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Evaluating the diagnostic accuracy of heat shock proteins and their combination with Alpha-Fetoprotein in the detection of hepatocellular carcinoma: a meta-analysis



A growing body of research suggests that heat shock proteins (HSPs) may serve as diagnostic biomarkers for hepatocellular carcinoma (HCC), but their results are still controversial. This meta-analysis endeavors to evaluate the diagnostic accuracy of HSPs both independently and in conjunction with alpha-fetoprotein (AFP) as novel biomarkers for HCC detection.


Pooled statistical indices, including sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) with 95% confidence intervals (CI), were computed to assess the diagnostic accuracy of HSPs, AFP, and their combinations. Additionally, the area under the summary receiver operating characteristic (SROC) curve (AUC) was determined.


A total of 2013 HCC patients and 1031 control subjects from nine studies were included in this meta-analysis. The summary estimates for HSPs and AFP are as follows: sensitivity of 0.78 (95% CI: 0.69–0.85) compared to 0.73 (95% CI: 0.65–0.80); specificity of 0.89 (95% CI: 0.81–0.95) compared to 0.86 (95% CI: 0.77–0.91); PLR of 7.4 (95% CI: 3.7–14.9) compared to 5.1 (95% CI: 3.3–8.1); NLR of 0.24 (95% CI: 0.16–0.37) compared to 0.31 (95% CI: 0.24–0.41); DOR of 30.19 (95% CI: 10.68–85.37) compared to 16.34 (95% CI: 9.69–27.56); and AUC of 0.90 (95% CI: 0.87–0.92) compared to 0.85 (95% CI: 0.82–0.88). The pooled sensitivity, specificity, PLR, NLR, DOR and AUC were 0.90 (95% CI: 0.82–0.95), 0.94 (95% CI: 0.82–0.98), 14.5 (95% CI: 4.6–45.4), 0.11 (95% CI: 0.06–0.20), 133.34 (95% CI: 29.65–599.61), and 0.96 (95% CI: 0.94–0.98) for the combination of HSPs and AFP.


Our analysis suggests that HSPs have potential as a biomarker for clinical use in the diagnosis of HCC, and the concurrent utilization of HSPs and AFP shows notable diagnostic effectiveness for HCC.

Peer Review reports


Hepatocellular carcinoma (HCC) ranks second in cancer-related mortality rates globally, with approximately 90,000 new cases and over 800,000 cancer-related deaths reported worldwide in 2020, nearly half of which were in China [1,2,3]. Surgical resection and liver transplantation are the primary treatment options for early-stage HCC in current clinical practice [4, 5]. However, due to the absence of typical clinical symptoms and early warning signs, a significant proportion of patients are diagnosed at advanced stages, precluding potentially curative resection and resulting in a dismal 5-year survival rate [4,5,6]. Timely identification and effective intervention are essential factors in improving the prognosis of individuals diagnosed with HCC. Alpha-fetoprotein (AFP) serves as the primary screening and diagnostic biomarker for HCC but suffers from limited sensitivity, as approximately 40% of HCC patients have normal AFP levels, and only 20% of those with early-stage HCC exhibit elevated AFP levels [7, 8]. Hence, there is an urgent need for more accurate diagnostic biomarkers for early HCC detection.

Heat shock proteins (HSPs) are molecular chaperones that are ubiquitously present in archaea, fungi, and eukaryotes, exhibiting a high degree of conservation [9, 10]. These proteins are typically categorized into distinct groups based on their molecular weight, including HSP27, HSP40, HSP70, HSP90, HSP110, and chaperonins [11]. It has been observed that these proteins are integral in maintaining protein homeostasis by facilitating the proper folding and unfolding of proteins, and they also play crucial roles in the regulation of apoptosis [12,13,14,15]. Initially identified as intracellular chaperones, HSPs have also been detected in the extracellular environment. In extracellular spaces, HSPs have been linked to tumor invasiveness, tumor immunity, resistance to anti-tumor treatments, and unfavorable clinical outcomes, thereby playing a significant role in tumor progression and development [16,17,18]. Elevated levels of HSP expression have been observed in various human malignancies, including HCC, colorectal cancer, cervical cancer, breast cancer, prostate cancer, and lung cancer [19]. Several studies have demonstrated that HSPs are potential biomarkers for cancer diagnosis and prognosis [19,20,21]. Recently, a few studies estimated the diagnostic value of HSPs for detecting HCC, but the diagnostic accuracies are inconsistent and even conflicting [22,23,24,25,26,27,28,29,30]. Thus, we conducted this systematic review and meta-analysis to evaluate the diagnostic efficacy of HSPs for detecting HCC. Additionally, we also compared the diagnostic value of HSPs, AFP, and the combination of both based on the pooled statistical indicators.


