Cancer research has been significantly advanced by the integration of transcriptomic data through high-throughput sequencing technologies like RNA sequencing (RNA-seq). This paper reviews the transformative impact of transcriptomics on understanding cancer biology, focusing on the use of extensive datasets such as The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). While transcriptomic data provides crucial insights into gene expression patterns and disease mechanisms, the analysis is fraught with technical and biological biases. Technical biases include issues related to microarray, RNA-seq, and nanopore sequencing methods, while biological biases arise from factors like tumor heterogeneity and sample purity. Additionally, misinterpretations often occur when correlational data is erroneously assumed to imply causality or when bulk data is misattributed to specific cell types. This review emphasizes the need for researchers to understand and mitigate these biases to ensure accurate data interpretation and reliable clinical outcomes. By addressing these challenges, the paper aims to enhance the robustness of cancer research and improve the application of transcriptomic data in developing effective therapies and diagnostic tools.

Cancer is one of the most concerning public health issues and breast cancer is one of the most common cancers in the world. The immune cells within the tumor microenvironment regulate cancer development. In this study, single immune cell data sets were used to identify marker gene sets for exhausted CD8 + T cells (CD8Tex) in breast cancer. Machine learning methods were used to cluster subtypes and establish the prognostic models with breast cancer bulk data using the gene sets to evaluate the impacts of CD8Tex. We analyzed breast cancer overexpressing and survival-associated marker genes and identified CD8Tex hub genes in the protein–protein-interaction network. The relevance of the hub genes for CD8 + T-cells in breast cancer was evaluated. The clinical associations of the hub genes were analyzed using bulk sequencing data and spatial sequencing data. The pan-cancer expression, survival, and immune association of the hub genes were analyzed. We identified biomarker gene sets for CD8Tex in breast cancer. CD8Tex-based subtyping systems and prognostic models performed well in the separation of patients with different immune relevance and survival. CRTAM, CLEC2D, and KLRB1 were identified as CD8Tex hub genes and were demonstrated to have potential clinical relevance and immune therapy impact. This study provides a unique view of the critical CD8Tex hub genes for cancer immune therapy.

Background: Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide. Hypericumperforatum (HP) is a traditional herb that has been shown to have antidepressant effects, but its mechanism is unclear. This study aims to identify the molecular targets of HP for the treatment of MDD. Methods: We performed differential analysis and weighted gene co-expression network analysis (WGCNA) with blood mRNA expression cohort of MDD and healthy control to identify DEGs and significant module genes (gene list 1). Three databases, CTD, DisGeNET, and GeneCards, were used to retrieve MDD-related gene intersections to obtain MDD-predicted targets (gene list 2). The validated targets were retrieved from the TCMSP database (gene list 3). Based on these three gene lists, 13 key pathways were identified. The PPI network was constructed by extracting the intersection of genes and HP-validated targets on all key pathways. Key therapeutic targets were obtained using MCODE and machine learning (LASSO, SVM-RFE). Clinical diagnostic assessments (Nomogram, Correlation, Intergroup expression), and gene set enrichment analysis (GSEA) were performed for the key targets. In addition, immune cell analysis was performed on the blood mRNA expression cohort of MDD to explore the association between the key targets and immune cells. Finally, molecular docking prediction was performed for the targets of HP active ingredients on MDD. Results: Differential expression analysis and WGCNA module analysis yielded 933 potential targets for MDD. Three disease databases were intersected with 982 MDD-predicted targets. The TCMSP retrieved 275 valid targets for HP. Separate enrichment analysis intersected 13 key pathways. Five key targets (AKT1, MAPK1, MYC, EGF, HSP90AA1) were finally screened based on all enriched genes and HP valid targets. Combined with the signaling pathway and immune cell analysis suggested the effect of peripheral immunity on MDD and the important role of neutrophils in immune inflammation. Finally, the binding of HP active ingredients (quercetin, kaempferol, and luteolin) and all 5 key targets were predicted based on molecular docking. Conclusions: The active constituents of Hypericumperforatum can act on MDD and key targets and pathways of this action were identified.

Background: Cancer remains one of the most significant public health challenges worldwide. A widely recognized hallmark of cancer is the ability to sustain proliferative signaling, which is closely tied to various cell cycle processes. Centromere Protein A (CENPA), a variant of the standard histone H3, is crucial for selective chromosome segregation during the cell cycle. Despite its importance, a comprehensive pan-cancer bioinformatic analysis of CENPA has not yet been conducted. Methods: Data on genomes, transcriptomes, and clinical information were retrieved from publicly accessible databases. We analyzed CENPA’s genetic alterations, mRNA expression, functional enrichment, association with stemness, mutations, expression across cell populations and cellular locations, link to the cell cycle, impact on survival, and its relationship with the immune microenvironment. Additionally, a prognostic model for glioma patients was developed to demonstrate CENPA’s potential as a biomarker. Furthermore, drugs targeting CENPA in cancer cells were identified and predicted using drug sensitivity correlations and protein-ligand docking. Results: CENPA exhibited low levels of gene mutation across various cancers. It was found to be overexpressed in nearly all cancer types analyzed in TCGA, relative to normal controls, and was predominantly located in the nucleus of malignant cells. CENPA showed a strong association with the cancer cell cycle, particularly as a biomarker for the G2 phase. It also emerged as a valuable diagnostic and prognostic biomarker across multiple cancer types. In glioma, CENPA demonstrated reliable prognostic potential when used alongside other prognostic factors. Additionally, CENPA was linked to the immune microenvironment. Drugs such as CD-437, 3-Cl-AHPC, Trametinib, BI-2536, and GSK461364 were predicted to target CENPA in cancer cells. Conclusion: CENPA serves as a crucial biomarker for the cell cycle in cancers, offering both diagnostic and prognostic value.

Background: The mitotic DNA integrity checkpoint signaling pathway is potentially involved in cancers that regulate genomic stability where protein kinases play a pivotal role. 16 total protein kinase genes are involved in this pathway: ATM, BRSK1, CDK1, CDK2, CHEK1, CHEK2, MAP3K20, NEK11, PLK1, PLK2, PLK3, PRKDC, STK33, TAOK1, TAOK2, and TAOK3. This study aims to provide pan-cancer profiles of the protein kinases in mitotic DNA integrity checkpoint signaling gene set for potential prognostic and diagnostic purposes, as well as future potential therapeutic targets for cancer in a clinical setting. Methods: Multi-omic data was acquired for the 16 genes; over 9000 samples of 33 types of cancer were analyzed to create pan-cancer profiles of SNV, CNV, methylation, mRNA expression, pathway crosstalk, and microRNA regulation networks. Results: The SNV profile showed that most of these genes have a high SNV mutation frequency across some cancer types, such as UCEC and SKCM. The CNVs of some of these genes are associated with the survival of UCEC, KIRP, and LGG. BRCA, KIRC, LUAD, and STAD might be affected by the mRNA expression of these genes which might involve regulation of copy number, methylation, and miRNA. In addition, these genes also cross-talk with some known cancer pathways. Conclusion: The protein kinases in mitotic DNA integrity checkpoint signaling may play a role in cancer development and, with adequate research, could potentially be developed as biomarkers for cancer diagnosis and prognosis. However, further efforts are necessary to validate their clinical value for diagnosis and prognosis and to develop practical applications in clinical settings. Nevertheless, these pan-cancer profiles offer a better overall understanding as well as useful information for future reference regarding mitotic DNA integrity checkpoint signaling in cancer.