Low-risk Subgroups Research Articles

Functional near-infrared spectroscopy measures of frontal hemodynamic responses in Parkinson's patients and controls performing the Timed-Up-and-Go test

Using functional near-infrared spectroscopy (fNIRS), hemodynamic responses (i.e., changes in oxygenated and deoxygenated hemoglobin) were measured while participants with Parkinson’s disease (PD) and healthy controls performed the Timed-Up-and-Go test (TUGT), and differences in cortical activity at baseline and three different intervals were examined between the two groups. Seventeen PD patients and twenty-two controls participated in the study, but two PD patients were excluded from statistical analysis due to the presence of freezing of gait and using walking aids during the TUGT. During the TUGT, activity in the front, left, right and total frontal cortices initially decreased significantly, then significantly increased in PD participants and low-risk faller PD participants, compared to when in a sitting position. ΔHbO (HbO change from baseline) over the front, left and total frontal cortices in the PD group was significantly lower than the control group in interval 1 (P = 0.019, P = 0.014 and P = 0.031, respectively), while significantly higher than the control group in interval 2 over the left frontal cortex (P = 0.010). No significant differences were observed between the high-risk faller and low-risk faller subgroups of PD participants in ΔHbO and ΔHbR in the three intervals (P > 0.05). In the high-risk faller subgroup, ΔHbO over the left frontal cortex was significantly higher than the right frontal cortex in interval 2 and interval 3 (P = 0.015, P = 0.030, respectively). There was a strong positive correlation between education and HbR concentration over the right frontal cortex in PD participants (rho = 0.557, P = 0.031), while there were strong negative correlations between PD duration and HbR concentration over the right and total frontal cortices in the high-risk faller subgroup of PD participants (rho = −0.854, P = 0.014 for the right; rho = −0.784, P = 0.037 for the total). The falls prediction cutoff TUGT time for PD participants was 14.2 s. These results suggest that frontal cognition training, along with exercise training, could be used as an effective training method to improve motor performance in PD patients, especially for those at high-risk for falls.

Prognostic Factors and a Model for Occult Breast Cancer: A Population-Based Cohort Study.

Occult breast cancer (OBC) is a special type of breast cancer of an unknown primary origin. Early stage OBC is treated as stage II-III breast cancer. Currently, there are no models for predicting the survival outcomes. Hence, we aimed to evaluate the role of the positive lymph node ratio (PLNR) in OBC and further establish and validate a prognostic nomogram. Patients with stage T0N+M0 breast cancer were enrolled from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox analyses were used to evaluate the effects of prognostic factors on breast-cancer-specific survival (BCSS), and a nomogram was established and validated for OBC. Overall, 843 patients were included, and the 5-year BCSS rate was 92.4%. Patients with a PLNR < 0.54 had better BCSS rates than those with a PLNR ≥ 0.54. The nomogram combined clinicopathological parameters, including the PLNR, pN stage, and estrogen receptor status, and showed a higher accuracy than the TNM staging system in predicting the BCSS. The patients could be stratified into different risk groups based on their prognostic scores. Patients in the low-risk subgroup showed an improved BCSS compared those in the high-risk subgroup. In conclusion, the PLNR is an independent prognostic factor for OBC. The PLNR-based nomogram has a better predictive ability than the TNM staging system and could be of great value for the treatment of OBC and prediction of its prognosis.

Robust Validation of the UKALL High Hyperdiploid Risk Profile Using Individual Patient Data Collected By the Harmony Alliance