This meta-analysis was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement [31], and the PRISMA checklist is shown in the Supplementary Table 1. The study protocol was registered on PROSPERO with registration number CRD42023442862.

Literature search

Unrestricted by language, year of publication, or publication status, a comprehensive search was conducted for relevant studies on the diagnostic utility of HSPs in detecting HCC up to January 1, 2024. This search encompassed multiple databases including PubMed, Cochrane Library, Web of Science, Embase, EBSCO, Scopus, Chinese National Knowledge Infrastructure (CNKI), Wan Fang, and VIP. Two researchers independently performed the search using specified search terms outlined in Supplementary Table 2, which included keywords such as "heat shock proteins" and "hepatocellular carcinoma." Additional eligible articles were identified by manually searching the references of included studies.

Inclusion and exclusion criteria

Two authors conducted a screening of relevant articles by reviewing titles and abstracts, followed by a thorough examination of the full-text based on predetermined inclusion and exclusion criteria. Any discrepancies were resolved through discussion with a third author to achieve a final consensus. The inclusion criteria for the present meta-analysis were as follows: 1) articles that evaluated the diagnostic value of serum or plasma HSPs in detection of HCC; 2) the HSPs were tested by enzyme-linked immunosorbent assay (ELISA); 3) the diagnosis of HCC was made on the basis of histopathology; 4) the sample size of patients and controls, true positive (TP), false positive (FP), true negative (TN) and false negative (FN) were reported or could be calculated; 5) study was published in English or Chinese with full-text available. In addition, the exclusion criteria were applied: Letters, reviews, conference abstracts, animal experiments, fundamental research, case reports and duplicated reports; sample size of case and control patients was less than 20.

Data extraction and quality assessment

Two authors independently extracted the following data: first author, publication year, country, sample size of patients and controls, control populations, cut-off values, assay method of the biomarkers, TP, FP, TN and FN.

The quality of included studies is assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool, which is reliable for quality assessment of diagnostic accuracy tests [32]. The QUADAS-2 tool comprises four domains: patient selection, index test, reference standard, and flow and trimming. Each domain is evaluated for risk of bias, with the first three domains also assessed for applicability. Key areas crucial for quality assessment include participant selection, blinding, and missing data. Risk of bias and applicability concerns are rated as "high," "unclear," or "low" by the QUADAS-2. Disagreements were resolved through discussion with a third investigator to achieve a final consensus in data extraction and quality assessment.

Statistical analysis

The meta-analysis was performed using Stata 16.0 and Review Manager 5.3 software. Threshold effect was assessed by Spearman correlation coefficient and P- value. P < 0.05 indicated the existence of a threshold effect. Heterogeneity induced by non-threshold effect was estimated using the I2 value and Cochran’s Q test, with I2 > 50% and P < 0.05 suggesting significant heterogeneity. When P > 0.05 and I2 < 50%, a fixed-effect model was used for meta-analysis, while a random-effect model was used. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) with 95% confidence interval (CI) were calculated and presented in the form of forest plots. Summary receiver operator characteristic (SROC) curves were plotted, and the area under curve (AUC) was calculated. The AUC was used for grading the overall diagnostic accuracy of HSPs and AFP in HCC. A diagnostic tool is described as perfect if AUC is 1.00, excellent if the AUC is greater than 0.90, good if it is greater than 0.80, moderate if it is less than 0.80 [33]. Subgroup analysis was conducted to further explore the source of heterogeneity. Furthermore, we planned to use funnel plots to assess the publication bias if there were greater than or equal to 10 included studies [34].


Characteristics and quality evaluation of included studies

As shown in Fig. 1, a total of 2083 relevant articles were retrieved based on our search strategy. Nine articles were enrolled in our meta-analysis, including 2013 patients and 1031 controls. All the studies were published between 2011 and 2021, with eight studies from China and one study from Italy [22,23,24,25,26,27,28,29,30]. The basic characteristic of included studies is shown in Table 1.

