Exploring biomarkers for prognosis and neoadjuvant chemosensitivity in rectal cancer: Multi-omics and ctDNA sequencing collaboration.

Introduction: This study aimed to identified the key genes and sequencing metrics for predicting prognosis and efficacy of neoadjuvant chemotherapy (nCT) in rectal cancer (RC) based on genomic DNA sequencing in samples with different origin and multi-omics association database. Methods: We collected 16 RC patients and obtained DNA sequencing data from cancer tissues and plasma cell-free DNA before and after nCT. Various gene variations were analyzed, including single nucleotide variants (SNV), copy number variation (CNV), tumor mutation burden (TMB), copy number instability (CNI) and mutant-allele tumor heterogeneity (MATH). We also identified genes by which CNV level can differentiate the response to nCT. The Cancer Genome Atlas database and the Clinical Proteomic Tumor Analysis Consortium database were used to further evaluate the specific role of therapeutic relevant genes and screen out the key genes in multi-omics levels. After the intersection of the screened genes from differential expression analysis, survival analysis and principal components analysis dimensionality reduction cluster analysis, the key genes were finally identified. Results: The genes CNV level of principal component genes in baseline blood and cancer tissues could significantly distinguish the two groups of patients. The CNV of HSP90AA1, EGFR, SRC, MTOR, etc. were relatively gained in the better group compared with the poor group in baseline blood. The CNI and TMB was significantly different between the two groups. The increased expression of HSP90AA1, EGFR, and SRC was associated with increased sensitivity to multiple chemotherapeutic drugs. The nCT predictive score obtained by therapeutic relevant genes could be a potential prognostic indicator, and the combination with TMB could further refine prognostic prediction for patients. After a series of analysis in multi-omics association database, EGFR and HSP90AA1 with significant differences in multiple aspects were identified as the key predictive genes related to prognosis and the sensitivity of nCT. Discussion: This work revealed that effective combined application and analysis in multi-omics data are critical to search for predictive biomarkers. The key genes EGFR and HSP90AA1 could serve as an effective biomarker to predict prognose and neoadjuvant chemosensitivity.

Identification and Validation in a Novel Quantification System of Ferroptosis Patterns for the Prediction of Prognosis and Immunotherapy Response in Left- and Right-Sided Colon Cancer.

Background: This study aimed to establish a novel quantification system of ferroptosis patterns and comprehensively analyze the relationship between ferroptosis score (FS) and the immune cell infiltration (ICI) characterization, tumor mutation burden (TMB), prognosis, and therapeutic sensitivity in left-sided and right-sided colon cancers (LCCs and RCCs, respectively). Methods: We comprehensively evaluated the ferroptosis patterns in 444 LCCs and RCCs based on 59 ferroptosis-related genes (FRGs). The FS was constructed to quantify ferroptosis patterns by using principal component analysis algorithms. Next, the prognostic value and therapeutic sensitivities were evaluated using multiple methods. Finally, we performed weighted gene co-expression network analysis (WGCNA) to identify the key FRGs. The IMvigor210 cohort, TCGA-COAD proteomics cohort, and Immunophenoscores were used to verify the predictive abilities of FS and the key FRGs. Results: Two ferroptosis clusters were determined. Ferroptosis cluster B demonstrated a high degree of congenital ICI and stromal-related signal enrichment with a poor prognosis. The prognosis, response of targeted inhibitors, and immunotherapy were significantly different between high and low FS groups (HSG and LSG, respectively). HSG was characterized by high TMB and microsatellite instability-high subtype with poor prognosis. Meanwhile, LSG was more likely to benefit from immunotherapy. ALOX5 was identified as a key FRG based on FS. Patients with high protein levels of ALOX5 had poorer prognoses. Conclusion: This work revealed that the evaluation of ferroptosis subtypes will contribute to gaining insight into the heterogeneity in LCCs and RCCs. The quantification for ferroptosis patterns played a non-negligible role in predicting ICI characterization, prognosis, and individualized immunotherapy strategies.

Identification and quantification of immune infiltration landscape on therapy and prognosis in left- and right-sided colon cancer.

