Valid Signature Research Articles

Quantum Secure Undeniable Signature for Blockchain-Enabled Cold-Chain Logistics System

Data security and user privacy are two main security concerns in the cold-chain logistics system (CCLS). Many security issues exist in traditional CCLS, destroying data security and user privacy. The digital signature can provide data verification and identity authentication based on the mathematical difficulty problem for logistics data sharing in CCLS. This paper first established a blockchain-enabled cold-chain logistics system (BCCLS) based on union blockchain technology, which can provide secure data sharing among different logistics nodes and guarantee logistics data security with the untampered blockchain ledger. Meanwhile, a lattice-based undeniable signature scheme is designed to strengthen the security of logistics data and user privacy against quantum attacks. This scheme is based on the lattice assumption, which can provide anti-quantum security for BCCLS in the future quantum computer age. It also establishes the undeniable mechanism that guarantees that the actual signer cannot deny a valid signature. Then, security proof shows that this undeniable signature scheme is correct and safe, and an efficiency comparison shows that it is more efficient than other schemes in similar literature. The performance evaluations of transaction throughput and latency show that the proposed BCCLS is efficient and practical. This work can improve the security and efficiency of logistics data management and promote the reform and development of the logistics industry.

Троичная лемма о развлетвлении и ее приложение к анализу стойкости одной кодовой схемы подписи

The paper is devoted to the generalization of the so-called “forking lemma” to the case when the hash function returns a tuple of trits (trit is a variable that can take one out of the three values 0, 1, 2). It can be stated as follows. Let A(par, b) be an algorithm that, when run on randomly chosen par ←R Params and b ←R {0, 1, 2} δ , successfully stops and returns the correct answer x with probability ϵ. Then there exists an algorithm B that uses A as a subroutine and returns a triple (x1, x2, x3), where xi ← A(par, bi ), i = 1, 2, 3, with the additional condition that there exists a position j such that {b 1 j , b2 j , b3 j } = {0, 1, 2} (i.e., all trits in this position are different); the success probability of B can be bounded from above as follows: pB ⩾ ε 3 − ε (17/27)δ/2 , and the running time of B does not exceed 4 tA, where tA is the time complexity of A. The lemma is then applied to the analysis of code-based signature based on Stern identification protocol in the (standard) SUF-CMA model (with the outer hash function modelled as a programmable random oracle). First, we show that the SUF-CMA model can be reduced to the NMA model (in which the adversary makes no sign queries, only random oracle queries), thanks to the zero-knowledge property of the original Stern identification scheme and the programmability of an oracle. We then show that in the NMA model we can restrict attention only to the case, where the adversary makes a single random oracle query. The b value from the forking lemma acts as the random oracle’s answer to the adversarial query. Using the lemma, we are able to fork the process of forging a signature. Having three valid signatures for the same message, we can to extract a secret key (or to find a collision of an inner hash function). Hence, we can bound from above the probability of successful forgery in terms of the probability of successful execution of collision finding and syndrome decoding algorithms.

Comment on “Secure and Lightweight Conditional Privacy-Preserving Authentication for Securing Traffic Emergency Messages in VANETs”

In the above paper, Wei et al. proposed a lightweight conditional privacy-preserving authentication protocol in VANET to achieve both ultra-low transmission delay and SSK updating. To decrease communication overhead, their scheme adopts a signature scheme with message recovery to achieve message authentication. And they claimed that the adopted signature was secure against adaptively chosen message attacks, and gave the detailed security proof. Unfortunately, in this work, by analyzing the security of Wei et al. scheme, we show that their scheme is insecure, and it is universally forgeable, i.e., anyone can forge a valid signature on any message; it also makes their scheme does not satisfy conditional privacy. Finally, after analyzing the reasons to produce the attack, we give the corresponding suggestion to overcome the attacks.

Comprehensive analysis of a glycolysis and cholesterol synthesis-related genes signature for predicting prognosis and immune landscape in osteosarcoma.

