Comprehensive molecular classification predicted microenvironment profiles and therapy response for HCC.

BACKGROUND AND AIMS: Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for patients with HCC. The clinical applications of documented molecular subtypes are constrained by several issues. APPROACH AND RESULTS: We integrated 3 single-cell data sets to describe the TME landscape and identified 6 prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes. The predictive value of these molecular subtypes for prognosis and treatment response was explored in multiple external HCC cohorts and the Xiangya HCC cohort. TME features were estimated using single-cell immune repertoire sequencing, mass cytometry, and multiplex immunofluorescence. The prognosis-related score was constructed based on a machine-learning algorithm. Comprehensive single-cell analysis described TME heterogeneity in HCC. The 5 transcriptomic subtypes possessed different clinical prognoses, stemness characteristics, immune landscapes, and therapeutic responses. Class 1 exhibited an inflamed phenotype with better clinical outcomes, while classes 2 and 4 were characterized by a lack of T-cell infiltration. Classes 5 and 3 indicated an inhibitory tumor immune microenvironment. Analysis of multiple therapeutic cohorts suggested that classes 5 and 3 were sensitive to immune checkpoint blockade and targeted therapy, whereas classes 1 and 2 were more responsive to transcatheter arterial chemoembolization treatment. Class 4 displayed resistance to all conventional HCC therapies. Four potential therapeutic agents and 4 targets were further identified for high prognosis-related score patients with HCC. CONCLUSIONS: Our study generated a clinically valid molecular classification to guide precision medicine in patients with HCC.

A cancer-associated fibroblast subtypes-based signature enables the evaluation of immunotherapy response and prognosis in bladder cancer.

Bladder cancer (BLCA) is one of the most prevalent and heterogeneous urinary malignant tumors. Previous researches have reported a significant association between cancer-associated fibroblasts (CAFs) and poor prognosis of tumor patients. However, uncertainty surrounds the role of CAFs in the BLCA tumor microenvironment, necessitating further investigation into the CAFs-related gene signatures in BLCA. In this study, we identified three CAF subtypes in BLCA according to single-cell RNA-seq data and constructed CAFs-related risk score (CRRS) by screening 102,714 signatures. The survival analysis, ROC curves, and nomogram suggested that CRRS was a valuable predictor in 2,042 patients from 9 available public datasets and Xiangya real-world cohort. We further revealed the significant correlation between CRRS and clinicopathological characteristics, genome alterations, and epithelial-mesenchymal transition (EMT). A high CRRS indicated a non-inflamed phenotype and a lower remission rate of immunotherapy in BLCA. In conclusion, the CRRS had the potential to predict the prognosis and immunotherapy response of BLCA patients.

MTFR2 shapes a barrier of immune microenvironment in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death in the world. Mitochondrial fission regulator 2 (MTFR2) is involved in the development of various cancers. However, the roles of MTFR2 in HCC remain unknown. In this study, we conducted a comprehensive analysis of MTFR2 in HCC, which was generated from integrative MTFR2 analyses of eight HCC cell lines, and three datasets (public dataset, real-world dataset, and immunotherapy dataset) derived from bulk HCC tissues, survival, and immunotherapy data. We demonstrated that the expression level of MTFR2 is upregulated in HCC, leading to poor prognosis. MTFR2 is positively correlated with the level of immune cell infiltration, multiple immune checkpoints and immunotherapy response prediction pathways, and acts as an important role in cancer-immunity cycle. In conclusion, our work indicates that MTFR2 can shape a barrier of immune microenvironment and result in poor prognosis in hepatocellular carcinoma, but the immune barrier may be broken by immunotherapy.

Prognostic stratification based on m5C regulators acts as a novel biomarker for immunotherapy in hepatocellular carcinoma.

