Hyperresistinemia and metabolic dysregulation: a risky crosstalk in obese breast cancer

Cancer-cell-secreted miR-204-5p induces leptin signalling pathway in white adipose tissue to promote cancer-associated cachexia

Cancer-associated cachexia is a multi-organ weight loss syndrome, especially with a wasting disorder of adipose tissue and skeletal muscle. Small extracellular vesicles (sEVs) serve as emerging messengers to connect primary tumour and metabolic organs to exert systemic regulation. However, whether and how tumour-derived sEVs regulate white adipose tissue (WAT) browning and fat loss is poorly defined. Here, we report breast cancer cell-secreted exosomal miR-204-5p induces hypoxia-inducible factor 1A (HIF1A) in WAT by targeting von Hippel-Lindau (VHL) gene. Elevated HIF1A protein induces the leptin signalling pathway and thereby enhances lipolysis in WAT. Additionally, exogenous VHL expression blocks the effect of exosomal miR-204-5p on WAT browning. Reduced plasma phosphatidyl ethanolamine level is detected in mice lack of cancer-derived miR-204-5p secretion in vivo. Collectively, our study reveals circulating miR-204-5p induces hypoxia-mediated leptin signalling pathway to promote lipolysis and WAT browning, shedding light on both preventive screenings and early intervention for cancer-associated cachexia.

On the Applicability of Quantum Machine Learning

In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE). We provide a first evaluation on the performance of these classifiers when using a hyperparameter search on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples on two artificially generated test classification datasets. As quantum machine learning is based on unitary transformations, this paper explores data structures and application fields that could be particularly suitable for quantum advantages. Hereby, this paper introduces a novel dataset based on concepts from quantum mechanics using the exponential map of a Lie algebra. This dataset will be made publicly available and contributes a novel contribution to the empirical evaluation of quantum supremacy. We further compared the performance of VQC and QKE on six widely applicable datasets to contextualize our results. Our results demonstrate that the VQC and QKE perform better than basic machine learning algorithms, such as advanced linear regression models (Ridge and Lasso). They do not match the accuracy and runtime performance of sophisticated modern boosting classifiers such as XGBoost, LightGBM, or CatBoost. Therefore, we conclude that while quantum machine learning algorithms have the potential to surpass classical machine learning methods in the future, especially when physical quantum infrastructure becomes widely available, they currently lag behind classical approaches. Our investigations also show that classical machine learning approaches have superior performance classifying datasets based on group structures, compared to quantum approaches that particularly use unitary processes. Furthermore, our findings highlight the significant impact of different quantum simulators, feature maps, and quantum circuits on the performance of the employed quantum estimators. This observation emphasizes the need for researchers to provide detailed explanations of their hyperparameter choices for quantum machine learning algorithms, as this aspect is currently overlooked in many studies within the field. To facilitate further research in this area and ensure the transparency of our study, we have made the complete code available in a linked GitHub repository.

Endocrine Disruptors in Food, Estrobolome and Breast Cancer

The microbiota is now recognized as one of the major players in human health and diseases, including cancer. Regarding breast cancer (BC), a clear link between microbiota and oncogenesis still needs to be confirmed. Yet, part of the bacterial gene mass inside the gut, constituting the so called "estrobolome", influences sexual hormonal balance and, since the increased exposure to estrogens is associated with an increased risk, may impact on the onset, progression, and treatment of hormonal dependent cancers (which account for more than 70% of all BCs). The hormonal dependent BCs are also affected by environmental and dietary endocrine disruptors and phytoestrogens which interact with microbiota in a bidirectional way: on the one side disruptors can alter the composition and functions of the estrobolome, ad on the other the gut microbiota influences the metabolism of endocrine active food components. This review highlights the current evidence about the complex interplay between endocrine disruptors, phytoestrogens, microbiome, and BC, within the frames of a new "oncobiotic" perspective.

The first combined meta‐analytic approach for elucidating the relationship of circulating resistin levels and RETN gene polymorphisms with colorectal and breast cancer

Background

Evidence suggests that circulating resistin levels are altered in colorectal cancer (CRC) and breast cancer (BC). Again, polymorphisms in resistin-encoding gene RETN have been evaluated in CRC and BC. However, there is a scarcity of data establishing the relationship of resistin and RETN polymorphisms (rs1862513 and rs3745367) with these cancers. This study aimed to analyze the relationship of resistin levels and RETN polymorphisms with CRC and BC in a combined meta-analytic approach.

