SmulTCan: A Shiny application for multivariable survival analysis of TCGA data with gene sets

CGPA: multi-context insights from the cancer gene prognosis atlas

Cancer transcriptomic data are used extensively to interrogate the prognostic value of targeted genes, yet basic scientists and clinicians have predominantly relied on univariable survival analysis for this purpose. This method often fails to capture the full prognostic potential and contextual relevance of the genes under study, inadvertently omitting a group of genes we term univariable missed-opportunity prognostic (UMOP) genes. Recognizing the complexity of revealing multifaceted prognostic implications, especially when extending the analysis to include various covariates and thresholds, we present the Cancer Gene Prognosis Atlas (CGPA). This platform greatly enhances gene-centric biomarker research across cancer types by offering an interactive and user-friendly interface for highly customized, in-depth prognostic analysis. CGPA notably supports data-driven exploration of gene pairs and gene-hallmark relationships, elucidating key composite biological mechanisms like synthetic lethality and immunosuppression. It further expands its capabilities to assess multi-gene panels using both public and user-provided data, facilitating a seamless mechanism-to-machine analysis. Additionally, CGPA features a designated portal for discovering prognostic gene modules using curated cancer immunotherapy data. Ultimately, CGPA's comprehensive, accessible tools allow cancer researchers, including those without statistical expertise, to precisely investigate the prognostic landscape of genes, customizing the model to fit specific research hypotheses and enhancing biomarker discovery and validation through a synergy of mechanistic and data-driven strategies.

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance

Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.

Feature selection combined with top-down and bottom-up strategies for survival analysis: A case of prognostic prediction in glioblastoma

Over the last decades, molecular signatures have attracted extensive attention in cancer research. However, most of the reported biomarkers show a weak distinguishing ability in predicting the survival risks of patients. Actually, univariate analysis is generally considered in regression analysis, which makes the existing statistical methods ineffective. Furthermore, there is too much human involvement in the ways of classifying patients with high and low risk. Last but not least, the participation of therapy after conservative surgery also makes the survival analysis more complex. In order to solve these problems, we propose a solid method of feature selection which combines top-down and bottom-up strategies. The top-down strategy is to randomly extract some genes each time and select candidate genes through cumulative voting. The bottom-up strategy is to fully enumerate the selected genes and to use a clustering algorithm to classify samples. We analyzed glioblastoma data from the Cancer Genome Atlas (TCGA) and got candidate signatures. The results of simulation data, as well as an independent test set the Chinese Glioma Genome Atlas (CGGA), verified the reliability of the method and validity of the selected features.

Construction and Validation of a Novel Cuproptosis-Related Seven-lncRNA Signature to Predict the Outcomes, Immunotherapeutic Responses, and Targeted Therapy in Patients with Clear Cell Renal Cell Carcinoma

Background

Cuproptosis was recently recognized as a novel form of cell death, linked closely to the occurrence and progression of cancer. We aimed to identify prognostic cuproptosis-related long noncoding RNAs (lncRNAs) and build a risk signature to predict the prognosis and treatment responses of clear cell renal cell carcinoma (ccRCC) in this work.

Methods

LASSO-Cox regression was conducted to construct the signature based on prognostic cuproptosis-related lncRNAs (CR-lncRNAs). The signature's reliability and sensitivity were assessed by the Kaplan-Meier survival analysis and receiver operating characteristic analysis. External validation was performed via data from the International Cancer Genome Consortium database. On the basis of CR-lncRNAs, an lncRNA-microRNA-mRNA regulatory network was created, and functional enrichment analysis was used to investigate the underlying biological roles of these genes. In addition, the relationship between the risk signature and immunotherapy and targeted therapy responses was examined. Finally, the expression levels of seven candidate lncRNAs between tumor and normal cells were compared in vitro using quantitative real-time PCR.

Results

A seven-CR-lncRNA risk signature was constructed, which showed a stronger potential for survival prediction than standard clinicopathological features in patients with kidney cancer. Functional enrichment analysis showed that the CR-lncRNA risk signature was enriched in ion transport-related molecular functions as well as various immune-related biological processes. Furthermore, we discovered that individuals in the high-risk group were more likely than those in the low-risk group to respond to immunotherapy and targeted therapies with medications like sunitinib and pazopanib. Finally, quantitative real-time PCR revealed that the expression levels of seven candidate lncRNAs differed significantly between RCC and healthy kidney cells.

