Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining Study

Ovarian serous cancer (OSC) is one of the leading causes of death across the world. The role of the tumor microenvironment (TME) in OSC has received increasing attention. Targeted maximum likelihood estimation (TMLE) is developed under a counterfactual framework to produce effect estimation for both the population level and individual level. In this study, we aim to identify TME-related genes and using the TMLE method to estimate their effects on the 3-year mortality of OSC. In total, 285 OSC patients from the TCGA database constituted the studying population. ESTIMATE algorithm was implemented to evaluate immune and stromal components in TME. Differential analysis between high-score and low-score groups regarding ImmuneScore and StromalScore was performed to select shared differential expressed genes (DEGs). Univariate logistic regression analysis was followed to evaluate associations between DEGs and clinical pathologic factors with 3-year mortality. TMLE analysis was conducted to estimate the average effect (AE), individual effect (IE), and marginal odds ratio (MOR). The validation was performed using three datasets from Gene Expression Omnibus (GEO) database. Additionally, 355 DEGs were selected after differential analysis, and 12 genes from DEGs were significant after univariate logistic regression. Four genes remained significant after TMLE analysis. In specific, ARID3C and FREM2 were negatively correlated with OSC 3-year mortality. CROCC2 and PTF1A were positively correlated with OSC 3-year mortality. Combining of ESTIMATE algorithm and TMLE algorithm, we identified four TME-related genes in OSC. AEs were estimated to provide averaged effects based on the population level, while IEs were estimated to provide individualized effects and may be helpful for precision medicine.

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The causal effect and impact of reproductive factors on breast cancer using super learner and targeted maximum likelihood estimation: a case-control study in Fars Province, Iran

BMC Public Health ◽

10.1186/s12889-021-11307-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Amir Almasi-Hashiani ◽

Saharnaz Nedjat ◽

Reza Ghiasvand ◽

Saeid Safiri ◽

Maryam Nazemipour ◽

...

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Case Control Study ◽

Causal Effect ◽

Reproductive Factors ◽

Likelihood Estimation ◽

Case Control ◽

Targeted Maximum Likelihood Estimation ◽

Targeted Maximum Likelihood ◽

Control Study

Abstract Objectives The relationship between reproductive factors and breast cancer (BC) risk has been investigated in previous studies. Considering the discrepancies in the results, the aim of this study was to estimate the causal effect of reproductive factors on BC risk in a case-control study using the double robust approach of targeted maximum likelihood estimation. Methods This is a causal reanalysis of a case-control study done between 2005 and 2008 in Shiraz, Iran, in which 787 confirmed BC cases and 928 controls were enrolled. Targeted maximum likelihood estimation along with super Learner were used to analyze the data, and risk ratio (RR), risk difference (RD), andpopulation attributable fraction (PAF) were reported. Results Our findings did not support parity and age at the first pregnancy as risk factors for BC. The risk of BC was higher among postmenopausal women (RR = 3.3, 95% confidence interval (CI) = (2.3, 4.6)), women with the age at first marriage ≥20 years (RR = 1.6, 95% CI = (1.3, 2.1)), and the history of oral contraceptive (OC) use (RR = 1.6, 95% CI = (1.3, 2.1)) or breastfeeding duration ≤60 months (RR = 1.8, 95% CI = (1.3, 2.5)). The PAF for menopause status, breastfeeding duration, and OC use were 40.3% (95% CI = 39.5, 40.6), 27.3% (95% CI = 23.1, 30.8) and 24.4% (95% CI = 10.5, 35.5), respectively. Conclusions Postmenopausal women, and women with a higher age at first marriage, shorter duration of breastfeeding, and history of OC use are at the higher risk of BC.

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Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions

Statistics in Medicine ◽

10.1002/sim.8340 ◽

2019 ◽

Vol 38 (24) ◽

pp. 4888-4911 ◽

Cited By ~ 1

Author(s):

M. Schomaker ◽

M.A. Luque‐Fernandez ◽

V. Leroy ◽

M.A. Davies

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Likelihood Estimation ◽

Targeted Maximum Likelihood Estimation ◽

Targeted Maximum Likelihood ◽

Complex Settings

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Exercise During the First Trimester and Infant Size at Birth: Targeted Maximum Likelihood Estimation of the Causal Risk Difference

American Journal of Epidemiology ◽

10.1093/aje/kwz213 ◽

2019 ◽

Vol 189 (2) ◽

pp. 133-145 ◽

Cited By ~ 1

Author(s):

Samantha F Ehrlich ◽

Romain S Neugebauer ◽

Juanran Feng ◽

Monique M Hedderson ◽

Assiamira Ferrara

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Gestational Age ◽

Weight Status ◽

Likelihood Estimation ◽

First Trimester ◽

Normal Weight ◽

Targeted Maximum Likelihood Estimation ◽

Increased Risk ◽

Targeted Maximum Likelihood

Abstract This cohort study sought to estimate the differences in risk of delivering infants who were small or large for gestational age (SGA or LGA, respectively) according to exercise during the first trimester of pregnancy (vs. no exercise) among 2,286 women receiving care at Kaiser Permanente Northern California in 2013–2017. Exercise was assessed by questionnaire. SGA and LGA were determined by the sex- and gestational-age-specific birthweight distributions of the 2017 US Natality file. Risk differences were estimated by targeted maximum likelihood estimation, with and without data-adaptive prediction (machine learning). Analyses were also stratified by prepregnancy weight status. Overall, exercise at the cohort-specific 75th percentile was associated with an increased risk of SGA of 4.5 (95% CI: 2.1, 6.8) per 100 births, and decreased risk of LGA of 2.8 (95% CI: 0.5, 5.1) per 100 births; similar findings were observed among the underweight and normal-weight women, but no associations were found among those with overweight or obesity. Meeting Physical Activity Guidelines was associated with increased risk of SGA and decreased risk of LGA but only among underweight and normal-weight women. Any vigorous exercise reduced the risk of LGA in underweight and normal-weight women only and was not associated with SGA risk.

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