Fig. 1
figure 1

The flow chart of study selection

Table 1 Main characteristics and diagnostic performance of individual studies

The assessment of study quality was carried out using the QUADAS-2 tool, with the results summarized in Fig. 2. Regarding the patient selection domain, all studies were deemed to have an unclear risk of bias due to their case–control design [22,23,24,25,26,27,28,29,30]. Within the index test domain, two studies showed a high risk of bias [22, 30], with the remaining studies rated as unclear risk of bias [23,24,25,26,27,28,29]. None of the studies were found to have a high risk of bias in the reference standard or flow and timing domains. Further details of the assessment can be found in Supplementary Table 3.

Fig. 2
figure 2

Risk of bias assessment for each included study 

Meta-analysis of diagnostic efficacy

Nine studies were conducted to evaluate the diagnostic value of HSPs in HCC. Given the significant heterogeneity (sensitivity, I2 = 93.03% and specificity, I2 = 93.08%) among these studies, the random-effects model was utilized to synthesize the data, revealing no threshold effect (Spearman correlation coefficient: 0.59, P = 0.35). As depicted in Table 2, the pooled sensitivity and specificity were 0.78 (95% CI: 0.69- 0.85, I2 = 93.03%) and 0.89 (95% CI: 0.81- 0.95, I2 = 93.08%), respectively. The PLR and NLR were 7.4 (95% CI: 3.7- 14.9, I2 = 90.58%) and 0.24 (95% CI: 0.16- 0.37, I2 = 92.76%), respectively. The DOR of pooled studies was 30.19 (95% CI: 10.68- 85.37, I2 = 100%), and the AUC for SROC was 0.90 (95% CI: 0.87- 0.92), indicating good overall accuracy of HSPs for HCC. A Fagan nomogram was constructed to visually represent the diagnostic accuracy, demonstrating an increase in probability to 88% in patients with HSPs and a decrease to 20% in those without HSPs (Fig. 3A).

Table 2 Summary of the pooled diagnostic indices of heat shock proteins, alpha-fetoprotein and combination of both for hepatocellular carcinoma
Fig. 3
figure 3

Fagan diagram assessing the overall diagnostic value of heat shock proteins (A), alpha-fetoprotein (B) and heat shock proteins combination with alpha-fetoprotein (C) for hepatocellular carcinoma

In all of these studies, seven specifically investigated the diagnostic accuracy of AFP for HCC. The analysis revealed substantial heterogeneity (sensitivity, I2 = 84.36% and specificity, I2 = 86.18%) among the included studies, with no evidence of a threshold effect (Spearman correlation coefficient = -0.58, P = 0.33). The pooled sensitivity was 0.73 (95% CI: 0.65- 0.80, I2 = 84.36%), specificity was 0.86 (95% CI: 0.77- 0.91, I2 = 86.18%), PLR was 5.1 (95% CI: 3.3- 8.1, I2 = 71.66%), NLR was 0.31 (95% CI: 0.24- 0.41, I2 = 74.05%), and DOR was 16.34 (95% CI: 9.69- 27.56, I2 = 99.92%), respectively. The AUC for SROC was 0.85 (95% CI: 0.82- 0.88) (Table 2). Utilizing the Fagan plot, the likelihood of HCC diagnosis increased to 84% in patients with elevated AFP levels, while decreasing to 24% in those without elevated AFP levels, based on 50% of patients being diagnosed with HCC (Fig. 3B).

Furthermore, four studies assessed the diagnostic accuracy of the combination of HSPs and AFP, the pooled sensitivity was 0.90 (95%CI: 0.82- 0.95, I2 = 90.04%), specificity was 0.94 (95%CI: 0.82- 0.98, I2 = 92.53%), PLR was 14.5 (95%CI: 4.6- 45.4, I2 = 87.73%), NLR was 0.11 (95%CI: 0.06- 0.20, I2 = 89.85%), DOR was 133.34 (95%CI: 29.65- 599.61, I2 = 100%), and the corresponding AUC was 0.96 (95%CI: 0.94- 0.98) (Table 2). Additionally, the Fagan plot demonstrated that the combination of HSPs and AFP could increase the post-test probability to 94% in patients and decrease the post-test probability to 10% in patients with a pre-test probability of 50% (Fig. 3C), indicating a high overall accuracy of the combination of HSPs and AFP for detecting HCC.

Subgroup analysis

To investigate the heterogeneity resulting from the non-threshold effect, subgroup analysis was conducted based on various factors including control population, case sample size (≥ 100 or < 100), HSPs type, and specimen type. The findings of the subgroup analysis are presented in Table 3. None of the covariates mentioned above were found to contribute to heterogeneity in the HSPs group. However, in the AFP group, low heterogeneity was observed in the healthy control population group and the case sample size ≥ 100 group, with I2 values of 20.8% and 13.2%, respectively. This suggests that differences in control population and case sample size may be the underlying source of heterogeneity.