Conclusion

In summary, we generated a CR-lncRNA risk signature that may be utilized to predict outcomes in patients with ccRCC and responsiveness to immunotherapy and targeted treatment, potentially serving as a reference for clinical personalized medicine.

Comprehensive Molecular Analyses of Notch Pathway-Related Genes to Predict Prognosis and Immunotherapy Response in Patients with Gastric Cancer

Gastric cancer (GC) is a highly molecular heterogeneous tumor with unfavorable outcomes. The Notch signaling pathway is an important regulator of immune cell differentiation and has been associated with autoimmune disorders, the development of tumors, and immunomodulation caused by tumors. In this study, by developing a gene signature based on genes relevant to the Notch pathway, we could improve our ability to predict the outcome of patients with GC. From the TCGA database, RNA sequencing data of GC tumors and associated normal tissues were obtained. Microarray data were collected from GEO datasets. The Molecular Signature Database (MSigDB) was accessed in order to retrieve sets of human Notch pathway-related genes (NPRGs). The LASSO analysis performed on the TCGA cohort was used to generate a multigene signature based on prognostic NPRGs. In order to validate the gene signature, the GEO cohort was utilized. Using the CIBERSORT method, we were able to determine the amounts of immune cell infiltration in the GC. In this study, a total of 21 differentially expressed NPRGs were obtained between GC specimens and nontumor specimens. The construction of a prognostic prediction model for patients with GC involved the identification and selection of three different NPRGs. According to the appropriate cutoff value, the patients with GC were divided into two groups: those with a low risk and those with a high risk. The time-dependent ROC curves demonstrated that the new model had satisfactory performance when it came to prognostic prediction. Multivariate assays confirmed that the risk score was an independent marker that may be used to predict the outcome of GC. In addition, the generated nomogram demonstrated a high level of predictive usefulness. Moreover, the scores of immunological infiltration of the majority of immune cells were distinctly different between the two groups, and the low-risk group responded to immunotherapy in a significantly greater degree. According to the results of a functional enrichment study of candidate genes, there are multiple pathways and processes associated with cancer. Taken together, a new gene model associated with the Notch pathway may be utilized for the purpose of predicting the prognosis of GC. One potential method of treatment for GC is to focus on NPRGs.

Machine learning-based identification of a novel prognosis-related long noncoding RNA signature for gastric cancer

Gastric cancer (GC) is one of the most common malignancies with a poor prognosis. Immunotherapy has attracted much attention as a treatment for a wide range of cancers, including GC. However, not all patients respond to immunotherapy. New models are urgently needed to accurately predict the prognosis and the efficacy of immunotherapy in patients with GC. Long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and progression of cancers. Recent studies have identified a variety of prognosis-related lncRNA signatures in multiple cancers. However, these studies have some limitations. In the present study, we developed an integrative analysis to screen risk prediction models using various feature selection methods, such as univariate and multivariate Cox regression, least absolute shrinkage and selection operator (LASSO), stepwise selection techniques, subset selection, and a combination of the aforementioned methods. We constructed a 9-lncRNA signature for predicting the prognosis of GC patients in The Cancer Genome Atlas (TCGA) cohort using a machine learning algorithm. After obtaining a risk model from the training cohort, we further validated the model for predicting the prognosis in the test cohort, the entire dataset and two external GEO datasets. Then we explored the roles of the risk model in predicting immune cell infiltration, immunotherapeutic responses and genomic mutations. The results revealed that this risk model held promise for predicting the prognostic outcomes and immunotherapeutic responses of GC patients. Our findings provide ideas for integrating multiple screening methods for risk modeling through machine learning algorithms.

IOFS-SA: An interactive online feature selection tool for survival analysis

BACKGROUND

Survival analysis is a primary problem before clinical treatments to cancer patients after their operations. In order to make this kind of analysis simple, many corresponding tools have been proposed. Though these tools are easy to use, there exist still two fatal flaws. One is that sample grouping is commonly empirical and wrongly based on original gene expressions or survival time. The other is that their feature selection methods mostly depend univariate semi-supervised regression or the multivariate one without considering the small sample size compared with the high dimension.

