Two-stage Analysis Research Articles

The Predictive Validity of Individualised Load-Velocity Relationships for Predicting 1RM: A Systematic Review and Individual Participant Data Meta-analysis.

Load-velocity relationships are commonly used to estimate one-repetition maximums (1RMs). Proponents suggest these estimates can be obtained at high frequencies and assist with manipulating loads according to session-by-session fluctuations. Given their increasing popularity and development of associated technologies, a range of load-velocity approaches have been investigated. This systematic review and individual participant data (IPD) meta-analysis sought to quantify the predictive validity of individualised load-velocity relationships for the purposes of 1RM prediction. In September 2022, a search of MEDLINE, SPORTDiscus, Web of Science and Scopus was conducted for published research, with Google Scholar, CORE and British Ethos also searched for unpublished research. Studies were eligible if they were written in English, and directly compared a measured and predicted 1RM using load-velocity relationships in the squat, bench press, deadlift, clean or snatch. IPD were obtained through requests to primary authors and through digitisation of in-text plots (e.g. Bland-Altman plots). Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the review conducted in accordance with PRISMA-IPD guidelines and an a priori protocol. Absolute and scaled standard error of the estimates (SEE/SEE%) were calculated for two-stage aggregate analyses, with bootstrapping performed for sampling variances. Estimates were pooled using three-level hierarchical models with robust 95% confidence intervals (CIs). One-stage analyses were conducted with random intercepts to account for systematic differences across studies and prediction residuals calculated in the absolute scale (kg) and as a percentage of the measured 1RM. Moderator analyses were conducted by including a priori defined categorical variables as fixed effects. One hundred and thirty-seven models from 26 studies were included with each identified as having low, unclear or high risk of bias. Twenty studies comprising 434 participants provided sufficient data for meta-analyses, with raw data obtained for 8 (32%) studies. Two-stage analyses identified moderate predictive validity [SEE% 9.8, 95% CI 7.4% to 12.2%, with moderator analyses demonstrating limited differences based on the number of loads (β2Loads:>2Loads = 0.006, 95% CI - 1.6 to 1.6%) or the use of individual or group data to determine 1RM velocity thresholds (βGroup:Individualised = - 0.4, 95% CI - 1.9 to 1.0%)]. One-stage analyses identified that predictions tended to be overestimations (4.5, 95% CI 1.5to 7.4kg), which expressed as a percentage of measured 1RM was equal to 3.7 (95% CI 0.5 to 6.9% 1RM). Moderator analyses were consistent with those conducted for two-stage analyses. Load-velocity relationships tend to overestimate 1RMs irrespective of the modelling approach selected. On the basis of the findings from this review, practitioners should incorporate direct assessment of 1RM wherever possible. However, load-velocity relationships may still prove useful for general monitoring purposes (e.g. assessing trends across a training cycle), by providing high-frequency estimates of 1RM when direct assessment may not be logistically feasible. Given limited differences in predictions across popular load-velocity approaches, it is recommended that practitioners opting to incorporate this practice select the modelling approach that best suits their practical requirements. https://osf.io/agpfm/ .

Numerical study on the effects of non-uniform corrosion and confinement conditions on the bond performance of RC beams

A 3D model is established to simulate the bond performance of corroded RC beams, in which the non-uniform corrosion of tensile reinforcement and confinement conditions (characterized by the thickness-diameter ratios of 1.9–3.8 and stirrup confinement index of 0–8.3%) are investigated. In the 3D numerical model, fine modeling was adopted to consider the influences of corrosion on the steel-concrete interface and the non-uniform corrosion was considered by separating the tensile bars into two parts with inconsistent diameter loss. Meanwhile, a two-stage numerical analysis method was utilized, to reflect the corrosion-induced concrete cracking by applying forced displacement and subsequently analyzed their flexural bond performance. In comparison to the existing experiments, the proposed 3D numerical model is proved to be reasonable. In addition, the simulation results show that the increment of the (residual) bond strength generated by the increasing thickness-diameter ratio increases with the mass loss rate, while the increases of stirrup confinement index cause much more increases in the (residual) bond strength for the corroded RC beams than the un-corroded beams. Furthermore, a bond stress-slip relationship is proposed taking the non-uniform corrosion and confinement conditions into account, which correlates well with the experiments.