Table 3 Subgroup analysis of the included studies

Publication bias

Considering the small sample size (n < 10) in our meta-analysis, funnel plot analysis was not applicable for the determination of publication bias.


HCC poses a significant challenge to public health due to its high incidence and mortality rates, with a 5-year overall survival rate of less than 10% [1, 2, 5]. Prompt diagnosis plays a pivotal role for improving outcomes for individuals with HCC. AFP stands as the most extensively studied diagnostic biomarker for HCC, but its effectiveness is limited, with sensitivities ranging from 0.39 to 0.65 and specificities ranging from 0.76 to 0.97 [35, 36]. This hinders the utility of AFP in the diagnosis of HCC. In recent studies, alternative biomarkers such as des-γ-carboxy prothrombin (DCP), Glypican-3 (GPC-3), and Golgi protein 73 (GP73) have been utilized for the detection of HCC [37]. Zhao et al. conducted a meta-analysis to evaluate the diagnostic efficacy of GPC-3, resulting in a combined sensitivity of 0.59 and specificity of 0.93 in serum GPC-3 for HCC detection [38]. Another recent meta-analysis examined the diagnostic utility of GP73, revealing combined sensitivity, specificity, and AUC values of 0.79, 0.85, and 0.88, respectively [39]. Moreover, previous studies have reported that DCP exhibits sensitivities and specificities within the ranges of 0.61 to 0.77 and 0.70 to 0.82 [40, 41]. Despite advancements in diagnostic techniques over recent years, the timely detection of HCC continues to present challenges [42, 43]. So, there is a need to identify supplementary biomarkers that are closely associated with the progression of HCC in order to enhance the accuracy of diagnosis and treatment.

HSPs are ubiquitously present in biological cells [9]. Oncoproteins often rely on elevated levels of HSPs to sustain their functionality, with tumor cells exhibiting notably higher levels of HSPs compared to their normal counterparts in a range of cancers such as lung, colorectal, prostate cancers, and HCC [44,45,46,47,48]. Extensive research has been conducted in recent decades to elucidate the relationship between HSPs and tumor occurrence and progression, mainly focusing on HSP27, HSP70, and HSP90 in HCC [49,50,51,52,53,54,55]. HSP27, a member of the small HSP family, plays a critical role in the invasion and metastasis of HCC by binding to the N-terminus of AKT and connecting MAPK activated protein kinase 2 (MK2) to AKT, thereby regulating the synthesis of integrins α- Expression of 7 (ITGA7) and matrix metalloproteinase 2 (MMP2) [49, 50]. Zhang et al. demonstrated that elevated levels of HSP27 are associated with increased metastasis of HCC and established HSP27 as a valuable prognostic indicator for HCC outcomes [50]. Additionally, in the hypoxic and stressed tumor microenvironment of early-stage HCC, HSP70 is notably upregulated and may serve as a sensitive marker for precancerous lesions. Furthermore, in advanced stages of HCC, HSP70 expression is positively correlated with tumor size, portal and microvascular invasion, and inversely correlated with disease-free survival [51,52,53,54]. HSP90, a pivotal molecular chaperone, plays a crucial role in binding to the kinase SRPK2 and controlling the selective splicing of Numb PRR isoforms, ultimately facilitating HCC proliferation, invasion, and metastasis [55]. Overall, HSPs have a notable influence on the development of HCC, and may serve as a potential diagnosis biomarker for HCC.

In this meta-analysis, we systematically evaluated the diagnostic accuracy of HSPs, AFP, and the combination of HSPs with AFP in distinguishing HCC patients from non-HCC controls. To the best of our knowledge, this is the first systematic review and meta-analysis to estimate the diagnostic accuracy of HSPs and the combination of HSPs with AFP for HCC. We included nine studies with a total of 2013 patients in our analysis. The results of our study indicate that AFP exhibited a sensitivity of 0.73 and specificity of 0.86, with an AUC of 0.85. In comparison, HSPs demonstrated higher sensitivity (0.78), specificity (0.89), and AUC (0.90), suggesting that HSPs possess favorable diagnostic capabilities for distinguishing HCC patients from non-HCC controls. Our study supports the use of HSPs as an alternative to AFP for assessing HCC.

