Next-generation sequencing identified that RET variation associates with lymph node metastasis and the immune microenvironment in thyroid papillary carcinoma.

BACKGROUND: To date, although most thyroid carcinoma (THCA) achieves an excellent prognosis, some patients experience a rapid progression episode, even with differentiated THCA. Nodal metastasis is an unfavorable predictor. Exploring the underlying mechanism may bring a deep insight into THCA. METHODS: A total of 108 THCA from Chinese patients with next-generation sequencing (NGS) were recruited. It was used to explore the gene alteration spectrum of THCA and identify gene alterations related to nodal metastasis in papillary thyroid carcinoma (PTC). The Cancer Genome Atlas THCA cohort was further studied to elucidate the relationship between specific gene alterations and tumor microenvironment. A pathway enrichment analysis was used to explore the underlying mechanism. RESULTS: Gene alteration was frequent in THCA. BRAF, RET, POLE, ATM, and BRCA1 were the five most common altered genes. RET variation was positively related to nodal metastasis in PTC. RET variation is associated with immune cell infiltration levels, including CD8 naïve, CD4 T and CD8 T cells, etc. Moreover, Step 3 and Step 4 of the cancer immunity cycle (CIC) were activated, whereas Step 6 was suppressed in PTC with RET variation. A pathway enrichment analysis showed that RET variation was associated with several immune-related pathways. CONCLUSION: RET variation is positively related to nodal metastasis in Chinese PTC, and anti-tumor immune response may play a role in nodal metastasis triggered by RET variation.

A critical role of telomere chromatin compaction in ALT tumor cell growth.

ALT tumor cells often contain abundant DNA damage foci at telomeres and rely on the alternative lengthening of telomeres (ALT) mechanism to maintain their telomeres. How the telomere chromatin is regulated and maintained in these cells remains largely unknown. In this study, we present evidence that heterochromatin protein 1 binding protein 3 (HP1BP3) can localize to telomeres and is particularly enriched on telomeres in ALT cells. HP1BP3 inhibition led to preferential growth inhibition of ALT cells, which was accompanied by telomere chromatin decompaction, increased presence of C-circles, more pronounced ALT-associated phenotypes and elongated telomeres. Furthermore, HP1BP3 appeared to participate in regulating telomere histone H3K9me3 epigenetic marks. Taken together, our data suggest that HP1BP3 functions on telomeres to maintain telomere chromatin and represents a novel target for inhibiting ALT cancer cells.

Different Decision-Making Responses Occupy Different Brain Networks for Information Processing: A Study Based on EEG and TMS.

This study used large-scale time-varying network analysis to reveal the diverse network patterns during the different decision stages and found that the responses of rejection and acceptance involved different network structures. When participants accept unfair offers, the brain recruits a more bottom-up mechanism with a much stronger information flow from the visual cortex (O2) to the frontal area, but when they reject unfair offers, it displayed a more top-down flow derived from the frontal cortex (Fz) to the parietal and occipital cortices. Furthermore, we performed 2 additional studies to validate the above network models: one was to identify the 2 responses based on the out-degree information of network hub nodes, which results in 70% accuracy, and the other utilized theta burst stimulation (TBS) of transcranial magnetic stimulation (TMS) to modulate the frontal area before the decision-making tasks. We found that the intermittent TBS group demonstrated lower acceptance rates and that the continuous TBS group showed higher acceptance rates compared with the sham group. Similar effects were not observed after TBS of a control site. These results suggest that the revealed decision-making network model can serve as a potential intervention model to alter decision responses.

Brain Network Reconfiguration During Motor Imagery Revealed by a Large-Scale Network Analysis of Scalp EEG.

Mentally imagining rather physically executing the motor behaviors is defined as motor imagery (MI). During MI, the mu rhythmical oscillation of cortical neurons is the event-related desynchronization (ERD) subserving the physiological basis of MI-based brain-computer interface. In our work, we investigated the specific brain network reconfiguration from rest idle to MI task states, and also probed the underlying relationship between the brain network reconfiguration and MI related ERD. Findings revealed that comparing to rest state, the MI showed the enhanced motor area related linkages and the deactivated activity of default mode network. In addition, the reconfigured network index was closely related to the ERDs, i.e., the higher the reconfigured network index was, the more obvious the ERDs were. These findings consistently implied that the reconfiguration from rest to task states underlaid the reallocation of related brain resources, and the efficient brain reconfiguration corresponded to a better MI performance, which provided the new insights into understanding the mechanism of MI as well as the potential biomarker to evaluate the rehabilitation quality for those patients with deficits of motor function.