Optimising shared electric mobility hubs: Insights from performance analysis and factors influencing riding demand

In order to decarbonise the transport networks, systemic change is needed. One manifestation of this transformation is shared electric mobility, seeking to curtail car usage and ownership. This current case study aims to measure and optimise the operational performance of shared electric mobility hubs (eHUBs). From the performance results of eHUBs, one can get helpful insights to develop appropriate future planning and management policies for improving the transport chain. Incorporating data from September 2021 to October 2022, this research developed a novel dynamic two-stage data envelopment analysis (DEA) framework to assess the performance of the eHUB network in Inverness, Scotland. In the first stage, the DEA model computes relative efficiency scores related to the operational performance of the stations. The second stage focuses on network analysis and examining the factors that may influence the high or low obtained performance scores. Scrupulous analysis shows that the population in the catchment area of the eHUBs and the weather conditions (specifically, temperature) are among the most important factors influencing riding demand. The study also finds a weak association between eHUBs efficiency and proximity to public transport stops, suggesting that electric-assist bikes (e-bikes, pedelecs) may not strongly complement public transport, unlike bike-sharing systems. It indicates that e-bikes serve rather as a standalone mode for longer journeys. The findings of the case study can be used to improve sustainable mobility strategies, particularly related to e-bikes in other cities and urban areas.

The effect of universal testing and treatment for HIV on health-related quality of life – An analysis of data from the HPTN 071 (PopART) cluster randomised trial

BackgroundHIV treatment has clear Health-Related Quality-of-Life (HRQoL) benefits. However, little is known about how Universal Testing and Treatment (UTT) for HIV affects HRQoL. This study aimed to examine the effect of a combination prevention intervention, including UTT, on HRQoL among People Living with HIV (PLHIV). MethodsData were from HPTN 071 (PopART), a three-arm cluster randomised controlled trial in 21 communities in Zambia and South Africa (2013–2018). Arm A received the full UTT intervention of door-to-door HIV testing plus access to antiretroviral therapy (ART) regardless of CD4 count, Arm B received the intervention but followed national treatment guidelines (universal ART from 2016), and Arm C received standard care. The intervention effect was measured in a cohort of randomly selected adults, over 36 months. HRQoL scores, and the prevalence of problems in five HRQoL dimensions (mobility, self-care, performing daily activities, pain/discomfort, anxiety/depression) were assessed among all participants using the EuroQol-5-dimensions-5-levels questionnaire (EQ-5D-5L). We compared HRQoL among PLHIV with laboratory confirmed HIV status between arms, using adjusted two-stage cluster-level analyses. ResultsAt baseline, 7,856 PLHIV provided HRQoL data. At 36 months, the mean HRQoL score was 0.892 (95% confidence interval: 0.887–0.898) in Arm A, 0.886 (0.877–0.894) in Arm B and 0.888 (0.884–0.892) in Arm C. There was no evidence of a difference in HRQoL scores between arms (A vs C, adjusted mean difference: 0.003, -0.001-0.006; B vs C: -0.004, -0.014-0.005). The prevalence of problems with pain/discomfort was lower in Arm A than C (adjusted prevalence ratio: 0.37, 0.14–0.97). There was no evidence of differences for other HRQoL dimensions. ConclusionsThe intervention did not change overall HRQoL, suggesting that raising HRQoL among PLHIV might require more than improved testing and treatment. However, PLHIV had fewer problems with pain/discomfort under the full intervention; this benefit of UTT should be maximised during roll-out.