Glycolysis and cholesterol synthesis are crucial in cancer metabolic reprogramming. The aim of this study was to identify a glycolysis and cholesterol synthesis-related genes (GCSRGs) signature for effective prognostic assessments of osteosarcoma patients. Gene expression data and clinical information were obtained from GSE21257 and TARGET-OS datasets. Consistent clustering method was used to identify the GCSRGs-related subtypes. Univariate Cox regression and LASSO Cox regression analyses were used to construct the GCSRGs signature. The ssGSEA method was used to analyze the differences in immune cells infiltration. The pRRophetic R package was utilized to assess the drug sensitivity of different groups. Western blotting, cell viability assay, scratch assay and Transwell assay were used to perform cytological validation. Through bioinformatics analysis, patients diagnosed with osteosarcoma were classified into one of 4 subtypes (quiescent, glycolysis, cholesterol, and mixed subtypes), which differed significantly in terms of prognosis and tumor microenvironment. Weighted gene co-expression network analysis revealed that the modules strongly correlated with glycolysis and cholesterol synthesis were the midnight blue and the yellow modules, respectively. Both univariate and LASSO Cox regression analyses were conducted on screened module genes to identify 5 GCSRGs (RPS28, MCAM, EN1, TRAM2, and VEGFA) constituting a prognostic signature for osteosarcoma patients. The signature was an effective prognostic predictor, independent of clinical characteristics, as verified further via Kaplan-Meier analysis, ROC curve analysis, univariate and multivariate Cox regression analysis. Additionally, GCSRGs signature had strong correlation with drug sensitivity, immune checkpoints and immune cells infiltration. In cytological experiments, we selected TRAM2 as a representative gene to validate the validity of GCSRGs signature, which found that TRAM2 promoted the progression of osteosarcoma cells. Finally, at the pan-cancer level, TRAM2 had been correlated with overall survival, progression free survival, disease specific survival, tumor mutational burden, microsatellite instability, immune checkpoints and immune cells infiltration. Therefore, we constructed a GCSRGs signature that efficiently predicted osteosarcoma patient prognosis and guided therapy.

Predicting the prognosis of esophageal cancer based on extensive analysis of new inflammatory response-related signature.

The prognosis of esophageal cancer (ESCA) is very poor, with a 5-year survival rate of less than 20%. On the other hand, inflammation is the characteristic hallmark of ESCA; however, the prognostic relationship between inflammatory response-related genes and ESCA has not been clarified yet. Therefore, in the present manuscript, we intend to investigate the correlation and specific signature of inflammation for the prediction of the prognosis of ESCA. A total of 173 samples were obtained from The Cancer Genome Atlas(TCGA) database, including 162 tumors and 11 normal specimens. The prognostic signature was established by least absolute shrinkage and selection operator Cox regression analysis. The transcription factor regulatory network with genes of the prognostic signature was analyzed from the transcriptionalregulatoryrelationshipsunravelled bysentence-based text-mining database. Chemotherapy sensitivity and immunotherapy analysis were also performed. Multivariate Cox analysis showed that the signature was an independent prognostic risk factor. The low-risk group had poorer outcomes than the high-risk group. In the high-risk group, the infiltration of most immune cells was high and strongly correlated with the riskScore. In chemotherapeutic drug sensitivity analysis, OSM, AHR, and BTG2 were significantly correlated with the current chemotherapeutic drugs of ESCA. We have demonstrated a valid prognostic signature of inflammatory response-related genes and found strong associations with immune cells, targeted genes, and chemotherapeutic agents.

THE EVOLUTION OF THE SIMULATED SIGNATURE BY THE FORGER

Forgery is a very common case of Forensic Document Examination which might refer to several further distinctions of expertise between handwriting and signature. The imitation of a signature could come into effect in different methods or ways, depending on the type of document, the forger’s ability and other factors connected to the historical parameters of the case. The ability of the forger to reproduce morphologically the authentic specimen is even related to the range of his graphic variation, his training in forging the specific specimen or other various factors that could affect not only his graphic expression, but also his choices in applying forgery. In cases of long-lastingprofessional collaboration, the imitation of a person’s signature with his consent for a long period of time is a quite often phenomenon. Apart from the legal validity of such signatures, the evolution of the forged signature by the forger is not to be excluded, since the consent deliberates psychologically the forger, diminishes his anxiety and leaves space to a less accurate reproduction of the original specimen. In these cases,there are more parameters to be taken into consideration and the time of habitual execution of the forged signature is crucial, since the repetitive imitation can result in different variations of the original signature’s model. The eventual modifications are studied in a practical case which presents several complications.