BACKGROUND: The left-sided and right-sided colon cancer (LCCs and RCCs, respectively) have unique molecular features and clinical heterogeneity. This study aimed to identify the characteristics of immune cell infiltration (ICI) subtypes for evaluating prognosis and therapeutic benefits. METHODS: The independent gene datasets, corresponding somatic mutation and clinical information were collected from The Cancer Genome Atlas and Gene Expression Omnibus. The ICI contents were evaluated by "ESTIMATE" and "CIBERSORT." We performed two computational algorithms to identify the ICI landscape related to prognosis and found the unique infiltration characteristics. Next, principal component analysis was conducted to construct ICI score based on three ICI patterns. We analyzed the correlation between ICI score and tumor mutation burden (TMB), and stratified patients into prognostic-related high- and low- ICI score groups (HSG and LSG, respectively). The role of ICI scores in the prediction of therapeutic benefits was investigated by "pRRophetic" and verified by Immunophenoscores (IPS) (TCIA database) and an independent immunotherapy cohort (IMvigor210). The key genes were preliminary screened by weighted gene co-expression network analysis based on ICI scores. And they were further identified at various levels, including single cell, protein and immunotherapy response. The predictive ability of ICI score for prognosis was also verified in IMvigor210 cohort. RESULTS: The ICI features with a better prognosis were marked by high plasma cells, dendritic cells and mast cells, low memory CD4+ T cells, M0 macrophages, M1 macrophages, as well as M2 macrophages. A high ICI score was characterized by an increased TMB and genomic instability related signaling pathways. The prognosis, sensitivities of targeted inhibitors and immunotherapy, IPS and expression of immune checkpoints were significantly different in HSG and LSG. The genes identified by ICI scores and various levels included CA2 and TSPAN1. CONCLUSION: The identification of ICI subtypes and ICI scores will help gain insights into the heterogeneity in LCC and RCC, and identify patients probably benefiting from treatments. ICI scores and the key genes could serve as an effective biomarker to predict prognosis and the sensitivity of immunotherapy.

Identification and Validation of Immune-Related Gene for Predicting Prognosis and Therapeutic Response in Ovarian Cancer.

Ovarian cancer (OC) is a devastating malignancy with a poor prognosis. The complex tumor immune microenvironment results in only a small number of patients benefiting from immunotherapy. To explore the different factors that lead to immune invasion and determine prognosis and response to immune checkpoint inhibitors (ICIs), we established a prognostic risk scoring model (PRSM) with differential expression of immune-related genes (IRGs) to identify key prognostic IRGs. Patients were divided into high-risk and low-risk groups according to their immune and stromal scores. We used a bioinformatics method to identify four key IRGs that had differences in expression between the two groups and affected prognosis. We evaluated the sensitivity of treatment from three aspects, namely chemotherapy, targeted inhibitors (TIs), and immunotherapy, to evaluate the value of prediction models and key prognostic IRGs in the clinical treatment of OC. Univariate and multivariate Cox regression analyses revealed that these four key IRGs were independent prognostic factors of overall survival in OC patients. In the high-risk group comprising four genes, macrophage M0 cells, macrophage M2 cells, and regulatory T cells, observed to be associated with poor overall survival in our study, were higher. The high-risk group had a high immunophenoscore, indicating a better response to ICIs. Taken together, we constructed a PRSM and identified four key prognostic IRGs for predicting survival and response to ICIs. Finally, the expression of these key genes in OC was evaluated using RT-qPCR. Thus, these genes provide a novel predictive biomarker for immunotherapy and immunomodulation.

Comprehensive analysis of prognostic immune-related genes and drug sensitivity in cervical cancer.

BACKGROUND: Cervical cancer (CC) is the leading cause of cancer-related death in women. A limited number of studies have investigated whether immune-prognostic features can be used to predict the prognosis of CC. This study aimed to develop an improved prognostic risk scoring model (PRSM) for CC based on immune-related genes (IRGs) to predict survival and determine the key prognostic IRGs. METHODS: We downloaded the gene expression profiles and clinical data of CC patients from the TCGA and GEO databases. The ESTIMATE algorithm was used to calculate the score for both immune and stromal cells. Differentially expressed genes (DEGs) in different subpopulations were analyzed by "Limma". A weighted gene co-expression network analysis (WGCNA) was used to establish a DEG co-expression module related to the immune score. Immune-related gene pairs (IRGPs) were constructed, and univariate- and Lasso-Cox regression analyses were used to analyze prognosis and establish a PRSM. A log-rank test was used to verify the accuracy and consistency of the scoring model. Identification of the predicted key IRG was ensured by the application of functional enrichment, DisNor, protein-protein interactions (PPIs) and heatmap. Finally, we extracted the key prognostic immune-related genes from the gene expression data, validated the key genes by immunohistochemistry and analyzed the correlation between their expression and drug sensitivity. RESULTS: A new PRSM was developed based on 22 IRGPs. The prognosis of the low-risk group in the model group (P < 0.001) and validation group (P = 0.039) was significantly better than that in the high-risk group. Furthermore, M1 and M2 macrophages were highly expressed in the low-risk group. Retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs) and the Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway were significantly enriched in the low-risk group. Three representative genes (CD80, CD28, and LCP2) were markers of CC prognosis. CD80 and CD28 may more prominent represent important indicators to improve patient prognosis. These key genes was positively correlated with drug sensitivity. Finally, we found that differences in the sensitivity to JNK inhibitors could be distinguished based on the use and risk grouping of this PRSM. CONCLUSIONS: The prognostic model based on the IRGs and key genes have potential clinical significance for predicting the prognosis of CC patients, providing a foundation for clinical prognosis judgment and individualized treatment.