Main body of the abstract

After a comprehensive online literature search, screening and eligibility check, 41 articles (31 with resistin level and 10 with RETN polymorphisms) were retrieved for meta-analyses. The mean difference (MD) of resistin was calculated and pooled to investigate the effect sizes with a 95% confidence interval (CI), and the connection of genetic polymorphisms was analyzed with an odds ratio (OR) and 95% CI. The analysis showed that resistin level is significantly higher in CRC (MD = 3.39) and BC (MD = 3.91) patients. Subgroup analysis in CRC showed significantly higher resistin in serum (MD = 4.61) and plasma (MD = 0.34), and in BC, a significantly elevated resistin level was reported in premenopausal (MD = 7.82) and postmenopausal (MD = 0.37) patients. Again, RETN rs1862513 showed a significantly strong association with CRC (codominant 1—OR 1.24, codominant 2—OR 1.31, dominant model—OR 1.25, and allele model—OR 1.16) and with BC (codominant 2—OR 1.51, codominant 3—OR 1.51, recessive model—OR 1.51, and allele model—OR 1.21). RETN rs3745367 did not show any association with these cancers.

Short conclusion

Overall, our analysis indicates that higher circulating resistin levels are associated with an elevated risk of CRC and premenopausal and postmenopausal BC. Besides, rs1862513 in RETN gene is significantly connected with both CRC and BC.

Classification models for likelihood prediction of diabetes at early stage using feature selection

Purpose

Diabetes is one of the life-threatening chronic diseases, which is already affecting 422m people globally based on (World Health Organization) WHO report as at 2018. This costs individuals, government and groups a whole lot; right from its diagnosis stage to the treatment stage. The reason for this cost, among others, is that it is a long-term treatment disease. This disease is likely to continue to affect more people because of its long asymptotic phase, which makes its early detection not feasible.

Design/methodology/approach

In this study, the authors have presented machine learning models with feature selection, which can detect diabetes disease at its early stage. Also, the models presented are not costly and available to everyone, including those in the remote areas.

Findings

The study result shows that feature selection helps in getting better model, as it prevents overfitting and removes redundant data. Hence, the study result when compared with previous research shows the better result has been achieved, after it was evaluated based on metrics such as F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. This discovery has the potential to impact on clinical practice, when health workers aim at diagnosing diabetes disease at its early stage.

Originality/value

This study has not been published anywhere else.

Obesity-related protein biomarkers for predicting breast cancer risk: an overview of systematic reviews

BACKGROUND

Based on the biological mechanisms underlying the obesity-breast cancer connections, potential protein biomarkers involved in breast cancer development have been identified, which may be helpful for the estimation of breast cancer risk. This study aimed to carry out a comprehensive overview of systematic reviews on circulating levels of obesity-related protein biomarkers for female breast cancer risk to provide a solid reference for potential breast cancer predictors.

METHODS

Comprehensive literature searches were conducted in MEDLINE, EMBASE and Cochrane Database of Systematic Reviews up to Dec 2019. The AMSTAR tool was used for the methodological quality assessment of the included systematic reviews. Evidence was reported narratively.

RESULTS

A total of 28 relevant systematic reviews which were mostly of moderate quality were included in the overview. Protein biomarkers relating to adipokines, insulin/insulin-like growth factor-1 (IGF-1) axis, inflammatory cytokines and sex hormones were investigated. Higher levels of circulating IGF-1, IGF-binding protein-3, leptin and resistin were found to be associated with an increased risk of premenopausal breast cancer; lower levels of circulating adiponectin and higher levels of circulating c-reactive protein, leptin, and resistin were found to be associated with an increased risk of postmenopausal breast cancer.

CONCLUSIONS

We found sufficient evidence on the positive associations between certain obesity-related protein biomarkers with pre- and/or postmenopausal breast cancer risk. These biomarkers could be used jointly as predictors, so as to build a comprehensive risk predictive score for female breast cancer.

PROSPERO REGISTRATION NUMBER

CRD42020175328.