An Emerging Prognosis Prediction Model for Multiple Myeloma: Hypoxia-Immune Related Microenvironmental Gene Signature

Background: Multiple myeloma (MM) is a hematologic malignancy characterized by clonal proliferation of malignant plasma cells. Hypoxia and the immune microenvironment play vital roles in the carcinogenicity and evolution of MM. In this study, we attempted to construct a microenvironmental gene marker associated with hypoxia immunity for effective prognostic prediction and risk stratification. Methods: MM-related datasets (GSE136324, GSE47552 and GSE136337) were downloaded from Gene Expression Omnibus (GEO). A Kaplan-Meier (K-M) survival curve was applied to excavate survival differences among different hypoxia clusters, and the hypoxia state was divided into high hypoxia and low hypoxia groups according to the prognosis. The expression data algorithm was used to estimate the immune score of each MM patient. Based on the optimal truncation immune score value, MM patients were divided into high and low immune score subgroups. The R packages were applied to identify the risk differentially expressed genes (DEGs) and protective DEGs. Univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were applied to identify prognosis-related genes. Receiver Operating Characteristic curves and K-M survival curves were applied to verify the validity and agility of the prognostic signature. The cell type identification by estimating relative subsets of RNA transcripts algorithm was applied to analyze immune cell infiltration. Quantitative real-time PCR was used to test the signature in vitro. Cell proliferation was measured by a CCK-8 assay. The levels of apoptotic rate were analyzed by the flow cytometer. Cell migration and invasion ability were estimated by Transwell membranes and Matrigel. NOD/SCID mice were divided into four groups: normoxia control (CTL), normoxia shDDIT4 (CTL+shDDIT4), intermittent hypoxia control (IH), and IH shDDIT4 (IH+shDDIT4). CTL had an oxygen concentration of 21% while IH had a nadir of 6%~8%. Results: A total of 867 MM samples were classified according to hypoxia status and immune score and were classified into Hypoxia.low/Immune.high, Hypoxia.high/Immune, lowand mixed groups. K-M survival analysis showed patients in the Hypoxia.low/Immune.high group had a better prognosis than others. Overlapping the three groups of risk DEGs and protective DEGs. There were 472 protective DEGs and 205 risk DEGs with supporting evidence from hypoxia, immune and combing hypoxia/ immune groups. In GSE47522, 2633 DEGs were screened between normal and MM. 81 key hypoxia-immune-related genes were overlapped by the Venn diagram between MM and normal DEGs, protective DEGs and risk DEGs. Using GSE136324 as a training cohort, a univariate Cox regression analysis was performed on 81 key genes. We identified 44 hypoxia-immune-related prognostic DEGs statistically related to the overall survival time of MM patients. Then, an 8-gene signature (including CHRDL1, DDIT4, DNTT, FAM133A, MYB, PRR15, QTRT1, and ZNF275) was identified using the LASSO regression algorithm. MM patients were divided into high-risk and low-risk groups based on a threshold of the median risk score in the prognostic value of the 8-gene signature. K-M curves suggested that the MM patients in the low-risk group showed a statistically longer overall survival than those in the high-risk group. The AUC of 1 year in the training cohort was greater than 0.7. Univariate and multivariate Cox regression analyses showed that the risk score could be an independent prognostic risk factor. Moreover, we revealed that the proportions of 17 types of immune cells were significantly different between the two risk groups, . In the high-risk group, PD-L1 expression levels were higher than in the low-risk group. Finally, we found knockout of DDIT4 inhibited tumor formation in vitro under hypoxia. Further, the survival rate of the IH+shDDIT4 group was higher than that of the IH group. Moreover, both tumor volume and tumor weight in the IH+shDDIT4 group were reduced. Conclusions: We established a hypoxia-immune related prognostic signature. It could predict the prognosis of MM well and was related to the tumor microenvironment of MM. The downregulation of DDIT4 inhibited tumor formation and development in vitro and vivo under hypoxia. Meanwhile, our results suggest that the immune checkpoint inhibitors, PD-L1 inhibitors, may be helpful in the treatment of high-risk patients.

