A nonlinear correlation measure with applications to gene expression data.

Nonlinear correlation exists in many types of biomedical data. Several types of pairwise gene expression in humans and other organisms show nonlinear correlation across time, e.g., genes involved in human T helper (Th17) cells differentiation, which motivated this study. The proposed procedure, called Kernelized correlation (Kc), first transforms nonlinear data on the plane via a function (kernel, usually nonlinear) to a high-dimensional (Hilbert) space. Next, we plug the transformed data into a classical correlation coefficient, e.g., Pearson's correlation coefficient (r), to yield a nonlinear correlation measure. The algorithm to compute Kc is developed and the R code is provided online. In three simulated nonlinear cases, when noise in data is moderate, Kc with the RBF kernel (Kc-RBF) outperforms Pearson's r and the well-known distance correlation (dCor). However, when noise in data is low, Pearson's r and dCor perform slightly better than (equivalently to) Kc-RBF in Case 1 and 3 (in Case 2); Kendall's tau performs worse than the aforementioned measures in all cases. In Application 1 to discover genes involved in the early Th17 cell differentiation, Kc is shown to detect the nonlinear correlations of four genes with IL17A (a known marker gene), while dCor detects nonlinear correlations of two pairs, and DESeq fails in all these pairs. Next, Kc outperforms Pearson's and dCor, in estimating the nonlinear correlation of negatively correlated gene pairs in yeast cell cycle regulation. In conclusion, Kc is a simple and competent procedure to measure pairwise nonlinear correlations.

Acetyl Coenzyme A Synthase 2 Acts as a Prognostic Biomarker Associated with Immune Infiltration in Cervical Squamous Cell Carcinoma.

Cervical squamous cell carcinoma (CESC) is one of the most common malignant tumors in women worldwide with a low survival rate. Acetyl coenzyme A synthase 2 (ACSS2) is a conserved nucleosidase that converts acetate to acetyl-CoA for energy production. Our research intended to identify the correlations of ACSS2 with clinical prognosis and tumor immune infiltration in CESC. ACSS2 is highly expressed in many tumors and is involved in the progression and metastasis of these tumors. However, it is not clear how ACSS2 affects CESC progression and immune infiltration. Analysis of the cBioPortal, GEPIA2, UALCAN, and TCGA databases showed that ACSS2 transcript levels were significantly upregulated in multiple cancer types including CESC. Quantitative RT-PCR analysis confirmed that ACSS2 expression was significantly upregulated in human cervical cancer cells. Here, we performed tissue microarray analysis of paraffin-embedded tissues from 240 cervical cancer patients recorded at FIGO/TNM cancer staging. The results showed that ACSS2 and PDL1 were highly expressed in human CESC tissues, and its expression was associated with the clinical characteristics of CESC patients. TIMER database analysis showed that ACSS2 expression in CESC was associated with tumor infiltration of B cells, CD4+ and CD8+ T cells, and cancer-associated fibroblasts (CAF). Kaplan-Meier survival curve analysis showed that CESC with high ACSS2 expression was associated with shorter overall survival. Collectively, our findings establish ACSS2 as a potential diagnostic and prognostic biomarker for CESC.

The effect of timing of enteral nutrition support on feeding outcomes and dysphagia in patients with head and neck cancer undergoing radiotherapy or chemoradiotherapy: A systematic review.