High hyperdiploidy (HeH) is the most prevalent genetic subtype in paediatric acute lymphoblastic leukaemia (ALL) and is associated with a favourable outcome. HeH karyotypes have an increased modal number of 51-67 chromosomes and chromosome gain is non-random. Even though the outcome of paediatric patients with HeH is better than other subgroups, in adults it is far less favourable. The prevalence of the subgroup means that it accounts for a significant proportion of relapses; hence identifying risk factors within HeH is important. We recently developed a novel risk profile for HeH. Patients with both +17 and +18 together or patients with either +17 or +18 coupled with an absence of +5 or +20 defined a good risk (GR) group with a relapse risk <5% [Lancet Haem. 2021;8(11):e828-e39]. The remaining patients with HeH [poor risk (PR) group] had a relapse risk ~15% (i.e. similar to patients with intermediate risk genetics). In our original publication, we were restricted to using a single UK based clinical trial, UKALL2003, to validate our novel risk profile developed on data from ALL97/99. However, a comprehensive assessment of a novel risk profile requires validation on multiple independent cohorts but such cohorts are rarely readily available. The HARMONY alliance (http://www.harmony-alliance.eu) is a European big data platform which has collected data on >115,000 patients with various haematological malignancies; including >10,000 patients with ALL. The aims of this project were to (a) determine if the UKALL-HeH risk profile can be validated in a large independent cohort comprising data from multiple clinical trials and from both childhood and adults patients; and (b) assess the added benefit of validating a risk profile using data from the HARMONY platform compared with a single a country-specific cohort. The ALL cohort within HARMONY comprised cases from multiple paediatric and adult ALL trials including UKALL2003. Only cases with karyotype written according to ISCN were eligible for this study. A search of the cohort revealed 1169 karyotypes with a modal number of 51-67 chromosomes. Karyotypes with t(9;22)(q34;q11) or chromosome patterns indicative of masked haploidy or low hypodiploidy were excluded. Using a custom built R function, we sorted karyotypes according to trisomic status of chromosomes 5, 17, 18 and 20 (fig1A). Karyotypes not assigned definitively to a risk group due to being incomplete or the presence of marker chromosomes were assigned to provisional groups but later combined with the definitive cases because their relapse rate was equivalent. A total of 865 (74%) karyotypes had a UKALL HeH GR profile whilst 304 (26%) had a PR profile. The proportion of patients with a PR profile was significantly higher among those aged over 25 years compared to those under 25 (63% v 25%, p<0.001). Overall, patients with a PR profile had a significantly higher relapse rate at 5 years compared with those with a GR profile 6% (4-7) v 19% (14-23), log rank p<0.001. The hazard ratio from a univariate Cox model was 3.52 (SE 0.20 p<0.001). This effect was independent of MRD - hazard ratio for relapse from multivariate model was 3.30 (SE 0.26 p<0.001). The prognostic impact of the UKALL HeH profile was similar across all age groups examined including adults aged over 25 years but was greater among patients that were MRD negative (<0.01%) at the end of induction compared with MRD positive (≥0.01%) cases (fig1B). Compared to using the UKALL2003 cohort alone for validation, HARMONY provided a validation cohort that was 60% larger with a broader age range: 1-24 vs. 1-70 years. In addition, the Cox model estimates were more accurate with smaller standard errors. The standard error for the hazard ratio for risk of relapse reduced from 1.11 to 0.20. Moreover, validation on cohorts of patients treated on protocols from other study groups provides additional evidence for the robustness of the risk profile. In conclusion, we have validated the UKALL HeH risk profile on a larger and more heterogeneous cohort of patients. We confirm that the profile defines both a low risk subgroup of HeH paediatric patients that could be considered for treatment reduction, as well as a subgroup of HeH that has a risk of relapse similar to patients with intermediate risk genetics. In addition, we have demonstrated how the HARMONY data platform, which provides access to a standardised disease-specific datasets, can rapidly facilitate biomarker research. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

A Novel Risk Stratification Model for Cytogenetically Normal Acute Myeloid Leukemia with FLT3-ITD Mutation

Introduction Fms-like tyrosine kinase 3 internal tandem duplication (FLT3-ITD) mutations are observed in about 30 % of patients with newly diagnosed cytogenetically normal acute myeloid leukemia (CN-AML) and has now been acknowledged to be associated with a poor survival. However, long-term follow-up revealed a high degree of heterogeneity in the clinical prognosis of these patients. Further risk stratification to identify high-risk patients is urgently needed. Methods A total of 106 patients with CN-AML and FLT3-ITD mutations who visited the People's Hospital of Peking University from 2007-2019 were included, and target region sequencing was performed by 266 gene panel. Patients were randomly divided into training and validation cohort according to the ratio of 7:3. Molecular and clinical data in training cohort were incorporated to construct a nomogram model. Cumulative incidence of relapse (CIR), Relapse-free survival (RFS) and survival by allogenic hematopoietic stem cell transplantation (allo-HSCT) and consolidation chemotherapy were compared. Results Clinical variables, genes, and genetic groups with mutations occurring in more than 10 patients were enrolled for univariate analysis of RFS (allo-HSCT was recorded as a censored event). And to avoid bias patients with RFS ≤ 4.57 months (median interval from complete remission to allo-HSCT) would be excluded from the subsequent analyses unless receiving an allo-HSCT was treated as a censored event. Three variables were eventually entered into the nomogram analysis: DNMT3A mutation (+ vs. -), minimal residual disease detected by multiparameter flow cytometry (MPFC-MRD) at the end of the 2nd course of consolidation chemotherapy (CON2) (+ vs. -, represents ≥ vs. ＜0.01%), typical ITDs (+ vs. -, represents ITDs have insertion sequences that exactly match wild-type FLT3 gene vs. ITDs have insertion sequences containing other exogenous nucleotides). And the model demonstrated good discrimination in both the training and validation cohort with C-index value of 0.82 (95% confidence interval, 0.7-0.86), 0.86 (95% confidence interval, 0.76-0.95), respectively. The Nomogram model was constructed as: Score = 1.44×DNMT3A mutation+2.16×MFPC-MRD-0.94×typical ITDs (Figure 1, A). According to the variables in nomogram model, patients with risk scores ≤ 0 were assigned to the low-risk subgroup (N = 39) and the rest were assigned to the high-risk subgroup (N = 67). The 5-year RFS was significantly higher in the low-risk group than in the high-risk group (training cohort, HR 3.68 [1.16-11.66], P = 0.002; validation group, HR 4.45 [1.52-13.00], P = 0.03) (Figure 1, B-C). We then interrogated the effect of consolidation chemotherapy and allo-HSCT as post-remission therapies in the two risk subgroups. Allo-HSCT did not further improve the prognosis of patients in the low-risk group (allo-HSCT vs. chemotherapy, 5-year CIR, HR 0.46 [0.11-1.93], P = 0.29; 5-year RFS, HR 0.41 [0.09-1.86], P = 0.24; 5-year survival, HR 0.91 [0.18-4.68], P = 0.91) (Figure 2, A-D). In contrast, in the high-risk group of patients, transplantation showed a significant survival benefit (5-year CIR, HR 0.21 [0.06-0.74], P = 0.01; 5-year RFS, HR 0.23 [0.06-0.80], P = 0.01; 5-year survival, HR 0.25 [0.06-1.11], P = 0.05) (Figure 2, E-H). Conclusion Our study suggests that risk stratification by our novel nomogram scoring system can identify patients at high risk of relapse in CN-AML with FLT3-ITD mutations and allo-HSCT further benefits their prognosis. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Practices Regarding Human Papillomavirus and Cervical Cancer in A Sample of Paramedical Staff in Al- Najaf Governorate, Iraq

