Scientists from nearly all disciplines face the problem of simultaneously evaluating many hypotheses. Conducting multiple comparisons increases the likelihood that a non-negligible proportion of ...associations will be false positives, clouding real discoveries. Drawing valid conclusions require taking into account the number of performed statistical tests and adjusting the statistical confidence measures. Several strategies exist to overcome the problem of multiple hypothesis testing. We aim to summarize critical statistical concepts and widely used correction approaches while also draw attention to frequently misinterpreted notions of statistical inference. We provide a step-by-step description of each multiple-testing correction method with clear examples and present an easy-to-follow guide for selecting the most suitable correction technique. To facilitate multiple-testing corrections, we developed a fully automated solution not requiring programming skills or the use of a command line. Our registration free online tool is available at www.multipletesting.com and compiles the five most frequently used adjustment tools, including the Bonferroni, the Holm (step-down), the Hochberg (step-up) corrections, allows to calculate False Discovery Rates (FDR) and q-values. The current summary provides a much needed practical synthesis of basic statistical concepts regarding multiple hypothesis testing in a comprehensible language with well-illustrated examples. The web tool will fill the gap for life science researchers by providing a user-friendly substitute for command-line alternatives.
Multiple studies suggested using different miRNAs as biomarkers for prognosis of hepatocellular carcinoma (HCC). We aimed to assemble a miRNA expression database from independent datasets to enable ...an independent validation of previously published prognostic biomarkers of HCC. A miRNA expression database was established by searching the TCGA (RNA-seq) and GEO (microarray) repositories to identify miRNA datasets with available expression and clinical data. A PubMed search was performed to identify prognostic miRNAs for HCC. We performed a uni- and multivariate Cox regression analysis to validate the prognostic significance of these miRNAs. The Limma R package was applied to compare the expression of miRNAs between tumor and normal tissues. We uncovered 214 publications containing 223 miRNAs identified as potential prognostic biomarkers for HCC. In the survival analysis, the expression levels of 55 and 84 miRNAs were significantly correlated with overall survival in RNA-seq and gene chip datasets, respectively. The most significant miRNAs were hsa-miR-149, hsa-miR-139, and hsa-miR-3677 in the RNA-seq and hsa-miR-146b-3p, hsa-miR-584, and hsa-miR-31 in the microarray dataset. Of the 223 miRNAs studied, the expression was significantly altered in 102 miRNAs in tumors compared to normal liver tissues. In summary, we set up an integrated miRNA expression database and validated prognostic miRNAs in HCC.
Potential prognostic biomarker candidates for hepatocellular carcinoma (HCC) are abundant, but their generalizability is unexplored. We cross-validated markers of overall survival (OS) and vascular ...invasion in independent datasets.
The literature search yielded 318 genes related to survival and 52 related to vascular invasion. Validation was performed in three datasets (RNA-seq,
= 371; Affymetrix arrays,
= 91; Illumina gene chips,
= 135) by uni- and multivariate Cox regression and Mann-Whitney
-test, separately for Asian and Caucasian patients.
One hundred and eighty biomarkers remained significant in Asian and 128 in Caucasian subjects at
< 0.05. After multiple testing correction
(
= 1.9 × 10
),
(
= 2.5 × 10
) and
(
= 3 × 10
) endured as best performing genes in Asian patients; however, none remained significant in the Caucasian cohort. In a multivariate analysis, significance was reached by stage (
= 0.0018) and expression of
(
= 0.0038) and
(
= 0.038).
was the only gene predicting vascular invasion in the Affymetrix and Illumina cohorts (
= 0.003 and
= 0.025, respectively).
Overall, about half of biomarker candidates failed to retain prognostic value and none were better than stage predicting OS. Impact: Our results help to eliminate biomarkers with limited capability to predict OS and/or vascular invasion.
•Single-level data analysis produced by high-throughput technologies is limited by showing only a narrow window of cellular functions.•Data integration across different platforms, including genomics, ...epigenomics, transcriptomics, proteomics, and metabolomics, provides opportunities to understand causal relationships across multiple levels of cellular organization.•We review some of the most frequently used frameworks for multi-omics data integration.•We consider the significance of multi-omics in the functional identification of driver genomic alterations and discuss methods developed to exploit associations between mutations and downstream signaling pathways.•We provide an overview of the utility of multi-omics in tumor classifications, prognostications, diagnostics, and the role of data integration in the quest for novel biomarkers and therapeutic opportunities.•Translation of multi-omics technologies into tools accessible in daily medical routine is slow. One major obstacle is the uneven maturity of different omics approaches for routine clinical applications.
While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation.
We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations.
Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.
