Droplet digital PCR is the most robust method for absolute nucleic acid quantification. However, RNA is a very versatile molecule and its abundance is tissue-dependent. RNA quantification is dependent on a reference control to estimate the abundance. Additionally, in cancer, many cellular processes are deregulated which consequently affects the gene expression profiles. In this work, we performed microarray data mining of different childhood cancers and healthy controls. We selected four genes that showed no gene expression variations (PSMB6, PGGT1B, UBQLN2 and UQCR2) and four classical reference genes (ACTB, GAPDH, RPL4 and RPS18). Gene expression was validated in 40 acute lymphoblastic leukemia samples by means of droplet digital PCR. We observed that PSMB6, PGGT1B, UBQLN2 and UQCR2 were expressed ~100 times less than ACTB, GAPDH, RPL4 and RPS18. However, we observed excellent correlations among the new reference genes (p < 0.0001). We propose that PSMB6, PGGT1B, UBQLN2 and UQCR2 are housekeeping genes with low expression in childhood cancer.

Acute lymphoblastic leukemia (ALL) is a malignant neoplasm with the highest incidence in the pediatric population. Although the 5-year overall survival is greater than 85%, in emerging countries such as Mexico, the mortality rate is high. In Mexico, B-ALL is the most common type of childhood cancer; different characteristics suggest the presence of the disease; however, the prognosis is dependent on clinical and laboratory features, and no adverse prognostic molecular marker for B-ALL has yet been identified. The present research aimed to identify the prognostic value of HMMR expression in pediatric patients with B-ALL. The differential expression profile of B-ALL cells was determined via in silico analysis, and HMMR expression was subsequently measured via qRT–PCR and immunocytochemistry. The results were statistically analyzed via the ROUT test, Kolmogorov–Smirnov Z test, and Mann–Whitney U test. ROC curves and the Youden index were constructed, and Kaplan–Meier curves were plotted. We found that HMMR expression was increased in B-ALL patients (p < 0.0001). We observed that high expression was related to poor prognosis (p < 0.05). We observed that high expression was related to poor prognosis (p < 0.05). The increase in HMMR expression could be a potential early molecular prognostic marker and/or a new target in childhood B-ALL patients.

Leukemia is the most common childhood malignancy in Mexico, representing more than 50% of all childhood cancers. Although treatment leads to a survival of up to 90% in developing countries, in our country, it is less than 65%. Additionally, ~30% of patients relapse with poor prognosis. Alternative splicing plays an important role in transcriptome diversity and cellular biology. This mechanism promotes an increase in the assortment of proteins with potentially distinct functions from a single gene. The proliferating cell nuclear antigen (PCNA) gene encodes two transcripts for the same protein of 261 amino acids, which is associated with several important cellular processes and with several types of cancer. However, the diversity of the transcript variants expressed in this condition is not clear. Then, we used microarray gene expression to identify changes in the exon expression level of PCNA. The data were validated using RT-PCR and Sanger sequencing, and three additional transcripts (PCNA_V3, PCNA_V4, and PCNA_V5) were identified. Computational analyses were used to determine the potential proteins resulting, their structure, and interactions with PCNA native protein and themselves. Additionally, the PCNA transcript variants were inhibited using specific siRNA, determining that their inhibition contributes to the malignant characteristics in vitro. Finally, we quantified the PCNA transcript variants in acute lymphoblastic leukemia samples and identified their expression in this disease. Based on the clinical characteristics, we determined that PCNA_V2 and PCNA_V4 are expressed at significantly low levels in relapsed B-ALL patients. We conclude that the low expression of PCNA_V2 and PCNA_V4 could be a potential molecular marker of relapse in acute lymphoblastic leukemia patients.

Fifteen years ago, an omic study of medulloblastoma revealed the existence of four groups that are biologically and clinically different. Since then, various molecular classification methods have emerged, each with different diagnostic capabilities. Knowledge of these tools is essential for countries with low economic resources to be able to access the technology that is most within their reach since, at present, the precision of molecular classification is crucial for clinical practice, not only because of its relevance in the prognosis of patients but also because of its decisive impact on therapeutic strategies, which are essential for reducing mortality from this disease.

Background: Medulloblastoma is the childhood tumor with the highest morbidity and mortality worldwide. This type of cancer is characterized by a high degree of heterogeneity that gives rise to different molecular groups with disparities in the clinical presentation and prognosis. Among the molecular differences, one of the most relevant factors is alternative splicing, as it is responsible for transcriptomic diversity. EZH2 is a gene processed by alternative splicing that functions as an epigenetic regulator. In cancer, certain EZH2 mRNA variants are associated with tumorigenesis; however, in medulloblastoma, the alternative splicing pattern of EZH2 has not been studied. Currently, the best tool for identifying alternative splicing variants is long-read sequencing. Methods: We amplified the most variable region of EZH2 alternative splicing and used nanopore sequencing to obtain the transcriptional profile of the gene in patients with medulloblastoma. We verified the variants identified with Sanger sequencing and digital RT–PCR. Finally, we studied the relationship between the expression levels and the clinical–biological characteristics of the patients. Results: We identified seven mRNA variants of EZH2 expressed in medulloblastoma patients, five of which had not been reported previously. In addition, high expression of the novel variant EZH2_RetI8 was associated with patient mortality (p < 0.05). Conclusions: This is the first evidence of the EZH2 mRNA variant profile in medulloblastoma, revealing seven alternative transcripts, one of which is associated with patient mortality. This is a clear example of the complexity of the transcriptome and how long-read sequencing can resolve alternative splicing patterns.

Medulloblastoma is a heterogeneous solid tumor, and its molecular characteristics are the most important prognostic factors for this neoplasm. Unfortunately, the molecular classification of MB-G3 and MB-G-4 medulloblastoma is very complex because of molecular similarity. Therefore, in this work, through unsupervised machine learning-based gene expression profiling, we identified a low molecular profile associated with four molecular groups of medulloblastoma. We performed medulloblastoma expression microarray data mining via the Partek Genomics Suite and Transcriptome Analysis Console (TAC), and we included a total of 25 fresh medulloblastoma tumors that were obtained and hybridized into HG U133 Plus 2.0 Array microarrays. To identify the molecular groups of the 25 patients, we compared them against classified patients, which were obtained from free repositories, and through data mining based on gene expression, compared the expression profiles of our patients. To do so, we performed an analysis via the least squares method via PCA. The molecular groups MB-WNT and MB-SHH were confirmed via immunohistochemistry via β-catenin, YAP1 and GAB1 antibodies in tissue fixed in formalin and embedded in paraffin, and another tissue section was placed on a Visium Spatial slide to perform spatial RNA-seq via Illumina NextSeq 2000 platform sequencers. The data obtained were analyzed with R. We identified the expression profiles associated with the four molecular groups and formed a reference set. Through unsupervised analysis via the least squares method, we assigned the molecular profiles of 25 patients with medulloblastoma, via the integration of bulk and spatial tumor molecular gene expression profiling analysis and with immunohistochemical findings, this strategy was fast and accurate. We observed correlations in three of the trials carried out and, in part, in one study, a patient who presented two tumor strains and two molecular signatures (SHH and G4), which led us to believe that this patient presented mixed phenotypic characteristics. Multigene expression profile analysis of medulloblastoma represents a significant advance in precision medicine; integrating different layers of transcriptomic information allows us to demonstrate underlying molecular changes in the four molecular groups that are essential for personalized therapy.