LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis.

In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.

DriverDBv4: a multi-omics integration database for cancer driver gene research.

Advancements in high-throughput technology offer researchers an extensive range of multi-omics data that provide deep insights into the complex landscape of cancer biology. However, traditional statistical models and databases are inadequate to interpret these high-dimensional data within a multi-omics framework. To address this limitation, we introduce DriverDBv4, an updated iteration of the DriverDB cancer driver gene database (http://driverdb.bioinfomics.org/). This updated version offers several significant enhancements: (i) an increase in the number of cohorts from 33 to 70, encompassing approximately 24 000 samples; (ii) inclusion of proteomics data, augmenting the existing types of omics data and thus expanding the analytical scope; (iii) implementation of multiple multi-omics algorithms for identification of cancer drivers; (iv) new visualization features designed to succinctly summarize high-context data and redesigned existing sections to accommodate the increased volume of datasets and (v) two new functions in Customized Analysis, specifically designed for multi-omics driver identification and subgroup expression analysis. DriverDBv4 facilitates comprehensive interpretation of multi-omics data across diverse cancer types, thereby enriching the understanding of cancer heterogeneity and aiding in the development of personalized clinical approaches. The database is designed to foster a more nuanced understanding of the multi-faceted nature of cancer.

Using bioinformatics approaches to identify survival-related oncomiRs as potential targets of miRNA-based treatments for lung adenocarcinoma.

Lung cancer is a major cause of cancer-associated deaths worldwide, and lung adenocarcinoma (LUAD) is the most common lung cancer subtype. Micro RNAs (miRNAs) regulate the pattern of gene expression in multiple cancer types and have been explored as potential drug development targets. To develop an oncomiR-based panel, we identified miRNA candidates that show differential expression patterns and are relevant to the worse 5-year overall survival outcomes in LUAD patient samples. We further evaluated various combinations of miRNA candidates for association with 5-year overall survival and identified a four-miRNA panel: miR-9-5p, miR-1246, miR-31-3p, and miR-3136-5p. The combination of these four miRNAs outperformed any single miRNA for predicting 5-year overall survival (hazard ratio [HR]: 3.47, log-rank p-value = 0.000271). Experiments were performed on lung cancer cell lines and animal models to validate the effects of these miRNAs. The results showed that singly transfected antagomiRs largely inhibited cell growth, migration, and invasion, and the combination of all four antagomiRs considerably reduced cell numbers, which is twice as effective as any single miRNA-targeted transfected. The in vivo studies revealed that antagomiR-mediated knockdown of all four miRNAs significantly reduced tumor growth and metastatic ability of lung cancer cells compared to the negative control group. The success of these in vivo and in vitro experiments suggested that these four identified oncomiRs may have therapeutic potential.

Identification of a Steroid Hormone-Associated Gene Signature Predicting the Prognosis of Prostate Cancer through an Integrative Bioinformatics Analysis.

The importance of anti-androgen therapy for prostate cancer (PC) has been well recognized. However, the mechanisms underlying prostate cancer resistance to anti-androgens are not completely understood. Therefore, identifying pharmacological targets in driving the development of castration-resistant PC is necessary. In the present study, we sought to identify core genes in regulating steroid hormone pathways and associating them with the disease progression of PC. The selection of steroid hormone-associated genes was identified from functional databases, including gene ontology, KEGG, and Reactome. The gene expression profiles and relevant clinical information of patients with PC were obtained from TCGA and used to examine the genes associated with steroid hormone. The machine-learning algorithm was performed for key feature selection and signature construction. With the integrative bioinformatics analysis, an eight-gene signature, including CA2, CYP2E1, HSD17B, SSTR3, SULT1E1, TUBB3, UCN, and UGT2B7 was established. Patients with higher expression of this gene signature had worse progression-free interval in both univariate and multivariate cox models adjusted for clinical variables. The expression of the gene signatures also showed the aggressiveness consistently in two external cohorts, PCS and PAM50. Our findings demonstrated a validated eight-gene signature could successfully predict PC prognosis and regulate the steroid hormone pathway.

Identification of DNA Damage Repair-Associated Prognostic Biomarkers for Prostate Cancer Using Transcriptomic Data Analysis.