The role of financial covenants in pricing private investments in public equity

Our study examines the pricing of private placements for issuing firms subject to outstanding loan covenants. Using Private Investments in Public Equity (PIPE) deals from 2001 to 2018, we find that issuing firms restricted by loan covenants offer a discount of 3.9% larger than those without covenants. We corroborate the positive impact of financial covenants on discounts through channel tests that assess covenant violation history, covenant strictness, and the identities of lead PIPE investors. The increased probability of technical default and costly renegotiation in covenants potentially incentivizes borrowing firms to transition from the loan market to the PIPE market. To minimize endogeneity concerns, we use a matched sample, a Heckman selection model, and a two-stage least squares instrumental variable analysis, all of which present consistent results. Based on our findings, we conjecture that PIPE investors are concerned about the transfer of control rights to lenders and, as a result, demand deeper discounts in PIPE offerings.

Transmission mechanisms of geopolitical risks to the crude oil market——A pioneering two-stage geopolitical risk analysis approach

As an exogenous risk, geopolitical risk drives oil price volatility indirectly through multiple factors, which are ignored by most studies. This paper establishes a two-stage approach for the first time to analyze the indirect geopolitical risk transmission mechanisms, and applies the time-varying parametric vector autoregressive (TVPVAR) model to illustrate how geopolitical risk affects oil prices via different transmission paths including micro media (supply, demand, inventories, speculative behaviors) and macro media (global economic activities). Oil prices do not always rise in response to geopolitical events, sometimes even varies to the opposite direction, a phenomenon that can be well explained and modeled by the transmission mechanisms we developed. In this paper, we have identified two clear and unique geopolitical transmission paths to enhance energy-related decisions. Aside from the conventional economic phenomenon of geopolitical risks affecting oil prices through supply and demand behaviors, speculative behaviors increase during the period of high geopolitical risk, and the impact of speculative activity on oil prices is prominent. In addition, the most influential path is that geopolitical factors can indirectly impact oil prices by influencing economic fluctuations, which provides powerful and empirical supports for energy-related financial decisions and policy implications.

Underdetermined Blind Source Separation of Bioacoustic Signals

Bioacoustic signals have been used as a modality in environmental monitoring and biodiversity research. These signals also carry species or individual information, thus allowing the recognition of species and individuals based on vocals. Nevertheless, vocal communication in a crowded social environment is a challenging problem for automated bioacoustic recogniser systems due to interference problems in concurrent signals from multiple individuals. The bioacoustics sources are separated from the mixtures of multiple individual signals using a technique known as Blind source separation (BSS) to address the abovementioned issue. In this work, we explored the BSS of an underdetermined mixture based on a two-stage sparse component analysis (SCA) approach that consisted of (1) mixing matrix estimation and (2) source estimation. The key point of our procedure was to investigate the algorithm’s robustness to noise and the effect of increasing the number of sources. Using the two-stage SCA technique, the performances of the estimated mixing matrix and the estimated source were evaluated and discussed at various signal-to-noise ratios (SNRs). The use of different sources is also validated. Given its robustness, the SCA algorithm presented a stable and reliable performance in a noisy environment with small error changes when the noise level was increased.

Firms’ characteristics, corporate governance, and the adoption of sustainability reporting: evidence from Gulf Cooperation Council countries