Background: Immunotherapy is a promising anti-cancer strategy in hepatocellular carcinoma (HCC). However, a limited number of patients can benefit from it. There are currently no reliable biomarkers available to find the potential beneficiaries. Methylcytosine (m5C) is crucial in HCC, but its role in forecasting clinical responses to immunotherapy has not been fully clarified. Methods: In this study, we analyzed 371 HCC patients from The Cancer Genome Atlas (TCGA) database and investigated the expression of 18 m5C regulators. We selected 6 differentially expressed genes (DEGs) to construct a prognostic risk model as well as 2 m5C-related diagnostic models. Results: The 1-, 3-, and 5-year area under the curve (AUC) of m5C scores for the overall survival (OS) was 0.781/0.762/0.711, indicating the m5C score system had an ideal distinction of prognostic prediction for HCC. The survival analysis showed that patients with high-risk scores present a worse prognosis than the patients with low-risk scores (p< 0.0001). We got consistent results in 6 public cohorts and validated them in Xiangya real-world cohort by quantitative real-time PCR and immunohistochemical (IHC) assays. The high-m5C score group was predicted to be in an immune evasion state and showed low sensitivity to immunotherapy, but high sensitivity to chemotherapy and potential targeted drugs and agents, such as sepantronium bromide (YM-155), axitinib, vinblastine and docetaxel. Meanwhile, we also constructed two diagnostic models to distinguish HCC tumors from normal liver tissues or liver cirrhosis. Conclusion: In conclusion, our study helps to early screen HCC patients and select patients who can benefit from immunotherapy. Step forwardly, for the less likely beneficiaries, this study provides them with new potential targeted drugs and agents for choice to improve their prognosis.

Identification of tumour immune infiltration-associated snoRNAs (TIIsno) for predicting prognosis and immune landscape in patients with colon cancer via a TIIsno score model.

BACKGROUND: Colon cancer (CC) is the leading cause of tumour-related death worldwide. SnoRNA plays a critical role in the tumour microenvironment. The tumour microenvironment can be shaped by tumour-infiltrating immune cells, which control the destiny of immunotherapy efficacy. This study uniquely focused on snoRNAs derived from immune cells to identify new biomarkers for immune landscape. METHODS: A novel computational framework was initiated for identifying tumour immune infiltration-associated snoRNAs (TIIsno) signatures and developed a TIIsno score model from integrative snoRNA profiling analysis of 21 purified immune cell lines, 43 colon cancer cell lines, and three datasets (training, test, real-world validation set). FINDINGS: Our study found that a high TIIsno score was associated with poor CC prognosis. TIIsno scores were seen to be negatively correlated with (I) the infiltration level of most immune cells, (II) the inhibitory immune checkpoints expression level, and (III) the immune score. These findings, taken together with the observation that TIIsno score is lower in MSI-H patients, suggests that patients with a low TIIsno score may have a better response to immunotherapy. INTERPRETATION: In conclusion, we successfully identified TIIsno and constructed a TIIsno score model, a new potential biomarker of immunotherapy response, which can effectively predict the prognosis of CC patients as well. FUNDING: National Key R & D Program of China, National Natural Science Foundation of China, key projects from the Nature Science Foundation of Hunan Province, projects from Beijing CSCO Clinical Oncology Research Foundation, Fundamental Research Funds for the Central Universities of Central South University.

tRF3008A suppresses the progression and metastasis of colorectal cancer by destabilizing FOXK1 in an AGO-dependent manner.

BACKGROUND: tRNA-derived fragments (tRFs) have been shown to have critical regulatory roles in cancer biology. However, the contributions of tRFs to colorectal cancer (CRC) remain largely unknown. METHODS: tRF3008A (a tRFRNA derived from tRNAVal) was identified by RNA sequencing and validated by quantitative reverse transcription PCR. The role of tRF3008A in CRC progression was assessed both in vitro and in vivo, and its downstream target genes were identified and validated in CRC cells. RNA pull-down with mass spectrometry and AGO-RIP were used to confirm the interaction of tRF3008A and AGO proteins. The clinical implications of tRF3008A were assessed in CRC tissues and blood samples. RESULTS: The expression of tRF3008A was reduced in colorectal cancer, and its reduction was significantly correlated with advanced and metastatic disease in CRC. Patients with low tRF3008A expression showed significantly shorter DFS, and multivariate analysis identified tRF3008A as an independent prognostic biomarker in CRC. Functionally, tRF3008A inhibits the proliferation and migration of CRC in vivo and in vitro by repressing endogenous FOXK1, a positive regulator of the Wnt/ß-catenin pathway. Mechanistically, tRF3008A binds to AGO proteins as a guide to destabilize oncogenic FOXK1 transcript. CONCLUSIONS: tRF3008A suppresses the metastasis and progression of colorectal cancer by destabilizing FOXK1 in an AGO-dependent manner.