In the recent decade, the importance of DNA damage repair (DDR) and its clinical application have been firmly recognized in prostate cancer (PC). For example, olaparib was just approved in May 2020 to treat metastatic castration-resistant PC with homologous recombination repair-mutated genes; however, not all patients can benefit from olaparib, and the treatment response depends on patient-specific mutations. This highlights the need to understand the detailed DDR biology further and develop DDR-based biomarkers. In this study, we establish a four-gene panel of which the expression is significantly associated with overall survival (OS) and progression-free survival (PFS) in PC patients from the TCGA-PRAD database. This panel includes DNTT, EXO1, NEIL3, and EME2 genes. Patients with higher expression of the four identified genes have significantly worse OS and PFS. This significance also exists in a multivariate Cox regression model adjusting for age, PSA, TNM stages, and Gleason scores. Moreover, the expression of the four-gene panel is highly correlated with aggressiveness based on well-known PAM50 and PCS subtyping classifiers. Using publicly available databases, we successfully validate the four-gene panel as having the potential to serve as a prognostic and predictive biomarker for PC specifically based on DDR biology.

Using bioinformatics approaches to investigate driver genes and identify BCL7A as a prognostic gene in colorectal cancer.

Colorectal cancer (CRC) results from the uncontrolled growth of cells in the colon, rectum, or appendix. The 5-year relative survival rate for patients with CRC is 65% and is correlated with the stage at diagnosis (being 91% for stage I at diagnosis versus 12% for stage IV). This study aimed to identify CRC driver genes to assist in the design of a cancer panel to detect gene mutations during clinical early-stage screening and identify genes for use in prognostic assessments and the evaluation of appropriate treatment options. First, we utilized bioinformatics approaches to analyze 354 paired sequencing profiles from The Cancer Genome Atlas (TCGA) to identify CRC driver genes and analyzed the sequencing profiles of 38 patients with >5 years of follow-up data to search for prognostic genes. The results revealed eight driver genes and ten prognostic genes. Next, the presence of the identified gene mutations was verified using tissue and blood samples from Taiwanese CRC patients. The results showed that the set identified gene mutations provide high coverage for driver gene screening, and APC, TP53, PIK3CA, and FAT4 could be detected in blood as ctDNA test targets. We further found that BCL7A gene mutation was correlated with prognosis in CRC (log-rank p-value = 0.02), and that mutations of BCL7A could be identified in ctDNA samples. These findings may be of value in clinical early cancer detection, disease monitoring, drug development, and treatment efforts in the future.

Systematic identification of clinically relevant miRNAs for potential miRNA-based therapy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD), the most common histological type of non-small cell lung cancer, is one of the most malignant and deadly diseases. Current treatments for advanced LUAD patients are far from ideal and require further improvements. Here, we utilized a systematic integrative analysis of LUAD microRNA sequencing (miRNA-seq) and RNA-seq data from The Cancer Genome Atlas (TCGA) to identify clinically relevant tumor suppressor miRNAs. Three miRNA candidates (miR-195-5p, miR-101-3p, and miR-338-5p) were identified based on their differential expressions, survival significance levels, correlations with targets, and an additive effect on survival among them. We further evaluated mimics of the three miRNAs to determine their therapeutic potential in inhibiting cancer progression. The results showed not only that each of the miRNA mimics alone but also the three miRNA mimics in combination were efficient at inhibiting tumor growth and progression with equal final concentrations, meaning that the three miRNA mimics in combination were more effective than the single miRNA mimics. Moreover, the combined miRNA mimics provided significant therapeutic effects in terms of reduced tumor volume and metastasis nodules in lung tumor animal models. Hence, our findings show the potential of using the three miRNAs in combination to treat LUAD patients with poor survival outcomes.

Identification and validation of a miRNA-based prognostic signature for cervical cancer through an integrated bioinformatics approach.

