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.

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.

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.

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.

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.

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.

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.