Cancer Informatics最新文献

Objectives: Immunogenic cell death (ICD) has been demonstrated to play a critical role in the development and progression of malignant tumors by modulating the anti-tumor immune response. However, its function in cervical cancer (CC) remains largely unexplored. In this study, we aimed to construct an ICD-related gene signature to predict patient prognosis and immune cell infiltration in CC.

Methods: The gene expression profiles and clinical data of CC were downloaded from The Cancer Genome Alas (TCGA) and Gene Expression Omnibus (GEO) datasets, serving as the training and testing groups, respectively. An ICD-related gene signature was developed using the LASSO-Cox model. The expression levels of the associated ICD-related genes were evaluated using single-cell data, CC cell lines, and clinical samples in vitro.

Results: Two ICD-associated subtypes (cluster 1 and cluster 2) were identified through consensus clustering. Patients classified into cluster 2 demonstrated higher levels of immune cell infiltration and exhibited a more favorable prognosis. Subsequently, an ICD-related gene signature comprising 3 genes (IL1B, IFNG, and FOXP3) was established for CC. Based on the median risk score, patients in both training and testing cohorts were segregated into high-risk and low-risk groups. Further analyses indicated that the estimated risk score functioned as an independent prognostic factor for CC and influenced immune cell abundance within the tumor microenvironment. The up-regulation of the identified ICD-related genes was further validated in CC cell lines and collected clinical samples.

Conclusion: In summary, the stratification based on ICD-related genes demonstrated strong efficacy in predicting patient prognosis and immune cell infiltration, which also provides valuable new perspectives for the diagnosis and prognosis of CC.

Objectives: Prostate cancer stem cells (CSCs) play an important role in cancer cell survival, proliferation, metastasis, and recurrence; thus, removing CSCs is important for complete cancer removal. However, the mechanisms underlying CSC functions remain largely unknown, making it difficult to develop new anticancer drugs targeting CSCs. Herein, we aimed to identify novel factors that regulate stemness and predict prognosis.

Methods: We reanalyzed 2 single-cell RNA sequencing data of prostate cancer (PCa) tissues using Seurat. We used gene set enrichment analysis (GSEA) to estimate CSCs and identified common upregulated genes in CSCs between these datasets. To investigate whether its expression levels change over CSC differentiation, we performed a trajectory analysis using monocle 3. In addition, GSEA helped us understand how the identified genes regulate stemness. Finally, to assess their clinical significance, we used the Cancer Genome Atlas database to evaluate their impact on prognosis.

Results: The expression of thioredoxin (TXN), a redox enzyme, was approximately 1.2 times higher in prostate CSCs than in PCa cells (P < 1 × 10^-10), and TXN expression decreased over CSC differentiation. In addition, GSEA suggested that intracellular signaling pathways, including MYC, may be involved in stemness regulation by TXN. Furthermore, TXN expression correlated with poor prognosis (P < .05) in PCa patients with high stemness.

Conclusions: Despite the limited sample size in our study and the need for further in vitro and in vivo experiments to demonstrate whether TXN functionally regulates prostate CSCs, our findings suggest that TXN may serve as a novel therapeutic target against CSCs. Moreover, TXN expression in CSCs could be a useful marker for predicting the prognosis of PCa patients.

Objectives: The TGF-β signaling pathway is widely acknowledged for its role in various aspects of cancer progression, including cellular invasion, epithelial-mesenchymal transition, and immunosuppression. Immune checkpoint inhibitors (ICIs) and pharmacological agents that target TGF-β offer significant potential as therapeutic options for cancer. However, the specific role of TGF-β in prognostic assessment and treatment strategies for breast cancer (BC) remains unclear.

Methods: The Cancer Genome Atlas (TCGA) database was utilized to develop a predictive model incorporating five TGF-β signaling-related genes (TSRGs). The GSE161529 dataset from the Gene Expression Omnibus was employed to conduct single-cell analyses aimed at further elucidating the characteristics of these TSRGs. Additionally, an unsupervised clustering algorithm was applied to categorize BC patients into two distinct groups based on the five TSRGs, with a focus on immune response and overall survival (OS). Further investigations were conducted to explore variations in pharmacotherapy and the tumor microenvironment across different patient cohorts and clusters.

Results: The predictive model for BC identified five TSRGs: FUT8, IFNG, ID3, KLF10, and PARD6A. Single-cell analysis revealed that IFNG is predominantly expressed in CD8+ T cells. Consensus clustering effectively categorized BC patients into two distinct clusters, with cluster B demonstrating a longer OS and a more favorable prognosis. Immunological assessments indicated a higher presence of immune checkpoints and immune cells in cluster B, suggesting a greater likelihood of responsiveness to ICIs.

Conclusion: The findings of this study highlight the potential of the TGF-β signaling pathway for prognostic classification and the development of personalized treatment strategies for BC patients, thereby enhancing our understanding of its significance in BC prognosis.

Objectives: Lung adenocarcinoma (LUAD), a predominant form of lung cancer, is characterized by a high rate of metastasis and recurrence, leading to a poor prognosis for LUAD patients. This study aimed to identify and rigorously validate a highly precise biomarker, Cathepsin L (CTSL), for the prognostic prediction of lung adenocarcinoma.