Rho GDP-dissociation inhibitor α is a potential prognostic biomarker and controls telomere regulation in colorectal cancer.

Rho GDP-dissociation inhibitor α (RhoGDIα) is an essential regulator for Rho GTPases. Although RhoGDIα may serve as an oncogene in colorectal cancer (CRC), the underlying mechanism is still unclear. We investigated the function, mechanism, and clinical significance of RhoGDIα in CRC progression. We founded that downregulation of RhoGDIα repressed CRC cell proliferation, motility, and invasion. Overexpression of RhoGDIα increased DNA damage response signals at telomeres, and led to telomere shortening in CRC cells, also being validated in 26 pairs of CRC tissues. Mechanistic studies revealed that RhoGDIα could promote telomeric repeat factor 1 (TRF1) expression through the phosphatidylinositol 3-kinase-protein kinase B signal pathway. Moreover, RhoGDIα protein levels were strongly correlated with TRF1 in CRC tissues. A cohort of 297 CRC samples validated the positive relationship between RhoGDIα and TRF1, and revealed that RhoGDIα and TRF1 levels were negatively associated with CRC patients' survival. Taken together, our results suggest that RhoGDIα regulate TRF1 and telomere length and may be novel prognostic biomarkers in colorectal cancer.

TP53-specific mutations serve as a potential biomarker for homologous recombination deficiency in breast cancer: a clinical next-generation sequencing study.

Background: TP53 mutations and homologous recombination deficiency (HRD) occur frequently in breast cancer. However, the characteristics of TP53 pathogenic mutations in breast cancer patients with/without HRD are not clear. Methods: Clinical next-generation sequencing (NGS) of both tumor and paired blood DNA from 119 breast cancer patients (BRCA-119 cohort) was performed with a 520-gene panel. Mutations, tumor mutation burden (TMB), and genomic HRD scores were assessed from NGS data. NGS data from 47 breast cancer patients in the HRD test cohort were analyzed for further verification. Results: All TP53 pathogenic mutations in patients had somatic origin, which was associated with the protein expression of estrogen receptor and progestogen receptor. Compared to patients without TP53 pathologic mutations, patients with TP53 pathologic mutations had higher levels of HRD scores and different genomic alterations. The frequency of TP53 pathologic mutation was higher in the HRD-high group (HRD score ≥ 42) relative to that in the HRD-low group (HRD score < 42). TP53 has different mutational characteristics between the HRD-low and HRD-high groups. TP53-specific mutation subgroups had diverse genomic features and TMB. Notably, TP53 pathogenic mutations predicted the HRD status of breast cancer patients with an area under the curve (AUC) of 0.61. TP53-specific mutations, namely HRD-low mutation, HRD-high mutation, and HRD common mutation, predicted the HRD status of breast cancer patients with AUC values of 0.32, 0.72, and 0.58, respectively. Interestingly, TP53 HRD-high mutation and HRD common mutation combinations showed the highest AUC values (0.80) in predicting HRD status. Conclusions: TP53-specific mutation combinations predict the HRD status of patients, indicating that TP53 pathogenic mutations could serve as a potential biomarker for poly-ADP-ribose polymerase (PARP) inhibitors in breast cancer patients .

Somatic mutations in four novel genes contribute to homologous recombination deficiency in breast cancer: a real-world clinical tumor sequencing study.