A Lattice-Based Redactable Signature Scheme using Cryptographic Accumulators for Trees

Abstract Redactable signatures allow the signature holder to remove admissible data blocks in the signed data while generating valid signatures about different redacted data without communicating with the primary signer. Now, this sort of signature has attracted widespread attention due to its many application scenarios such as electronic medical records, smart grids and XML files. However, there are rarely redactable signature schemes that can resist quantum attacks so far. In the wake of quantum calculation era, it is essential to blossom more quantum-resistant redactable signatures for different data structures. Moreover, it is popular to use accumulators to design redactable signature schemes. Unfortunately, the existing accumulators do not support tree data structures. Therefore, this paper first gives the definition of accumulator schemes for trees, and designs a lattice-based accumulator scheme for trees. Our accumulator scheme features shorter accumulator values and a faster witness generation algorithm than existing lattice trapdoor accumulators. Second, this paper resorts to approximate trapdoors and the preimage sampleable technique, and presents a lattice-based redactable signature scheme for trees using our accumulator scheme. Meanwhile, this scheme fulfills unforgeability, transparency and privacy under adaptive chosen-message attacks. Furthermore, the experiment results show that the redactable signature scheme meets actual revision requirements well.

Implementing the electronic signature law in Tanzania – successes, challenges, and prospects

Abstract 
 In a bid to implement the Electronic Transactions Act 2015, Tanzania initiated the adoption of a National Public Key infrastructure (PKI) framework. However, the plan has not been executed as expected because of certain gaps and ambiguities in the laws. This article examines the existing laws providing for the legal validity, admissibility and enforceability of electronic signatures especially using PKI; identifies the weaknesses of the existing laws and recommends new laws relevant to PKI that should be considered, and their rationale. 
 Index words: Tanzania, electronic signature, PKI, cryptography, certification

A stratification system of ferroptosis and iron-metabolism related LncRNAs guides the prediction of the survival of patients with esophageal squamous cell carcinoma.

Ferroptosis and iron-metabolism have been widely reported to play an important role in cancer. Long non-coding RNAs (lncRNAs) are increasingly recognized as the crucial mediators in the regulation of ferroptosis and iron metabolism. A systematic understanding of ferroptosis and iron-metabolism related lncRNAs (FIRLs) in esophageal squamous cell carcinoma (ESCC) is essential for prognosis prediction. Herein, Pearson’s correlation analysis was carried out between ferroptosis and iron-metabolism-related genes (FIRGs) and all lncRNAs to derive the FIRLs. Based on weighted gene co-expression network exploration (WCGNA), least absolute shrinkage and selection operator (LASSO) regression and Cox regression analysis, a risk stratification system, including 3 FIRLs (LINC01068, TMEM92-AS1, AC243967.2), was established. According to Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, and univariate and multivariate Cox regression analyses, the risk stratification system had excellent predictive ability and clinical relevance. The validity of the established prognostic signature was further examined in TCGA (training set) and GEO (validation set) cohorts. A nomogram with enhanced precision for forecasting OS was set up on basis of the independent prognostic elements. Functional enrichment analysis revealed that three FIRLs took part in various cellular functions and signaling pathways, and the immune status was varied in the high-risk and low-risk groups. In the end, the oncogenic effects of LINC01068 was explored using in vitro researches. Overall, a risk stratification system of three FIRLs was found to have significant prognostic value for ESCC and may serve as a ferroptosis-associated therapeutic target in the clinic.

A Novel Construction Of Certificateless Aggregate Signature Scheme For Healthcare Wireless Medical Sensor Networks

Abstract To ensure privacy and security of healthcare wireless medical sensor networks (HWMSNs), several concrete constructions of efficient certificateless aggregate signature (CLAS) scheme without bilinear pairing were proposed in the last few years. However, many previous constructions of CLAS scheme were found to be impractical, which either fail to meet the claimed security or contain design flaws. For example, in some of the previous proposals, any adversary can forge a valid signature on any new message. In this paper, we first demonstrate some security issues and design flaws in the previous proposals of CLAS scheme. As follows, to further address the above deficiencies, a new construction of CLAS scheme with improved security is presented, and the formal security proof is given using Forking Lemma in the random oracle model, assuming that the discrete logarithm problem is hard. Compared with the previous CLAS schemes, our construction has similar computational costs, and it provides better security guarantees. Therefore, compared with the existing solutions, our proposal with strong security and high computational efficiency is more suitable for use in HWMSNs.