The papillomaviridae viral family includes HPV. When it comes to female sexually transmitted diseases, the genital tract is the primary site of infection for genital type HPVs, which are further divided into high- and low-risk subgroups depending on the likelihood that they will cause cancer of the genital tract. High-risk HPV strains 16 and 18 cause cervical cancer. Sexual contact or skin-to-skin contact is the primary mode of transmission of HPV. To assess the degree of Practice among a sample of paramedical workers concerning the Human Papillomavirus and Cervical Cancer. Five hospitals and 25 primary health care centers in the Al-Najaf governorate were studied in this cross-sectional observational descriptive cross-sectional research. Starting on December 19th, 2021 and concluding on April 18th, 2022, data was gathered for four months. According to the results of the present research, which included (five hundred) Paramedical employees aged 20-59 years, the age group with the largest proportion (66.4%) was that of 20-29 years. Whereas, in terms of gender, the largest proportion of women (64.6 percent). Almost all paramedics work in urban areas. In addition, almost half of the participants came from medical institutions, accounting for 51.6 percent of the total, while just 2.4 percent were graduates of secondary schools. Only 2.4% of the paramedical staff had any kind of education or training on HPV or cervical cancer. Excellent practice evaluations for the Human Papillomavirus and cervical cancer were much higher among paramedical providers (79 percent). Most of the paramedics who were evaluated for their practices addressing cervical cancer and the human papillomavirus (HPV) received good grades. Health authorities, social groupings, and non-governmental organizations should work together to communicate information about the human papillomavirus and cervical cancer to the general population. In order to educate and sensitize medical professionals about HPV screening technologies and the most current screening and treatment guidelines, conduct educational initiatives.

Postoperative Chemoradiotherapy versus Radiotherapy Alone in Major Salivary Gland Cancers: A Stratified Study Based on the External Validation of the Distant Metastasis Risk Score Model.

The role of additional chemoradiotherapy (CRT) for distant metastasis (DM) on the resected malignancy of the major salivary gland (SGM) remained unknown. We conducted this study to externally validate a recently reported DM risk score model and compare the survival outcome between adjuvant CRT and RT alone. We retrospectively reviewed the patients with SGM following postoperative radiotherapy (PORT). The cumulative incidence of DM was assessed using a competing risk method. Multivariate analysis was performed with Cox proportional-hazards regression to identify significant predictors for DM. Patients were classified as high- and low-risk subgroups with the cutoff value of the DM risk score model. The inverse probability of treatment weighting (IPTW) was conducted to minimize the bias of the groups. A total of 586 eligible patients were analyzed and 67 cases underwent adjuvant CRT. The 5-year incidence of DM was 19.5% (95% CI 16.0-23.0%). The model reasonably discriminated the DM risk between the high- and low-risk subgroup in our cohort, and the c-index was 0.75. No survival benefit was observed for the CRT group compared with RT alone in the entire cohort after IPTW (p = 0.095). After subgroup analysis, increased mortality was identified with the administration of CRT in the low-risk subset (p = 0.002) while no significant difference in OS was illustrated in the high-risk subgroup (p = 0.98). This external validation provides further exploration of the DM risk score model in major SGM. Our results demonstrated no support for the utility of additional chemotherapy to PORT in the major SGM, especially in the low-risk subgroup of patients with DM.

A Novel Insight into Paraptosis-Related Classification and Signature in Lower-Grade Gliomas.