Adipocytokines visfatin and resistin in breast cancer: Clinical relevance, biological mechanisms, and therapeutic potential

Obesity is one of the major modifiable risk factors in breast cancer, with obese adipose tissue showing a pathological role in breast cancer development and malignancy via the release of secretory factors, such as proinflammatory cytokines and adipocytokines. The current article focuses on visfatin and resistin, two such adipocytokines that have emerged over the last two decades as leading breast cancer promoting factors in obesity. The clinical association of circulating visfatin and resistin with breast cancer and their biological mechanisms are reviewed, in addition to their role in the context of tumor-stromal interactions in the breast cancer microenvironment. Recent findings have unraveled several mediators of visfatin and resistin that are involved in the crosstalk between breast cancer cells and adipose tissue in the breast tumor microenvironment, including growth differentiation factor 15 (GDF15), interleukin 6 (IL-6), and toll-like receptor 4 (TLR4). Finally, current therapeutics targeting visfatin and resistin and their respective pathways are discussed, including future therapeutic strategies such as new drug design or neutralizing peptides that target extracellular visfatin or resistin. These hold promise in the development of novel breast cancer therapies and are of increasing relevance as the prevalence of obesity-related breast cancer increases worldwide.

Elucidation of molecular links between obesity and cancer through microRNA regulation

Background

Obesity contributes to high cancer risk in humans and the mechanistic links between these two pathologies are not yet understood. Recent emerging evidence has associated obesity and cancer with metabolic abnormalities and inflammation where microRNA regulation has a strong implication.

Methods

In this study, we have developed an integrated framework to unravel obesity-cancer linkage from a microRNA regulation perspective. Different from traditional means of identifying static microRNA targets based on sequence and structure properties, our approach focused on the discovery of context-dependent microRNA-mRNA interactions that are potentially associated with disease progression via large-scale genomic analysis. Specifically, a meta-regression analysis and the integration of multi-omics information from obesity and cancers were presented to investigate the microRNA regulation in a dynamic and systematic manner.

Results

Our analysis has identified a total number of 2,143 unique microRNA-gene interactions in obesity and seven types of cancer. Common interactions in obesity and obesity-associated cancers are found to regulate genes in key metabolic processes such as fatty acid and arachidonic acid metabolism and various signaling pathways related to cell growth and inflammation. Additionally, modulated co-regulations among microRNAs targeting the same functional processes were reflected through the analysis.

Conclusion

We demonstrated the statistical modeling of microRNA-mediated gene regulation can facilitate the association study between obesity and cancer. The entire framework provides a powerful tool to understand multifaceted gene regulation in complex human diseases that can be generalized in other biomedical applications.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12920-020-00797-8).

Hyperresistinemia in Obese Female Dogs With Mammary Carcinoma in Benign-Mixed Tumors and Its Correlation With Tumor Aggressiveness and Survival

Resistin is associated with metabolic, inflammatory, and neoplastic disorders, and is also considered a prognostic marker in human oncology. Canine mammary tumors have epidemiological, clinical, biological, and genetic characteristics similar to those of women and are proposed as a comparative study model. Here, we evaluate the serum levels of resistin in female dogs with or without mammary carcinoma in mixed tumors (CBMT) and its correlation with the proliferative potential of the tumor, obesity, and survival. Eighty dogs grouped according to the presence (50) or absence (30) of CBMT, reproductive status and body condition were assessed for weight, fat percentage, and canine body mass index. The characteristic of the proliferative potential of the tumor (Ki-67) was evaluated. Ki-67 levels (p = 0.024), staging (p = 0.004), and grade (p = 0.016) influenced the survival of the female dogs. Through a multifactorial analysis, it could be seen that the parameters proliferation index (Ki-67) (p = 0.044) and staging (p = 0.036) influenced the survival of the animals. Neutered and overweight dogs from the control and CBMT groups showed hyperresistinemia. Ki-67 expression and resistin levels in dogs with CBMT were higher in overweight dogs than in dogs with normal weight (p = 0.0001). The survival rate of dogs with CBMT, obese and with high levels of resistin (8,400 μg L⁻¹) was lower when compared to those with lower levels of resistin. These results showed an important relationship between hyperresistinemia, tumor proliferative potential and excessive body fat, suggesting that resistin levels may act as an interesting prognostic marker in patients with CBMT.