Due to the limitations of single biomarkers in accurately determining both sensitivity and specificity, the combination of multiple biomarkers holds significant potential for improving the diagnosis of HCC. A recent meta-analysis revealed that the combination of AFP and DCP can enhance diagnostic accuracy, with pooled sensitivity and specificity rates of 0.82 and 0.85, respectively, and AUC of 0.90 [56]. Additionally, Zhao et al. demonstrated that combining GPC-3 and AFP resulted in a pooled sensitivity of 0.71 and specificity of 0.91, with an AUC of 0.85 [38]. In a separate meta-analysis conducted in 2020, the combined use of AFP, AFP-L3, and DCP demonstrated a high diagnostic efficacy in discriminating HCC, with a pooled sensitivity of 0.88, specificity of 0.79, and an AUC of 0.91 [57]. In the current investigation, we evaluated the diagnostic value of combining HSPs with AFP, revealing that this combined approach significantly improved diagnostic accuracy, resulting in a sensitivity of 0.90, specificity of 0.94, and an AUC of 0.96. Our findings confirmed that the combination of HSPs and AFP has better diagnostic performance than other biomarkers alone or combination, and may also further provide a new insight into the diagnosis of HCC patients. Further investigation through clinical trials is necessary to validate the potential utility of HSPs, either in combination or alone, as a biomarker for diagnosing HCC.

There was considerable heterogeneity between the included studies in our meta-analysis. Initially, we identified the threshold effect through the Spearman correlation analysis, and none of the results exhibited the threshold effect. Subsequently, a subgroup analysis was conducted to explore the potential sources of heterogeneity. As shown in Table 3, the subgroup results of HSPs suggested that the I2 of most subgroups was still more than 50%, indicating that these factors were not the source of heterogeneity. For AFP, the subgroup results of AFP suggested that the I2 of healthy control population and case sample size ≥ 100 group was 20.8% and 13.2%, respectively, indicating the different control population and case sample size may be the source of heterogeneity.

Several limitations need to be acknowledged in this study. Firstly, a significant proportion (88.89%) of the studies included in the analysis originated from China, potentially restricting the generalizability of our findings. This skewed representation may be attributed to the high incidence of new cancer cases and related deaths in China [1, 2]. Secondly, the level of evidence was low, as all the included studies were case–control, which may introduce the potential for bias. Thirdly, inconsistencies in the cut-off values used across the included studies could introduce variability in the results. Therefore, as a biomarker, HSPs still need to be tested for detecting HCC in future studies to analyze the suitable cut-off value. Additionally, the diagnostic value of HSPs for HCC patients at varying pathological stages was not assessed in this study due to the absence of original research data, highlighting the need for further investigation on this matter. Furthermore, significant heterogeneity persisted in certain subgroups, emphasizing the necessity for additional research in this area.


In summary, our meta-analysis indicates that HSPs serve as accurate biomarkers suitable for clinical use in the diagnosis of HCC, and the combination of HSPs and AFP significantly enhances diagnostic value compared to HSPs or AFP alone. However, further research studies characterized by rigorous methodology, substantial sample sizes, and collaboration across multiple centers are imperative to gather more conclusive evidence regarding the diagnostic utility of HSPs and the combined use of HSPs and AFP in the early detection of HCC.

Availability of data and materials

All data and materials during this study are presented within the manuscript.



Heat shock proteins


Hepatocellular carcinoma




Diagnostic odds ratio


Positive likelihood ratio


Negative likelihood ratio


95% Confidence interval


Summary receiver operator characteristic


Area under curve


  1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33.

    Article  PubMed  Google Scholar 

  2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49.

    Article  PubMed  Google Scholar 

  3. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32.

    Article  PubMed  Google Scholar 

  4. Cauchy F, Soubrane O, Belghiti J. Liver resection for HCC: patient’s selection and controversial scenarios. Best Pract Res Clin Gastroenterol. 2014;28(5):881–96.

    Article  PubMed  Google Scholar 

  5. Wang H, Lu Z, Zhao X. Tumorigenesis, diagnosis, and therapeutic potential of exosomes in liver cancer. J Hematol Oncol. 2019;12(1):133.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Vogel A, Meyer T, Sapisochin G, Salem R, Saborowski A. Hepatocellular carcinoma. Lancet. 2022;400(10360):1345–62.

    Article  CAS  PubMed  Google Scholar 

  7. Malek NP, Schmidt S, Huber P, Manns MP, Greten TF. The diagnosis and treatment of hepatocellular carcinoma. Dtsch Arztebl Int. 2014;111(7):101–6.