Gut microbiota imbalance in colorectal cancer patients, the risk factor of COVID-19 mortality.

BACKGROUND: COVID-19 pandemic is sweeping across the world. Previous studies have shown that gut microbiota is associated with COVID-19, and operational taxonomic unit (OTU) composed of Blautia genus, Lactobacillus genus, and Ruminococcus genus of Firmicutes is correlated with the severity of COVID-19. Gut microbiota imbalance in colorectal cancer patients may lead to the variation of OTU. RESULTS: Based on the GMrepo database, the gut microbiota of 1374 patients with colorectal neoplasms and 27,329 healthy people was analyzed to investigate the differences in the abundance of microbes between colorectal neoplasms patients and healthy people. Furthermore, We collected feces samples from 12 patients with colorectal cancer and 8 healthy people in Xiangya hospital for metabolomic analysis to investigate the potential mechanisms. Our study showed that the abundance of Blautia and Ruminococcus was significantly increased in colorectal neoplasms, which may increase the severity of COVID-19. The gender and age of patients may affect the severity of COVID-19 by shaping the gut microbiota, but the BMI of patients does not. CONCLUSIONS: Our work draws an initial point that gut microbiota imbalance is a risk factor of COVID-19 mortality and gut microbiota may provide a new therapeutic avenue for colorectal cancer patients.

A comprehensive analysis of the efficacy and safety of COVID-19 vaccines.

The numbers of cases and deaths from coronavirus disease 2019 (COVID-19) are continuously increasing. Many people are concerned about the efficacy and safety of the COVID-19 vaccines. We performed a comprehensive analysis of the published trials of COVID-19 vaccines and the real-world data from the Vaccine Adverse Event Reporting System. Globally, our research found that the efficacy of all vaccines exceeded 70%, and RNA-based vaccines had the highest efficacy of 94.29%; moreover, Black or African American people, young people, and males may experience greater vaccine efficacy. The spectrum of vaccine-related adverse drug reactions (ADRs) is extremely broad, and the most frequent ADRs are pain, fatigue, and headache. Most ADRs are tolerable and are mainly grade 1 or 2 in severity. Some severe ADRs have been identified (thromboembolic events, 21-75 cases per million doses; myocarditis/pericarditis, 2-3 cases per million doses). In summary, vaccines are a powerful tool that can be used to control the COVID-19 pandemic, with high efficacy and tolerable ADRs. In addition, the spectrum of ADRs associated with the vaccines is broad, and most of the reactions appear within a week, although some may be delayed. Therefore, ADRs after vaccination need to be identified and addressed in a timely manner.

M6A "Writer" Gene METTL14: A Favorable Prognostic Biomarker and Correlated With Immune Infiltrates in Rectal Cancer.

Rectal cancer (RC) is the leading cause of tumor-related death among both men and women. The efficacy of immunotherapy for rectal cancer is closely related to the immune infiltration level. The N6-methyladenosine (m6A) modification may play a pivotal role in tumor-immune interactions. However, the roles of m6A-related genes in tumor-immune interactions of rectal cancer remain largely unknown. After an evaluation on the expression levels of m6A-related genes and their correlations with the prognosis of rectal cancer patients, we found that METTL14 was the only gene to be significantly correlated with prognosis in rectal cancer patients. Therefore, we further observed the impact of METTL14 expression and m6A modification on the immune infiltration in rectal cancer. Our study indicates that low expression of the m6A "writer" gene METTL14 in rectal cancer may lead to the downregulation of m6A RNA modification, thus reducing the level of immune cell infiltration and resulting in poor prognosis. METTL14 expression level is an independent prognostic factor in rectal cancer and is positively correlated with the immune infiltration level. Our study identified METTL14 as a potential target for enhancing immunotherapy efficacy in rectal cancer.