BACKGROUND & AIMS: Patients with head and neck cancer (HNC) often require enteral nutrition (EN). This systematic review reports the effect of EN timing in patients with HNC undergoing curative-intent and definitive or adjuvant radiotherapy or chemoradiotherapy on tube feeding duration, tube-related complications and dysphagia. METHODS: Randomised controlled trials (RCTs) published between January 2015-April 2020 were obtained from Medline, CINAHL and Embase. Study quality and certainty of evidence were assessed with Cochrane Risk-of-Bias and Grading of Recommendations Assessment, Development and Evaluation (GRADE). RESULTS: Two RCTs (n = 265) in five manuscripts were included. The risk of bias was moderate in one RCT and low in the other RCT. Timing of EN (prophylactic vs. reactive) may have little or no effect on tube feeding duration or complications, however, the effect on dysphagia was uncertain. Certainty of evidence was low for short-term and moderate for long-term tube feeding duration, low for tube-related complications and very low for dysphagia. There was imprecision due to small sample sizes, heterogeneity in the definitions and protocols for prophylactic and reactive EN, variations in time points for outcome assessment and indirect dysphagia measures. CONCLUSION: Larger well-designed trials are warranted to increase certainty of evidence regarding EN timing in patients with HNC.

UBE2C Drives Human Cervical Cancer Progression and Is Positively Modulated by mTOR.

Cervical cancer is a common gynecological malignancy, accounting for 10% of all gynecological cancers. Recently, targeted therapy for cervical cancer has shown unprecedented advantages. Several studies have shown that ubiquitin conjugating enzyme E2 (UBE2C) is highly expressed in a series of tumors, and participates in the progression of these tumors. However, the possible impact of UBE2C on the progression of cervical squamous cell carcinoma (CESC) remains unclear. Here, we carried out tissue microarray analysis of paraffin-embedded tissues from 294 cervical cancer patients with FIGO/TNM cancer staging records. The results indicated that UBE2C was highly expressed in human CESC tissues and its expression was related to the clinical characteristics of CESC patients. Overexpression and knockdown of UBE2C enhanced and reduced cervical cancer cell proliferation, respectively, in vitro. Furthermore, in vivo experiments showed that UBE2C regulated the expression and activity of the mTOR/PI3K/AKT pathway. In summary, we confirmed that UBE2C is involved in the process of CESC and that UBE2C may represent a molecular target for CESC treatment.

Sequential adaptive variables and subject selection for GEE methods.

Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time-consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process-such as the active-learning methods found in the machine-learning literature-will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.

Nutritional support in chronic obstructive pulmonary disease (COPD): an evidence update.

Chronic obstructive pulmonary disease (COPD) primarily affects the lungs but due to the accompanying chronic systematic inflammation and the symptoms associated with the disease there are many extrapulmonary effects which include complex physical and metabolic adaptations. These changes have been associated with reduced exercise capacity, increased nutritional requirements, altered metabolic processes and compromised nutritional intake. As a result, nutritional depletion in COPD is multi-faceted and can involve imbalances of energy (weight loss), protein (sarcopenia), and periods of markedly increased inflammation (pulmonary cachexia) which can increase nutritional losses. As a result, depletion of both fat-mass (FM) and fat-free mass (FFM) can occur. There is good evidence that nutritional support, in the form of oral nutritional supplements (ONS), can overcome energy and protein imbalances resulting in improved nutritional status and functional capacity. However, in order to treat the aetiology of sarcopenia, frailty and cachexia, it is likely that targeted multi-modal interventions are required to address energy and protein imbalance, specific nutrient deficiencies, reduced androgens and targeted exercise training. Furthermore, interventions taking a disease-course approach, are likely to hold the key to effectively managing the common and costly problem of nutritional depletion in COPD.

Sample size determination and treatment screening in two-stage phase II clinical trials via ROC curve.

In pharmaceutical-related research, we usually use clinical trials methods to identify valuable treatments and compare their efficacy with that of a standard control therapy. Although clinical trials are essential for ensuring the efficacy and postmarketing safety of a drug, conducting clinical trials is usually costly and time-consuming. Moreover, to allocate patients to the little therapeutic effect treatments is inappropriate due to the ethical and cost imperative. Hence, there are several 2-stage designs in the literature where, for reducing cost and shortening duration of trials, they use the conditional power obtained from interim analysis results to appraise whether we should continue the lower efficacious treatments in the next stage. However, there is a lack of discussion about the influential impacts on the conditional power of a trial at the design stage in the literature. In this article, we calculate the optimal conditional power via the receiver operating characteristic curve method to assess the impacts on the quality of a 2-stage design with multiple treatments and propose an optimal design using the minimum expected sample size for choosing the best or promising treatment(s) among several treatments under an optimal conditional power constraint. In this paper, we provide tables of the 2-stage design subject to optimal conditional power for various combinations of design parameters and use an example to illustrate our methods.