OBJECTIVE

In order to solve the two problems, we design an automatic feature selection web tool which can also satisfy interactive sample grouping.

METHODS

An automatic feature selection is performed on user-defined data or TCGA data. users can also perform manual feature selection. Then, hierarchical clustering is used and an automatic re-clustering strategy is proposed after interactive risk score split. Kaplan-Meier survival curve and log-rank test are utilized as the measurement.

RESULTS

Experimental results on 53 datasets from TCGA demonstrate the effectiveness of our method. The tree view, heat map and scatter map can intuitively display the result of the selected genes to the doctors for further research.

CONCLUSIONS

This method is suitable for survival analysis of high-dimensional small sample data sets. At the same time, it also provides a platform for researchers to analyze custom data. It solves the problems of the existing web tools and provides an effective feature selection method for survival analysis.

AVAILABILITY

The full code package is freely available and can be downloaded at https://github.com/Yuan-23/IOFS-SA-ecp-data-main, and the online version at https://bioinfor.nefu.edu.cn/IOFS-SA/ is ready for use freely.

Doxorubicin induces prolonged DNA damage signal in cells overexpressing DEK isoform-2

DEK has a short isoform (DEK isoform-2; DEK2) that lacks amino acid residues between 49–82. The full-length DEK (DEK isoform-1; DEK1) is ubiquitously expressed and plays a role in different cellular processes but whether DEK2 is involved in these processes remains elusive. We stably overexpressed DEK2 in human bone marrow stromal cell line HS-27A, in which endogenous DEKs were intact or suppressed via short hairpin RNA (sh-RNA). We have found that contrary to ectopic DEK1, DEK2 locates in the nucleus and nucleolus, causes persistent γH2AX signal upon doxorubicin treatment, and couldn’t functionally compensate for the loss of DEK1. In addition, DEK2 overexpressing cells were more sensitive to doxorubicin than DEK1-cells. Expressions of DEK1 and DEK2 in cell lines and primary tumors exhibit tissue specificity. DEK1 is upregulated in cancers of the colon, liver, and lung compared to normal tissues while both DEK1 and DEK2 are downregulated in subsets of kidney, prostate, and thyroid carcinomas. Interestingly, only DEK2 was downregulated in a subset of breast tumors suggesting that DEK2 can be modulated differently than DEK1 in specific cancers. In summary, our findings show distinct expression patterns and subcellular location and suggest non-overlapping functions between the two DEK isoforms.

Discovery of Cancer-Specific and Independent Prognostic Gene Subsets of the Slit-Robo Family Using TCGA-PANCAN Datasets

The Slit-Robo family of axon guidance molecules works in concert, playing important roles in organ development and cancer. Expressions of individual Slit-Robo genes have been used in calculating univariable hazard ratios (HR_uni) for predicting cancer prognosis in the literature. However, Slit-Robo members do not act independently; hence, hazard ratios from multivariable Cox regression (HR_multi) on the whole gene set can further lead to identification of cancer-specific, novel, and independent prognostic gene pairs or modules. Herein, we obtained mRNA expressions of the Slit-Robo family consisting of four Robos (ROBO1/2/3/4) and three Slits (SLIT1/2/3), along with four types of survival outcome across cancers found in the Cancer Genome Atlas (TCGA). We used cluster heat maps to visualize closely associated pairs/modules of prognostic genes across 33 different cancers. We found a smaller number of significant genes in HR_multi than in HR_uni, suggesting that the former analysis was less redundant. High ROBO4 expression emerged as relatively protective within the family, in both types of HR analyses. Multivariable Cox regression, on the other hand, revealed significantly more HR signatures containing Slit-Robo pairs acting in opposing directions than those containing Slit-Slit or Robo-Robo pairs for disease-specific survival. Furthermore, we discovered, through the online app SmulTCan's lasso regression, Slit-Robo gene subsets that significantly differentiated between high- versus low-risk prognosis patient groups, particularly for renal cancers and low-grade glioma. The statistical pipeline reported herein can help test independent and significant pairs/modules within a codependent gene family for cancer prognostication, and thus should also prove useful in personalized/precision medicine research.