic effect of pretreatment anemia for (A) OS, (B) DSS, and (C) DFS. DFS = disease-free survival, DSS = disease-specific survival, OS = overall survival.
Six studies provide data on the relationship between pre-treatment anemia and DSS in patients with STS.[20–25] Our study shows that pre-treatment anemia also has a reliable prognostic value in DSS (HR: 1.53, 95%CI: 1.20–1.96, P = .02), and significant heterogeneity is detected between studies (I2 = 61%; Fig. 1).
Only 4 studies explored the prognostic significance of pre-treatment anemia in DFS.[24,26,27,29] The random-effects model shows that pretreatment anemia is related to poor DFS (HR: 1.55, 95%CI: 1.10–2.17, P = .01). Significant heterogeneity was observed (I2 = 60%; Fig. 1).
3.3. Subgroup analysis of pretreatment anemia
We conducted a large number of subgroup analysis and sensitivity analysis to find sources of heterogeneity. As shown in Table 2, each outcome had 3 subgroups including tumor stage, treatment, and ethnicity. Significant prognostic value was observed in most subgroup analyses, while the DSS mixed treatment group and several other subgroups did not show such prognostic value. Results of the sensitivity analysis are shown in Figure 2, and omitting each study, in turn, does not bring significant changes in the results.
Table 2.
Subgroup analysis of the prognostic value of HB.
| Survival analysis | No. of studies | I2 (%) | HR (95%CI) | P |
| OS | ||||
| Total | 9 | 75.9% | 2.13 (1.52–2.98) | P < .00001 |
| Treatment | ||||
| Surgery | 3 | 88% | 1.72 (1.08–2.73) | P = .02 |
| Mixed | 3 | 0% | 2.15 (1.46–3.16) | P < .0001 |
| Drug | 2 | 59% | 3.29 (1.37–7.92) | P = .008 |
| Stage | ||||
| Nonmetastatic | 1 | 7% | 2.63 (1.89–3.67) | P < .00001 |
| Metastatic | 3 | 38% | 2.56 (1.61–4.06) | P < .0001 |
| Mixed | 4 | 0% | 2.11 (1.52–2.92) P < .00001 I2 = 0% | P < .00001 |
| Ethnicity | ||||
| Asian | 1 | 0% | 2.02 (1.35–3.01) | P = .0006 |
| Caucasian | 7 | 81% | 2.16 (1.48–3.16) | P < .0001 |
| DSS | ||||
| Total | 6 | 61% | 1.53 (1.20–1.96) | P = .02 |
| Treatment | ||||
| Surgery | 4 | 52% | 1.55 (1.20–1.99) | P = .0006 |
| Mixed | 2 | 84% | 1.52 (0.65–3.54) | P = .33 |
| Stage | ||||
| Nonmetastatic | 3 | 0% | 2.23 (1.63–3.06) | P < .00001 |
| Metastatic | 1 | NA | 1.01 (0.69–1.48) | P = .95 |
| Mixed | 2 | 44% | 1.35 (1.08–1.70) | P = .008 |
| Ethnicity | ||||
| Asian | 1 | NA | 1.01 (0.69–1.48) | P = 0.95 |
| Caucasian | 5 | 56% | 1.52 (1.32–1.76) | P < .00001 |
| DFS | ||||
| Total | 4 | 60% | 1.55 (1.10–2.17) | P = .01 |
| Treatment | ||||
| Surgery | 1 | NA | 1.19 (1.03–1.37) | P = .02 |
| Mixed | 1 | NA | 1.52 (0.90–2.56) | P = .12 |
| Drug | 2 | 27% | 2.01 (1.34–3.01) | P = .0008 |
| Stage | ||||
| Metastatic | 3 | 2% | 1.81 (1.31–2.49) | P = .0003 |
| Mixed | 1 | NA | 1.19 (1.03–1.37) | P = .02 |
| Ethnicity | ||||
| Asian | 1 | NA | 1.52 (0.90–2.56) | P = .12 |
| Caucasian | 3 | 72% | 1.62 (1.02–2.59) | P = .04 |
DFS = disease-free survival, DSS = disease-specific survival, OS = overall survival.
Figure 2.
Sensitivity analysis of the prognostic effect of pretreatment anemia for (A) OS, (B) DSS, and (C) DFS. DFS = disease-free survival, DSS = disease-specific survival, OS = overall survival.