PurposeThe purpose of the study is to investigate the factors that influence the adoption of new sustainability reporting (SDG) and external assurance (EXTA) practices. This study also examines the relationship between sustainability reporting activity and corporate economic performance for a sample of 99 companies in Gulf Cooperation Council (GCC) countries that addressed SDGs in their sustainability reports published in 2019.Design/methodology/approachUsing a two-stage analysis, this study examines how firms’ characteristics and corporate governance variables affect SDG and economic performance, as well as the firm’s decision to adopt EXTA statements for a sample of companies in that addressed SDGs in their sustainability reports published in 2019. The authors collected data from the Global Reporting Initiative’s (GRI) Sustainability Disclosure database and the Bureau van Dijk for Orbis database.FindingsThe results show that the variables firm size, profitability, big 4 auditors and government ownership significantly affect SDG and economic performance. The results also reveal that firms operating in the manufacturing sector are positively correlated with SDG and the firm’s decision to adopt EXTA statements. Furthermore, the results indicate that board independence positively affects SDGs and EXTA.Research limitations/implicationsThe results can be particularly relevant and timely in helping large GCC companies promote their engagement to sustainable development practices by adopting more sustainable long-term strategies and policies. The findings could also guide managers in the strategic direction to identify firms’ characteristics and corporate governance features essential to promote sustainability reporting, an increasingly important performance indicator for investors and to enhance their confidence in the capital market. The results may also have practical implications to policymakers and other regulators in GCC countries to define effective frameworks that promote sustainable development reports and the use of EXTA.Originality/valueThe results make significant contributions by providing new insights to the existing literature on sustainability reporting in emerging markets by examining a unique perspective on the influence of firms’ characteristics and corporate governance features on the adoption of new sustainability reporting practices. The authors further add to the previous literature on the relationship between a firm’s economic performance and sustainable reporting by providing evidence from large companies in GCC countries, which might benefit from the adoption of multiple conceptual lenses, in this case, legitimacy and stakeholder theories. Lastly, through the empirical findings, this study provides economic validity to the 2018 joint initiative of the GRI and the United Nations Global Compact to strengthen corporate actions to achieve the United Nations SDGs.

Measurement and determinants of smart destinations’ sustainable performance: a two-stage analysis using DEA-Tobit model

ABSTRACT With a two-stage analysis framework, this paper investigates how information communication technology can improve the sustainable performance of smart tourism destinations. It analyzes the impact of online attention (in the form of activity on a search engine) and the level of the digital economy on sustainable performance of smart tourism destinations, measured using an advanced data envelopment analysis model. The empirical results from 50 key smart tourism destinations in China show that: (1) the average value of smart destination performance from 2008 to 2019 is 0.74; (2) both online attention and digital economy have significant positive impacts on sustainable performance; (3) digital economy positively moderates the impact of online attention on sustainable performance; (4) undesirable outputs (air pollution) and tourist satisfaction are two factors that can be considered in measuring the sustainable performance of a destination.

Role of perceived COVID-19 disruption, personality traits and risk perception in determining the investment behavior of retail investors: a hybrid regression-neural network approach

PurposeThis study aims to assess the impact of personality traits, risk perception and perceived coronavirus disease 2019 (COVID-19) disruption on the investment behavior of individual investors in the Indian stock market.Design/methodology/approachThis study adopts a survey approach. The sample comprises 315 active retail investors investing in the Indian stock exchange. Two-stage analysis technique regression and Artificial Neural Network (ANN) were used for data analysis. Study hypotheses were tested through regression and ANN was adopted to validate the regression results.FindingsTwo regression models were modeled to test the research hypotheses. Findings showed that risk perception and COVID-19 disruption have a significant positive and neuroticism has a significant negative impact on short-term investment decisions, while the role of conscientiousness in determining short-term investment decisions was not found significant. Results also showed a positive impact of neuroticism and conscientiousness and a negative impact of risk perception on long-term investment decisions. The role of COVID-19 disruption was found negative but insignificant in predicting long-term investment decisions.Practical implicationsThis study has practical implications for many parties like retail investors, financial advisors and policymakers. This study will assist the investors to realize that they do not always take rational financial decisions. This study will suggest the financial advisors to use the knowledge of behavioral finance in making the advisors' advisory and wealth management decisions. This study will also assist the policymakers to outline behaviorally well-informed policy decisions to protect the interests of investors.Originality/valueIndia is one of the fast-growing economies in the world. India has a vast population of active investors and determining investors' investment behavior adds novelty to this study as developed economies have remained the main focus of previous studies. The other novel feature of this study is that this study tries to assess the impact of COVID-19 disruption along with personality traits and risk perception on investment behavior. The other valuable factor of this study is the use of ANN to predict the relative importance of the exogenous variables.

