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Testicular cancer (TCa) is a rare malignancy affecting young men worldwide. Sociodemographic factors, especially socioeconomic level (SEL) and healthcare access, seem to impact TCa incidence and outcomes, particularly among Hispanic populations. However, limited research has explored these variables in Hispanic groups. This study aimed to investigate sociodemographic and clinical factors in Mexico and their role in health disparities among Hispanic TCa patients. We retrospectively analyzed 244 Mexican TCa cases between 2007 and 2020 of a representative cohort with diverse social backgrounds from a national reference cancer center. Logistic regression identified risk factors for fatality: non-seminoma histology, advanced stage, and lower education levels. Age showed a significant trend as a risk factor. Patient delay and healthcare distance lacked significant associations. Inadequate treatment response and chemotherapy resistance were more likely in advanced stages, while higher education positively impacted treatment response. Cox regression highlighted non-seminoma histology, below-median SEL, higher education, and advanced-stage survival rates. Survival disparities emerged based on tumor histology and patient SEL. This research underscores the importance of comprehensive approaches that integrate sociodemographic, biological, and environmental factors to address health disparities improving outcomes through personalized interventions in Hispanic individuals with TCa.

Background: Risk stratification or progression in prostate cancer is performed with the support of clinical-pathological data such as the sum of the Gleason score and serum levels PSA. For several decades, methods aimed at the early detection of prostate cancer have included the determination of PSA serum levels. The aim of this systematic review is to provide an overview about recent advances in the discovery of new molecular biomarkers through transcriptomics, genomics and artificial intelligence that are expected to improve clinical management of the prostate cancer patient. Methods: An exhaustive search was conducted by Pubmed, Google Scholar and Connected Papers using keywords relating to the genetics, genomics and artificial intelligence in prostate cancer, it includes “biomarkers”, “non-coding RNAs”, “lncRNAs”, “microRNAs”, “repetitive sequence”, “prognosis”, “prediction”, “whole-genome sequencing”, “RNA-Seq”, “transcriptome”, “machine learning”, and “deep learning”. Results: New advances, including the search for changes in novel biomarkers such as mRNAs, microRNAs, lncRNAs, and repetitive sequences, are expected to contribute to an earlier and accurate diagnosis for each patient in the context of precision medicine, thus improving the prognosis and quality of life of patients. We analyze several aspects that are relevant for prostate cancer including its new molecular markers associated with diagnosis, prognosis, and prediction to therapy and how bioinformatic approaches such as machine learning and deep learning can contribute to clinic. Furthermore, we also include current techniques that will allow an earlier diagnosis, such as Spatial Transcriptomics, Exome Sequencing, and Whole-Genome Sequencing. Conclusion: Transcriptomic and genomic analysis have contributed to generate knowledge in the field of prostate carcinogenesis, new information about coding and non-coding genes as biomarkers has emerged. Synergies created by the implementation of artificial intelligence to analyze and understand sequencing data have allowed the development of clinical strategies that facilitate decision-making and improve personalized management in prostate cancer.

ABSTRACTBackground: Prolonged mitotic arrest in response to anti-cancer chemotherapeutics, such as DNA-damaging agents, induces apoptosis, mitotic catastrophe, and senescence. Disruptions in mitotic checkpoints contribute resistance to DNA-damaging agents in cancer. MAD2 has been associated with checkpoint failure and chemotherapy response. In this study, a novel splice variant of MAD2, designated MAD2γ, was identified, and its association with the DNA damage response was investigated.Methods: Endogenous expression of MAD2γ and full-length MAD2 (MAD2α) was measured using RT-PCR in cancer cell lines, normal foreskin fibroblasts, and tumor samples collected from patients with testicular germ cell tumors (TGCTs). A plasmid expressing MAD2γ was transfected into HCT116 cells, and its intracellular localization and checkpoint function were evaluated according to immunofluorescence and mitotic index.Results: MAD2γ was expressed in several cancer cell lines and non-cancerous fibroblasts. Ectopically expressed MAD2γ localized to the nucleus and reduced the mitotic index, suggesting checkpoint impairment. In patients with TGCTs, the overexpression of endogenous MAD2γ, but not MAD2α, was associated with resistance to cisplatin-based chemotherapy. Likewise, cisplatin induced the overexpression of endogenous MAD2γ, but not MAD2α, in HCT116 cells.Conclusions: Overexpression of MAD2γ may play a role in checkpoint disruption and is associated with resistance to cisplatin-based chemotherapy in TGCTs.

Breast cancer is one of the leading causes of mortality in women worldwide, and neoadjuvant chemotherapy has emerged as an option for the management of locally advanced breast cancer. Extensive efforts have been made to identify new molecular markers to predict the response to neoadjuvant chemotherapy. Transcripts that do not encode proteins, termed long noncoding RNAs (lncRNAs), have been shown to display abnormal expression profiles in different types of cancer, but their role as biomarkers in response to neoadjuvant chemotherapy has not been extensively studied. Herein, lncRNA expression was profiled using RNA sequencing in biopsies from patients who subsequently showed either response or no response to treatment. The GATA3-AS1transcript was overexpressed in the nonresponder group and was the most stable feature when performing selection in multiple random forest models. GATA3-AS1was experimentally validated by RT-qPCR in an extended group of 68 patients. Expression analysis confirmed that GATA3-AS1is overexpressed primarily in patients who were nonresponsive to neoadjuvant chemotherapy, with a sensitivity of 92.9%, a specificity of 75.0%, and an area under the curve of approximately 0.90, as measured by receiver operating characteristic curve analysis. The statistical model was based on luminal B-like patients and adjusted by menopausal status and phenotype (odds ratio, 37.49; 95% CI, 6.74–208.42; P = 0.001); GATA3-AS1was established as an independent predictor of response. Thus, lncRNA GATA3-AS1is proposed as a potential predictive biomarker of nonresponse to neoadjuvant chemotherapy.