Circulating obesity-driven biomarkers are associated with risk of clear cell renal cell carcinoma: a two-stage, case-control study

Obesity is one of modifiable risk factors for clear cell renal cell cancer (ccRCC). We aim to identify the association between obesity-driven biomarkers and ccRCC risk. This is a retrospective, two-phase, case-control study involving 682 cases and 733 controls. Obesity-driven biomarkers [gastric inhibitory polypeptide (GIP), C-peptide, insulin, resistin, adipsin, peptide YY, pancreatic polypeptide, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), plasminogen activator inhibitor-1, monocyte chemoattractant protein 1, lipocalin2, leptin, adiponectin] were measured using the Milliplex method. Multivariate logistic regression was used to assess the associations between biomarkers and ccRCC risk. Results revealed that GIP, C-peptide, IL-6 and TNF-α levels were consistently distinct between cases and controls. These markers were significantly associated with ccRCC risk in both phases (except C-peptide). In the combined population, compared with individuals with low levels of the biomarkers, individuals with high level of GIP [odds ratio (OR) = 0.52, 95% confidence interval (CI): 0.40-0.67] had lower risk, whereas individuals with high levels of C-peptide (OR = 1.46, 95% CI: 1.15-1.87), IL-6 (OR = 2.20, 95% CI: 1.50-3.22), TNF-α (OR = 1.90, 95% CI: 1.49-2.43) had significantly higher risk. Stratified analysis showed consistent associations with ccRCC risk in most subgroups (P < 0.05). The risk score based on the IL-6, TNF-α and GIP was positively associated with ccRCC risk in a dose-response manner (P for trend = 2.18E-13). Data from The Cancer Genome Atlas indicate that insulin signaling, IL-6 signaling and TNF-α signaling were enhanced in tumors. Collectively, our study demonstrates the integrative effect of insulin resistance and inflammation in ccRCC development, which may elucidate the basis of association between obesity and carcinogenesis. Further confirmation in prospective cohort studies are warranted for clinical applications in prevention and precision medicine of ccRCC.

Association of serum leptin with breast cancer: A meta-analysis

BACKGROUND

Accumulating evidence has demonstrated that leptin is associated to the tumorigenesis and progression of breast cancer (BC). However, these studies remain inconsistent. Thus, a meta-analysis was conducted to investigate the role of leptin in the patients with BC.

METHOD

A systematic search in PubMed, Embase, ISI Web of Science, and Chinese National Knowledge Infrastructure (CNKI) databases was conducted up to September 1, 2017. The standardized mean difference (SMD) with 95% confidence interval (CI) was applied to pool the effect size. A funnel plot and Egger test were used to evaluate publication bias.

RESULTS

Finally, 43 eligible studies were included in the current meta-analysis. Overall, serum leptin levels in BC cases were significantly higher compared with the controls (SMD = 0.61, P <.0001). When subgroup analyses were restricted to ethnicity and menstrual status, higher serum leptin concentration was also detected in patients with BC. Moreover, BC cases with body mass index (BMI) >25 indicated significantly higher serum leptin levels (SMD = 1.48, P = .034). Furthermore, the BC cases with lymph node metastases showed significantly higher serum leptin concentration (SMD = 0.53, P = .015).

CONCLUSION

The present meta-analysis suggests that the serum leptin may profiles as a pivotal role in the pathogenesis and metastasis of BC. In addition, leptin will provide useful information for a therapeutic target to treat BC.

Association of serum adiponectin with breast cancer: A meta-analysis of 27 case-control studies

BACKGROUND

Emerging published studies have indicated that adiponectin is involved in tumorigenesis of breast cancer. However, the results of available studies were inconsistent. The aim of this updated meta-analysis was to assess the association of adiponectin with breast cancer.

MATERIALS AND METHODS

PubMed, EMBASE, Wanfang databases, and the China National Knowledge Infrastructure (CNKI) were systematically searched from inception to June 2018. The mean difference (MD) with 95% confidence interval (CI) were estimated and pooled to investigate the effect sizes.