Methods: We employed a multicenter and omics-based approach, analyzing RNA sequencing data and mutation information from public databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The DepMap portal with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR/Cas9) technology was used to assess the functional impact of CTSL. Immunohistochemistry (IHC) was conducted on a local cohort to validate the prognostic significance of CTSL at the protein expression level.

Results: Our findings revealed a significant correlation between elevated CTSL expression and advanced disease stage in LUAD patients. Kaplan-Meier survival analysis and Cox regression modeling revealed that high CTSL expression is associated with poor overall survival. The in vitro studies corroborated these findings, revealing notable suppression of tumor proliferation following CTSL knockout in cell lines, particularly in LUAD. Functional enrichment revealed that CTSL activated pathways associated with tumor progression, such as angiogenesis and Transforming growth factor beta (TGF-beta) signaling, and inhibited pathways such as apoptosis and DNA repair. Mutation analysis revealed distinct variations in the CTSL expression groups.

Conclusion: This study highlights the crucial role of CTSL as a prognostic biomarker in LUAD. This combined multicenter and omics-based analysis provides comprehensive insights into the biological role of CTSL, supporting its potential as a target for therapeutic intervention and a marker for prognosis in patients with LUAD.

Objectives: Colorectal cancer (CRC) is a prevalent disease characterized by significant dysregulation of gene expression. Non-invasive tests that utilize microRNAs (miRNAs) have shown promise for early CRC detection. This study aims to determine the association between miRNAs and key genes in CRC.

Methods: Two datasets (GSE106817 and GSE23878) were extracted from the NCBI Gene Expression Omnibus database. Penalized logistic regression (PLR) and artificial neural networks (ANN) were used to identify relevant miRNAs and evaluate the classification accuracy of the selected miRNAs. The findings were validated through bipartite miRNA-mRNA interactions.

Results: Our analysis identified 3 miRNAs: miR-1228, miR-6765-5p, and miR-6787-5p, achieving a total accuracy of over 90%. Based on the results of the mRNA-miRNA interaction network, CDK1 and MAD2L1 were identified as target genes of miR-6787-5p.

Conclusions: Our results suggest that the identified miRNAs and target genes could serve as non-invasive biomarkers for diagnosing colorectal cancer, pending laboratory confirmation.

Objectives: Emerging evidence suggests that N6-methyladenosine (m⁶A) methylation plays a critical role in cancers through various mechanisms. This work aims to reveal the essential role of m⁶A methylation "readers" in regulation of cancer prognosis at the pan-cancer level.

Methods: Herein, we focused on one special protein family of the "readers" of m⁶A methylation, YT521-B homology (YTH) domain family genes, which were observed to be frequently dysregulated in tumor tissues and closely associated with cancer prognosis. Then, a comprehensive analysis of modulation in cancer prognosis was conducted by integrating RNA sequencing (RNAseq) datasets of YTH family genes and clinical information at the pan-cancer level.

Results: YTH family genes were significantly differentially expressed in most of the cancers, particularly increased in Gastrointestinal cancers, and decreased in Endocrine and Urologic cancers. In addition, they were observed to be associated with overall survival (OS) and disease-specific survival (DSS) with various extent, especially in lower grade glioma (LGG), thyroid cancer (THCA), liver hepatocellular carcinoma (LIHC) and kidney clear cell carcinoma (KIRC), so were some "writers" (METLL3, METLL14, WTAP) and "erasers" (FTO, ALKBH5). Further survival analysis illustrated that YTH family genes specifically YTHScore constructed by combining 5 YTH family genes, as well as RWEScore calculated by combining genes from "readers"-"writers"-"erasers" could dramatically distinguish tumor prognosis in 4 representative cancers. As expected, YTHScore presented an equally comparable prognostic classification with RWEScore. Finally, analysis of immune signatures and clinical characteristics implied that, the activity of the innate immune, diagnostic age, clinical stage, Tumor-Node-Metastasis (TNM) stage and immune types, might play specific roles in modulating tumor prognosis.

Conclusions: The study demonstrated that YTH family genes had the potential to predict tumor prognosis, in which the YTHScore illustrated equal ability to predict tumor prognosis compared to RWEScore, thus providing insights into prognostic biomarkers and therapeutic targets at the pan-cancer level.

Objectives: Breast cancer (BC) stands as the second-leading cause of female-specific cancer-related fatalities globally, necessitating comprehensive research to address its critical aspects. This study aimed to explore the time intervals between surgery and disease recurrence in BC patients and their survival utilizing various parametric and semi-parametric models.

Methods: After the examination of data collected from 2010 to 2021 at a BC Center in Tehran, Iran, 171 cases met the criteria for analysis out of 2246 datasets. Model fitting, was assessed through the Akaike Information Criterion (AIC), and indicated the logistic distribution as the most fit one among concurrent and independent variable models.

