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Abstract This study aimed to investigate the role of cluster of differentiation 276 (CD276) in evaluating the prognosis of clear cell renal carcinoma (ccRCC) and to build a nomogram for predicting ccRCC progression post‐surgery. Using data downloaded from The Cancer Genome Atlas (TCGA) database, we constructed a Kaplan–Meier (KM) curve depicting the relationship between CD276 expression levels and the progression‐free interval (PFI) in 539 ccRCC cases. We further validated this by plotting a KM curve of the relationship between CD276 expression levels and PFI in 116 ccRCC patients from our hospital. Using clinical data collected from 116 patients, we identified independent risk factors affecting postoperative PFI in patients with ccRCC through univariate and multivariate COX analyses and created a nomogram for visual representation. Both TCGA and clinical data revealed a negative correlation between the expression levels of CD276 and PFI (p

Abstract Background Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS. Results Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. Conclusions Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach.

This study aimed to investigate the role of cluster of differentiation 276 (CD276) in evaluating the prognosis of clear cell renal carcinoma (ccRCC) and to build a nomogram for predicting ccRCC progression post‐surgery. Using data downloaded from The Cancer Genome Atlas (TCGA) database, we constructed a Kaplan–Meier (KM) curve depicting the relationship between CD276 expression levels and the progression‐free interval (PFI) in 539 ccRCC cases. We further validated this by plotting a KM curve of the relationship between CD276 expression levels and PFI in 116 ccRCC patients from our hospital. Using clinical data collected from 116 patients, we identified independent risk factors affecting postoperative PFI in patients with ccRCC through univariate and multivariate COX analyses and created a nomogram for visual representation. Both TCGA and clinical data revealed a negative correlation between the expression levels of CD276 and PFI (p < 0.05). Univariate COX analysis revealed that the prognostic nutritional index, neutrophil‐to‐lymphocyte ratio, platelet‐to‐lymphocyte ratio, systemic inflammatory index, World Health Organization grading, tumor diameter, CD276 expression levels, T stage, and N stage were related to PFI (p < 0.05). Furthermore, multivariate COX analysis indicated that tumor diameter and CD276 expression levels were independent risk factors for postoperative PFI in patients with ccRCC (p < 0.05). The calibration curve of the established nomogram exhibited a slope close to 1, with a Hosmer–Lemeshow goodness‐of‐fit test result of 2.335 and a p‐value of 0.311. In patients with ccRCC, a negative correlation was noted between tumor CD276 expression and PFI. The larger the tumor diameter and the higher the tumor CD276 expression level, the shorter is the PFI. [ABSTRACT FROM AUTHOR]

Background: Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS. Results: Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. Conclusions: Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach. [ABSTRACT FROM AUTHOR]

Abstract Background Thyroid cancer (THCA) has become increasingly common in recent decades, and women are three to four times more likely to develop it than men. Evidence shows that estrogen has a significant impact on THCA proliferation and growth. Nevertheless, the effects of estrogen-related genes (ERGs) on THCA stages, immunological infiltration, and treatment susceptibility have not been well explored. Methods Clinicopathological and transcriptome data of patients with THCA from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were cleaned before consensus clustering. Differential expression analysis was performed on the genes expressed between THCA and paraneoplastic tissues in TCGA, and Wayne analysis was performed on the ERGs obtained from the Gene Set Enrichment Analysis MsigDB and differentially expressed genes (DEGs). Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were used to identify the set of estrogen-related differentially expressed genes (ERDEGs) associated with progression-free intervals (PFI) and to establish a prediction model. Receiver operating characteristic curves were plotted to calculate the risk scores and PFI status to validate the predictive effect of the model. Enrichment analyses and immune infiltration analyses were performed to analyze DEGs between the high- and low-risk groups, and a nomogram plot was used in the risk model to predict the PFI of THCA. Results The expression of 120 ERDEGs differed significantly between the two groups (P < 0.05). Five (CD24, CAV1, TACC1, TIPARP, and HSD17B10) of the eight ERDEGs identified using univariate Cox and LASSO regression were validated via RT-qPCR and immunohistochemistry analysis of clinical tissue samples and were used for clinical staging and drug sensitivity analysis. Risk-DEGs were shown to be associated with immune modulation and tumor immune evasion, as well as defense systems, signal transduction, the tumor microenvironment, and immunoregulation. In 19 of the 28 immune cells, infiltration levels differed between the high- and low-risk groups. High-risk patients in the immunotherapy dataset had considerably shorter survival times than low-risk patients. Conclusion We identified and confirmed eight ERDEGs using a systematic analysis and screened sensitive drugs for ERDEGs. These results provide molecular evidence for the involvement of ERGs in controlling the immunological microenvironment and treatment response in THCA.