Breast cancers involving mutations in homologous recombination (HR) genes, most commonly BRCA1 and BRCA2 (BRCA1/2), respond well to PARP inhibitors and platinum-based chemotherapy. However, except for these specific HR genes, it is not clear which other mutations contribute to homologous recombination defects (HRD). Here, we performed next-generation sequencing of tumor tissues and matched blood samples from 119 breast cancer patients using the OncoScreen Plus panel. Genomic mutation characteristics and HRD scores were analyzed. In the HR genes, we found that BRCA1/2 and PLAB2 mutations were related to HRD. HRD was also detected in a subset of patients without germline or somatic mutations in BRCA1/2, PLAB2, or other HR-related genes. Notably, LRP1B, NOTCH3, GATA2, and CARD11 (abbreviated as LNGC) mutations were associated with high HRD scores in breast cancer patients. Furthermore, functional experiments demonstrated that silencing CARD11 and GATA2 impairs HR repair efficiency and enhances the sensitivity of tumor cells to olaparib treatment. In summary, in the absence of mutations in the HR genes, the sensitivity of tumor cells to PARP inhibitors and platinum-based chemotherapy may be enhanced in a subset of breast cancer patients with LNGC somatic mutations.

The time-varying networks of the wrist extension in post-stroke hemiplegic patients.

Hemiplegia is a common dysfunction caused by the brain stroke and leads to movement disability. Although the lateralization of movement-related potential, the event-related desynchronization, and more complicated inter-regional information coupling have been investigated, seldom studies have focused on investigating the dynamic information exchanging among multiple brain regions during motor execution for post-stroke hemiplegic patients. With high temporal-resolution electroencephalogram (EEG), the time-varying network is able to reflect the dynamical complex network modalities corresponding to the movements at a millisecond level. In our present study, the wrist extension experiment was designed, along with related EEG datasets being collected. Thereafter, the corresponding time-varying networks underlying the wrist extension were accordingly constructed by adopting the adaptive directed transfer function and then statistically explored, to further uncover the dynamic network deficits (i.e., motor dysfunction) in post-stroke hemiplegic patients. Results of this study found the effective connectivity between the stroked motor area and other areas decreased in patients when compared to healthy controls; on the contrary, the enhanced connectivity between non-stroked motor areas and other areas, especially the frontal and parietal-occipital lobes, were further identified for patients during their accomplishing the designed wrist extension, which might dynamically compensate for the deficited patients' motor behaviors. These findings not only helped deepen our knowledge of the mechanism underlying the patients' motor behaviors, but also facilitated the real-time strategies for clinical therapy of brain stroke, as well as providing a reliable biomarker to predict the future rehabilitation. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-021-09738-2.

Upregulated YTHDF1 associates with tumor immune microenvironment in head and neck squamous cell carcinomas.

Background: The nature of the tumor immune microenvironment (TME) is essential for the head and neck squamous cell carcinomas (HNSCC) initiation, prognosis, and response to immunotherapy. However, its gene regulatory network remains to be elucidated. Methods: To identify N6-methyladenosine (m6A) regulators that are involved in regulating the HNSCC TME, a computational screen was applied to The Cancer Genome Atlas (TCGA) HNSCC patient samples. The effects of mutation, copy number variation (CNV), and transcriptional regulation on YTHDF1 expression were analyzed. We analyzed the TME infiltration, cancer-immunity cycle activities, and YTHDF1-related Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results: Among the 24 m6A regulators, 3 factors (YTHDF1, ELAVL1, and METTL3) were highly correlated with TME infiltration. As the top candidate, YTHDF1 was up-regulated and amplified in HNSCC. YTHDF1 promoter gains active histone marks and high chromatin accessibility, which might be transcriptionally activated by SOX2 and TP63. Moreover, YTHDF1 expression significantly associates with tumor malignant phenotype in HNSCC, which has a positive correlation with CD4+ T cells and a negative correlation with CD8+ T cells infiltration. Specifically, YTHDF1 expression is negatively associated with the cancer-immunity cycle and immune checkpoint inhibitors. In terms of the underlying biological mechanisms, YTHDF1 may interact with YTHDF2/3 to regulate several vital immune-related pathways. Conclusions: We identify YTHDF1 associated with TME and elucidate an underlying mechanism of immune escape in HNSCC, which might be used as a predictive marker in guiding immunotherapy.

A 25-gene panel predicting the benefits of immunotherapy in head and neck squamous cell carcinoma.