Lower-grade gliomas (LGG) are the most common intracranial malignancies that readily evolve to high-grade gliomas and increase drug resistance. Paraptosis is defined as a nonapoptotic form of programmed cell death, which is gradually focused on patients with gliomas to develop treatment options. However, the specific role of paraptosis in LGG and its correlation is still vague. In this study, we first establish the novel paraptosis-based prognostic model for LGG patients. The relevant data of LGG patients were acquired from The Cancer Genome Atlas database, and we found that LGG patients could be divided into three different clusters based on paraptosis via consensus cluster analysis. Through least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis, 10-paraptosis-related gene (PRG) signatures (CDK4, TNK2, DSTYK, CDKN3, CCR4, CASP9, HSPA5, RGR, LPAR1, and PDCD6IP) were identified to separate LGG patients into high- and low-risk subgroups successfully. The Kaplan-Meier analysis and time-dependent receiver-operating characteristic showed that the performances of predicting overall survival (OS) were dramatically high. The parallel results were reappeared and verified by using the Chinese Glioma Genome Atlas and Gene Expression Omnibus databases. Independent prognostic analysis and nomogram construction implied that risk scores could be considered the independent factor to predict OS. Enrichment analysis indicated that immune-related biological processes were generally enriched, and different immune statuses were highly infiltrated in high-risk group. We also confirmed the potential relationship of 10-PRG signatures and drug sensitivity of Food and Drug Administration-approved drugs. In summary, our findings provide a novel knowledge of paraptosis status and crucial direction to further explore the role of PRG signatures in LGG.

Abstract 14875: Topological Data Analysis of Electronic Health Record Features Predicts Major Cardiovascular Outcomes After Revascularization for Acute Myocardial Infarction

Introduction: Topological data analysis (TDA) is an emerging mathematical technique that imposes structure on a dataset and examines the shapes (or topological features) that arise. The TDA algorithm Mapper Plus is proposed to study the spatial characteristics of clinical data for the purpose of patient classification. This is a data-driven and hypothesis-free approach to clinical risk stratification. Hypothesis: The hypothesis is that Mapper Plus can classify distinctive subsets of patients receiving optimal treatments post-acute myocardial infarction (AMI). Our goal is to identify subgroups who remained at risk of having a future adverse event (AE) i.e., death, heart failure hospitalization, or recurrent MI Methods: A single-center, retrospective analysis of 31 clinical variables from the electronic health record (EHR) was conducted on 798 AMI subjects. Risk was defined as high or low in relation to the average probability of survival without AE for the entire cohort at the 1 year mark. Results: TDA identified six subsets of patients. Four subsets (n=597) had &gt; 1-fold change on the probability of survival free of AEs; these became the low-risk subgroup. Two subsets (n=344) had &lt; 1-fold change on the probability of survival free of AEs; these became the high-risk subgroup. Because Mapper Plus allows for subject overlap across subgroups, 143 subject (18%) were shared between the high (n=201, 25%)- and low-risk (n=454, 57%) subgroups and were extracted into a 3rd intermediate risk subgroup (see figure). Conclusions: TDA significantly stratified AMI patients into 3 subgroups with distinctive incidences of AE up to 3 years post AMI. This is a new framework for EHR-based risk stratification that requires no additional patient interaction, is agnostic to prior knowledge, and is driven by the topological features of the included data. Further studies will be needed to validate TDA across different patient cohorts before it can be applied to clinical practice.

Immunogenic cell death-related gene landscape predicts the overall survival and immune infiltration status of ovarian cancer.

Background: Ovarian cancer (OC) is the most troubling malignant tumor of the female reproductive system. It has a low early diagnosis rate and a high tumor recurrence rate after treatment. Immunogenic cell death (ICD) is a unique form of regulated cell death that can activate the adaptive immune system through the release of DAMPs and cytokines in immunocompromised hosts and establish long-term immunologic memory. Therefore, this study aims to explore the prognostic value and underlying mechanisms of ICD-related genes in OC on the basis of characteristics. Methods: The gene expression profiles and related clinical information of OC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. ICD-related genes were collected from the Genecards database. ICD-related prognostic genes were obtained by intersecting ICD-related genes with the OC prognostic-related genes that were analyzed in the TCGA database. Functional enrichment, genetic mutation, and immune infiltration correlation analyses were further performed to identify underlying mechanisms. Subsequently, we developed a TCGA cohort-based prognostic risk model that included a nine-gene signature through univariate and multivariate Cox regression and LASSO regression analyses. Meanwhile, external validation was performed on two sets of GEO cohorts and the TCGA training cohort for three other common tumors in women. In addition, a nomogram was established by integrating clinicopathological features and ICD-related gene signature to predict survival probability. Finally, functional enrichment and immune infiltration analyses were performed on the two risk subgroups. Results: By utilizing nine genes (ERBB2, RB1, CCR7, CD38, IFNB1, ANXA2, CXCL9, SLC9A1, and SLAMF7), we constructed an ICD-related prognostic signature. Subsequently, patients were subdivided into high- and low-risk subgroups in accordance with the median value of the risk score. In multivariate Cox regression analyses, risk score was an independent prognostic factor (hazard ratio = 2.783; p < 0.01). In the TCGA training cohort and the two GEO validation cohorts, patients with high-risk scores had worse prognosis than those with low-risk scores (p < 0.05). The time-dependent receiver operating characteristic curve further validated the prognostic power of the gene signature. Finally, gene set enrichment analysis indicated that multiple oncological pathways were significantly enriched in the high-risk subgroup. By contrast, the low-risk subgroup was strongly related to the immune-related signaling pathways. Immune infiltration analysis further illustrated that most immune cells showed higher levels of infiltration in the low-risk subgroup than in the high-risk subgroup. Conclusion: We constructed a novel ICD-related gene model for forecasting the prognosis and immune infiltration status of patients with OC. In the future, new ICD-related genes may provide novel potential targets for the therapeutic intervention of OC.