3.4. Publication bias
Figure 3 shows results of publication bias. The Begg P for OS, DSS, and DFS are 0.251, 0.452, and 0.308, so the publication bias in this meta-analysis is not significant.
Figure 3.
Begg’ funnel plots of the prognostic effect of pretreatment anemia for (A) OS, (B) DSS, and (C) DFS. DFS = disease-free survival, DSS = disease-specific survival, OS = overall survival.
4. Discussion
For cancer patients, the cause of anemia may be multifaceted.[11] Studies have shown that anemia is mostly caused by the tumor itself and diverse therapies.[30] For example, surgery frequently causes blood loss, and radiotherapy and chemotherapy could induce bone marrow inhibition, etc. However, the anemia before treatment is more likely caused by the tumor itself.
Many recent studies have shown that pre-treatment anemia is associated with poor survival in a variety of cancer patients.[31,32] However, the relationship between preoperative anemia and the prognosis of STS remains controversial. Our current results obtained from the meta-analysis of 12 studies including 2445 patients suggest that pre-treatment anemia is associated with the prognosis of STS. This conclusion is supported by the results of subgroup analysis. However, our results need to be interpreted with caution. In our study, the majority of patients had high-grade sarcomas. In addition, only a few patients have initial metastases. Therefore, the relationship between pretreatment anemia and the prognosis of patients with low-grade sarcoma and patients with primary metastases needs further study.
Although the exact mechanism between the treatment of anemia and survival of cancer patients is not clear, it might be explained by tumor hypoxia. As we know, anemia is the most important factor in tumor hypoxia, and tumor hypoxia will further lead to a series of adverse consequences.[33] First, tumor hypoxia increases tumor aggressiveness by stimulating angiogenesis.[11] Second, tumor hypoxia can slow down the cell cycle by reducing the formation of reactive oxygen species. This process promotes the resistance of tumors to radiation therapy. In addition, low blood flow in the tumor exacerbates the resistance to chemotherapy.[34] Third, hypoxia stabilizes hypoxia-inducible factor 1α and dimerizes with HIF-1β, and then binds to hypoxia response elements.[35] This process will activate the transcription of a variety of oncogenes. In addition to promoting the survival, proliferation, invasion, and metastasis of tumor cells, this process will also promote the generation of tumors with undifferentiated phenotypes.[33]
Anemia is a lack of hemoglobin and red blood cells. The harm it causes is that the normal physiological functions of red blood cells and hemoglobin cannot be exerted, resulting in tissue hypoxia. Tissue hypoxia is damage at the organ level, and its performance in the body is a series of changes in the normal physiological functions of the organs. Although anemia is often only a systemic manifestation, it will affect all tissues, organs, and organs throughout the body. For cancer patients, the decrease in hemoglobin causes a decrease in the blood's oxygen-carrying capacity, thereby reducing the tumor's arterial oxygen supply. Severe anemia leads to a very poor oxygenation state and aggravates the original hypoxia of the tumor. Hypoxia has direct damage and impact on artificial hematopoietic stem cells, and affects the proliferation and differentiation of artificial hematopoietic stem cells. Under the hypoxic environment, the lactate dehydrogenase of artificial hematopoietic stem cells leaks out, and the adenosine triphosphate content in the cells decreases. Mitochondria are the body's aerobic oxidation sites. Hypoxia reduces the body's ATP, which first affects the normal function of the oxygen-dependent enzyme system and cell membrane structure, cell damage, cell proliferation, and differentiation are inhibited.
According to the state of preoperative anemia, a personalized treatment plan should be developed. Correcting anemia can make surgery safer and allows patients to better tolerate the side effects of radiation and chemotherapy. Blood transfusion and erythropoietin have been widely used to improve anemia in patients. Unfortunately, although blood transfusion or erythropoietin stimulator can improve the symptoms of anemia, it does not improve the outcome of patients.[36] Recent studies have shown that perioperative blood transfusion does not improve outcomes of STS patients and is associated with all-cause mortality, cancer-related mortality, and relapse.[37,38] Similarly, the use of erythropoietin preparations does not improve patient survival and increases the risk of venous thrombosis in patients.[39,40] Yet, there are few related studies in STS, so it is necessary to study the application of these measures in STS.