Background: Thyroid cancer (THCA) has become increasingly common in recent decades, and women are three to four times more likely to develop it than men. Evidence shows that estrogen has a significant impact on THCA proliferation and growth. Nevertheless, the effects of estrogen-related genes (ERGs) on THCA stages, immunological infiltration, and treatment susceptibility have not been well explored. Methods: Clinicopathological and transcriptome data of patients with THCA from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were cleaned before consensus clustering. Differential expression analysis was performed on the genes expressed between THCA and paraneoplastic tissues in TCGA, and Wayne analysis was performed on the ERGs obtained from the Gene Set Enrichment Analysis MsigDB and differentially expressed genes (DEGs). Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were used to identify the set of estrogen-related differentially expressed genes (ERDEGs) associated with progression-free intervals (PFI) and to establish a prediction model. Receiver operating characteristic curves were plotted to calculate the risk scores and PFI status to validate the predictive effect of the model. Enrichment analyses and immune infiltration analyses were performed to analyze DEGs between the high- and low-risk groups, and a nomogram plot was used in the risk model to predict the PFI of THCA. Results: The expression of 120 ERDEGs differed significantly between the two groups (P < 0.05). Five (CD24, CAV1, TACC1, TIPARP, and HSD17B10) of the eight ERDEGs identified using univariate Cox and LASSO regression were validated via RT-qPCR and immunohistochemistry analysis of clinical tissue samples and were used for clinical staging and drug sensitivity analysis. Risk-DEGs were shown to be associated with immune modulation and tumor immune evasion, as well as defense systems, signal transduction, the tumor microenvironment, and immunoregulation. In 19 of the 28 immune cells, infiltration levels differed between the high- and low-risk groups. High-risk patients in the immunotherapy dataset had considerably shorter survival times than low-risk patients. Conclusion: We identified and confirmed eight ERDEGs using a systematic analysis and screened sensitive drugs for ERDEGs. These results provide molecular evidence for the involvement of ERGs in controlling the immunological microenvironment and treatment response in THCA. [ABSTRACT FROM AUTHOR]

This study aimed to explore the prognostic and predictive value of autophagy-related lncRNAs in papillary thyroid carcinoma (PTC). The expression data of autophagy-related genes and lncRNAs of the PTC patients were obtained from TCGA database. Autophagy-related-differentially expressed lncRNAs (DElncs) were identified and used to establish the lncRNAs signature predicting patients’ progression-free interval (PFI) in the training cohort. Its performance was assessed in the training cohort, validation cohort, and entire cohort. Effects of the signature on I-131 therapy were also explored. We identified 199 autophagy-related-DElncs and constructed a novel six-lncRNAs signature was constructed based on these lncRNAs. This signature had a good predictive performance and was superior to TNM stages and previous clinical risk scores. I-131 therapy was found to be associated with favorable prognosis in patients with high-risk scores but not those with low-risk scores. Gene set enrichment analysis suggested that a series of hallmark gene sets were enriched in the high-risk subgroup. Single-cell RNA sequencing analysis suggested that the lncRNAs were mainly expressed in thyroid cells but not stromal cells. In conclusion, our study constructed a well-performed six-lncRNAs signature to predict PFI and I-131 therapy benefits in PTC.