Pre-donation water and salty snacks to prevent vasovagal reactions among blood donors.

Minimizing the risk of vasovagal reactions (VVRs) can prevent donor harms and improve donor return. We report the results of a program to reduce VVR rates. The program was implemented on June 11, 2017 and consisted in drinking water and eating a salty snack before donating blood, plasma, or platelets. All donations made during the "pre-program period" (October 11, 2015-June 10, 2017) and "post-program period" (June 11, 2017-May 11, 2019) were included. Study outcomes comprised VVRs (any severity) and syncopal VVRs, whether employee- or donor-reported. An interrupted time series (ITS) analysis proxied causality based on the "pre-program trend," the "immediate trend" (i.e., immediately before versus after the program), and the "post-program trend". The relative risk (RR) of VVR (along with confidence intervals [CIs]) was reported, overall and stratified by subgroups based on age, sex, donor type (i.e., first-time versus repeat), and donation type (i.e., whole blood versus apheresis). The monthly VVR rate (any severity) dropped from 4.6% in the pre-program period to 4.3% in the post-program period, and never reached its pre-program level. The ITS analysis revealed a statistically significant and increasing pre-program trend (RR [95% CI]=1.011 [1.002-1.020]), a statistically significant and decreasing immediate trend (RR [95% CI]=0.848 [0.743-0.969]), and a non-statistically-significant and stable post-program trend (RR [95% CI]=0.999 [0.993-1.006]). Similar trends were observed for nearly all high- and low-risk subgroups. No statistically significant trend was observed for syncopal VVRs. These results suggest that the herein-described program durably reduced the incidence of VVRs (any severity) by ~15%.

Establishment and validation of a multigene model to predict the risk of relapse in hormone receptor-positive early-stage Chinese breast cancer patients.

Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery. In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan-Meier analysis, receiver operating characteristic curve (ROC). A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes ( CKMT1B , SMR3B , and OR11M1P ) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model. A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemotherapy after surgery for breast cancer patients.

A novel senescence-related lncRNA signature that predicts prognosis and the tumor microenvironment in patients with lung adenocarcinoma.

Background: Cellular senescence has recently been considered a new cancer hallmark. However, the factors regulating cellular senescence have not been well characterized. The aim of this study is to identify long non-coding RNAs (lncRNAs) associated with senescence and prognosis in patients with lung adenocarcinoma (LUAD). Methods: Using RNA sequence data from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and senescence genes from the CellAge database, a subset of senescence-related lncRNAs was first identified. Then, using univariate and multivariate Cox regression analyses, a senescence lncRNA signature (LUADSenLncSig) associated with LUAD prognosis was developed. Based on the median LUADSenLncSig risk score, LUAD patients were divided into high-risk and low-risk groups. Kaplan-Meier analysis was used to compare the overall survival (OS) in the high- and low-risk score subgroups. Differences in Gene Set Enrichment Analysis (GSEA), immune infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) module score, chemotherapy, and targeted therapy selection were also compared between the high-risk and low-risk groups. Results: A prognostic risk model was obtained consisting of the following nine senescence-related lncRNAs: LINC01116, AC005838.2, SH3PXD2A-AS1, VIMS-AS1, SH3BP5-AS1, AC092279.1, AC026355.1, AC027020.2, and LINC00996. The LUADSenLncSig high-risk group was associated with poor OS (hazard ratio = 1.17, 95% confidence interval = 1.102-1.242; p < 0.001). The accuracy of the model was further supported based on receiver operating characteristic (ROC), principal component analysis (PCA), and internal validation cohorts. In addition, a nomogram was developed consisting of LUADSenLncSig for LUAD prognosis, which is consistent with the actual probability of OS. Furthermore, immune infiltration analysis showed the low-risk group had a stronger anti-tumor immune response in the tumor microenvironment. Notably, the levels of immune checkpoint genes such as CTLA-4, PDCD-1, and CD274, and the TIDE scores were significantly higher in the low-risk subgroups than in high-risk subgroups (p < 0.001). This finding indicates the LUADSenLncSig can potentially predict immunotherapy efficacy. Conclusion: In this study, a lncRNA signature, LUADSenLncSig, that has dual functions of senescence phenotype identification and prognostic prediction as well as the potential to predict the LUAD response to immunotherapy was developed.

Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods.

Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model. A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the "limma" R package. The "clusterProfiler" R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan-Meier (K-M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the "pRRophetic" R package. A total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs. Our study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.

Identification and validation of a novel tumor driver gene signature for diagnosis and prognosis of head and neck squamous cell carcinoma.

Objective: Head and neck squamous cell carcinoma (HNSCC) is a common heterogeneous cancer with complex carcinogenic factors. However, the current TNM staging criteria to judge its severity to formulate treatment plans and evaluate the prognosis are particularly weak. Therefore, a robust diagnostic model capable of accurately diagnosing and predicting HNSCC should be established. Methods: Gene expression and clinical data were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Key prognostic genes associated with HNSCC were screened with the weighted gene co-expression network analysis and least absolute shrinkage and selection operator (LASSO) Cox regression model analysis. We used the timeROC and survival R packages to conduct time-dependent receiver operating characteristic curve analyses and calculated the area under the curve at different time points of model prediction. Patients in the training and validation groups were divided into high- and low-risk subgroups, and Kaplan-Meier (K-M) survival curves were plotted for all subgroups. Subsequently, LASSO and support vector machine algorithms were used to screen genes to construct diagnostic model. Furthermore, we used the Wilcoxon signed-rank test to compare the half-maximal inhibitory concentrations of common chemotherapy drugs among patients in different risk groups. Finally, the expression levels of eight genes were measured using quantitative real-time polymerase chain reaction and immunohistochemistry. Results: Ten genes (SSB, PFKP, NAT10, PCDH9, SHANK2, PAX8, CELSR3, DCLRE1C, MAP2K7, and ODF4) with prognostic potential were identified, and a risk score was derived accordingly. Patients were divided into high- and low-risk groups based on the median risk score. The K-M survival curves confirmed that patients with high scores had significantly worse overall survival. Receiver operating characteristic curves proved that the prognostic signature had good sensitivity and specificity for predicting the prognosis of patients with HNSCC. Univariate and multivariate Cox regression analyses confirmed that the gene signature was an independent prognostic risk factor for HNSCC. Diagnostic model was built by identifying eight genes (SSB, PFKP, NAT10, PCDH9, CELSR3, DCLRE1C, MAP2K7, and ODF4). The high-risk group showed higher sensitivity to various common chemotherapeutic drugs. DCLRE1C expression was higher in normal tissues than in HNSCC tissues. Conclusion: Our study identified the important role of tumor-driver genes in HNSCC and their potential clinical diagnostic and prognostic values to facilitate individualized management of patients with HNSCC.

Association between cancer stem cell gene expression signatures and prognosis in head and neck squamous cell carcinoma

BackgroundVarious cancer stem cell (CSC) biomarkers and the genes encoding them in head and neck squamous cell carcinoma (HNSCC) have been identified and evaluated. However, the validity of these factors in the prognosis of HNSCC has been questioned and remains unclear. In this study, we examined the clinical significance of CSC biomarker genes in HNSCC, using five publicly available HNSCC cohorts.MethodsTo predict the prognosis of patients with HNSCC, we developed and validated the expression signatures of CSC biomarker genes whose mRNA expression levels correlated with at least one of the four CSC genes (CD44, MET, ALDH1A1, and BMI1).ResultsPatients in The Cancer Genome Atlas (TCGA) HNSCC cohort were classified into CSC gene expression-associated high-risk (CSC-HR; n = 285) and CSC gene expression-associated low-risk (CSC-LR; n = 281) subgroups. The 5-year overall survival and recurrence-free survival rates were significantly lower in the CSC-HR subgroup than in the CSC-LR subgroup (p = 0.04 and 0.02, respectively). The clinical significance of the CSC gene expression signature was validated using four independent cohorts. Analysis using Cox proportional hazards models showed that the CSC gene expression signature was an independent prognostic factor of non-oropharyngeal HNSCC which mostly indicates HPV (–) status. Furthermore, the CSC gene expression signature was associated with the prognosis of HNSCC patients who received radiotherapy.ConclusionThe CSC gene expression signature is associated with the prognosis of HNSCC and may help in personalized treatments for patients with HNSCC, especially in cases with HPV (–) status who were classified in more detail.

Characterization of sialylation-related long noncoding RNAs to develop a novel signature for predicting prognosis, immune landscape, and chemotherapy response in colorectal cancer.