Determinants of Return On Asset (ROA) at Sharia Commercial Banks 2016 – 2021

ROA is a ratio that serves to assess a company's ability to make a profit. The amount of ROA can be influenced by many things such as NPF, FDR, and CAR. This study aims to find the effect of NPF, FDR, and CAR on ROA at Islamic Commercial Banks (BUS) in 2016-2021. This research uses a two-stage analysis. The first stage will use multiple linear analysis with the Ordinary Least Square method to find the influence between variables, then the second stage will examine the ROA scheme in Islamic banking, and whether it is in accordance with Islamic principles. The results showed that NPF had a significant negative impact on ROA, FDR had an insignificant positive impact on ROA, and CAR had a significant positive impact on ROA. The results of the calculation of the coefficient of determination show that NPF, FDR, and CAR affect ROA by 78.78% while the rest is explained by other variables that are not taken into account in the study. In addition, the ROA business scheme is in accordance with existing Sharia aspects because nothing is violated in Sharia principles, and the existence of DPS is one of the factors the researchers believe that this business model is in accordance with Sharia values. This research is useful to determine the effect between ROA variables and to find out whether business schemes with ROA are in accordance with Sharia.

Regional Differences and Convergence of Urban Water Use Efficiency in the Yangtze River Economic Belt

This study used a two-stage network data envelopment analysis model to measure the water use efficiency of 108 cities in the Yangtze River Economic Belt in the initial water use and wastewater treatment phases from 2009 to 2019. We divided the urban water use efficiency of six significant urban clusters in the Yangtze River Economic Belt using the Dagum Gini coefficient. We also tested the convergence characteristics of urban water use efficiency in six significant urban clusters in the Yangtze River Economic Belt using convergence and convergence kinds. According to this report, the Yangtze River Economic Zone’s cities often have low levels of water use efficiency, which is primarily due to ineffective wastewater treatment. The 108 cities in the Yangtze River Economic Zone are divided into four types based on the average values of water use efficiency in the initial use and wastewater treatment phases; the highest number of cities are in the double-low category, with low average values of water use efficiency in the initial use and wastewater treatment phases. During the study period, spatial differences in urban water use efficiency in the Yangtze River Economic Zone narrowed, with the differences stemming mainly from hyperdensity, followed by intra- and inter-regional differences. Meanwhile, there is convergence in urban water use efficiency in the Yangtze River Economic Belt, significant β convergence in the urban agglomerations of the Yangtze River Delta, Jianghuai, middle reaches of the Yangtze River, Chengdu–Chongqing, and Central Yunnan, and insignificant β convergence in the Central Qian urban agglomeration. After considering control factors, such as industrial structure, financial development level, environmental regulation, economic development level, and science and education development level, the water use efficiency of the six major urban clusters in the Yangtze River Economic Belt converges faster, but the influence of these control factors on the water use efficiency of each urban cluster is heterogeneous. Research results have reference value for the development of improvement strategies on differentiated urban water use efficiency in the Yangtze River Economic Belt. By measuring the regional differences in water use efficiency of urban agglomerations in the Yangtze River Economic Belt and clarifying their convergence mechanism, it provides a basis for analyzing the spatial pattern of water use efficiency in urban agglomerations and has reference value for formulating differentiated urban water use efficiency improvement strategies in the Yangtze River Economic Belt.

