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Cervical cancer remains a leading cause of cancer-related deaths in women worldwide, with limited treatment options for advanced stages and therapy-resistant cases. Despite advances in treatment, the variability in the patient response to standard therapies underscores the need for molecular biomarkers to guide personalized treatment strategies. This study aimed to explore the transcriptomic changes associated with the therapeutic response in locally advanced cervical cancer, focusing on 770 immune-related genes. We employed a digital multiplexed gene expression analysis, comparing gene expression profiles between matching pre- and post-treatment samples. The results revealed the significant upregulation of C7 and EGR2 in the post-treatment samples, suggesting that enhanced immune activity is a key factor in therapeutic success. Conversely, IL17RB, S100A7, and SAA1 were upregulated in the pre-treatment samples, potentially indicating resistance mechanisms. Pathway enrichment analysis highlighted that the immune response and apoptosis pathways are crucial to post-treatment changes. These findings suggest that C7, EGR2, and IL17RB may serve as biomarkers for predicting therapeutic outcomes and could inform the development of more effective, individualized treatment strategies for cervical cancer. This study provides new insights into the molecular mechanisms underlying treatment response and resistance.

Cervical cancer remains a leading cause of cancer-related deaths in women worldwide, with limited treatment options for advanced stages and therapy-resistant cases. Despite advances in treatment, the variability in the patient response to standard therapies underscores the need for molecular biomarkers to guide personalized treatment strategies. This study aimed to explore the transcriptomic changes associated with the therapeutic response in locally advanced cervical cancer, focusing on 770 immune-related genes. We employed a digital multiplexed gene expression analysis, comparing gene expression profiles between matching pre- and post-treatment samples. The results revealed the significant upregulation of C7 and EGR2 in the post-treatment samples, suggesting that enhanced immune activity is a key factor in therapeutic success. Conversely, IL17RB, S100A7, and SAA1 were upregulated in the pre-treatment samples, potentially indicating resistance mechanisms. Pathway enrichment analysis highlighted that the immune response and apoptosis pathways are crucial to post-treatment changes. These findings suggest that C7, EGR2, and IL17RB may serve as biomarkers for predicting therapeutic outcomes and could inform the development of more effective, individualized treatment strategies for cervical cancer. This study provides new insights into the molecular mechanisms underlying treatment response and resistance.

Tag-Seq is a high-throughput approach used for discovering SNPs and characterizing gene expression. In comparison to RNA-Seq, Tag-Seq eases data processing and allows detection of rare mRNA species using only one tag per transcript molecule. However, reduced library complexity raises the issue of PCR duplicates, which distort gene expression levels. Here we present a novel Tag-Seq protocol that uses the least biased methods for RNA library preparation combined with a novel approach for joint PCR template and sample labeling. In our protocol, input RNA is fragmented by hydrolysis, and poly(A)-bearing RNAs are selected and directly ligated to mixed DNA—RNA P5 adapters. The P5 adapters contain i5 barcodes composed of sample-specific (moderately) degenerate base regions (mDBRs), which later allow detection of PCR duplicates. The P7 adapter is attached via reverse transcription with individual i7 barcodes added during the amplification step. The resulting libraries can be sequenced on an Illumina sequencer. After sample demultiplexing and PCR duplicate removal with a free software tool we designed, the data are ready for downstream analysis. Our protocol was tested on RNA samples from predator-induced and control Daphnia microcrustaceans.