Aberrant sialylation plays a key biological role in tumorigenesis and metastasis, including tumor cell survival and invasion, immune evasion, angiogenesis, and resistance to therapy. It has been proposed as a possible cancer biomarker and a potential therapeutic target of tumors. Nevertheless, the prognostic significance and biological features of sialylation-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC) remain unclear. This study aimed to develop a novel sialylation-related lncRNA signature to accurately evaluate the prognosis of patients with CRC and explore the potential molecular mechanisms of the sialylation-related lncRNAs. Here, we identified sialylation-related lncRNAs using the Pearson correlation analysis on The Cancer Genome Atlas (TCGA) dataset. Univariate and stepwise multivariable Cox analysis were used to establish a signature based on seven sialylation-related lncRNAs in the TCGA dataset, and the risk model was validated in the Gene Expression Omnibus dataset. Kaplan-Meier curve analysis revealed that CRC patients in the low-risk subgroup had a better survival outcome than those in the high-risk subgroup in the training set, testing set, and overall set. Multivariate analysis demonstrated that the sialylation-related lncRNA signature was an independent prognostic factor for overall survival, progression-free survival, and disease-specific survival prediction. The sialylation lncRNA signature-based nomogram exhibited a robust prognostic performance. Furthermore, enrichment analysis showed that cancer hallmarks and oncogenic signaling were enriched in the high-risk group, while inflammatory responses and immune-related pathways were enriched in the low-risk group. The comprehensive analysis suggested that low-risk patients had higher activity of immune response pathways, greater immune cell infiltration, and higher expression of immune stimulators. In addition, we determined the sialylation level in normal colonic cells and CRC cell lines by flow cytometry combined with immunofluorescence, and verified the expression levels of seven lncRNAs using real-time quantitative polymerase chain reaction. Finally, combined drug sensitivity analysis using the Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal, and Profiling Relative Inhibition Simultaneously in Mixtures indicated that the sialylation-related lncRNA signature could serve as a potential predictor for chemosensitivity. Collectively, this is the first sialylation lncRNA-based signature for predicting the prognosis, immune landscape, and chemotherapeutic response in CRC, and may provide vital guidance to facilitate risk stratification and optimize individualized therapy for CRC patients.

Identification of a necroptosis-related prognostic gene signature associated with tumor immune microenvironment in cervical carcinoma and experimental verification

Cervical carcinoma (CC) has been associated with high morbidity, poor prognosis, and high intratumor heterogeneity. Necroptosis is the significant cellular signal pathway in tumors which may overcome tumor cells’ apoptosis resistance. To investigate the relationship between CC and necroptosis, we established a prognostic model based on necroptosis-related genes for predicting the overall survival (OS) of CC patients. The gene expression data and clinical information of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) patients were obtained from The Cancer Genome Atlas (TCGA). We identified 43 differentially expressed necroptosis-related genes (NRGs) in CESC by examining differential gene expression between CESC tumors and normal tissues, and 159 NRGs from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Gene ontology (GO) and KEGG enrichment analysis illustrated that the genes identified were mainly related to cell necrosis, extrinsic apoptosis, Influenza A, I − kappaB kinase/NF − kappaB, NOD − like receptor, and other signaling pathways. Subsequently, least absolute shrinkage and selection operator (LASSO) regression and univariate and multivariate Cox regression analyses were used to screen for NRGs that were correlated with patient prognosis. A prognostic signature that includes CAMK2A, CYBB, IL1A, IL1B, SLC25A5, and TICAM2 was established. Based on the prognostic model, patients were stratified into either the high-risk or low-risk subgroups with distinct survival. Receiver operating characteristic (ROC) curve analysis was used to identify the predictive accuracy of the model. In relation to different clinical variables, stratification analyses were performed to demonstrate the associations between the expression levels of the six identified NRGs and the clinical variables in CESC. Immunohistochemical (IHC) validation experiments explored abnormal expressions of these six NRGs in CESC. We also explored the relationship between risk score of this necroptosis signature and expression levels of some driver genes in TCGA CESC database and Gene Expression Omnibus (GEO) datasets. Significant relationships between the six prognostic NRGs and immune-cell infiltration, chemokines, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoints in CESC were discovered. In conclusion, we successfully constructed and validated a novel NRG signature for predicting the prognosis of CC patients and might also play a crucial role in the progression and immune microenvironment in CC.

Investigation on the regulatory T cells signature and relevant Foxp3/STAT3 axis in esophageal cancer.

Regulatory T cells (Tregs) have an important role in accelerating the immunosuppression of tumor. Tregs regulation is a hopeful strategy to improve the dismal prognosis of Esophageal cancer (EC), while its mechanisms have not yet been fully clarified. To characterize the role of Tregs in EC, we comprehensively explored its prognostic value, clinical pathology partnership, related biological functions and potential mechanisms at transcriptome level. Through the integrated analysis of GEO and TCGA datasets, we comprehensively evaluated the Tregs infiltration patterns in EC patients. The correlation between Tregs infiltration and genomic characteristics, as well as biological functions were analyzed by a variety of computational algorithms. We observed that Tregs were significantly upregulated in EC and involved in various immune processes. According to TCGA and GEO transcriptional classification schemes, Tregs specific genes were observed to be highly expressed in tumor samples, as well as were closely associated with poor prognosis and worse clinical outcomes. In addition, EC patients can be stratified into high-risk and low-risk immune subgroups according to Tregs/macrophages infiltration level, and the results showed significant differences in tumor development, biological processes and probe gene expression pattern. The multi-variate analysis revealed that the interaction between STAT3 and Foxp3 was a potential prognostic signature of Tregs in EC, especially the modulation effect of STAT3 on Foxp3 expression, which has not been well studied in EC. We also identified that STAT3 and Foxp3 expression presented a high accuracy in predicting Tregs infiltration level in EC patients (AUC: 0.817; 95% CI: 0.756-0.878). Our results revealed that Tregs have the potential to predict prognosis and tumor deterioration in EC patients. A comprehensive landscape of Tregs regulation mechanisms will help us interpret the immunosuppression of tumor microenvironment (TME) and novel strategies for EC immunotherapy.