Medulloblastoma (MB) is the most common malignant childhood tumor of the brain. Multimodal treatment consisting of surgery, radiation therapy, and chemotherapy reduced cumulative incidence of late ...mortality but increased the incidence of subsequent neoplasms and severe, incapacitating chronic health conditions. Present treatment strategies fail to recognize heterogeneity within patients despite wide divergence in individual responses. The persistent mortality rates and serious side effects of non-targeted cytotoxic therapies indicate a need for more refined therapeutic approaches. Advanced genomic research has led to the accumulation of an enormous amount of genetic information and resulted in a consensus distinguishing four molecular subgroups, WNT-activated, SHH-activated, and Group 3 and 4 medulloblastomas. These have distinct origin, demographics, molecular alterations, and clinical outcomes. Although subgroup affiliation does not predict response to therapy, new subgroup-specific markers of prognosis can enable a more layered risk stratification with additional subtypes within each primary subgroup. Here, we summarize subgroup-specific genetic alterations and their utility in current treatment strategies. The transition toward molecularly targeted interventions for newly diagnosed MBs remains slow, and prospective trials are needed to confirm stratifications based on molecular alterations. At the same time, numerous studies focus at fine-tuning the intensity of invasive radio- and chemotherapies to reduce intervention-related long-term morbidity. There are an increasing number of immunotherapy-based treatment strategies including immune checkpoint-inhibitors, oncolytic viruses, CAR-T therapy, and NK cells in recurrent and refractory MBs. Although most trials are in early phase, there is hope for therapeutic breakthroughs for advanced MBs within the next decade.
Renewed interest in tumor metabolism sparked an enthusiasm for dietary interventions to prevent and treat cancer. Changes in diet impact circulating nutrient levels in the plasma and the tumor ...microenvironment, and preclinical studies suggest that dietary approaches, including caloric and nutrient restrictions, can modulate tumor initiation, progression, and metastasis. Cancers are heterogeneous in their metabolic dependencies and preferred energy sources and can be addicted to glucose, fructose, amino acids, or lipids for survival and growth. This dependence is influenced by tumor type, anatomical location, tissue of origin, aberrant signaling, and the microenvironment.
This review summarizes nutrient dependencies and the related signaling pathway activations that provide targets for nutritional interventions. We examine popular dietary approaches used as adjuvants to anticancer therapies, encompassing caloric restrictions, including time-restricted feeding, intermittent fasting, fasting-mimicking diets (FMDs), and nutrient restrictions, notably the ketogenic diet. Despite promising results, much of the knowledge on dietary restrictions comes from in vitro and animal studies, which may not accurately reflect real-life situations. Further research is needed to determine the optimal duration, timing, safety, and efficacy of dietary restrictions for different cancers and treatments. In addition, well-designed human trials are necessary to establish the link between specific metabolic vulnerabilities and targeted dietary interventions. However, low patient compliance in clinical trials remains a significant challenge.
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The introduction of trastuzumab for anti-HER2 therapy dramatically changed the clinical outcome for HER2 (ERBB2, neu) positive breast cancer patients. Today, patients eligible for trastuzumab are ...selected using HER2 expression/amplification status of the primary tumor. However, acquired and inherent resistance to anti-HER2 therapy in these patients poses a significant challenge, and better patient stratification will be needed to improve clinical response. Here, we provide a wide-ranging overview of potential biomarkers capable of stratifying patients regarding their response to trastuzumab. These include HER2 amplification, impaired access to the binding site (p95HER2, Δ16HER-2, MUC4), augmented signaling through other ERBB family receptors (HER1, HER3, HER4) and their ligands, activation of HER2 targets by alternate heterodimers (EphA2, IGF-1R, GDF15, MUC1*), signaling triggered by downstream members (PIK3CA, PTEN, SRC, mTOR), altered expression of cell cycle and apoptotic regulators (CDKs, p27(kip1), Bcl-2), hormone receptor status, resistance to antibody-dependent cellular cytotoxicity (FcγR), and altered miRNA expression signatures. Multigenic molecular profile analyses have revealed further genes not directly associated with classical oncogenic pathways. Although numerous biomarkers have shown promise in pre-clinical studies, many have delivered controversial results when evaluated in clinical trials. One of the keys for targeting ERBB2 will be to consider the entire ERBB family and downstream associated pathways responsible for the malignant transformation. The heterogeneity of the disease is likely to represent a significant obstacle to accurately predicting the course of resistance. The future most probably involves the incorporation of multiple biomarkers into a unified predictor enabling selection of patients for superior targeted drug administration.