Influence Analysis for the Area Under the Receiver Operating Characteristic Curve.

Classification measures play essential roles in the assessment and construction of classifiers. Hence, determining how to prevent these measures from being affected by individual observations has become an important problem. In this paper, we propose several indexes based on the influence function and the concept of local influence to identify influential observations that affect the estimate of the area under the receiver operating characteristic curve (AUC), an important and commonly used measure. Cumulative lift charts are also used to equipoise the disagreements among the proposed indexes. Both the AUC indexes and the graphical tools only rely on the classification scores, and both are applicable to classifiers that can produce real-valued classification scores. A real data set is used for illustration.

Multiple-stage sampling procedure for covariate-adjusted response-adaptive designs.

Covariate-adjusted response-adaptive (CARA) design becomes an important statistical tool for evaluating and comparing the performance of treatments when targeted medicine and adaptive therapy become important medical innovations. Due to the nature of the adaptive therapies of interest and how subjects accrue to a sampling procedure, it is of interest how to control the sample size sequentially such that the estimates of treatment effects have satisfactory precision in addition to its asymptotic properties. In this paper, we apply a multiple-stage sequential sampling method to CARA design in such a way that the control of the sample size is more feasible. The theoretical properties of the proposed method, including the estimates of regression parameters and the allocation probabilities under this randomly stopped sampling procedure, are discussed. The numerical results based on synthesized data and a real example are presented.

Assessing the predictive power of newly added biomarkers.

As medical research and technology advance, there are always new biomarkers found and predictive models proposed for improving the diagnostic performance of diseases. Therefore, in addition to the existing biomarkers and predictive models, how to assess new biomarkers becomes an important research problem. Many classification performance measures, which are usually based on the performance on the whole cut-off values, were applied directly to this type of problems. However, in a medical diagnosis, some cut-off points are more important, such as those points within the range of high specificity. Thus, as the partial area under the ROC curve to the area under ROC curve, we study the partial integrated discriminant improvement (pIDI) for evaluating the predictive ability of a newly added marker at a prespecified range of cut-offs. Theoretical property of estimate of the proposed measure is reported. The performance of this new measure is then compared with that of the partial area under an ROC curve. The numerical results use synthesized are presented, and a liver cancer dataset is used for demonstration purposes.

Obesity and inflammatory biomarkers in women with polycystic ovary syndrome.

OBJECTIVE: To evaluate the roles of obesity and inflammatory biomarkers associated with medical complications in women with PCOS. STUDY DESIGN: Retrospective, BMI-matched study. A total of 330 patients, including 165 women with PCOS and 165 women without PCOS, were evaluated. The insulin resistance (homeostasis model assessment insulin resistance index - HOMA) and lipid profiles were assessed. The adiponectin, leptin, ghrelin, resistin, anti-müllerian hormone (AMH), sex hormone-binding globulin (SHBG), high sensitivity C-reactive protein (hs-CRP), and interleukin-6 (IL-6) levels were also measured. RESULTS: Women with PCOS had significantly higher AMH, fasting insulin, total cholesterol, and low-density lipoprotein levels and lower SHBG levels compared with the controls. There was no difference in the serum obesity and inflammatory biomarkers between the PCOS cases and the controls. After adjusting for BMI and age, IL-6 was positively correlated with HOMA, and SHBG was negatively correlated with HOMA, triglyceride, and LDL. CONCLUSIONS: The serum adipokines levels are not good markers for PCOS. PCOS patients were characterized by their high AMH and low SHBG levels. A low level of SHBG should play an important role in the pathogenesis of the medical complications observed in women with PCOS. Clinical trial registration number NCT01989039.