Results: The Cox proportional hazard regression model consistently demonstrated superior fitting, characterized by the lowest AIC values. The average age at diagnosis was 50.39 years, with a standard deviation of 11.13. Typical survival time was estimated 53.44 months, falling within a confidence interval of 51.41-55.48 months at a 95% confidence level. The 1-year survival rate was determined at 0.92 (95% CI: 0.89-0.94). Notably, patient age while cancer diagnosis, progesterone receptor (PR), tumor grade, and tumor stage were found to be statistically significant (P < .05) risk factors for prediction of BC recurrence after surgery in Iran by Cox model.

Conclusions: Our findings underscore the importance of further exploration and consideration of the identified risk factors in BC research and treatment strategies.

Objectives: For prostate cancer (PCa), hundreds of risk variants have been identified. It remains unknown whether the polygenic risk score (PRS) that combines the effects of these variants is also a sufficiently informative metric with relevance to the molecular mechanisms of carcinogenesis in prostate. We aimed to understand the biological basis of PRS and racial disparities in the cancer.

Methods: We performed a comprehensive analysis of the data generated (deposited in) by several genomic and/or transcriptomic projects (databases), including the GTEx, TCGA, 1000 Genomes, GEO and dbGap. PRS was constructed from 260 PCa risk variants that were identified by a recent trans-ancestry meta-analysis and contained in the GTEx dataset. The dosages of risk variants and the multi-ancestry effects on PCa incidence estimated by the meta-analysis were used in calculating individual PRS values.

Results: The following novel results were obtained from our analyses. (1) In normal prostate samples from healthy European Americans (EAs), the expression levels of 540 genes (termed PRS genes) were associated with the PRS (P < .01). (2) Ubiquitin-proteasome system in high-PRS individuals' prostates was more active than that in low-PRS individuals' prostates. (3) Nine PRS genes play roles in the cancer progression-relevant parts, which are frequently hit by somatic mutations in PCa, of PI3K-Akt/RAS-MAPK/mTOR signaling pathways. (4) The expression profiles of the top significant PRS genes in tumor samples were capable of predicting malignant PCa relapse after prostatectomy. (5) The transcriptomic differences between African American and EA samples were incompatible with the patterns of the aforementioned associations between PRS and gene expression levels.

Conclusions: This study provided unique insights into the relationship between PRS and the molecular mechanisms of carcinogenesis in prostate. The new findings, alongside the moderate but significant heritability of PCa susceptibility contributed by the risk variants, suggest the aptness and inaptness of PRS for explaining PCa and disparities.

Image-based diagnosis has become a crucial tool in the identification and management of various cancers, particularly lung and colon cancer. This review delves into the latest advancements and ongoing challenges in the field, with a focus on deep learning, machine learning, and image processing techniques applied to X-rays, CT scans, and histopathological images. Significant progress has been made in imaging technologies like computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), which, when combined with machine learning and artificial intelligence (AI) methodologies, have greatly enhanced the accuracy of cancer detection and characterization. These advances have enabled early detection, more precise tumor localization, personalized treatment plans, and overall improved patient outcomes. However, despite these improvements, challenges persist. Variability in image interpretation, the lack of standardized diagnostic protocols, unequal access to advanced imaging technologies, and concerns over data privacy and security within AI-based systems remain major obstacles. Furthermore, integrating imaging data with broader clinical information is crucial to achieving a more comprehensive approach to cancer diagnosis and treatment. This review provides valuable insights into the recent developments and challenges in image-based diagnosis for lung and colon cancers, underscoring both the remarkable progress and the hurdles that still need to be overcome to optimize cancer care.

Objectives: Patients with intermediate or advanced hepatocellular carcinoma (HCC) require repeated disease monitoring, prognosis assessment and treatment planning. In 2018, a novel machine learning methodology "survival path" (SP) was developed to facilitate dynamic prognosis prediction and treatment planning. One year after, a deep learning approach called Dynamic Deephit was developed. The performance of the two state-of-art models in dynamic prognostication have not been compared.

Methods: We trained and tested the SP and Dynamic DeepHit models in a large cohort of 2511 HCC patients using time-series data. The time-series data were converted into data of time slices, with an interval of three months. The time-dependent c-index for OS at given prediction time (t = 1, 6, 12, 18 months) and evaluation time (∆t = 3, 6, 9, 12, 18, 24, 36, 48 months) were compared.

Results: The comparison between SP model and Dynamic DeepHit-HCC model showed the latter had significant better performance at the time of initial admission. The time-dependent c-index of Dynamic DeepHit-HCC model gradually decreased with the extension of time (from 0.756 to 0.639 in the training set; from 0.787 to 0.661 in internal testing set; from 0.725 to 0.668 in multicenter testing set); while the time-dependent c-index of SP model displayed an increased trend (from 0.665 to 0.748 in the training set; from 0.608 to 0.743 in internal testing set; from 0.643 to 0.720 in multicenter testing set). When the prediction time comes to 6 months or later since initial treatment, the survival path model outperformed the dynamic DeepHit model at late evaluation times (∆t > 12 months).

Conclusions: This research highlighted the unique strengths of both models. The SP model had advantage in long term prediction while the Dynamic DeepHit-HCC model had advantages in prediction at near time points. Fine selection of models is needed in dealing with different scenarios.