Construction and validation of a histone acetylation-related lncRNA prognosis signature for ovarian cancer.

Ovarian cancer (OC) leads to the most deaths among gynecological malignancies. The various epigenetic regulatory mechanisms of histone acetylation in cancer have attracted increasing attention from scientists. Long non-coding RNA (lncRNA) also plays an important role in multiple biology processes linked to OC. This study aimed to identify the histone acetylation-related lncRNAs (HARlncRNAs) with respect to the prognosis in OC. We obtained the transcriptome data from Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA); HARlncRNAs were first identified by co-expression and differential expression analyses, and then univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) were used to construct the HARlncRNAs risk signature. Kaplan–Meier analysis, time-dependent receiver operating characteristics (ROC), univariate Cox regression, multivariate Cox regression, nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analysis (GSEA) in risk groups were conducted to explore the tightly correlated pathways with the risk group. A risk signature with 14 HARlncRNAs in OC was finally established and further validated in the International Cancer Genome Consortium (ICGC) cohort; the 1-, 3-, and 5-year ROC value, nomogram, and calibration results confirmed the good prediction power of this model. The patients were grouped into high- and low-risk subgroups according to the risk score by the median value. The low-risk group patients exhibited a higher homologous recombination deficiency (HRD) score, LOH_frac_altered, and mutLoad_nonsilent. Furthermore, consensus clustering analysis was employed to divide OC patients into three clusters based on the expression of the 14 HARlncRNAs, which presented different survival probabilities. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were also performed to evaluate the three clusters. In conclusion, the risk signature composed of 14 HARlncRNAs might function as biomarkers and prognostic indicators with respect to predicting the response to the anti-cancer drugs in OC.

Fetal growth trajectories of babies born large-for-gestational age in the LIFECODES Fetal Growth Study

Babies born large-for-gestational age have an increased risk of adverse health outcomes, including birth injuries, childhood obesity, and cardiometabolic disorders. However, little work has been done to characterize patterns of fetal growth among large-for-gestational age births, which may further elucidate high- and low-risk subgroups. This study aimed to identify subgroups of large-for-gestational age births based on trajectories of fetal growth derived from prenatal ultrasound measurements and explore differences in sociodemographic, pregnancy, and birth outcome characteristics across subgroups. This study identified and described trajectories of fetal growth among large-for-gestational age births (n=235) in the LIFECODES Fetal Growth Study. Ultrasound measurements of fetal growth in middle to late pregnancy were abstracted from health records. Group-based multi-trajectory modeling was applied to measurements of head circumference, abdominal circumference, and femur length z-scores to identify multivariate trajectories of fetal growth. Moreover, sociodemographic variables, pregnancy characteristics, and birth outcomes based on trajectory membership were summarized. This study identified 4 multivariate trajectories of fetal growth among large-for-gestational age births: catch-up growth (n=28), proportional abdominal circumference-to-femur length growth (n=67), disproportional abdominal circumference-to-femur length growth (n=96), and consistently large (n=44). Fetuses in the "catch-up growth" group exhibited small relative sizes in midpregnancy (ie, below average head circumference, abdominal circumference, and femur length z-scores) and large relative sizes in late pregnancy. Growth among these births was driven by increases in relative abdominal circumference and head circumference sizes. Participants who delivered births assigned to this group were less likely to have normal glucose control (40% vs 65%-75%) and more likely to have pregestational diabetes mellitus (36% vs 10%-17%) than other large-for-gestational age subgroups. In addition, the babies in this trajectory group were more likely to have macrosomia (86% vs 67%-73%) and to be admitted to the neonatal intensive care unit (32% vs 14%-21%) than other large-for-gestational age subgroups. In contrast, babies in the "consistently large" group had the largest relative size for all growth parameters throughout gestation and experienced a lower risk of adverse birth outcomes than other large-for-gestational age subgroups. This study characterized several trajectories of fetal growth among large-for-gestational age births, which were related to different pregnancy characteristics and the distribution of adverse birth outcomes. Although the number of individuals within some trajectories was small, a subgroup that exhibited a catch-up growth phenotype during gestation was identified, which may be uniquely associated with exposure to pregestational diabetes mellitus and a higher risk of admission to the neonatal intensive care unit. These results have highlighted that the risk of adverse outcomes may not be evenly distributed across all large-for-gestational age births.