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

The cell cycle plays a vital role in tumorigenesis and progression. Long non-coding RNAs (lncRNAs) are key regulators of cell cycle processes. Therefore, understanding cell cycle–related lncRNAs (CCR-lncRNAs) is crucial for determining the prognosis of papillary thyroid carcinoma (PTC). RNA-seq and clinical data of PTC were acquired from The Cancer Genome Atlas, and CCR-lncRNAs were selected based on Pearson’s correlation coefficients. According to univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses, a five-CCR-lncRNA signature (FOXD2-AS1, LOC100507156, BSG-AS1, EGOT, and TMEM105) was established to predict the progression-free interval (PFI) in PTC. Kaplan–Meier survival, time-dependent receiver operating characteristic curve, and multivariate Cox regression analyses proved that the signature had a reliable prognostic capability. A nomogram consisting of the risk signature and clinical characteristics was constructed that effectively predicted the PFI in PTC. Functional enrichment analyses indicted that the signature was involved in cell cycle– and immune-related pathways. Furthermore, we also analyzed the correlation between the signature and immune cell infiltration. Finally, we verified the differential expression of CCR-lncRNAs in vitro using quantitative real-time polymerase chain reaction. Overall, the newly developed prognostic risk signature based on five CCR-lncRNAs may become a marker for predicting the PFI in PTC.

BackgroundThis study aimed to explore the diagnostic value of alpha-l-fucosidase (AFU) in prostate cancer (PCa) patients with “gray-zone PSA” and to investigate the correlation between AFU expression and clinicopathological characteristics of PCa patients.MethodsThe level of AFU and other necessary clinicopathological variables of patients were retrieved from electronic medical records. The transcriptome profiling and clinical information of PCa patients were obtained from The Cancer Genome Atlas (TCGA) database. The protein level of AFU in tissue was assessed by immunohistochemistry (IHC). All the data were processed by appropriate analysis methods. The p-value of

Background: This study aimed to explore the diagnostic value of alpha-l-fucosidase (AFU) in prostate cancer (PCa) patients with "gray-zone PSA" and to investigate the correlation between AFU expression and clinicopathological characteristics of PCa patients. Methods: The level of AFU and other necessary clinicopathological variables of patients were retrieved from electronic medical records. The transcriptome profiling and clinical information of PCa patients were obtained from The Cancer Genome Atlas (TCGA) database. The protein level of AFU in tissue was assessed by immunohistochemistry (IHC). All the data were processed by appropriate analysis methods. The p-value of <0.05 was considered statistically significant. Results: AFU showed ideal diagnostic value for PCa with prostate-specific antigen (PSA) levels ranging from 4 to 10 ng/ml, and its optimal cutoffs were 19.5 U/L. Beyond this, low AFU expression was associated with high pathological grade, T stage and N stage, more postoperative residual tumors, and poor primary therapy outcome, as well as shorter progression-free interval. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis illustrated that FUCA1/FUCA2 exerted tumor-suppressive function by regulating the glycosylation. Conclusions: AFU (<19.5 U/L) could effectively distinguish the PCa from the patients with "gray-zone PSA", and low expression of AFU was an independent unfavorable predictor for the clinicopathological characteristics of PCa patients. [ABSTRACT FROM AUTHOR]

Background: Accurate risk assessment of post-surgical progression in papillary thyroid carcinoma (PTC) patients is critical. Exploring key differentially expressed mRNAs (DE-mRNAs) regulated by differentially expressed circular RNAs (circRNAs) via the ceRNA mechanism could help establish a novel assessment tool.Methods: ceRNA network was established based on differentially expressed RNAs and correlation analysis. DE-mRNAs within the ceRNA network associated with progression-free interval (PFI) of PTC were identified to construct a prognostic ceRNA regulatory subnetwork. least absolute shrinkage and selection operator (LASSO)–Cox regression was applied to identify hub DE-mRNAs and establish a novel DE-mRNA signature in predicting PFI of PTC.Results: Six hub DE-mRNAs, namely, CLCNKB, FXBO27, FXYD6, RIMS2, SPC24, and CDKN2A, were identified to be most significantly related to the PFI of PTC, and a prognostic DE-mRNA signature was proposed. A nomogram incorporating the DE-mRNA signature and clinical parameters was established to improve the progression risk assessment in post-surgical PTC, which was superior to the American Thyroid Association risk stratification system and distant Metastasis, patient Age, Completeness of resection, local Invasion, and tumor Size (MACIS) score American Joint Committee on Cancer staging system.Conclusions: Based on the circRNA-associated ceRNA RNA mechanism, a DE-mRNA signature and prognostic nomogram was established, which may improve the progression risk assessment in post-surgical PTC.