RESULTS

Twenty-seven eligible articles that met the study criteria were included in the current meta-analysis. Overall, there was an evident inverse association between serum adiponectin levels and breast cancer (MD = -0.29, 95%CI = (-0.38, -0.21), P < .001). Asian subgroup showed a significant negative association between serum adiponectin concentrations and breast cancer in subgroup analysis by ethnicity (MD = -2.19, 95%CI = (-3.45, -0.94), P < .001). However, no statistical significance was found in Caucasian subgroup (MD = -0.65, 95%CI = (-1.47, 0.17), P = 0.12). Additionally, a further subgroup analysis of Asian stratified by menopausal status showed higher concentrations of adiponectin in healthy control group, whether they were premenopausal (MD = -0.85, 95%CI = (-1.50, -0.19), P = .01) or postmenopausal (MD = -2.17, 95%CI = (-4.17, -0.18), P = .03). No significant difference was observed concerning the association between serum adiponectin and breast cancer metastasis (MD = -1.56, 95%CI = (-4.90, 1.78), P = .36).

CONCLUSION

The current meta-analysis suggests that the serum adiponectin may be inversely associated with breast cancer. Decreased serum adiponectin levels in premenopausal women may also be inversely associated with breast cancer risk other than postmenopausal status. In addition, low serum adiponectin levels in Asian women were more likely to be associated with breast cancer risk than Caucasian women.

Serum adiponectin in breast cancer: A meta-analysis

BACKGROUND

Accumulating data have found that adiponectin is involved in development of breast cancer (BC). However, these results were inconsistent.

METHOD

A systematic search in PubMed, Embase, ISI Web of Science, and Chinese National Knowledge Infrastructure databases were conducted up to October 1, 2017. The standardized mean difference (SMD) with 95% confidence interval was applied to pool the effect size.

RESULTS

Finally, 31 eligible studies were included in this meta-analysis. The overall results indicated that serum adiponectin levels in BC cases were significantly lower than the controls (SMD = -0.33, P < 0.0001). As for the subgroup analysis of menstrual status, serum adiponectin levels were significantly lower in pre- and postmenopausal BC cases. Moreover, the subgroup analysis by ethnicity in pre- and postmenopausal group indicated an inverse association between adiponectin levels and BC risk in Asian population, but not in Caucasian population.

CONCLUSION

The present meta-analysis suggests that low serum adiponectin concentration may be associated with an increased BC risk in premenopausal and postmenopausal women, especially among Asians. Adiponectin may serve as a biomarker of BC risk and help to identify subjects at high risk for BC development.

Association between serum leptin levels and breast cancer risk: An updated systematic review and meta-analysis

BACKGROUND

Many studies have indicated that leptin is correlated with breast cancer occurrence and tumor behavior. However, this issue remains controversial. Therefore, we conducted an updated meta-analysis to investigate the role of leptin in breast cancer.

METHODS

We performed a systematic literature search and identified relevant papers up to 1 September 2017. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were used to evaluate effect sizes.

RESULTS

Thirty-five eligible studies were included in the current meta-analysis. Serum leptin levels were related to breast cancer risk as demonstrated by calculations of the overall SMD = 0.46 (95% CI = 0.31-0.60, I = 93.5%). A subgroup analysis of BMI identified an association between breast cancer and serum leptin levels in patients who are overweight and obese (overweight: SMD = 0.35, 95% CI = 0.13-0.57, I = 88.1%; obesity: SMD = 1.38, 95% CI = 0.64-2.12, I = 89.6%). Additionally, menopausal status subgroup analysis revealed a significant association in postmenopausal women (SMD = 0.26, 95% CI = 0.12-0.40, I = 77.9%). Furthermore, we identified a significant association between breast cancer and serum leptin levels in Chinese women (SMD = 0.61, 95% CI = 0.44-0.79, I = 40.6%).

CONCLUSION

The results of this meta-analysis suggested that leptin could be a potential biomarker for breast cancer risk in women, especially overweight/obese or postmenopausal women. Therefore, it may be useful for identifying subjects with a high risk for breast cancer who may benefit from preventive treatments.