The findings should be taken into account. First, since STS is a group of heterogeneous tumors containing more than 50 subtypes, there is some heterogeneity in our findings, even though we conducted a subgroup analysis. Therefore, we communicated with the authors of included studies via email. The effect of hypoxia on different subtypes is inherently heterogeneous. Nakamura et al found that pretreatment anemia was not associated with the prognosis of liposarcoma, while malignant fibrous histiocytoma was the opposite.[23,42] Patients may differ from study to study, so heterogeneity is acceptable. Second, the cutoff values between different studies are different. Third, all the studies included are retrospective. Therefore, the validation of our current results in more randomized controlled trials and studies on specific subtypes of STS are needed. There is also a need to explore reasonable interventions of anemia to benefit STS patients.
To the best of our knowledge, this is the first meta-analysis of the relationship between pretreatment anemia and the prognosis of STS. Nakamura et al[23] described that anemia was an adverse prognostic factor for disease-specific survival for malignant fibrous histiocytoma and other sarcomas, but not for liposarcoma. The focus of Maretty-Kongstad et al[25] research is that a battery of serum biomarkers comprised of 5 proinflammatory biomarkers integrated into a prognostic score named Aarhus Composite Biomarker Score is prognostic for localized nonmetastatic bone sarcomas even after adjusting for various confounders including comorbidity. To the best of our knowledge, only 1 similar sized study in STS patients reported a poor prognostic value of Hb levels.[23] Therefore, this study aims to verify the prognostic significance of preoperative Hb level on OS, DSS, and DFS of STS patients. We included enough studies and excluded the confounding factor of osteogenic tumors. This reinforces the advantages of our research. Our results suggest that pretreatment anemia is associated with poor prognosis in patients with STS. Therefore, pre-treatment anemia may serve as a hematological biomarker to predict the prognosis of STS. Clinicians may be able to pay more attention to these hematological markers.
5. Conclusions
Preconditioning anemia may be an important marker for predicting the prognosis of STS. A well-designed randomized controlled trial is needed to validate our results.
Author contributions
All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by Dean Chou, Yao Zhao, Shuhao Zhang, Limin Wang, and Min Zhang. The first draft of the manuscript was written by Landa Shi, Longqing Li, Yilin Liu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Conceptualization: Landa Shi, Longqing Li, Dean Chou, Yilin Liu.
Data curation: Landa Shi, Longqing Li, Yao Zhao, Shuhao Zhang, Limin Wang, Min Zhang.
Formal analysis: Landa Shi, Dean Chou, Yao Zhao, Shuhao Zhang, Limin Wang, Min Zhang.
Investigation: Yuqiang Wang.
Methodology: Landa Shi, Yilin Liu.
Project administration: Yuqiang Wang.
Resources: Yuqiang Wang, Yilin Liu.
Software: Landa Shi.
Supervision: Yilin Liu.
Validation: Yilin Liu.
Writing – original draft: Landa Shi, Longqing Li.
Writing – review & editing: Dean Chou.
Footnotes
Abbreviations: CI = confidence intervals, DFS = disease-free survival, DSS = disease-specific survival, HR = hazard ratios, LDH = lactate dehydrogenase, NLR = neutrophil-to-lymphocyte ratio, OS = overall survival, PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses, STS = soft tissue sarcomas.
How to cite this article: Shi L, Wang Y, Li L, Chou D, Zhao Y, Zhang S, Wang L, Zhang M, Liu Y. Prognostic value of pretreatment anemia in patients with soft tissue sarcoma: a meta-analysis. Medicine. 2021;100:37(e27221).
This article does not contain any studies with human participants or animals performed by any of the authors.