Purpose: Immunotherapy-based approaches are standard first-line treatments for advanced/metastatic lung cancer or for chemoradiotherapy consolidation in locally advanced disease. Uncertainty on how to treat patients at disease progression prompted us to develop a consensus document on post-immunotherapy options in Spain for patients with advanced wild-type lung adenocarcinoma. Methods: After extensive literature review, a 5-member scientific committee generated 33 statements in 4 domains: general aspects (n = 4); post-durvalumab in locally advanced disease (n = 6); post-first-line immunotherapy ± chemotherapy in advanced/metastatic disease (n = 11); and post-first-line platinum-based chemotherapy in advanced/metastatic disease (n = 12). A panel of 26 lung cancer experts completed 2 Delphi iterations through an online platform rating their degree of agreement/disagreement (first-round scale 1–5 and second-round scale 1–4, 1 = strongly disagree, 4/5 = strongly agree) for each statement. Second-round consensus: ≥ 70% of responses were in categories 1/2 (disagreement) or 3/4 (agreement). Results: Consensus was reached for 2/33 statements in the first Delphi round and in 29/31 statements in the second round. Important variables informing treatment at disease progression with an immunotherapy-based treatment include: disease aggressiveness, previous treatment, accumulated toxicity, progression-free interval, PD-L1 expression, and tumour mutational burden. A platinum-based chemotherapy should follow a first-line immunotherapy treatment without chemotherapy. Treatment with docetaxel + nintedanib may be appropriate post-durvalumab in refractory patients or following progression to first-line chemotherapy + immunotherapy, or second-line chemotherapy after first-line immunotherapy, or first-line chemotherapy in some patients with low/negative PD-L1 expression, or second-line immunotherapy after first-line chemotherapy. Conclusions: To support decision making following progression to immunotherapy-based treatment in patients with advanced wild-type lung adenocarcinoma, a consensus document has been developed. [ABSTRACT FROM AUTHOR]

Background: The incidence of papillary thyroid carcinoma (PTC) is high and increasing worldwide. Although prognosis is relatively good, it is important to select the minority of patients with poorer prognosis to avoid side effects associated with unnecessary over-treatment in low-risk patients; this requires accurate prognostic predictions.Materials and Methods: Six PTC expression datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas Thyroid Cancer (TCGA–THCA) database. Through integrated analysis of these datasets, highly reliable differentially-expressed genes (DEGs) between tumor and normal tissue were identified and lasso Cox regression was applied to identify DEGs related to the progression-free interval (PFI) and to establish a prognostic gene signature. The performance of a five-gene signature was evaluated based on a Kaplan–Meier curve, receiver operating characteristic (ROC), and Harrell's concordance index (C-index). Multivariate Cox regression analysis was used to identify factors associated with PTC prognosis. Finally, a prognostic nomogram was established based on the TCGA-THCA dataset.Results: A novel five-gene signature was established to predict the PTC PFI, which included PLP2, LYVE1, FABP4, TGFBR3, and FXYD6, and the ROC curve and C-index showed good performance in both training and validation datasets. This could classify patients into high- and low-risk groups with distinct PFIs and differentiate PTC tumors from normal tissue. Univariate Cox regression revealed that this signature was an independent prognostic factor for PTC. The established nomogram, incorporating the prognostic gene signature and clinical parameters, was able to predict the PFI with high efficiency. The gene signature-based nomogram was superior to the American Thyroid Association (ATA) risk stratification to predict PTC PFI.Conclusions: Our study identified a five-gene signature and established a prognostic nomogram, which were reliable in predicting the PFI of PTC; this could be beneficial for individualized treatment and medical decision making.