    PubMed  PubMed Central  Google Scholar 

  8. Fong ZV, Tanabe KK. The clinical management of hepatocellular carcinoma in the United States, Europe, and Asia: a comprehensive and evidence-based comparison and review. Cancer. 2014;120(18):2824–38.

    Article  PubMed  Google Scholar 

  9. Burdon RH. The heat shock proteins. Endeavour. 1988;12(3):133–8.

    Article  CAS  PubMed  Google Scholar 

  10. Wu J, Liu T, Rios Z, Mei Q, Lin X, Cao S. Heat Shock Proteins and Cancer. Trends Pharmacol Sci. 2017;38(3):226–56.

    Article  CAS  PubMed  Google Scholar 

  11. Kampinga HH, Hageman J, Vos MJ, Kubota H, Tanguay RM, Bruford EA, et al. Guidelines for the nomenclature of the human heat shock proteins. Cell Stress Chaperones. 2009;14(1):105–11.

    Article  CAS  PubMed  Google Scholar 

  12. Hartl FU, Bracher A, Hayer-Hartl M. Molecular chaperones in protein folding and proteostasis. Nature. 2011;475(7356):324–32.

    Article  CAS  PubMed  Google Scholar 

  13. Murshid A, Eguchi T, Calderwood SK. Stress proteins in aging and life span. Int J Hyperthermia. 2013;29(5):442–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Neckers L, Blagg B, Haystead T, Trepel JB, Whitesell L, Picard D. Methods to validate Hsp90 inhibitor specificity, to identify off-target effects, and to rethink approaches for further clinical development. Cell Stress Chaperones. 2018;23(4):467–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Eguchi T, Sogawa C, Okusha Y, Uchibe K, Iinuma R, Ono K, et al. Organoids with cancer stem cell-like properties secrete exosomes and HSP90 in a 3D nanoenvironment. PLoS ONE. 2018;13(2): e0191109.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Balogi Z, Multhoff G, Jensen TK, Lloyd-Evans E, Yamashima T, Jäättelä M, et al. Hsp70 Interactions With Membrane Lipids Regulate Cellular Functions in Health and Disease. Prog Lipid Res. 2019;74:18–30.

    Article  CAS  PubMed  Google Scholar 

  17. Juhasz K, Lipp AM, Nimmervoll B, Sonnleitner A, Hesse J, Haselgruebler T, et al. The complex function of Hsp70 in metastatic cancer. Cancers (Basel). 2013;6(1):42–66.

    Article  PubMed  Google Scholar 

  18. Pfister K, Radons J, Busch R, Tidball JG, Pfeifer M, Freitag L, et al. Patient survival by Hsp70 membrane phenotype: association with different routes of metastasis. Cancer. 2007;110(4):926–35.

    Article  PubMed  Google Scholar 

  19. Albakova Z, Siam MKS, Sacitharan PK, Ziganshin RH, Ryazantsev DY, Sapozhnikov AM. Extracellular heat shock proteins and cancer: New perspectives. Transl Oncol. 2021;14(2): 100995.

    Article  CAS  PubMed  Google Scholar 

  20. Xiang D, Jiang M, Chen Y, Liu C, Li L. Clinicopathological and prognostic significance of heat shock proteins in hepatocellular carcinoma: a systematic review and meta-analysis. Front Oncol. 2023;13:1169979.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang C, Zhang Y, Guo K, Wang N, Jin H, Liu Y, et al. Heat shock proteins in hepatocellular carcinoma: Molecular mechanism and therapeutic potential. Int J Cancer. 2016;138(8):1824–34.

    Article  CAS  PubMed  Google Scholar 

  22. Zhang Q, Hou S, Tang X, Qin G, Liang Y. Diagnostic and prognostic value of plasma heat shock proteins 90α in patients with hepatocellular carcinoma. Oncology Progress. 2019;17(24):2965–8.

    Google Scholar 

  23. Cheng T, Tang Z. Clinical significance of AFP-L3, Hsp90α test in the diagnosis of hepatocellular carcinoma with low and medium concentration of AFP. Chin J Cancer Prev Treat. 2018;25(21):1511–4.

    Google Scholar 

  24. Tang Y, Li K, Cai Z, Xie Y, Tan X, Su C, Li J. HSP90α combined with AFP and TK1 improved the diagnostic value for hepatocellular carcinoma. Biomark Med. 2020;14(10):869–78.