The overall prognosis for colorectal cancer (CRC) remains challenging as the survival time varies widely, even in patients with the same stage of disease. Recent studies suggest prognostic relevance ...of the novel markers of systemic inflammation, the systemic immune–inflammation index (SII), and the systemic inflammation response index (SIRI). We conducted a comprehensive meta-analysis to assess the prognostic significance of the SII and the SIRI in CRC. We searched the relevant literature for observational studies, and random effects models were employed to conduct a statistical analysis using the metaanalysisonline.com platform. Pooled effect sizes were reported with hazard ratios (HRs) and corresponding 95% confidence intervals (CI). Data from 29 studies published between 2016 and 2024, comprising 10,091 participants, were included in our meta-analysis on SII. CRC patients with high SII levels had worse disease outcomes, which were associated with poor OS (HR: 1.75; 95% CI: 1.4–2.19) and poor PFS/DFS/RFS (HR: 1.25; 95% CI: 1.18–1.33). This increased risk of worse OS was present irrespective of the treatment strategy, sample size (<220 and ≥220), and cutoff used to define high and low SII (<550 and ≥550) groups. Based on data from five studies comprising 2362 participants, we found a strong association between the high SIRI and worse OS (HR: 2.65; 95% CI: 1.6–4.38) and DFS/RFS (HR: 2.04; 95% CI: 1.42–2.93). According to our results, both the SII and SIRI hold great promise as prognostic markers in CRC. Further validations are needed for their age- and stage-specific utility in the clinical routine.
The hallmarks of cancer capture the most essential phenotypic characteristics of malignant transformation and progression. Although numerous factors involved in this multi-step process are still ...unknown to date, an ever-increasing number of mutated/altered candidate genes are being identified within large-scale cancer genomic projects. Therefore, investigators need to be aware of available and appropriate techniques capable of determining characteristic features of each hallmark.
We review the methods tailored to experimental cancer researchers to evaluate cell proliferation, programmed cell death, replicative immortality, induction of angiogenesis, invasion and metastasis, genome instability, and reprogramming of energy metabolism. Selecting the ideal method is based on the investigator's goals, available equipment and also on financial constraints. Multiplexing strategies enable a more in-depth data collection from a single experiment — obtaining several results from a single procedure reduces variability and saves time and relative cost, leading to more robust conclusions compared to a single end point measurement. Each hallmark possesses characteristics that can be analyzed by immunoblot, RT-PCR, immunocytochemistry, immunoprecipitation, RNA microarray or RNA-seq. In general, flow cytometry, fluorescence microscopy, and multiwell readers are extremely versatile tools and, with proper sample preparation, allow the detection of a vast number of hallmark features. Finally, we also provide a list of hallmark-specific genes to be measured in transcriptome-level studies.
Although our list is not exhaustive, we provide a snapshot of the most widely used methods, with an emphasis on methods enabling the simultaneous evaluation of multiple hallmark features.
•Selecting appropriate in vitro assays to monitor hallmarks of cancer is challenging.•We cover frequently used methods for analyzing each hallmark.•Principles, advantages, and limitations are discussed for each method.•Procedures supporting the study of multiple hallmarks simultaneously are highlighted.
Despite much effort on the treatment of breast cancer over the decades, a great uncertainty regarding the appropriate molecular biomarkers and optimal therapeutic strategy still exists. This research ...was performed to analyze the association of SPAG5 gene expression with clinicopathological factors and survival outcomes.
We used a breast cancer database including 5667 patients with a mean follow-up of 69 months. Kaplan-Meier survival analyses for relapse free survival (RFS), overall survival (OS), and distant metastasis-free survival (DMFS) were performed. In addition, ROC analysis was performed to validate SPAG5 as a prognostic candidate gene.
Mean SPAG5 expression value was significantly higher with some clinicopathological factors that resulted in tumor promotion and progression, including poor differentiated type, HER2 positive or TP53 mutated breast cancer. Based on ROC-analysis SPAG 5 is a suitable prognostic marker of poor survival. In patients who received chemotherapy alone, SPAG5 had only a moderate and not significant predictive impact on survival outcomes. However, in hormonal therapy, high SPAG5 expression could strongly predict prognosis with detrimental RFS (HR = 1.57, 95% CI 1.2-2.06, p = 0.001), OS (HR = 2, 95% CI 1.05-3.8, p = 0.03) and DMFS (HR = 2.36, 95% CI 1.57-3.54, p < 0.001), respectively. In addition, SPAG5 could only serve as a survival predictor in ER+, but not ER- breast cancer patients. Patients might also be at an increased risk of relapse despite being diagnosed with a lower grade cancer (well differentiated type).
SPAG5 could be used as an independent prognostic and predictive biomarker that might have clinical utility, especially in ER+ breast cancer patients who received hormonal therapy.
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