The footprint of human-induced climate change on heat-related deaths in the summer of 2022 in Switzerland

Human-induced climate change is leading to an increase in the intensity and frequency of extreme weather events, which are severely affecting the health of the population. The exceptional heat during the summer of 2022 in Europe is an example, with record-breaking temperatures only below the infamous 2003 summer. High ambient temperatures are associated with many health outcomes, including premature mortality. However, there is limited quantitative evidence on the contribution of anthropogenic activities to the substantial heat-related mortality observed in recent times. Here we combined methods in climate epidemiology and attribution to quantify the heat-related mortality burden attributed to human-induced climate change in Switzerland during the summer of 2022. We first estimated heat-mortality association in each canton and age/sex population between 1990 and 2017 in a two-stage time-series analysis. We then calculated the mortality attributed to heat in the summer of 2022 using observed mortality, and compared it with the hypothetical heat-related burden that would have occurred in absence of human-induced climate change. This counterfactual scenario was derived by regressing the Swiss average temperature against global mean temperature in both observations and CMIP6 models. We estimate 623 deaths [95% empirical confidence interval (95% eCI): 151–1068] due to heat between June and August 2022, corresponding to 3.5% of all-cause mortality. More importantly, we find that 60% of this burden (370 deaths [95% eCI: 133–644]) could have been avoided in absence of human-induced climate change. Older women were affected the most, as well as populations in western and southern Switzerland and more urbanized areas. Our findings demonstrate that human-induced climate change was a relevant driver of the exceptional excess health burden observed in the 2022 summer in Switzerland.

COVID-19 crisis and the efficiency of Indian banks: Have they weathered the storm?

The purpose of this study is to determine whether Indian banks were able to weather the COVID-19 storm. We estimate banks’ deposits-generating and operating efficiencies using a two-stage directional distance function-based network data envelopment analysis (DDF-NDEA) approach and seek to capture the immediate impact of COVID-19 on these efficiency measures by comparing their magnitudes in the pre-pandemic (2014/15–2019/20), just 1-year prior to the pandemic (2019/20), and during the pandemic year (2020/21) periods. The study looks at whether the impact of the COVID-19 pandemic was uniform across ownership types and size classes. The empirical findings suggest that the Indian banking system was resilient and withstood the immediate impact of the COVID-19 pandemic. During the study period, however, the large and medium-sized banks experienced some efficiency losses. By and large, regardless of bank group, banks have shown resilience to the shock of the global health pandemic and improvements in efficiency.

A PLS – SEM study to test the role of Social media in influencing Purchase Intention

In light of the growing importance of social networking marketing (SMM) to the profitability of tiny and medium-sized businesses (SMEs) and the relatively modest adoption rate of SMM among SMEs, this study seeks to determine which factors influence SMEs’ adoption of SMM. This study, unlike the majority of others, proposed a two-stage analysis combining the partial least squares (PLS) method with an artificial intelligence technique called an artificial neural network (ANN). Using a deep ANN architecture, the proposed model can make predictions with a 91% success rate. The marketing rate of social networking site adoption was found to be significantly affected by the strength of the relationship between perceived efficiency, user approval of use, perceived expenses, and encouragement from upper management, beneficial conditions, and vendor pressure. The findings of this study contribute to the expanding body of literature on online advertising by shedding light on the role played by technological, corporate, and ecological (TOE) variables in consumers’ adoption of social media promotional activities. Investment choices in digital marketing in comparable and non-competitive industries can benefit from the study’s findings, which can be used by policymakers as well as managers of SMM and consumer behavior.

Analysis of the Buried Pipeline Response Induced by Twin Tunneling Using the Generalized Hermite Spectral Method