Background: The incidence of papillary thyroid carcinoma (PTC) is high and increasing worldwide. Although prognosis is relatively good, it is important to select the minority of patients with poorer prognosis to avoid side effects associated with unnecessary over-treatment in low-risk patients; this requires accurate prognostic predictions. Materials and Methods: Six PTC expression datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas Thyroid Cancer (TCGA–THCA) database. Through integrated analysis of these datasets, highly reliable differentially-expressed genes (DEGs) between tumor and normal tissue were identified and lasso Cox regression was applied to identify DEGs related to the progression-free interval (PFI) and to establish a prognostic gene signature. The performance of a five-gene signature was evaluated based on a Kaplan–Meier curve, receiver operating characteristic (ROC), and Harrell's concordance index (C-index). Multivariate Cox regression analysis was used to identify factors associated with PTC prognosis. Finally, a prognostic nomogram was established based on the TCGA-THCA dataset. Results: A novel five-gene signature was established to predict the PTC PFI, which included PLP2, LYVE1, FABP4, TGFBR3 , and FXYD6 , and the ROC curve and C-index showed good performance in both training and validation datasets. This could classify patients into high- and low-risk groups with distinct PFIs and differentiate PTC tumors from normal tissue. Univariate Cox regression revealed that this signature was an independent prognostic factor for PTC. The established nomogram, incorporating the prognostic gene signature and clinical parameters, was able to predict the PFI with high efficiency. The gene signature-based nomogram was superior to the American Thyroid Association (ATA) risk stratification to predict PTC PFI. Conclusions: Our study identified a five-gene signature and established a prognostic nomogram, which were reliable in predicting the PFI of PTC; this could be beneficial for individualized treatment and medical decision making. [ABSTRACT FROM AUTHOR]

Background: Gastrointestinal stromal tumors (GISTs) are uncommon intestinal neoplasms in the dog. Literature regarding adjunctive therapy for GISTs in dogs is sparse. High‐risk GISTs in humans respond to tyrosine kinase inhibition in the adjuvant setting. Objectives: To review cases of toceranib phosphate use in dogs with GISTs and provide initial assessment of possible biological activity. A secondary aim was to evaluate patient and tumor characteristics for possible prognostic value. Animals Twenty‐seven dogs with confirmed GISTs based on histopathology and immunohistochemistry treated with toceranib. Methods: Retrospective study in which cases of toceranib use in dogs with GIST were solicited using the American College of Veterinary Internal Medicine Oncology and Small Animal Internal Medicine listservs. Results: Five of 7 dogs with gross disease experienced clinical benefit (71%; 3 complete responses, 1 partial response, 1 stable disease). These included 2 dogs with durable responses after toceranib discontinuation. Median progression‐free interval (PFI) in dogs with gross disease was 110 weeks (range, 36‐155 weeks). Median PFI in dogs with microscopic disease was 67 weeks (range, 9‐257 weeks). Metastasis at diagnosis (P = 0.04) and high mitotic index (P < 0.001) were associated with shorter PFI in toceranib‐treated dogs. Conclusions and Clinical Importance: Biological activity of toceranib is evident in dogs with gross disease. Metastasis of GIST at diagnosis, as well as high tumor mitotic index, was associated with shorter PFI in toceranib‐treated dogs. Larger studies are needed to define postsurgical risk and refine the use of toceranib in dogs with gross and microscopic GIST. [ABSTRACT FROM AUTHOR]