Tumor mutation burden high (TMB-H) is widely used in the guidance of immune checkpoint blocking (ICB) therapy for head and neck squamous cell carcinoma (HNSCC) patients. However, a few patients still had a poor response. Therefore, it is necessary to investigate a better model to guide ICB therapy. We constructed a genomic mutation model conducive to ICB therapy using an available HNSCC dataset. Moreover, treatment procedures for patients with HNSCC from our internal cohort confirmed this model. Here, a genomic mutation signature based on a list of 25 candidate genes that are favorable for immunotherapy was established. Patients with combined mutation had a respectable clinical outcome under ICB treatment. Notably, compared with patients who obtained TMB-H (TMB ≥ 10, but did not have combined mutation), those patients with TMB-L (TMB < 10) and combined mutation acquired remarkably beneficial overall survival. Moreover, the combined mutation signature predicting the survival status of patients was superior to TMB, with a Youden index of 0.55. Furthermore, higher immune cell infiltration levels, more active cancer-immunity cycle activities and immune response pathways were observed in patients with combined mutation. Finally, our internal cohort further confirmed that combined mutated patients can benefit from ICB therapy rather than any other patients.

Rehabilitation of motor function in children with cerebral palsy based on motor imagery.

To promote the rehabilitation of motor function in children with cerebral palsy (CP), we developed motor imagery (MI) based training system to assist their motor rehabilitation. Eighteen CP children, ten in short- and eight in long-term rehabilitation, participated in our study. In short-term rehabilitation, every 2 days, the MI datasets were collected; whereas the duration of two adjacency MI experiments was ten days in the long-term protocol. Meanwhile, within two adjacency experiments, CP children were requested to daily rehabilitate the motor function based on our system for 30 min. In both strategies, the promoted motor information processing was observed. In terms of the relative signal power spectra, a main effect of time was revealed, as the promoted power spectra were found for the last time of MI recording, compared to that of the first one, which first validated the effectiveness of our intervention. Moreover, as for network efficiency related to the motor information processing, compared to the first MI, the increased network properties were found for the last MI, especially in long-term rehabilitation in which CP children experienced a more obvious efficiency promotion. These findings did validate that our MI-based rehabilitation system has the potential for CP children to assist their motor rehabilitation.

The state of memory-matched distractor in working memory influence the visual attention.

Information in working memory (WM) can guide visual attention towards matched features. While recent work has suggested that cognitive control can act upon WM guidance of visual attention, little is known about how the state of memorized items retaining in WM contribute to its influence over attention. Here, we disentangle the role of inhibition and maintenance on WM-guided attention with a novel delayed match-to-sample dual-task. The results showed that active inhibition facilitated searching by diminishing sensory processing and deterring attentional guidance, indexed by an attenuated P1 amplitude and unaffected N2pc amplitude, respectively. By contrast, active maintenance impaired searching by attentional guidance while sensory processing remained unimpaired, indexed by an enhanced N2pc amplitude and unchanged P1 amplitude, respectively. Furthermore, multivariate pattern analyses could sucessfully decode maintenance and inhibition, suggesting that two states differed in modulating visual attention. We propose that remembered contents may play an anchoring role for attentional guidance, and the state of those contents retaining in WM may directly influence the shifting of attention. The maintenance could guide attention by accessing input information, while the inhibition could deter the shifting of attention by suppressing sensory processing. These findings provide a possible reinterpretation of the influence of WM on attention.

Constructing large-scale cortical brain networks from scalp EEG with Bayesian nonnegative matrix factorization.

A large-scale network provides a high hierarchical level for understanding the adaptive adjustment of the human brain during cognition processes. Since high spatial resolution is required, most of the related works are based on functional magnetic resonance imaging (fMRI); however, fMRI lacks the temporal information that is important in investigating the high cognition processes. Although combining electroencephalography (EEG) inverse solution and independent component analysis (ICA), researchers detected large-scale functional subnetworks recently, few researchers focus on the unreasonable negative activation, which is biased from the nonnegative electrical source activations in the brain. In this study, considering the favorable nonnegative property of Bayesian nonnegative matrix factorization (Bayesian NMF) and combining EEG source imaging, we developed a robust approach for EEG large-scale network construction and applied it to two independent real EEG datasets (i.e., decision-making and P300). Eight and nine best-fit networks, including such important subnetworks as the somatosensory-motor network (SMN), the default mode network (DMN), etc., were successfully identified for decision-making and P300, respectively. Compared to the networks acquired with ICA, these networks not only lacked confusing negative activations but also showed clear spatial distributions that are compatible with specific brain function. Based on the constructed large-scale network, we further probed that the self-referential network (SRN), the primary visual network (PVN), and the visual network (VN) demonstrated different interaction patterns with other networks between different responses in decision-making. Our results confirm the possibility of probing the neural mechanisms of high cognition processes at a very high temporal and spatial resolution level.