Adiponectin and leptin in overweight/obese and lean women with polycystic ovary syndrome.

AIM: The objective of this study was to evaluate the adiponectin and leptin levels in overweight/obese and lean women with polycystic ovary syndrome (PCOS). DESIGN: This was a retrospective study. PATIENTS: Of the 422 studied patients, 224 women with PCOS and 198 women without PCOS were evaluated. MAIN OUTCOME MEASURE(S): Insulin resistance and the metabolic components were assessed. The adiponectin and leptin levels were also evaluated. RESULTS: Adiponectin was negatively correlated with insulin resistance, body mass index (BMI), and total testosterone, triglyceride, and low-density lipoprotein (LDL) levels; conversely, leptin reversed the aforementioned reaction and was negatively correlated with adiponectin levels. The adiponectin to leptin ratios were significantly lower in PCOS women than in those without PCOS. Compared to women with non-PCOS, overweight/obese women with PCOS had lower serum adiponectin levels than women without PCOS, which was not the case for lean women. Conversely, lean women with PCOS had higher serum leptin levels than those without PCOS, which was not the case for overweight/obese women. CONCLUSIONS: Adipose tissue might play an important role in the metabolic complications in women with PCOS. To study the impact of obesity biomarkers in women with PCOS, overweight/obese and lean women should be considered separately.

Comparison of Paired ROC Curves through a Two-Stage Test.

The area under the receiver operating characteristic (ROC) curve (AUC) is a popularly used index when comparing two ROC curves. Statistical tests based on it for analyzing the difference have been well developed. However, this index is less informative when two ROC curves cross and have similar AUCs. In order to detect differences between ROC curves in such situations, a two-stage nonparametric test that uses a shifted area under the ROC curve (sAUC), along with AUCs, is proposed for paired designs. The new procedure is shown, numerically, to be effective in terms of power under a wide range of scenarios; additionally, it outperforms two conventional ROC-type tests, especially when two ROC curves cross each other and have similar AUCs. Larger sAUC implies larger partial AUC at the range of low false-positive rates in this case. Because high specificity is important in many classification tasks, such as medical diagnosis, this is an appealing characteristic. The test also implicitly analyzes the equality of two commonly used binormal ROC curves at every operating point. We also apply the proposed method to synthesized data and two real examples to illustrate its usefulness in practice.

Cluster analysis of cardiovascular and metabolic risk factors in women of reproductive age.

OBJECTIVE: To study the association between endocrine disturbances and metabolic complications in women seeking gynecologic care. DESIGN: Retrospective study, cluster analysis. SETTING: Outpatient clinic, university medical center. PATIENT(S): 573 women, including 384 at low risk and 189 at high risk of cardiometabolic disease. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Cardiovascular and metabolic parameters and clinical and biochemical characteristics. RESULT(S): Risk factors for metabolic disease are associated with a low age of menarche, high levels of high-sensitivity C-reactive protein and liver enzymes, and low levels of sex hormone-binding globulin. Overweight/obese status, polycystic ovary syndrome, oligo/amenorrhea, and hyperandrogenism were found to increase the risk of cardiometabolic disease. However, hyperprolactinemia and premature ovarian failure were not associated with the risk of cardiometabolic disease. In terms of androgens, the serum total testosterone level and free androgen index but not androstenedione or dehydroepiandrosterone sulfate (DHEAS) were associated with cardiometabolic risk. CONCLUSION(S): Although polycystic ovary syndrome is associated with metabolic risk, obesity was the major determinant of cardiometabolic disturbances in reproductive-aged women. Hyperprolactinemia and premature ovarian failure were not associated with the risk of cardiovascular and metabolic diseases. CLINICAL TRIAL REGISTRATION NUMBER: NCT01826357.

Biomarker selection for medical diagnosis using the partial area under the ROC curve.