    Article  CAS  PubMed  Google Scholar 

  25. Gruden G, Carucci P, Lolli V, Cosso L, Dellavalle E, Rolle E, et al. Serum heat shock protein 27 levels in patients with hepatocellular carcinoma. Cell Stress Chaperones. 2013;18(2):235–41.

    Article  CAS  PubMed  Google Scholar 

  26. Li X, Li J, Luo X, Hou Q, He S. Value of combined detection of serum GP73, HSP27 and AFP in the diagnosis of hepatitis B virus-associated early hepatocellular carcinoma. Pract Prev Med. 2016;23(11):1319–21.

    Google Scholar 

  27. Wei W, Liu M, Ning S, Wei J, Zhong J, Li J, et al. Diagnostic value of plasma HSP90α levels for detection of hepatocellular carcinoma. BMC Cancer. 2020;20(1):6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Han Y, Zhang Y, Cui L, Li Z, Feng H, Zhang Y, et al. Plasma heat shock protein 90alpha as a biomarker for the diagnosis of liver cancer: in patients with different clinicopathologic characteristics. World J Surg Oncol. 2021;19(1):228.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Fu Y, Xu X, Huang D, Cui D, Liu L, Liu J, et al. Plasma Heat Shock Protein 90alpha as a Biomarker for the Diagnosis of Liver Cancer: An Official, Large-scale, and Multicenter Clinical Trial. EBioMedicine. 2017;24:56–63.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wang J, Hu S, Pan D, Lu M, Li Z, Yang S, et al. The application of serum HSP27 on early diagnosis of hepatocellular carcinoma. Anat Res. 2011;33(3):194–7+207.

    CAS  Google Scholar 

  31. Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med. 2015;162:777–84.

    Article  PubMed  Google Scholar 

  32. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36.

    Article  PubMed  Google Scholar 

  33. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240(4857):1285–93.

    Article  CAS  PubMed  Google Scholar 

  34. Sterne JA, Sutton AJ, Ioannidis JP, Terrin N, Jones DR, Lau J, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343:d4002.

    Article  PubMed  Google Scholar 

  35. Oka H, Tamori A, Kuroki T, Kobayashi K, Yamamoto S. Prospective study of alpha-fetoprotein in cirrhotic patients monitored for development of hepatocellular carcinoma. Hepatology. 1994;19(1):61–6.

    Article  CAS  PubMed  Google Scholar 

  36. Lok AS, Sterling RK, Everhart JE, Wright EC, Hoefs JC, Di Bisceglie AM, et al. Des-gamma-carboxy prothrombin and alpha-fetoprotein as biomarkers for the early detection of hepatocellular carcinoma. Gastroenterology. 2010;138(2):493–502.

    Article  CAS  PubMed  Google Scholar 

  37. Chen F, Wang J, Wu Y, Gao Q, Zhang S. Potential Biomarkers for Liver Cancer Diagnosis Based on Multi-Omics Strategy. Front Oncol. 2022;12:822449.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zhao S, Long M, Zhang X, Lei S, Dou W, Hu J, et al. The diagnostic value of the combination of Golgi protein 73, glypican-3 and alpha-fetoprotein in hepatocellular carcinoma: a diagnostic meta-analysis. Ann Transl Med. 2020;8(8):536.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zhang X, Wu LN, Li XQ, Luo X, Liu SW, Zhang L, et al. Whether the Golgi protein 73 could be a diagnostic serological marker in hepatocellular carcinoma: a metaanalysis. BMC Gastroenterol. 2023;24(23(1)):85.

    Article  CAS  Google Scholar 

  40. Marrero JA, Feng Z, Wang Y, Nguyen MH, Befeler AS, Roberts LR, et al. Alpha-fetoprotein, des-gamma carboxyprothrombin, and lectin-bound alpha-fetoprotein in early hepatocellular carcinoma. Gastroenterology. 2009;137(1):110–8.

    Article  CAS  PubMed  Google Scholar 

  41. Caviglia GP, Abate ML, Petrini E, Gaia S, Rizzetto M, Smedile A. Highly sensitive alpha-fetoprotein, Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein and des-gamma-carboxyprothrombin for hepatocellular carcinoma detection. Hepatol Res. 2016;46(3):E130–5.

    Article  CAS  PubMed  Google Scholar 

  42. Yang JD, Heimbach JK. New advances in the diagnosis and management of hepatocellular carcinoma. BMJ. 2020;371:m3544.

    Article  PubMed  Google Scholar 

  43. Sartoris R, Gregory J, Dioguardi Burgio M, Ronot M, Vilgrain V. HCC advances in diagnosis and prognosis: Digital and Imaging. Liver Int. 2021;41(Suppl 1):73–7.