For the sustainability of economic, ecological and social development, the safety of infrastructure, including buried pipelines, is extremely important. Undercrossing tunneling can compromise the safety of buried pipelines because of deformations, cracks and dislocations, which can result in wasted resources, environmental pollution and economic losses. Therefore, it is important to assess the pipeline response accurately during tunnel excavation. This paper proposes a generalized Hermite spectral solution to estimate the pipeline response induced by twin tunneling. The proposed solution is formulated by a truncated series of Hermite functions and it is available in an unbounded domain. On the basis of the two-stage analysis method, a general formula for calculating the soil greenfield displacement induced by twin tunneling is first derived using the superposition principle. To obtain the final solution, the soil greenfield displacement and pipeline displacement are expanded using two truncated series of Hermite functions, and the governing differential equation of pipeline displacement is subsequently simplified into a linear algebraic system. After solving this system, a general solution for calculating pipeline displacement is formulated. Then, the convergence of the developed solution is proven, and its validity is verified against existing theoretical solutions and centrifuge test results. The effects of the truncated series number and its scaling factor are investigated. Finally, parametric studies are conducted to discuss pipeline responses induced by twin tunneling.

Do Indonesians rationally or irrationally vote? Evidence from the 2014 Indonesian general election

This study investigated the determinants of voters’ trust level regarding official candidates at presidential, legislative, and governor levels in the 2014 general election in Indonesia. Data from the 2014 National Socioeconomic Survey was utilized in this study. The data was analyzed using two-stage regression analysis. The first stage regression analyzed the determinants of individual trust in official candidates, while the second stage analyzed probabilities of individuals to vote. This study found that voters’ experiences in vote buying reduced their trust in official candidates in the 2014 election. The second-stage regression results indicated that voters’ experiences in disasters reduced their intention to vote at all.

Determinants of shock-coping mechanisms adoption and rural household consumption in Rwanda: A two-stage analysis considering both idiosyncratic and covariate shocks

Determinants of shock-coping mechanisms adoption and rural household consumption in Rwanda: A two-stage analysis considering both idiosyncratic and covariate shocks

Sustainability performance efficiency in the banking sector

Traditional sustainability reporting is based on an internally developed methodology and does not fully cope with the sustainability performance evaluation, especially from an external perspective. This study aims to fill this gap by offering a tool that may be used to assess the company’s performance based on the results presented to external users. For this purpose, a two-stage data envelopment analysis (DEA) is proposed, that enables a comparison of the sustainability performance disclosed in non-financial reports in the banking industry in Poland, Croatia, and Romania. The findings of our study identify differences between the efficiency of sustainability performance in the banking sectors of all three countries. The proposed approach to compare hard-to-compare sustainability performance may significantly contribute to the decision-making process for stakeholders and, therefore, to the advancement of sustainability performance measurement research.

Potentially functional genetic variants in RPS6KA4 and MAP2K5 in the MAPK signaling pathway predict HBV-related hepatocellular carcinoma survival.

Hepatocellular carcinoma (HCC) ranks the third leading cause of cancer deaths with a dismal 5-year survival rate. The mitogen-activated protein kinase (MAPK) signaling pathway is abnormally activated in HCC to promote growth and aggressive metastatic potential of cancer cells. Therefore, genetic variants in the MAPK signaling pathway may serve as potential predictors of Hepatitis B virus (HBV)-related HCC survival. In the present study, we performed a two-stage survival analysis to evaluate the associations between 10,912 single nucleotide polymorphisms (SNPs) in 79 MAPK signaling pathway genes and the overall survival (OS) of 866 HBV-related HCC patients, followed by functional annotation. In combined datasets, we identified two novel and potential functional SNPs (RPS6KA4 rs600377 T>G and MAP2K5 rs17300363 A>C) as prognostic factors for HBV-related HCC, with adjusted allelic hazards ratios of 1.24 (95% confidence interval [CI] = 1.05-1.46, p = 0.010) and 1.48 (1.15-1.91, p = 0.001), respectively. Furthermore, their combined risk genotypes also predicted a poor survival in a dose-response manner in the combined data set (Ptrend < 0.001). Additional functional analysis showed that RPS6KA4 rs600377 G and MAP2K5 rs17300363 C alleles were associated with elevated mRNA expression levels of the corresponding genes in normal tissues. These results provide new insights into the role of genetic variants in the MAPK signaling pathway genes in HBV-related HCC survival.