Robust method for identification of prognostic gene signatures from gene expression profiles

VSTM2L is a promising therapeutic target and a prognostic soluble-biomarker in cholangiocarcinoma

The aim of the present study is to provide a rational background for silencing the V-set and transmembrane domain containing 2 like (VSTM2L) in consort with recognising soluble VSTM2L against cholangiocarcinoma. A therapeutic target against cholangiocarcinoma was selected using iterative patient partitioning (IPP) calculation, and it was verified by in vitro and in silico analyses. VSTM2L was selected as a potential therapeutic target against cholangiocarcinoma. Silencing the VSTM2L expression significantly attenuated the viability and survival of cholangiocarcinoma cells through blockade of the intracellular signalling pathway. In silico analysis showed that VSTM2L affected the positive regulation of cell growth in cholangiocarcinoma. Liptak's z value revealed that the expression of VSTM2L worsened the prognosis of cholangiocarcinoma patients. In addition, soluble VSTM2L was significantly detected in the whole blood of cholangiocarcinoma patients compared with that of healthy donors. Our report reveals that VSTM2L might be the potential therapeutic target and a soluble prognostic biomarker against cholangiocarcinoma. [BMB Reports 2024; 57(7): 324-329].

Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection

Simple Summary

Patient stratification is clinically important because it allows us to understand the characteristics and establish treatment strategies for a group. Transcriptomic data play an important role in determining molecular subtypes and predicting survival. In the case of breast cancer, although the order of prognosis according to molecular subtypes is well known, there is heterogeneity even within a subtype. Therefore, patient stratification considering both molecular subtypes and survival outcomes is required. In this study, a methodology to handle this problem is presented. A genetic algorithm is used to select a set of genes, and a risk score is assigned to each patient using their expression level. According to the risk score, patients are ordered and stratified considering molecular subtypes and survival outcomes. Consequently, informative genes for patient stratification with respect to both aspects could be nominated, and the usefulness of the risk score was shown through comparison with other indicators.

Abstract

Patient stratification is a clinically important task because it allows us to establish and develop efficient treatment strategies for particular groups of patients. Molecular subtypes have been successfully defined using transcriptomic profiles, and they are used effectively in clinical practice, e.g., PAM50 subtypes of breast cancer. Survival prediction contributed to understanding diseases and also identifying genes related to prognosis. It is desirable to stratify patients considering these two aspects simultaneously. However, there are no methods for patient stratification that consider molecular subtypes and survival outcomes at once. Here, we propose a methodology to deal with the problem. A genetic algorithm is used to select a gene set from transcriptome data, and their expression quantities are utilized to assign a risk score to each patient. The patients are ordered and stratified according to the score. A gene set was selected by our method on a breast cancer cohort (TCGA-BRCA), and we examined its clinical utility using an independent cohort (SCAN-B). In this experiment, our method was successful in stratifying patients with respect to both molecular subtype and survival outcome. We demonstrated that the orders of patients were consistent across repeated experiments, and prognostic genes were successfully nominated. Additionally, it was observed that the risk score can be used to evaluate the molecular aggressiveness of individual patients.

Soluble TGFBI aggravates the malignancy of cholangiocarcinoma through activation of the ITGB1 dependent PPARγ signalling pathway

BACKGROUND

Cholangiocarcinoma is a devastating cancer with a poor prognosis. Previous reports have presented conflicting results on the role of transforming growth factor-β-induced protein (TGFBI) in malignant cancers. Currently, our understanding of the role of TGFBI in cholangiocarcinoma is ambiguous. The aim of the present study was to investigate the role of TGFBI in human cholangiocarcinoma.

METHODS

Iterative patient partitioning (IPP) scoring and consecutive elimination methods were used to select prognostic biomarkers. mRNA and protein expression levels were determined using Gene Expression Omnibus (GEO), Western blot and ELISA analyses. Biological activities of selected biomarkers were examined using both in vitro and in vivo assays. Prognostic values were assessed using Kaplan-Meier and Liptak's z score analyses.

RESULTS

TGFBI was selected as a candidate cholangiocarcinoma biomarker. GEO database analysis revealed significantly higher TGFBI mRNA expression levels in cholangiocarcinoma tissues compared to matched normal tissues. TGFBI protein was specifically detected in a soluble form in vitro and in vivo. TGFBI silencing evoked significant anti-cancer effects in vitro. Soluble TGFBI treatment aggravated the malignancy of cholangiocarcinoma cells both in vitro and in vivo through activation of the integrin beta-1 (ITGB1) dependent PPARγ signalling pathway. High TGFBI expression was associated with a poor prognosis in patients with cholangiocarcinoma.

CONCLUSIONS

Our data suggest that TGFBI may serve as a promising prognostic biomarker and therapeutic target for cholangiocarcinoma.