    Article  PubMed  Google Scholar 

  44. Asgharzadeh F, Moradi-Marjaneh R, Marjaneh MM. The Role of Heat Shock Protein 27 in Carcinogenesis and Treatment of Colorectal Cancer. Curr Pharm Des. 2022;28(32):2677–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Fu Z, Jia B. Advances in the role of heat shock protein 90 in prostate cancer. Andrologia. 2022;54(4):e14376.

    Article  CAS  PubMed  Google Scholar 

  46. Mittal S, Rajala MS. Heat shock proteins as biomarkers of lung cancer. Cancer Biol Ther. 2020;21(6):477–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Xiong J, Jiang XM, Mao SS, Yu XN, Huang XX. Heat shock protein 70 downregulation inhibits proliferation, migration and tumorigenicity in hepatocellular carcinoma cells. Oncol Lett. 2017;14(3):2703–8.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Ding Q, Sun Y, Zhang J, Yao Y, Huang D, Jiang Y. Utility and specificity of plasma heat shock protein 90 alpha, CEA, and CA199 as the diagnostic test in colorectal cancer liver metastasis. J Gastrointest Oncol. 2022;13(5):2497–504.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wu R, Kausar H, Johnson P, Montoya-Durango DE, Merchant M, Rane MJ. Hsp27 regulates Akt activation and polymorphonuclear leukocyte apoptosis by scaffolding MK2 to Akt signal complex. J Biol Chem. 2007;282(30):21598–608.

    Article  CAS  PubMed  Google Scholar 

  50. Zhang Y, Tao X, Jin G, Jin H, Wang N, Hu F, et al. A Targetable Molecular Chaperone Hsp27 Confers Aggressiveness in Hepatocellular Carcinoma. Theranostics. 2016;6(4):558–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Sheng X, Huang T, Qin J, Yang L, Sa ZQ, Li Q. Identification of the Differential Expression Profiles of Serum and Tissue Proteins During Rat Hepatocarcinogenesis. Technol Cancer Res Treat. 2018;17:1533034618756785.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Shin E, Ryu HS, Kim SH, Jung H, Jang JJ, Lee K. The clinicopathological significance of heat shock protein 70 and glutamine synthetase expression in hepatocellular carcinoma. J Hepatobiliary Pancreat Sci. 2011;18(4):544–50.

    Article  PubMed  Google Scholar 

  53. Kang GH, Lee BS, Lee ES, Kim SH, Lee HY, Kang DY. Prognostic significance of p53, mTOR, c-Met, IGF-1R, and HSP70 overexpression after the resection of hepatocellular carcinoma. Gut Liver. 2014;8(1):79–87.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Yu YQ, Wang L, Jin Y, Zhou JL, Geng YH, Jin X, et al. Identification of serologic biomarkers for predicting microvascular invasion in hepatocellular carcinoma. Oncotarget. 2016;7(13):16362–71.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Lu Y, Xu W, Ji J, Feng D, Sourbier C, Yang Y, et al. Alternative splicing of the cell fate determinant Numb in hepatocellular carcinoma. Hepatology. 2015;62(4):1122–31.

    Article  CAS  PubMed  Google Scholar 

  56. Chen H, Chen S, Li S, Chen Z, Zhu X, Dai M, et al. Combining des-gamma-carboxyprothrombin and alpha-fetoprotein for hepatocellular carcinoma diagnosing: an update meta-analysis and validation study. Oncotarget. 2017;8(52):90390–401.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Wang X, Zhang Y, Yang N, He H, Tao X, Kou C, et al. Evaluation of the Combined Application of AFP, AFP-L3%, and DCP for Hepatocellular Carcinoma Diagnosis: A Meta-analysis. Biomed Res Int. 2020;2020:5087643.

    PubMed  PubMed Central  Google Scholar 

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All authors contributed to this work. Study design and writing: DX and CL. Figures, tables and data analysis: DX and LF. Screening of abstracts and articles: LF, YY and CL. Manuscript critical review: YH. All authors have approved the final version of the manuscript.

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Xiang, D., Fu, L., Yang, Y. et al. Evaluating the diagnostic accuracy of heat shock proteins and their combination with Alpha-Fetoprotein in the detection of hepatocellular carcinoma: a meta-analysis. BMC Gastroenterol 24, 178 (2024).

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