Prognostic Immunity and Therapeutic Sensitivity Analyses Based on Differential Genomic Instability-Associated LncRNAs in Left- and Right-Sided Colon Adenocarcinoma.

Background: The purpose of our study was to develop a prognostic risk model based on differential genomic instability-associated (DGIA) long non-coding RNAs (lncRNAs) of left-sided and right-sided colon cancers (LCCs and RCCs); therefore, the prognostic key lncRNAs could be identified. Methods: We adopted two independent gene datasets, corresponding somatic mutation and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Identification of differential DGIA lncRNAs from LCCs and RCCs was conducted with the appliance of "Limma" analysis. Then, we screened out key lncRNAs based on univariate and multivariate Cox proportional hazard regression analysis. Meanwhile, DGIA lncRNAs related prognostic model (DRPM) was established. We employed the DRPM in the model group and internal verification group from TCGA for the purpose of risk grouping and accuracy verification of DRPM. We also verified the accuracy of key lncRNAs with GEO data. Finally, the differences of immune infiltration, functional pathways, and therapeutic sensitivities were analyzed within different risk groups. Results: A total of 123 DGIA lncRNAs were screened out by differential expression analysis. We obtained six DGIA lncRNAs by the construction of DRPM, including AC004009.1, AP003555.2, BOLA3-AS1, NKILA, LINC00543, and UCA1. After the risk grouping by these DGIA lncRNAs, we found the prognosis of the high-risk group (HRG) was significantly worse than that in the low-risk group (LRG) (all p < 0.05). In all TCGA samples and model group, the expression of CD8+ T cells in HRG was lower than that in LRG (all p < 0.05). The functional analysis indicated that there was significant upregulation with regard to pathways related to both genetic instability and immunity in LRG, including cytosolic DNA sensing pathway, response to double-strand RNA, RIG-Ⅰ like receptor signaling pathway, and Toll-like receptor signaling pathway. Finally, we analyzed the difference and significance of key DGIA lncRNAs and risk groups in multiple therapeutic sensitivities. Conclusion: Through the analysis of the DGIA lncRNAs between LCCs and RCCs, we identified six key DGIA lncRNAs. They can not only predict the prognostic risk of patients but also serve as biomarkers for evaluating the differences of genetic instability, immune infiltration, and therapeutic sensitivity.

Identification of genetic variants in a pedigree associated with epilepsy by using whole exome sequencing and whole genome sequencing.

Epilepsy is a common heterogeneous group of neurological disorders including electroencephalographic and brain imaging. We used whole exome sequencing and whole genome sequencing to identify variants in a pedigree associated with epilepsy. Cranium CT scan showed that the lateral right parietal lobe was hyperdense, and there were no clear boundaries with brain tissue in affected cases. Using WES, one exclusive nonsynonymous mutant in gene TSC2 (Chr16:2138307; c.5240 T > G; p.Ile1747Ser) was involved in this disease. Further analysis showed that de novo variant in TSC2 was high conserved across different species. Moreover, the two affected sisters and their father had the same compound heterozygous variants in TSC2, while the father had no epilepsy but depigmentation. These variants demonstrated that variant in TSC2 may result in epilepsy with incomplete penetrance in humans, and the CNV and SV variants we identified probably be involved in this disease.

Deubiquitinating Enzymes Orchestrate the Cancer Stem Cell-Immunosuppressive Niche Dialogue: New Perspectives and Therapeutic Potential.

Cancer stem cells (CSCs) are sparks for igniting tumor recurrence and the instigators of low response to immunotherapy and drug resistance. As one of the important components of tumor microenvironment, the tumor associated immune microenvironment (TAIM) is driving force for the heterogeneity, plasticity and evolution of CSCs. CSCs create the inhibitory TAIM (ITAIM) mainly through four stemness-related signals (SRSs), including Notch-nuclear factor-κB axis, Hedgehog, Wnt and signal transducer and activator of transcription. Ubiquitination and deubiquitination in proteins related to the specific stemness of the CSCs have a profound impact on the regulation of ITAIM. In regulating the balance between ubiquitination and deubiquitination, it is crucial for deubiquitinating enzymes (DUBs) to cleave ubiquitin chains from substrates. Ubiquitin-specific peptidases (USPs) comprise the largest family of DUBs. Growing evidence suggests that they play novel functions in contribution of ITAIM, including regulating tumor immunogenicity, activating stem cell factors, upregulating the SRSs, stabilizing anti-inflammatory receptors, and regulating anti-inflammatory cytokines. These overactive or abnormal signaling may dampen antitumor immune responses. The inhibition of USPs could play a regulatory role in SRSs and reversing ITAIM, and also have great potential in improving immune killing ability against tumor cells, including CSCs. In this review, we focus on the USPs involved in CSCs signaling pathways and regulating ITAIM, which are promising therapeutic targets in antitumor therapy.