BACKGROUND: A biomarker is usually used as a diagnostic or assessment tool in medical research. Finding an ideal biomarker is not easy and combining multiple biomarkers provides a promising alternative. Moreover, some biomarkers based on the optimal linear combination do not have enough discriminatory power. As a result, the aim of this study was to find the significant biomarkers based on the optimal linear combination maximizing the pAUC for assessment of the biomarkers. METHODS: Under the binormality assumption we obtain the optimal linear combination of biomarkers maximizing the partial area under the receiver operating characteristic curve (pAUC). Related statistical tests are developed for assessment of a biomarker set and of an individual biomarker. Stepwise biomarker selections are introduced to identify those biomarkers of statistical significance. RESULTS: The results of simulation study and three real examples, Duchenne Muscular Dystrophy disease, heart disease, and breast tissue example are used to show that our methods are most suitable biomarker selection for the data sets of a moderate number of biomarkers. CONCLUSIONS: Our proposed biomarker selection approaches can be used to find the significant biomarkers based on hypothesis testing.

Hyperhomocysteinaemia is associated with biochemical hyperandrogenaemia in women with reproductive age.

OBJECTIVE: Hyperhomocysteinaemia is a well-established risk factor for cardiovascular disease. This study investigated the relationship between hyperhomocysteinaemia and factors related to polycystic ovary syndrome (PCOS). STUDY DESIGN: Case-control study. Three hundred and thirty-nine women were included; of these, 84 had hyperhomocysteinaemia (homocysteine>12.4 µmol/l) and 255 had normal homocysteine levels. Homocysteine, high-sensitivity C-reactive protein, insulin resistance, metabolic disturbance and PCOS-related disturbance were evaluated. The clinical and biochemical characteristics of women with hyperhomocysteinaemia and normal homocysteine levels, including insulin resistance, metabolic disturbance and PCOS-related disturbance, were compared. RESULTS: Correlation was found between serum homocysteine level and serum total testosterone level and diastolic blood pressure. No correlation was found between serum homocysteine level and age, body mass index, insulin resistance and lipid profile. Women with hyperhomocysteinaemia had a significantly higher risk for biochemical hyperandrogenaemia and higher serum total testosterone levels than women with normal homocysteine levels. The prevalence rates of PCOS, oligo-amenorrhoea, polycystic ovary morphology and metabolic disturbance did not differ between the two groups. The parameters of insulin resistance and lipid profiles were similar between the two groups, and signs of clinical hyperandrogenism (hirsutism and the modified Ferriman-Gallwey score) did not differ between the two groups. Logistic regression analysis found a significant association between hyperandrogenaemia and hyperhomocysteinaemia (odds ratio 2.24, 95% confidence interval 1.26-4.01). CONCLUSIONS: For women with PCOS, an elevated serum total testosterone level is the main factor associated with hyperhomocysteinaemia. The association between biochemical hyperandrogenism and hyperhomocysteinaemia may contribute to cardiovascular risk for women with PCOS.

Dietary intakes consistent with the DASH dietary pattern reduce blood pressure increase with age and risk for stroke in a Chinese population.

The Dietary Approaches to Stop Hypertension (DASH) diet has been proven to effectively lower blood pressure(BP), and associate with a lower cardiovascular disease and stroke risk in mainly non-Asians. Further, it is unclear if adhering to the DASH target nutrients has similar BP impact as adhering to the recommended DASH food groups. Associations between adherence to DASH foods or nutrients and BP or stroke risk were assessed in 1420 and 2061 Taiwanese adults from 1989 to 2002, respectively. The DASH food score (p=0.053), dairy(p=0.030) and calcium (p=0.020) intake were significantly and inversely associated with follow up systolic BP change in univariate analyses. Both dairy (p=0.020) and calcium (p=0.001) also showed a consistent inverse association with systolic BP change in multivariate analysis. None of the factors examined was associated with diastolic BP change. Both DASH nutrient score and magnesium intakes were significantly associated with the hazard ratio (HR) for total stroke in an inverse relationship. The HR of total stroke comparing the highest to the lowest tertile was 0.63 (95% CI: 0.41-0.98, p=0.037) for the DASH nutrient score, and 0.62 (95% CI: 0.40-0.97,p=0.030) for magnesium intake. Similar findings were observed for DASH nutrient score (p=0.011) and magnesium intake (p=0.043) with the HR for ischemic stroke. The HR for total and ischemic stroke for calcium intake also showed a borderline trend (p=0.071 and 0.051, respectively). In conclusion, adhering to the DASH diet is beneficial for long term BP control and reduction of stroke risk in this Chinese population.