Transcranial Magnetic Stimulation to the Middle Frontal Gyrus During Attention Modes Induced Dynamic Module Reconfiguration in Brain Networks.

The interaction between dorsal and ventral attention networks (VANs) is mediated by the middle frontal gyrus (MFG), which is functionally connected to both networks. However, the direct role of the MFG in selective and sustained attention remains controversial. In the current study, we used transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to probe the connectivity dynamic changes of MFG-associated regions during different attention modes. The participants underwent visual, selective, and sustained attention tasks to observe TMS-induced network changes. Twenty healthy participants received single-pulse TMS over the left or right MFG during tasks, while synchronous EEG data was acquired. Behavioral results were recorded and time-varying brain network analyses were performed. We found that the MFG is involved in attention processing and that sustained attention was preferentially controlled by the right MFG. Moreover, compared with the right hemisphere, the left hemisphere was associated with selective attention tasks. Visual and selective attention tasks induced MFG-related changes in network nodes were within the left hemisphere; however, sustained attention induced changes in network nodes were in the bilateral posterior MFG. Our findings indicated that the MFG plays a crucial role in regulating attention networks. In particular, TMS-induced MFG alterations influenced key nodes of the time-varying brain network, leading to the reorganization of brain network modules.

The Dynamic Brain Networks of Motor Imagery: Time-Varying Causality Analysis of Scalp EEG.

Motor imagery (MI) requires subjects to visualize the requested motor behaviors, which involves a large-scale network that spans multiple brain areas. The corresponding cortical activity reflected on the scalp is characterized by event-related desynchronization (ERD) and then by event-related synchronization (ERS). However, the network mechanisms that account for the dynamic information processing of MI during the ERD and ERS periods remain unknown. Here, we combined ERD/ERS analysis with the dynamic networks in different MI stages (i.e. motor preparation, ERD and ERS) to probe the dynamic processing of MI information. Our results show that specific dynamic network structures correspond to the ERD/ERS evolution patterns. Specifically, ERD mainly shows the contralateral networks, while ERS has the symmetric networks. Moreover, different dynamic network patterns are also revealed between the two types of MIs, in which the left-hand MIs exhibit a relatively less sustained contralateral network, which may be the network mechanism that accounts for the bilateral ERD/ERS observed for the left-hand MIs. Similar to the network topologies, the three MI stages also appear to be characterized by different network properties. The above findings all demonstrate that different MI stages that involve specific brain networks for dynamically processing the MI information.

Predicting individual decision-making responses based on the functional connectivity of resting-state EEG.

OBJECTIVE: Despite increasing evidence revealing the relationship between task-related brain activity and decision-making, the association between resting-state functional connectivity and decision-making remains unknown. APPROACH: In this study, we investigated the potential relationship between the network revealed in the resting-state electroencephalogram (EEG) and decision responses and further predicted individuals' acceptance rates during the ultimatum game (UG) based on the functional connectivity revealed in the resting-state EEG. MAIN RESULTS: The results of this study demonstrated a significant relationship between the resting-state frontal-occipital connectivity and the UG acceptance rate in the alpha band. Increased acceptance rates were accompanied by a larger clustering coefficient and global and local efficiency as well as a shorter characteristic path length. Compared to the low-acceptance group, the high-acceptance group exhibited stronger frontal-occipital linkages. Finally, a multiple linear regression model based on the resting-state EEG network properties was adopted to predict the acceptance rates when subjects made their decision in the UG task. SIGNIFICANCE: Together, the findings of this study may deepen our knowledge of decision-making and provide a potential physiological biomarker to predict the decision-making responses of subjects.

Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis.

