Gene Expression Analysis for Uterine Cervix and Corpus Cancer Characterization.

The analysis of gene expression quantification data is a powerful and widely used approach in cancer research. This work provides new insights into the transcriptomic changes that occur in healthy uterine tissue compared to those in cancerous tissues and explores the differences associated with uterine cancer localizations and histological subtypes. To achieve this, RNA-Seq data from the TCGA database were preprocessed and analyzed using the KnowSeq package. Firstly, a kNN model was applied to classify uterine cervix cancer, uterine corpus cancer, and healthy uterine samples. Through variable selection, a three-gene signature was identified (VWCE, CLDN15, ADCYAP1R1), achieving consistent 100% test accuracy across 20 repetitions of a 5-fold cross-validation. A supplementary similar analysis using miRNA-Seq data from the same samples identified an optimal two-gene miRNA-coding signature potentially regulating the three-gene signature previously mentioned, which attained optimal classification performance with an 82% F1-macro score. Subsequently, a kNN model was implemented for the classification of cervical cancer samples into their two main histological subtypes (adenocarcinoma and squamous cell carcinoma). A uni-gene signature (ICA1L) was identified, achieving 100% test accuracy through 20 repetitions of a 5-fold cross-validation and externally validated through the CGCI program. Finally, an examination of six cervical adenosquamous carcinoma (mixed) samples revealed a pattern where the gene expression value in the mixed class aligned closer to the histological subtype with lower expression, prompting a reconsideration of the diagnosis for these mixed samples. In summary, this study provides valuable insights into the molecular mechanisms of uterine cervix and corpus cancers. The newly identified gene signatures demonstrate robust predictive capabilities, guiding future research in cancer diagnosis and treatment methodologies.

Eco-efficiency assessment of municipal solid waste services: Influence of exogenous variables.

Improving the eco-efficiency of municipalities in the provision of municipal solid waste (MSW) services is fundamental in the context of a circular economy. This study evaluates the eco-efficiency of a sample of Spanish municipalities, integrating the total cost as input, recyclable waste as desirable output, and unsorted waste as undesirable output. Following a pioneering approach, the weighted Russell directional distance model (a non-radial data envelopment analysis model) was employed, which allowed us to obtain a global inefficiency score and individual inefficiency scores for each variable integrated in the model. In the second stage of analysis, the potential factors affecting the previously computed inefficiency scores were investigated. The results indicated that one third of the municipalities evaluated were eco-efficient in the provision of MSW services with the total cost being the variable in which the municipalities exhibited the best performance. Moreover, the size of the municipalities, population served, population density, tourism, and availability of containers for separative collection of paper, glass, and plastic significantly affect the eco-efficiency of the municipalities. The findings of this study provide detailed information to support decision-making for the policy makers to improve the eco-efficiency of the municipalities in managing MSW.