The standard postoperative chemotherapy for epithelial ovarian cancer is a combination therapy including platinum and taxanes. The aim this study was to investigate the degree of platinum sensitivity in patients with relapsed epithelial ovarian cancer according to the treatment-free interval (TFI) and the histological tumor type. The medical records of 405 patients diagnosed with stage III/IV ovarian cancer, including 107 patients who relapsed after attaining a clinical complete response with first-line treatment, were retrospectively reviewed. The degree of platinum sensitivity was assessed by comparing the progression-free survival (PFS) following the second-line treatment. In patients with serous/endometrioid adenocarcinoma who were treated with platinum following relapse, there were significant differences in the PFS between the following groups of patients: those who relapsed within 6 months and those who relapsed between 6 and 12 months; those who relapsed between 6 and 12 months and those who relapsed between 12 and 18 months; and those who relapsed between 12 and 18 months and those who relapsed after 18 months. By contrast, in patients with clear cell/mucinous adenocarcinoma who were treated with platinum following a relapse, there were no significant differences in the PFS between patients who relapsed within 6 months and those who relapsed between 6 and 12 months, while there were significant differences in the PFS between those who relapsed between 6 and 12 months and those who relapsed after 12 months. With regard to the patients who relapsed after 12 months, the PFS of those with clear cell/mucinous adenocarcinoma was significantly shorter compared with the PFS of those with serous/endometrioid adenocarcinoma. Therefore, we considered it justified to classify patients with clear cell/mucinous adenocarcinoma who relapsed within 12 months as platinum-resistant and those who relapsed after 12 months as platinum-sensitive. [ABSTRACT FROM AUTHOR]

Objective: Many patients with ovarian cancer disease relapse within 6 months after adjuvant chemotherapy, with a limited prognosis. Epigenetic modifications have been shown to play an important role in tumor development and formation. Therefore, global analysis of DNA methylation patterns might reveal specific CpG sites that correlate with progression-free interval (PFI) after therapy. Methods: Twenty samples of advanced ovarian cancer with a predominantly serous papillary histological subtype were subjected to DNA methylation profiling. Illumina HumanMethylation27 BeadChip technology was used for simultaneous analysis of 27,578 CpG sites in >14,000 genes. Results: Differential DNA methylation of various cytosines correlated with PFI. However, this becomes only significant by classification according to PFI with a cutoff of >28 months. Longer survival was associated with hypomethylation at specific CpG sites (e.g. GREB1, TGIF and TOB1) and hypermethylation in other genes (e.g. TMCO5, PTPRN and GUCY2C). Gene ontology analysis revealed that differentially methylated genes were significantly overrepresented in the categories telomere organization, mesoderm development and immune regulation. Conclusion: Epigenetic modifications at specific CpG sites correlate with PFI in ovarian cancer. Therefore, such analysis might be of prognostic value. Copyright © 2011 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Abstract: After so many years of research, clinical value of HER2 (Human epidermal growth factor receptor 2) is unclear. Perhaps the main reason is variability of testing methods that produce controversial results. There is a lack of studies regarding prognostic value of CISH especially in metastatic breast cancer (MBC) when risk evaluation is based on different parameters than for primary breast cancer. Aim of this study was to compare prognostic relevance of HER2 status in MBC tested by two different methods i.e. immunohistochemistry (IHC) and chromogenic in situ hybridization (CISH). HER2 status of the same group of 107 MBC patients was determined by IHC (protein overexpression) and by CISH (gene amplification). HER2 results obtained by IHC and CISH showed significant correlation, beside the existence of discrepancies. Beside the significant correlation in two methods, there was a difference in prognostic values of compared methods during the course of metastatic disease. There was a significant difference in progression-free interval (PFI) between HER2 non-amplified and HER2 amplified cases determined by CISH, in postmenopausal subgroup and node-positive subgroup, but no significant difference for IHC stratified MBC patients. CISH seems to be accurate and more informative method than IHC regarding prognostic value of HER2 in metastatic breast cancer. [Copyright &y& Elsevier]