Background Gene expression profiling (GEP) studies recognized a prognostic role for tumor microenvironment (TME) in diffuse large B-cell lymphoma (DLBCL), but the routinely adoption of prognostic stromal signatures remains limited. Patients and methods Here, we applied the computational method CIBERSORT to generate a 1028-gene matrix incorporating signatures of 17 immune and stromal cytotypes. Then, we carried out a deconvolution on publicly available GEP data of 482 untreated DLBCLs to reveal associations between clinical outcomes and proportions of putative tumor-infiltrating cell types. Forty-five genes related to peculiar prognostic cytotypes were selected and their expression digitally quantified by NanoString technology on a validation set of 175 formalin-fixed, paraffin-embedded DLBCLs from two randomized trials. Data from an unsupervised clustering analysis were used to build a model of clustering assignment, whose prognostic value was also assessed on an independent cohort of 40 cases. All tissue samples consisted of pretreatment biopsies of advanced-stage DLBCLs treated by comparable R-CHOP/R-CHOP-like regimens. Results In silico analysis demonstrated that higher proportion of myofibroblasts (MFs), dendritic cells, and CD4+ T cells correlated with better outcomes and the expression of genes in our panel is associated with a risk of overall and progression-free survival. In a multivariate Cox model, the microenvironment genes retained high prognostic performance independently of the cell-of-origin (COO), and integration of the two prognosticators (COO   +   TME) improved survival prediction in both validation set and independent cohort. Moreover, the major contribution of MF-related genes to the panel and Gene Set Enrichment Analysis suggested a strong influence of extracellular matrix determinants in DLBCL biology. Conclusions Our study identified new prognostic categories of DLBCL, providing an easy-to-apply gene panel that powerfully predicts patients' survival. Moreover, owing to its relationship with specific stromal and immune components, the panel may acquire a predictive relevance in clinical trials exploring new drugs with known impact on TME. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are small (20-24 nucleotide long) endogenous regulatory RNAs that play important roles in plant growth and development. They regulate gene expression at the post-transcriptional level by translational repression or target degradation and gene silencing. In this study, we identified 116 conserved miRNAs belonging to 23 families from the flax ( Linum usitatissimum L.) genome using a computational approach. The precursor miRNAs varied in length; while most of the mature miRNAs were 21 nucleotide long, intergenic and showed conserved signatures of RNA polymerase II transcripts in their upstream regions. Promoter region analysis of the flax miRNA genes indicated prevalence of MYB transcription factor binding sites. Four miRNA gene clusters containing members of three phylogenetic groups were identified. Further, 142 target genes were predicted for these miRNAs and most of these represent transcriptional regulators. The miRNA encoding genes were expressed in diverse tissues as determined by digital expression analysis as well as real-time PCR. The expression of fourteen miRNAs and nine target genes was independently validated using the quantitative reverse transcription PCR (qRT-PCR). This study suggests that a large number of conserved plant miRNAs are also found in flax and these may play important roles in growth and development of flax. [ABSTRACT FROM AUTHOR]

Gene expression profiling holds tremendous promise for dissecting the regulatory mechanisms and transcriptional networks that underlie biological processes. Here we provide details of approaches used by others and ourselves for gene expression profiling in plants with emphasis on cDNA microarrays and discussion of both experimental design and downstream analysis. We focus on methods and techniques emphasizing fabrication of cDNA microarrays, fluorescent labeling, cDNA hybridization, experimental design, and data processing. We include specific examples that demonstrate how this technology can be used to further our understanding of plant physiology and development (specifically fruit development and ripening) and for comparative genomics by comparing transcriptome activity in tomato and pepper fruit. [ABSTRACT FROM AUTHOR]

Tag-Seq is a high-throughput approach used for discovering SNPs and characterizing gene expression. In comparison to RNA-Seq, Tag-Seq eases data processing and allows detection of rare mRNA species using only one tag per transcript molecule. However, reduced library complexity raises the issue of PCR duplicates, which distort gene expression levels. Here we present a novel Tag-Seq protocol that uses the least biased methods for RNA library preparation combined with a novel approach for joint PCR template and sample labeling. In our protocol, input RNA is fragmented by hydrolysis, and poly(A)-bearing RNAs are selected and directly ligated to mixed DNA-RNA P5 adapters. The P5 adapters contain i5 barcodes composed of sample-specific (moderately) degenerate base regions (mDBRs), which later allow detection of PCR duplicates. The P7 adapter is attached via reverse transcription with individual i7 barcodes added during the amplification step. The resulting libraries can be sequenced on an Illumina sequencer. After sample demultiplexing and PCR duplicate removal with a free software tool we designed, the data are ready for downstream analysis. Our protocol was tested on RNA samples from predator-induced and control Daphnia microcrustaceans.