Cervical cancer is the fourth most common cancer in women worldwide. Increasing evidence has shown that miRNAs are related to the progression of cervical cancer. However, the mechanisms that affect the prognosis of cancer are still largely unknown. In the present study, we sought to identify miRNAs associated with poor prognosis of patient with cervical cancer, as well as the possible mechanisms regulated by them. The miRNA expression profiles and relevant clinical information of patients with cervical cancer were obtained from The Cancer Genome Atlas (TCGA). The selection of prognostic miRNAs was carried out through an integrated bioinformatics approach. The most effective miRNAs with synergistic and additive effects were selected for validation through in vitro experiments. Three miRNAs (miR-216b-5p, miR-585-5p, and miR-7641) were identified as exhibiting good performance in predicting poor prognosis through additive effects analysis. The functional enrichment analysis suggested that not only pathways traditionally involved in cancer but also immune system pathways might be important in regulating the outcome of the disease. Our findings demonstrated that a synergistic combination of three miRNAs may be associated, through their regulation of specific pathways, with very poor survival rates for patients with cervical cancer.

DriverDBv3: a multi-omics database for cancer driver gene research.

An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics' sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the 'Cancer' and 'Gene' sections. The 'Survival' panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, 'Survival Analysis' in 'Customized-analysis,' allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics' sophisticated information, and also constructed a Summary panel in the 'Cancer' and 'Gene' sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.

Impact of body-mass factors on setup displacement during pelvic irradiation in patients with lower abdominal cancer.

Background The aim of the study was investigate the impact of body-mass factors (BMF) on setup displacement during pelvic radiotherapy in patients with lower abdominal cancers. Patients and methods The clinical data of a training cohort composed of 60 patients with gynecological, rectal, or prostate cancer were analyzed. The daily alignment data from image-guided radiotherapy (IGRT) were retrieved. Setup errors for were assessed by systematic error (SE) and random error (RE) through the superior-inferior (SI), anterior-posterior (AP), and medial-lateral (ML) directions. Several BMFs and patient-related parameters were analyzed with binary logistic regression and receiver-operating characteristic curves. A scoring system was proposed to identify those with greater setup displacement during daily treatment. The results were validated by another cohort. Results A large hip lateral diameter correlated with a greater SI-SE and AP-SE, whereas a large umbilical AP diameter correlated with a greater ML-SE and ML-RE. A higher SI-RE was associated with a large hip circumference. The positive predictors for setup uncertainty were chosen to dichotomize patients into groups at high risk and low risk for setup displacement. Based on the scoring system, the adequate treatment margins for the SI direction in the high-and low-risk groups were 5.4 mm and 3.8 mm, whereas those for the ML direction were 8.2 mm and 4.2 mm, respectively. The validated cohort showed a similar trend. Conclusions Large BMFs including hip lateral diameter, hip circumference, and umbilical AP diameter are associated with greater setup uncertainty. Based on the scores, IGRT or required treatment margins can be adapted for patients with high risk features.

Increased risk of varicella-zoster virus infection in patients with breast cancer after adjuvant radiotherapy: A population-based cohort study.

BACKGROUND/PURPOSE: Limited evidence has been obtained on varicella-zoster virus (VZV) infection in patients with breast cancer as a complication related to adjuvant radiotherapy. We conducted a cohort study aimed to assess the risk of VZV infection in this patient setting. MATERIALS AND METHODS: We used the National Health Insurance Research Database to identify 65,981 patients with breast cancer in Taiwan who underwent breast surgery between 2000 and 2011. After a 1:1 propensity score match was obtained between patients with and without radiotherapy, a competing risk regression model was constructed to estimate the hazard ratios and the incidence rate difference (IRD) of VZV infection in the patients with breast cancer receiving radiotherapy and those not receiving radiotherapy. RESULTS: After adjusting for covariates, the radiotherapy cohort showed a 1.51-fold higher risk (95% confidence interval = 1.06-5.16, p = 0.02, IRD = 4.98/10000 person-years) of subsequent VZV infection than the nonradiotherapy cohort. Furthermore, VZV infection risk was 3.85-fold higher among patients aged >65 years who received radiotherapy than among those of the same age who did not receive radiotherapy (95% confidence interval = 1.1-13.4, p < 0.05, IRD = 11.09/10000 person-years). The risk increased with adjusted hazard ratio of 6.6 (95% confidence interval I = 1.51-28.8, p < 0.05, IRD = 32.01/10,000 person-years) and 7.08 (95% confidence interval = 1.64-30.5, p < 0.01, IRD = 35.72/10,000 person-years) in follow-up period less than 3 months and 3-5 months respectively. CONCLUSION: Radiotherapy was associated with an increased risk of VZV infection among patients with breast cancer. The risk was significantly higher in older patient (>65 years old) and/or those who received chemotherapy. Regular clinical follow-up and additional serological testing in the first 5 months after radiotherapy are recommended.