Epilepsy is a neurological disorder in the brain that is characterized by unprovoked seizures. Epileptic seizures are attributed to abnormal synchronous neuronal activity in the brain. To detect the seizure as early as possible, the identification of specific electroencephalogram (EEG) dynamics is of great importance in investigating the transition of brain activity as the epileptic seizure approaches. In this study, we investigated the transition of brain activity from interictal to preictal states preceding a seizure by combining EEG network and clustering analyses together in different frequency bands. The findings of this study demonstrated the best clustering performance of k-medoids in the beta band; in addition, compared to the interictal state, the preictal state experienced increased synchronization of EEG network connectivity, characterized by relatively higher network properties. These findings can provide helpful insight into the mechanism of epilepsy, which can also be used in the prediction of epileptic seizures and subsequent intervention.

Differentiation of Schizophrenia by Combining the Spatial EEG Brain Network Patterns of Rest and Task P300.

The P300 is regarded as a psychosis endophenotype of schizophrenia and a putative biomarker of risk for schizophrenia. However, the brain activity (i.e., P300 amplitude) during tasks cannot always provide satisfying discrimination of patients with schizophrenia (SZs) from healthy controls (HCs). Spontaneous activity at rest indices the potential of the brain, such that if the task information can be efficiently processed, it provides a compensatory understanding of the cognitive deficits in SZs. In this paper, based on the resting and P300 task electroencephalogram (EEG) data sets, we constructed functional EEG networks and then extracted the inherent spatial pattern of network (SPN) features for both brain states. Finally, the combined SPN features of the rest and task networks were used to recognize SZs. The findings of this paper revealed that the combined SPN features could achieve the highest accuracy of 90.48%, with the sensitivity of 89.47%, and specificity of 91.30%. These findings consistently implied that the rest and task P300 EEGs could actually provide comprehensive information to reliably classify SZs from HCs, and the SPN is a promising tool for the clinical diagnosis of SZs.

Different Contexts in the Oddball Paradigm Induce Distinct Brain Networks in Generating the P300.

Despite the P300 event-related potential (ERP) differences between distinct stimulus sequences, the effect of stimulus sequence on the brain network is still left unveiled. To uncover the corresponding effect of stimulus sequence, we thus investigated the differences of functional brain networks, when a target (T) or standard (S) stimulus was presented preceding another T as background context. Results of this study demonstrated that, when an S was first presented preceding a T (i.e., ST sequence), the P300 experiencing large amplitude was evoked by the T, along with strong network architecture. In contrast, if a T was presented in advance [i.e., target-to-target (TT) sequence], decreased P300 amplitude and attenuated network efficiency were demonstrated. Additionally, decreased activations in regions, such as inferior frontal gyrus and superior frontal gyrus were also revealed in TT sequence. Particularly, the effect of stimulus sequence on P300 network could be quantitatively measured by brain network properties, the increase in network efficiency corresponded to large P300 amplitude evoked in P300 task.

Top-Down Disconnectivity in Schizophrenia During P300 Tasks.

Cognitive deficits in schizophrenia are correlated with the dysfunctions of distinct brain regions including anterior cingulate cortex (ACC) and prefrontal cortex (PFC). Apart from the dysfunctions of the intrinsic connectivity of related areas, how the coupled neural populations work is also crucial in related processes. Twenty-four patients with schizophrenia (SZs) and 24 matched healthy controls (HCs) were recruited in our study. Based on the electroencephalogram (EEG) datasets recorded, the Dynamic Causal Modeling (DCM) was then adopted to estimate how the brain architecture adapts among related areas in SZs and to investigate the mechanism that accounts for their cognitive deficits. The distinct winning models in SZs and HCs consistently emphasized the importance of ACC in regulating the elicitations of P300s. Specifically, comparing to that in HCs, the winning model in SZs uncovered a compensatory pathway from dorsolateral PFC to intraparietal sulcus that promised the SZs' accomplishing P300 tasks. The findings demonstrated that the "disconnectivity hypothesis" is helpful and useful in explaining the cognitive deficits in SZs, while the brain architecture adapted with related compensatory pathway promises the limited brain cognitions in SZs. This study provides a new viewpoint that deepens our understanding of the cognitive deficits in schizophrenia.