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
References
- [1].Clark MA, Fisher C, Judson I, Thomas JM. Soft-tissue sarcomas in adults. N Engl J Med 2005;353:701–11. [DOI] [PubMed] [Google Scholar]
- [2].Jo VY, Fletcher CD. WHO classification of soft tissue tumours: an update based on the 2013 (4th) edition. Pathology 2014;46:95–104. [DOI] [PubMed] [Google Scholar]
- [3].Fillon M. Surgery remains the best solution for patients with soft-tissue sarcomas. CA Cancer J Clin 2019;69:03–4. [DOI] [PubMed] [Google Scholar]
- [4].Cormier JN, Pollock S RE. Soft tissue sarcomas. CA Cancer J Clin 2004;54:94–109. [DOI] [PubMed] [Google Scholar]
- [5].Lahat G, Tuvin D, Wei C, et al. Molecular prognosticators of complex karyotype soft tissue sarcoma outcome: a tissue microarray-based study. Ann Oncol 2010;21:1112–20. [DOI] [PubMed] [Google Scholar]
- [6].Kandel RA, Yao X, Dickson BC, et al. Molecular analyses in the diagnosis and prediction of prognosis in non-GIST soft tissue sarcomas: a systematic review and meta-analysis. Cancer Treat Rev 2018;66:74–81. [DOI] [PubMed] [Google Scholar]
- [7].Que Y, Xiao W, Guan YX, et al. PD-L1 expression is associated with FOXP3+ regulatory T-cell infiltration of soft tissue sarcoma and poor patient prognosis. J Cancer 2017;8:2018–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Boxberg M, Steiger K, Lenze U, et al. PD-L1 and PD-1 and characterization of tumor-infiltrating lymphocytes in high grade sarcomas of soft tissue – prognostic implications and rationale for immunotherapy. Oncoimmunology 2018;7:e1389366. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Szkandera J, Gerger A, Liegl-Atzwanger B, et al. The lymphocyte/monocyte ratio predicts poor clinical outcome and improves the predictive accuracy in patients with soft tissue sarcomas. Int J Cancer 2014;135:362–70. [DOI] [PubMed] [Google Scholar]
- [10].Nakamura T, Grimer R, Gaston C, et al. The value of C-reactive protein and comorbidity in predicting survival of patients with high grade soft tissue sarcoma. Eur J Cancer 2013;49:377–85. [DOI] [PubMed] [Google Scholar]
- [11].Knight K, Wade S, Balducci L. Prevalence and outcomes of anemia in cancer: a systematic review of the literature. Am J Med 2004;116:11S–26S. [DOI] [PubMed] [Google Scholar]
- [12].Dai D, Han S, Li L, et al. Anemia is associated with poor outcomes of metastatic castration-resistant prostate cancer, a systematic review and meta-analysis. Am J Transl Res 2018;10:3877–86. [PMC free article] [PubMed] [Google Scholar]
- [13].Huang XZ, Yang YC, Chen Y, et al. Preoperative anemia or low hemoglobin predicts poor prognosis in gastric cancer patients: a meta-analysis. Dis Markers 2019;2019:7606128. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Wang Z, Shi N, Naing A, et al. Survival of patients with metastatic leiomyosarcoma: the MD Anderson Clinical Center for targeted therapy experience. Cancer Med 2016;5:3437–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Szkandera J, Gerger A, Liegl-Atzwanger B, et al. Pre-treatment anemia is a poor prognostic factor in soft tissue sarcoma patients. PloS One 2014;9:e107297. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Stang A. Critical evaluation of the Newcastle–Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol 2010;25:603–5. [DOI] [PubMed] [Google Scholar]
- [17].DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986;7:177–88. [DOI] [PubMed] [Google Scholar]
- [18].Mantel N, Haenszel##W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 1959;22:719–48. [PubMed] [Google Scholar]
- [19].Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994;50:1088–101. [PubMed] [Google Scholar]
- [20].Nakamura T, Katagiri H, Shido Y, et al. Analysis of factors for predicting survival in soft-tissue sarcoma with metastatic disease at initial presentation. Anticancer Res 2017;37:3137–41. [DOI] [PubMed] [Google Scholar]
- [21].Panotopoulos J, Posch F, Alici B, et al. Hemoglobin, alkalic phosphatase, and C-reactive protein predict the outcome in patients with liposarcoma. J Orthop Res 2015;33:765–70. [DOI] [PubMed] [Google Scholar]
- [22].Szkandera J, Gerger A, Liegl-Atzwanger B, et al. Pre-treatment anemia is a poor prognostic factor in soft tissue sarcoma patients. PLoS One 2014;9:e107297. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [23].Nakamura T, Grimer R, Gaston C, et al. The relationship between pretreatment anaemia and survival in patients with adult soft tissue sarcoma. J Orthop Sci 2013;18:987–93. [DOI] [PubMed] [Google Scholar]