Biological imaging in clinical oncology: radiation therapy based on functional imaging.

Radiation therapy is one of the most effective tools for cancer treatment. In recent years, intensity-modulated radiation therapy has become increasingly popular in that target dose-escalation can be done while sparing adjacent normal tissues. For this reason, the development of measures to pave the way for accurate target delineation is of great interest. With the integration of functional information obtained by biological imaging with radiotherapy, strategies using advanced biological imaging to visualize metabolic pathways and to improve therapeutic index and predict treatment response are discussed in this article.

Early radiation-induced liver toxicities are associated with poor survival in patients with advanced hepatocellular carcinoma.

AIM: Little is known about the impact of radiotherapy associated early hepatic toxicities. This study is to investigate the risk factors and outcome of early radiation-induced liver disease (early-RILD) in patients with hepatocellular carcinoma. METHODS: One hundred patients with advanced hepatocellular carcinoma receiving hepatic radiotherapy were included in this retrospective analysis. All had no evidence of intrahepatic tumor progression within 3 months after initiating radiotherapy. The toxicities were graded according to the Common Terminology Criteria for adverse events version 4.0. Early-RILD was defined as any detectable events of RILD occurring during or within 2 weeks after the ending of radiotherapy. Patient- and radiotherapy-related data, and several staging/scoring parameters were retrieved for analysis. Logistic regression analysis was used to find risk factors for early-RILD. Cox regression model was performed to explore prognosticators for survival. RESULTS: Child-Turcotte-Pugh (CTP) score >5 was the predictor for early-RILD (odds ratio 5.38, P = 0.004). The incidence of early-RILD in patients with CTP scores 6/7 and 5 was 34% and 13.2%, respectively. Early-RILD and a Cancer of the Liver Italian Program (CLIP) score > 2 were the two prognostic factors associated with inferior overall survival (hazard ratio 2.79, P = 0.04; hazard ratio = 3.79, P = 0.04, respectively). The median overall survival for patients with early-RILD was 3.5 months compared with 12.7 months in those without this event. CONCLUSION: The occurrence of early-RILD is associated with high mortality. A CTP score >5 is the most informative factor predicting early-RILD.

Impact of body-mass factors on setup displacement in patients with head and neck cancer treated with radiotherapy using daily on-line image guidance.

BACKGROUND: To determine the impact of body-mass factors (BMF) before radiotherapy and changes during radiotherapy on the magnitude of setup displacement in patients with head and neck cancer (HNC). METHODS: The clinical data of 30 patients with HNC was analyzed using the alignment data from daily on-line on-board imaging from image-guided radiotherapy. BMFs included body weight, body height, and the circumference and bilateral thickness of the neck. Changes in the BMFs during treatment were retrieved from cone beam computed tomography at the 10th and 20th fractions. Setup errors for each patient were assessed by systematic error (SE) and random error (RE) through the superior-inferior (SI), anterior-posterior (AP), and medial-lateral (ML) directions, and couch rotation (CR). Using the median values of the BMFs as a cutoff, the impact of the factors on the magnitude of displacement was assessed by the Mann-Whitney U test. RESULTS: A higher body weight before radiotherapy correlated with a greater AP-SE (p = 0.045), SI-RE (p = 0.023), and CR-SE (p = 0.033). A longer body height was associated with a greater SI-RE (p = 0.002). A performance status score of 1 or 2 was related to a greater AP-SE (p = 0.043), AP-RE (p = 0.015), and SI-RE (p = 0.043). Among the ratios of the BMFs during radiotherapy, the values at the level of mastoid tip at the 20th fraction were associated with greater setup errors. CONCLUSIONS: To reduce setup errors in patients with HNC receiving RT, the use of on-line image-guided radiotherapy is recommended for patients with a large body weight or height, and a performance status score of 1-2. In addition, adaptive planning should be considered for those who have a large reduction ratio in the circumference (<1) and thickness (<0.94) over the level of the mastoid tip during the 20th fraction of treatment.