Predicting primary treatment failure using interim FDG-PET scanning in diffuse large B-cell lymphoma.

Interim FDG-PET (iPET) in diffuse large B-cell lymphoma (DLBCL) is increasingly practised and used in clinical trials to adapt further therapy. However, the optimum timing and methodology of iPET remains controversial. We retrospectively analysed the iPET results and outcomes of 200 DLBCL patients where FDG-PET was routinely performed at baseline, after 2 cycles (iPET2) and at completion of chemoimmunotherapy. iPET was also performed after 4 cycles (iPET4) where at iPET2, Deauville score (DS) was ≥4. Scans were assessed by blinded expert lymphoma PET physicians for DS, maximum standard uptake value (SUVmax), total metabolic tumour volume (TMTV) and total lesion glycolysis (TLG). Treatment failure was defined as death, progression or refractory disease. 95.5% of patients received R-CHOP. No baseline PET parameter was predicted for EFS or OS independent of the NCCN-IPI. The multivariable analysis at iPET2 showed DS5 (19.5% of cases) predicted treatment failure (HR 6.29, 95% CI 3.01-13.17, P < .001), but DS4 was equivalent to DS1-3. At iPET4, ΔSUVmax < 66% predicted treatment failure (HR 5.49, 95% CI 3.03-9.99, P < .001). By multivariable analysis of all time points, high NCCN-IPI and DS5 at iPET2 were negative predictors of survival. These findings were independent of novel prognostic markers.

Low expression of NLRP1 is associated with a poor prognosis and immune infiltration in lung adenocarcinoma patients.

NLRP1 (NLR family, pyrin domain containing 1), the first NLR protein, described to form an inflammasome, plays critical roles in innate immunity and inflammation. However, NLRP1 has not been reported to be linked to LUAD (lung adenocarcinoma) risk, prognosis, immunotherapy or any other treatments. This research aimed to explore the prognostic value and mechanism of NLRP1 in LUAD. We performed bioinformatics analysis on LUAD data downloaded from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), and jointly analyzed with online databases such as TCGAportal, LinkedOmics, TIMER, ESTIMATE and TISIDB. NLRP1 expression of LUAD tissue was considerably lower than that in normal lung tissue. Decreased NLRP1 expression of LUAD was associated with relatively high pathological, T and N stages. Kaplan-Meier survival analysis indicated that patients with low NLRP1 expression had a worse prognosis than those with high expression. Multivariate Cox analysis further showed that NLRP1 expression level was an independent prognostic factor of LUAD. Moreover, the level of NLRP1 expression was positively linked to the degree of infiltration of various TIICs (tumor-infiltrating immune cells). Our findings confirmed that reduced expression of NLRP1 was significantly related to poor prognosis and low degree of immune cell infiltration in LUAD patients.

A comprehensive analysis of the fatal toxic effects associated with CD19 CAR-T cell therapy.

To determine the incidence, spectrum, timing, and clinical features of CD19 Chimeric antigen receptor (CAR-T) cell therapy-associated fatal toxic effects. We initiated a comprehensive analysis. First, we retrospectively queried the real-world data from a World Health Organization (WHO) pharmacovigilance database (Vigilyze). Furthermore, we performed a meta-analysis of published trials of CD19 CAR-T cell therapy. From screening the WHO database, we identified 1200 patients: 499 received Kymriah therapy, and 701 received Yescarta therapy. We compared the adverse reactions of the two drugs. We evaluated all published clinical trials, comprising 19 trials and 890 patients. Our meta-analysis showed that the incidence of fatal toxic effects associated with death was 5.4%. Infections and infestations appeared to present the highest risk of death. The toxic effect specific median time to death was 30, 30, and 68 days for total, cytokine release syndrome (CRS), and nervous system disorders (NSD), respectively. We observed a high mortality rate for some toxic effects and an early onset of death with varied causes, indicating the need for clinicians to pay more attention to the monitoring and treatment of these fatal toxic effects when using CD19 CAR-T cell therapy, especially for infections and infestations.