- [24].Willegger M, Posch F, Schieder S, et al. Serum creatinine and albumin predict sarcoma-specific survival in patients with myofibroblastic and fibroblastic sarcomas. J Orthop Res 2017;35:2815–24. [DOI] [PubMed] [Google Scholar]
- [25].Maretty-Kongstad K, Aggerholm-Pedersen N, Keller J, Safwat A. A validated prognostic biomarker score for adult patients with nonmetastatic soft tissue sarcomas of the trunk and extremities. Transl Oncol 2017;10:942–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].de Nonneville A, Barbolosi D, Andriantsoa M, et al. Validation of neutrophil count as an algorithm-based predictive factor of progression-free survival in patients with metastatic soft tissue sarcomas treated with trabectedin. Cancers (Basel) 2019;11: [DOI] [PMC free article] [PubMed] [Google Scholar]
- [27].Iqbal N, Shukla NK, Deo SV, et al. Prognostic factors affecting survival in metastatic soft tissue sarcoma: an analysis of 110 patients. Clin Transl Oncol 2016;18:310–6. [DOI] [PubMed] [Google Scholar]
- [28].Stefanovski PD, Bidoli E, De Paoli A, et al. Prognostic factors in soft tissue sarcomas: a study of 395 patients. Eur J Surg Oncol 2002;28:153–64. [DOI] [PubMed] [Google Scholar]
- [29].Kasper B, Sleijfer S, Litière S, et al. Long-term responders and survivors on pazopanib for advanced soft tissue sarcomas: subanalysis of two European Organisation for Research and Treatment of Cancer (EORTC) clinical trials 62043 and 62072. Ann Oncol 2014;25:719–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30].Shander A, Knight K, Thurer R, Adamson J, Spence R. Prevalence and outcomes of anemia in surgery: a systematic review of the literature. Am J Med 2004;116:58S–69S. [DOI] [PubMed] [Google Scholar]
- [31].Kawase A, Inoue Y, Hirosoko M, Sugihara Y, Shimada H, Iwaki M. Decrease in multidrug resistance-associated protein 2 activities by knockdown of phosphatidylinositol 4-phosphate 5-kinase in hepatocytes and cancer cells. J Pharm Pharm Sci 2019;22:576–84. [DOI] [PubMed] [Google Scholar]
- [32].Pergialiotis V, Daskalakis G, Thomakos N, et al. Prechemotherapy hemoglobin levels as a predictive factor of ovarian cancer survival: a systematic review and meta-analysis. Am J Clin Oncol 2019;42:725–31. [DOI] [PubMed] [Google Scholar]
- [33].Harris AL. Hypoxia – a key regulatory factor in tumour growth. Nat Rev Cancer 2002;2:38–47. [DOI] [PubMed] [Google Scholar]
- [34].Vaupel P, Thews O, Hoeckel M. Treatment resistance of solid tumors: role of hypoxia and anemia. Med Oncol 2001;18:243–59. [DOI] [PubMed] [Google Scholar]
- [35].Tsai YP, Wu KJ. Hypoxia-regulated target genes implicated in tumor metastasis. J Biomed Sci 2012;19:102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [36].Yang J, Yu S, Yang Z, et al. Efficacy and safety of supportive care biosimilars among cancer patients: a systematic review and meta-analysis. BioDrugs 2019;33:373–89. [DOI] [PubMed] [Google Scholar]
- [37].Tsai YP, Wu KJ, Tsai YP, Wu KJ. Allogeneic blood transfusion and the prognosis of gastric cancer patients: systematic review and meta-analysis. Int J Surg 2015;13:102–10. [DOI] [PubMed] [Google Scholar]
- [38].Postlewait LM, Squires MH, Kooby DA, et al. The relationship of blood transfusion with peri-operative and long-term outcomes after major hepatectomy for metastatic colorectal cancer: a multi-institutional study of 456 patients. HPB (Oxford) 2016;18:192–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [39].Bohilus J, Schmidlin K, Brillant C, et al. Recombinant human erythropoiesis-stimulating agents and mortality in patients with cancer: a meta-analysis of randomised trials. Lancet (London, England) 2009;373:1532–42. [DOI] [PubMed] [Google Scholar]
- [40].Hoskin PJ, Robinson M, Slevin N, Morgan D, Harrington K, Gaffney C. Effect of epoetin alfa on survival and cancer treatment-related anemia and fatigue in patients receiving radical radiotherapy with curative intent for head and neck cancer. J Clin Oncol 2009;27:5751–6. [DOI] [PubMed] [Google Scholar]
- [41].Mahyudin F, Edward M, Basuki MH, et al. Analysis of prognostic factors in soft tissue sarcoma: cancer registry from a single tertiary hospital in Indonesia. A retrospective cohort study. Ann Med Surg 2020;57:257–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [42].Shi L, Chou D, Wang Y, et al. Efficacy of computed tomography-assisted limited decompression in the surgical management of thoracolumbar fractures with neurological deficit. J Orthop Surg Res 2021;16:263. [DOI] [PMC free article] [PubMed] [Google Scholar]