Association of RETN and CAP1 SNPs, Expression and Serum Resistin Levels with Breast Cancer in Mexican Women

BACKGROUND

Breast cancer is the most common cancer in women worldwide. Approximately 70% of female breast cancer patients have a body mass index (BMI) >25. In obesity, adipose tissue secretes additional resistin, which prompts a proinflammatory effect through its action on adenylate cyclase-associated protein 1 (CAP1). Several studies have associated the RETN gene single nucleotide polymorphism (SNP) rs1862513 (-420C<G) with serum resistin levels and breast cancer. The CAP1 gene SNP rs35749351 (missense, Arg294His), located in the extracellular domain, has not previously been studied in cancer. These two SNPs, the mRNA expression levels of the two alleles for each of the cognate genes, and the serum resistin levels were compared between patients and controls to determine their association with breast cancer in Mexican women in this study.

MATERIALS AND METHODS

This study included 308 controls and 100 female patients with breast cancer. SNPs were detected by PCR-RFLP from DNA isolated from peripheral blood. Gene expression was performed with hydrolysis probes in tumor tissue. Resistin levels were quantified from serum samples by ELISA.

RESULTS

The RETN rs1862513CG/GG and CAP1 rs35749351GA/AA genotypes were associated with 1.61 and 2.193-fold increased risks of breast cancer, respectively, compared with the CC and GG genotypes. Similarly, carriers of the G allele of rs1862513 and the A allele of rs35749351, had 1.51 and 2.217-fold increased risks of breast cancer compared with the C and G alleles, respectively. The rs1862513GG/rs35749351AA genotype combination increased breast cancer risk by twofold. Serum resistin levels in postmenopausal breast cancer women were higher compared with postmenopausal controls. Tissue CAP1 expression showed differences with regard to molecular subtypes and metastases.

CONCLUSION

The RETN and CAP1 polymorphisms and gene expression may be potential biomarkers for breast cancer risk.

Using Resistin, glucose, age and BMI to predict the presence of breast cancer

Background

The goal of this exploratory study was to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis.

Methods

For each of the 166 participants several clinical features were observed or measured, including age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, Resistin and MCP-1. Machine learning algorithms (logistic regression, random forests, support vector machines) were implemented taking in as predictors different numbers of variables. The resulting models were assessed with a Monte Carlo Cross-Validation approach to determine 95% confidence intervals for the sensitivity, specificity and AUC of the models.

Results

Support vector machines models using Glucose, Resistin, Age and BMI as predictors allowed predicting the presence of breast cancer in women with sensitivity ranging between 82 and 88% and specificity ranging between 85 and 90%. The 95% confidence interval for the AUC was [0.87, 0.91].

Conclusions

These findings provide promising evidence that models combining age, BMI and metabolic parameters may be a powerful tool for a cheap and effective biomarker of breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s12885-017-3877-1) contains supplementary material, which is available to authorized users.

Circulating resistin levels and obesity-related cancer risk: A meta-analysis

Resistin levels have been reported to be abnormal in obesity-related cancer patients with epidemiological studies yielding inconsistent results. Therefore, a meta-analysis was performed to assess the association between blood resistin levels and obesity-related cancer risk. A total of 13 studies were included for pooling ORs analysis. High resistin levels were found in cancer patients (OR= 1.20, 95% CI= 1.10-1.30). After excluding one study primarily contributing to between-study heterogeneity, the association between resistin levels and cancer risk was still significant (OR=1.18, 95% CI = 1.09-1.28). Stratification analysis found resistin levels were not associated with cancer risk in prospective studies. Meta-regression analysis identified factors such as geographic area, detection assay, or study design as confounders to between-study variance. The result of 18 studies of pooling measures on SMD analysis was that high resistin levels were associated with increased cancer risk (SMD = 0.94, 95% CI = 0.63-1.25), but not in the pooling SMD analysis of prospective studies. Except for the studies identified as major contributors to heterogeneity by Galbraith plot, resistin levels were still higher in cancer patients (SMD = 0.75, 95% CI = 0.63-0.87) in retrospective studies. Meta-regression analysis found factors, such as geographic area, BMI-match, size, and quality score, could account for 66.7% between-study variance in pooling SMD analysis of retrospective studies. Publication bias was not found in pooling ORs analysis. Our findings indicated high resistin levels were associated with increased obesity-related cancer risk. However, it may not be a predictor.