Prognostic Immune-Related Analysis Based on Differentially Expressed Genes in Left- and Right-Sided Colon Adenocarcinoma.

BACKGROUND: Colon adenocarcinoma (COAD) can be divided into left-sided and right-sided COAD (LCCs and RCCs, respectively). They have unique characteristics in various biological aspects, particularly immune invasion and prognosis. The purpose of our study was to develop a prognostic risk scoring model (PRSM) based on differentially expressed immune-related genes (IRGs) between LCCs and RCCs, therefore the prognostic key IRGs could be identified. METHODS: The gene sets and clinical information of COAD patients were derived from TCGA and GEO databases. The comparison of differentially expressed genes (DEGs) of LCCs and RCCs were conducted with appliance of "Limma" analysis. The establishment about co-expression modules of DEGs related with immune score was conducted by weighted gene co-expression network analysis (WGCNA). Furthermore, we screened the module genes and completed construction of gene pairs. The analysis of the prognosis and the establishment of PRSM were performed with univariate- and lasso-Cox regression. We employed the PRSM in the model group and verification group for the purpose of risk group assignment and PRSM accuracy verification. Finally, the identification of the prognostic key IRGs was guaranteed by the adoption of functional enrichment, "DisNor" and protein-protein interaction (PPI). RESULTS: A total of 215 genes were screened out by differential expression analysis and WGCNA. A PRSM with 16 immune-related gene pairs (IRGPs) was established upon the genes pairing. Furthermore, we confirmed that the risk score was an independent factor for survival by univariate- and multivariate-Cox regression. The prognosis of high-risk group in model group (P < 0.001) and validation group (P = 0.014) was significantly worse than that in low-risk group. Treg cells (P < 0.001) and macrophage M0 (P = 0.015) were highly expressed in the high-risk group. The functional analysis indicated that there was significant up-regulation with regard of lymphocyte and cytokine related terms in low-risk group. Finally, we identified five prognostic key IRGs associated with better prognosis through PPI and prognostic analysis, including IL2RB, TRIM22, CIITA, CXCL13, and CXCR6. CONCLUSION: Through the analysis and screening of the DEGs between LCCs and RCCs, we constructed a PRSM which could predicate prognosis of LCCs and RCCs, and five prognostic key IRGs were identified as well. Therefore, the basis for identifying the benefits of immunotherapy and immunomodulatory was built.

Ultrasound Elastic Parameters Predict Central Lymph Node Metastasis of Papillary Thyroid Carcinoma.

BACKGROUND: This study aimed to explore the new factors that can predict central lymph node metastasis (CLNM) of papillary thyroid carcinoma (PTC) independently from ultrasound characteristics, elastic parameters, and endocrine indicators. METHODS: A total of 391 patients with PTC undergoing thyroidectomy and prophylactic central lymph node dissection from January 2017 to June 2019 were collected to determine the independent predictors of CLNM by single-factor and multivariate logistic regression analysis. RESULTS: Multivariate logistic regression analysis showed 9 independent predictors of CLNM, age, male, tumors in the middle or lower poles (without tumors in the isthmus), tumors in the isthmus, multiple tumors, and maximum tumor diameter measured by ultrasound, microcalcification, visible surrounding blood flow signal, and the maximum value of elastic modulus (Emax).We used the aforementioned factors to establish a scoring prediction model: predictive score Y(P) = 1/[1 + exp (1.444 + 0.084 ∗ age - 0.834 ∗ men - 0.73 ∗ multifocality - 2.718 ∗ tumors in the isthmus - 0.954 ∗ tumors in the middle or lower poles - 0.086 ∗ tumor maximum diameter - 1.070 ∗ microcalcification - 0.892 ∗ visible surrounding blood flow signal - 0.021 ∗ Emax)]. The area under the curve of the receiver operating characteristic was 0.827. It was found that 0.524 was the highest index of Youden, and the best cutoff value for predicting CLNM. When Y(P)≥0.524, the risk of CLNM in patients with PTC is predicted to be high. Predictive accuracy was 78.5% and 72.4% in the internal validation group and 78.6% in the external validation group. CONCLUSIONS: These data indicate that the scoring prediction model could provide a scientific and quantitative way to predict CLNM in patients with PTC.