MLH1 Deficiency Induces Cetuximab Resistance in Colon Cancer via Her-2/PI3K/AKT Signaling.

The rapid onset of resistance to cetuximab (CTX) limits its clinical utility in colorectal cancer (CRC) patients. This study aims to understand a potential role of mismatch repair gene mutL homolog 1 (MLH1) in CTX response. Functional analysis of MLH1 in Her-2/phosphoinositide 3-kinases (PI3K)/PKB protein kinase (AKT)-regulated CTX sensitivity is performed using human CRC specimens, CRC cell lines with different MLH1 expression levels, and a subcutaneous xenograft model. Overexpression, knockdown, small interfering RNA, and inhibitors are used to examine the role of MLH1 and HER-2 downstream signaling and apoptotic targets in CTX sensitivity. Reduced MLH1 expression is correlated with unfavorable prognosis in cetuximab-treated patients. MLH1 loss decreases CTX sensitivity through Her-2/PI3K/AKT signaling and apoptosis resistance in culture and in xenografts, while MLH1 overexpression increases CTX sensitivity. Blocking Her-2 signaling increases CTX sensitivity of microsatellite instability CRC in vitro and in vivo. MLH1 loss induces activation of Her-2/PI3K/AKT signaling and leads to cetuximab resistance in colon cancer.

Leadless Pacemaker: Report of the First Experience in Hawai'i.

We had the opportunity to implant the first leadless pacemakers in Hawai'i. This device represents a major change in pacemaker technology. This is a report of the first five cases and a review of the literature. All these devices were implanted via femoral venous access (versus conventional upper chest axillary/subclavian/cephalic routes), with an unique fixation mechanism allowing direct attachment to the ventricular myocardium (dispensing the usage of long transvenous electrode leads). The miniature generator can is over an order of magnitude smaller and lighter than the currently available ones. This article provides an understanding of the device design, implantation technique, the advantages and limitations, and the potential of this new pacemaker.

GReTA-A Novel Global and Recursive Tracking Algorithm in Three Dimensions.

Tracking multiple moving targets allows quantitative measure of the dynamic behavior in systems as diverse as animal groups in biology, turbulence in fluid dynamics and crowd and traffic control. In three dimensions, tracking several targets becomes increasingly hard since optical occlusions are very likely, i.e., two featureless targets frequently overlap for several frames. Occlusions are particularly frequent in biological groups such as bird flocks, fish schools, and insect swarms, a fact that has severely limited collective animal behavior field studies in the past. This paper presents a 3D tracking method that is robust in the case of severe occlusions. To ensure robustness, we adopt a global optimization approach that works on all objects and frames at once. To achieve practicality and scalability, we employ a divide and conquer formulation, thanks to which the computational complexity of the problem is reduced by orders of magnitude. We tested our algorithm with synthetic data, with experimental data of bird flocks and insect swarms and with public benchmark datasets, and show that our system yields high quality trajectories for hundreds of moving targets with severe overlap. The results obtained on very heterogeneous data show the potential applicability of our method to the most diverse experimental situations.

Emergence of collective changes in travel direction of starling flocks from individual birds' fluctuations.