Maximizing an ROC-type measure via linear combination of markers when the gold reference is continuous.

Effectively combining many classification instruments or diagnostic measurements together to improve the classification accuracy of individuals is a common idea in disease diagnosis or classification. These ensemble-type diagnostic methods can be constructed with respect to different kinds of performance criterions. Among them, the receiver operating characteristic (ROC) curve is the most popular criterion, which, together with some indexes derived from it, is commonly used to evaluate and summarize the performance of a classification instrument, such as a biomarker or a classifier. However, the usefulness of ROC curve and its related indexes relies on the existence of a binary label for each individual subject. In many disease diagnosis situations, such a binary variable may not exist, but only the continuous measurement of the true disease status is available. This true disease status is often referred to as the 'gold standard'. The modified area under ROC curve (AUC)-type measure defined by Obuchowski is a method proposed to accommodate such a situation. However, there is still no method for finding the optimal combination of diagnostic measurements, with respect to such an index, to have better diagnostic power than that of each individual measurement. In this paper, we propose an algorithm for finding the optimal combination with respect to such an extended AUC-type measure such that the combined measurement can have more diagnostic power. We illustrate the performance of our algorithm by using some synthesized data and a diabetes data set.

Optimal sampling in retrospective logistic regression via two-stage method.

Case-control sampling is popular in epidemiological research because of its cost and time saving. In a logistic regression model, with limited knowledge on the covariance matrix of the point estimator of the regression coefficients a priori, there exists no fixed sample size analysis. In this study, we propose a two-stage sequential analysis, in which the optimal sample fraction and the required sample size to achieve a predetermined volume of a joint confidence set are estimated in an interim analysis. Additionally required observations are collected in the second stage according to the estimated optimal sample fraction. At the end of the experiment, data from these two stages are combined and analyzed for statistical inference. Simulation studies are conducted to justify the proposed two-stage procedure and an example is presented for illustration. It is found that the proposed two-stage procedure performs adequately in the sense that the resultant joint confidence set has a well-controlled volume and achieves the required coverage probability. Furthermore, the optimal sample fractions among all the selected scenarios are close to one. Hence, the proposed procedure can be simplified by always considering a balance design.

Marker selection via maximizing the partial area under the ROC curve of linear risk scores.

Rather than viewing receiver operating characteristic (ROC) curves directly to compare the performances of diagnostic methods, the whole and the partial areas under the ROC curve (area under the ROC curve [AUC] and partial area under the ROC curve [pAUC]) are 2 of the most popularly used summaries of the curve. Moreover, when high specificity is a prerequisite, as in some medical diagnostics, pAUC is preferable. In this paper, we propose a wrapper-type algorithm to select the best linear combination of markers that has high sensitivity within a confined specificity range. The markers selected by the proposed algorithm are different from those selected by AUC-based algorithms and therefore provide different information for further studies. Most notably, for example, within the given range of specificity, the markers selected by the proposed algorithm always have higher individual sensitivities than those selected by other AUC-based methods. This characteristic makes the proposed method a good addition to existing methods. Without assuming the underlying distributions of markers, we prove that the pAUC obtained with the proposed algorithm is a strongly consistent estimate of the true pAUC and then illustrate its performance with numerical studies using synthesized data and 2 real examples. The results are compared with those obtained by its AUC-based counterpart. We found that the classification performance of the final classifier based on the selected markers is very competitive.