Background. The purpose of this study was to report the duration of the progression-free interval (PFI) in advanced ovarian cancer patients who were treated with intermittent maintenance chemotherapy. Methods. Between 1991 and 1998, 25 patients with stage III or IV ovarian cancer were enrolled in a trial of intermittent maintenance chemotherapy. All patients underwent cytoreduction surgery, and received adjuvant chemotherapy, after which they were treated with intermittent maintenance chemotherapy every 3 to 4 months for 2 years. Results. The median PFI in the 25 women in the intermittent chemotherapy group was 25 months, while in the 32 patients in the control group it was 18 months ( P = 0.0124). The median survival of women treated with the intermittent chemotherapy was 34 months, and for the control group patients, it was 35 months ( P = 0.0672). Multivariate analysis in the intermittent chemotherapy group revealed that the only factor that correlated significantly with PFI was the status after adjuvant chemotherapy ( P = 0.0137). In patients with no evidence of disease after the adjuvant chemotherapy, the median survival was 39 months in the intermittent chemotherapy group, and 35 months in the control group ( P = 0.0156). The median PFI was 28 months in the intermittent chemotherapy group, and 18 months in the control group ( P = 0.0012). Conclusion. It would be warranted to perform intermittent maintenance chemotherapy for patients with advanced ovarian cancer, if a clinically disease-free status could be achieved after completion of the standard treatment procedure. [ABSTRACT FROM AUTHOR]

Long non-coding RNA ITGB1-DT is involved in the regulation of cancer growth and metastasis. However, the roles of ITGB1-DT in non-small cell lung cancer (NSCLC) progression and sensitivity to cisplatin has not been elucidated. ITGB1-DT expression in NSCLC tissues, and the relationship between ITGB1-DT expression with NSCLC diagnosis, prognosis, clinicopathological features, and immune cell infiltration were investigated in The Cancer Gene Atlas (TCGA) database. The roles and mechanisms of ITGB1-DT in cell growth, migration, and drug sensitivity of NSCLC cells were explored in the cell model. The prognostic nomograms of ITGB1-DT-related genes were evaluated using bioinformatics. ITGB1-DT was overexpressed in NSCLC. Elevated ITGB1-DT expression was related to the late T stage, N stage, M stage, short overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) of NSCLC patients. ITGB1-DT was the independent risk factors for poor prognosis, and had diagnostic value for NSCLC patients. Interfering with the ITGB1-DT expression can inhibit the proliferation, migration, and invasion of A549, H1299, and drug-resistant A549/DDP, possibly due to the inhibition of p38 MAPK and ERK phosphorylation levels. ITGB1-DT expression was correlated with the levels of NSCLC immune infiltration cells, such as the TReg, Th, and NK cells. ITGB1-DT-related gene nomograms were associated with the prognosis, and were expected to evaluate the prognosis of NSCLC patients. In conclusion, inhibition of ITGB1-DT expression delayed the growth and metastasis of NSCLC using the MAPK/ERK signaling mechanism and enhanced the sensitivity of NSCLC to cisplatin drugs. These results indicate that ITGB1-DT might be a biomarker for evaluating the diagnosis and prognosis of NSCLC patients., Competing Interests: None., (AJCR Copyright © 2022.)

Aim: To identify patients with colorectal cancer (CRC) who are at a truly higher risk of progression, which is key for individualized approaches to precision therapy. Materials & methods: We developed a predictor associated with progression-free interval (PFI) using The Cancer Genome Atlas CRC methylation data. Results: The risk score was associated with PFI in the whole cohort (p < 0.001). A nomogram consisting of the risk score and other significant clinical features was generated to predict the 3- and 5-year PFI in the whole set (area under the curve: 0.79 and 0.71, respectively). Conclusion: The risk score based on 23 DNA-methylation sites may serve as the basis for improved prediction of progression in patients with CRC in future clinical practice.

Efficacy of liposomal doxorubicin (LD) in treating desmoid fibromatosis (DF) in children has not been well evaluated. This retrospective case series examines five children with progressive DF, treated with LD. We report progression-free intervals (PFIs) and radiographic as well as clinical responses for each medication received. LD was well tolerated, with an average 4.5% reduction in tumor size and median PFI of 29 months. Treatment with LD conferred the longest PFI of all medical therapies pursued. Thus, LD is an important treatment option for DF in pediatrics., (© 2016 Wiley Periodicals, Inc.)