Transcriptomic Profiling Identifies DCBLD2 as a Diagnostic and Prognostic Biomarker in Pancreatic Ductal Adenocarcinoma

Background: Accumulating evidence shows that the elevated expression of DCBLD2 (discoidin, CUB and LCCL domain-containing protein 2) is associated with unfavorable prognosis of various cancers. However, the correlation of DCBLD2 expression value with the diagnosis and prognosis of pancreatic ductal adenocarcinoma (PDAC) has not yet been elucidated. Methods: Univariate Cox regression analysis was used to screen robust survival-related genes. Expression pattern of selected genes was investigated in PDAC tissues and normal tissues from multiple cohorts. Kaplan–Meier (K–M) survival curves, ROC curves and calibration curves were employed to assess prognostic performance. The relationship between DCBLD2 expression and immune cell infiltrates was conducted by CIBERSORT software. Biological processes and KEGG pathway enrichment analyses were adopted to clarify the potential function of DCBLD2 in PDAC. Results: Univariate analysis, K–M survival curves and calibration curves indicated that DCBLD2 was a robust prognostic factor for PDAC with cross-cohort compatibility. Upregulation of DCBLD2 was observed in dissected PDAC tissues as well as extracellular vesicles from both plasma and serum samples of PDAC patients. Both DCBLD2 expression in tissue and extracellular vesicles had significant diagnostic value. Besides, DCBLD2 expression was correlated with infiltrating level of CD8⁺ T cells and macrophage M2 cells. Functional enrichment revealed that DCBLD2 might be involved in cell motility, angiogenesis, and cancer-associated pathways. Conclusion: Our study systematically analyzed the potential diagnostic, prognostic and therapeutic value of DCBLD2 in PDAC. All the findings indicated that DCBLD2 might play a considerably oncogenic role in PDAC with diagnostic, prognostic and therapeutic potential. These preliminary results of bioinformatics analyses need to be further validated in more prospective studies.

The clinical relevance of gene expression based prognostic signatures in colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent cancers, with more than one million new cases every year. In the last few decades, several advancements in therapeutic and preventative levels have reduced the mortality rate, but new biomarkers are required for improved prognosis. The alterations at the genetic and epigenetic level have been recognized as major players in tumorigenesis. The products of gene expression in the form of mRNA, microRNA, and long-noncoding RNA, have started to emerge as important regulatory molecules, playing an important role in cancer. Gene-expression based prognostic risk scores, which quantify and compare their expression, have emerged as promising biomarkers with enormous clinical value. These composite multi-gene models in which more than one gene is used to predict prognosis have been shown to be significantly effective in identifying patients with multiple clinico-pathological risks like overall mortality, response to chemotherapy, risk of metastasis, etc. The advent of microarray and advanced sequencing technologies have led to the generation of large datasets like TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), which have fueled the search for new biomarkers. Continuous evaluation of these candidate biomarkers in clinical settings is promising to improve the management of CRC. These composite gene signatures provide potential in identifying high-risk patients, which might help clinicians to better manage these patients and design appropriate personalized therapeutic interventions. In this review, we emphasize on composite prognostic scores from diverse resources with clinical utility in CRC.

Predicting the effect of 5-fluorouracil-based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles

It is critical to identify patients with stage II and III colorectal cancer (CRC) who will benefit from adjuvant chemotherapy (ACT) after curative surgery, while the only use of clinical factors is insufficient to predict this beneficial effect. In this study, we performed genetic algorithm (GA) to select ACT candidate genes, and built a predictive model of support vector machine (SVM) using gene expression profiles from the Gene Expression Omnibus database. The model contained four ACT candidate genes (EDEM1, MVD, SEMA5B, and WWP2) and TNM stage (stage II or III). After using Subpopulation Treatment Effect Pattern Plot to determine the optimal cutoff value of predictive scores, the validated patients from The Cancer Genome Atlas database can be divided into the predictive ACT-benefit/-futile groups. Patients in the predictive ACT-benefit group with 5-fluorouracil (5-Fu)-based ACT had significantly longer relapse-free survival (RFS) compared to those without ACT (P = .015); However, the difference in RFS in the predictive ACT-futile group was insignificant (P = .596). The multivariable analysis found that the predictive groups were significantly associated with the effect of ACT (P_interaction  = .011). Consequently, we developed a predictive model based on the SVM and GA algorithm which was further validated to define patients who benefit from ACT on recurrence.

Blockade of integrin α3 attenuates human pancreatic cancer via inhibition of EGFR signalling

The prognosis of pancreatic cancer remains dismal despite continuous and considerable efforts. Integrins (ITGs) are highly expressed in various malignant cancers. However, very few studies investigated the role of integrin α3 (ITGα3) in malignant cancers. Here, we determined the functional role of ITGα3 in pancreatic cancer. Analysis of public microarray databases and Western blot analysis indicated a unique expression of ITGα3 in human pancreatic cancer. Silencing ITGα3 expression significantly inhibited the viability and migration of human pancreatic cancer cells. Notably, ablation of ITGα3 expression resulted in a significant decrease of epidermal growth factor receptor (EGFR) expression compared with transfection of control-siRNA through an increased number of leucine-rich repeats and immunoglobulin-like domain protein 1 (LRIG1) expression. In addition, ablating ITGα3 inhibited tumour growth via blockade of EGFR signalling in vivo. Furthermore, the highly expressed ITGα3 led to a poor prognosis of pancreatic cancer patients. Our results provide novel insights into ITGα3-induced aggressive pancreatic cancer.