One of the most impressive features of moving animal groups is their ability to perform sudden coherent changes in travel direction. While this collective decision can be a response to an external alarm cue, directional switching can also emerge from the intrinsic fluctuations in individual behaviour. However, the cause and the mechanism by which such collective changes of direction occur are not fully understood yet. Here, we present an experimental study of spontaneous collective turns in natural flocks of starlings. We employ a recently developed tracking algorithm to reconstruct three-dimensional trajectories of each individual bird in the flock for the whole duration of a turning event. Our approach enables us to analyse changes in the individual behaviour of every group member and reveal the emergent dynamics of turning. We show that spontaneous turns start from individuals located at the elongated tips of the flocks, and then propagate through the group. We find that birds on the tips deviate from the mean direction of motion much more frequently than other individuals, indicating that persistent localized fluctuations are the crucial ingredient for triggering a collective directional change. Finally, we quantitatively verify that birds follow equal-radius paths during turning, the effects of which are a change of the flock's orientation and a redistribution of individual locations in the group.

Gait assessment using the Kinect RGB-D sensor.

Patients with concussions, strokes and neuromuscular disease such as Parkinson's disease, often have difficulties in keeping balance and suffer from abnormal gaits. Gait assessment conducted by a physician or therapist in clinics is standard clinical practice for assessing such injuries. However, this approach is subjective, leading to potential problems of unrepeatability, poor sensitivity and unreliability. To conduct the assessment in an objective way, a computer-based gait assessment system is designed and presented in this paper. The system performs assessments on dynamic balance and gaits by analyzing the skeleton frames of a subject captured by the Microsoft Kinect RGB-D sensor. Results show that the proposed system effectively scores subjects.

Finite-size scaling as a way to probe near-criticality in natural swarms.

Collective behavior in biological systems is often accompanied by strong correlations. The question has therefore arisen of whether correlation is amplified by the vicinity to some critical point in the parameters space. Biological systems, though, are typically quite far from the thermodynamic limit, so that the value of the control parameter at which correlation and susceptibility peak depend on size. Hence, a system would need to readjust its control parameter according to its size in order to be maximally correlated. This readjustment, though, has never been observed experimentally. By gathering three-dimensional data on swarms of midges in the field we find that swarms tune their control parameter and size so as to maintain a scaling behavior of the correlation function. As a consequence, correlation length and susceptibility scale with the system's size and swarms exhibit a near-maximal degree of correlation at all sizes.

Information transfer and behavioural inertia in starling flocks.

Collective decision-making in biological systems requires all individuals in the group to go through a behavioural change of state. During this transition fast and robust transfer of information is essential to prevent cohesion loss. The mechanism by which natural groups achieve such robustness, though, is not clear. Here we present an experimental study of starling flocks performing collective turns. We find that information about direction changes propagates across the flock with a linear dispersion law and negligible attenuation, hence minimizing group decoherence. These results contrast starkly with current models of collective motion, which predict diffusive transport of information. Building on spontaneous symmetry breaking and conservation laws arguments, we formulate a new theory that correctly reproduces linear and undamped propagation. Essential to the new framework is the inclusion of the birds' behavioural inertia. The new theory not only explains the data, but also predicts that information transfer must be faster the stronger the group's orientational order, a prediction accurately verified by the data. Our results suggest that swift decision-making may be the adaptive drive for the strong behavioural polarization observed in many living groups.

Collective behaviour without collective order in wild swarms of midges.

Collective behaviour is a widespread phenomenon in biology, cutting through a huge span of scales, from cell colonies up to bird flocks and fish schools. The most prominent trait of collective behaviour is the emergence of global order: individuals synchronize their states, giving the stunning impression that the group behaves as one. In many biological systems, though, it is unclear whether global order is present. A paradigmatic case is that of insect swarms, whose erratic movements seem to suggest that group formation is a mere epiphenomenon of the independent interaction of each individual with an external landmark. In these cases, whether or not the group behaves truly collectively is debated. Here, we experimentally study swarms of midges in the field and measure how much the change of direction of one midge affects that of other individuals. We discover that, despite the lack of collective order, swarms display very strong correlations, totally incompatible with models of non-interacting particles. We find that correlation increases sharply with the swarm's density, indicating that the interaction between midges is based on a metric perception mechanism. By means of numerical simulations we demonstrate that such growing correlation is typical of a system close to an ordering transition. Our findings suggest that correlation, rather than order, is the true hallmark of collective behaviour in biological systems.