Amino acid metabolism genes associated with immunotherapy responses and clinical prognosis of colorectal cancer

Based on amino acid metabolism-related genes (AAMRGs), this study aimed at screening out key prognosis-related genes and finding the underlying correlation between the amino acid metabolism and tumor immune microenvironment of colorectal cancer. A total of 448 amino acid metabolism-related genes were obtained from MsigDB. The risk signature was built based on differential expression genes, univariate Cox, and LASSO analyses with 403 patients’ data downloaded from the TCGA database. Survival analysis and independence tests were performed to confirm the validity of the risk signature. Single-sample gene set enrichment analysis (ssGSEA), tumor mutation burden (TMB), the score of tumor immune dysfunction and exclusion (TIDE), the immunophenoscore obtained from The Cancer Immunome Atlas database, and the IC50 of drugs were used to find the relationship among the risk signature, immune status, immunotherapy response, and drug sensitivity of colorectal cancer. We identified five amino acid metabolism-related genes for the construction of the risk signature, including ENOPH1, ACAT1, ALDH4A1, FAS, and ASPG. The low-risk group was significantly associated with a better prognosis (p < 0.0001). In the entire set, the area under the curve (AUC) for 1, 3, and 5 years was 0.717, 0.734, and 0.764, respectively. We also discovered that the low-risk subgroup was related to more activity of immune cells, had higher expression of some immune checkpoints, and was more likely to benefit from immunotherapy. ssGSEA revealed that except the processes of glutamine histidine, lysine, tyrosine, and L-phenylalanine metabolism, the other amino acid metabolism pathways were more active in the samples with the low risk scores, whereas the activities of synthesis and transportation of most amino acids were similar. Hedgehog signaling, WNT/β-catenin signaling, mitotic, notch signaling, and TGF-β signaling were the top five pathways positively associated with the risk score. To sum up, AAMRGs were associated with the immune microenvironment of CRC patients and could be applied as biomarkers to predict the prognosis and immunotherapy response of patients.

A novel signature derived from metabolism-related genes GPT and SMS to predict prognosis of laryngeal squamous cell carcinoma

BackgroundA growing body of evidence has suggested the involvement of metabolism in the occurrence and development of tumors. But the link between metabolism and laryngeal squamous cell carcinoma (LSCC) has rarely been reported. This study seeks to understand and explain the role of metabolic biomarkers in predicting the prognosis of LSCC.MethodsWe identified the differentially expressed metabolism-related genes (MRGs) through RNA-seq data of The Cancer Genome Atlas (TCGA) and Gene set enrichment analysis (GSEA). After the screening of protein–protein interaction (PPI), hub MRGs were analyzed by least absolute shrinkage and selection operator (LASSO) and Cox regression analyses to construct a prognostic signature. Kaplan–Meier survival analysis and the receiver operating characteristic (ROC) was applied to verify the effectiveness of the prognostic signature in four cohorts (TCGA cohort, GSE27020 cohort, TCGA-sub1 cohort and TCGA-sub2 cohort). The expressions of the hub MRGs in LSCC cell lines and clinical samples were verified by quantitative reverse transcriptase PCR (qRT-PCR). The immunofluorescence staining of the tissue microarray (TMA) was carried out to further verify the reliability and validity of the prognostic signature. Cox regression analysis was then used to screen for independent prognostic factors of LSCC and a nomogram was constructed based on the results.ResultsAmong the 180 differentially expressed MRGs, 14 prognostic MRGs were identified. A prognostic signature based on two MRGs (GPT and SMS) was then constructed and verified via internal and external validation cohorts. Compared to the adjacent normal tissues, SMS expression was higher while GPT expression was lower in LSCC tissues, indicating poorer outcomes. The prognostic signature was proven as an independent risk factor for LSCC in both internal and external validation cohorts. A nomogram based on these results was developed for clinical application.ConclusionsDifferentially expressed MRGs were found and proven to be related to the prognosis of LSCC. We constructed a novel prognostic signature based on MRGs in LSCC for the first time and verified it via different cohorts from both databases and clinical samples. A nomogram based on this prognostic signature was developed.

Abstract 3868: High throughput tissue phenotyping and imaging of the tumor immune microenvironment using novel FlexVUE™ multiplexed immunofluorescence assays

Abstract Immunotherapy has transformed the treatment of metastatic and recurrent solid tumors. Advances in technology in the past few years have created unprecedented opportunities to identify biomarkers of disease processes, especially by using multi-omics technologies and datasets to derive valid and useful signatures of disease. Advances in multiplexed immunofluorescence (mIF) staining and imaging technologies that allow for accurate phenotyping of individual cells while also preserving architectural features of the sample are critical to our understanding of the interplay of tumor and immune cells in the tumor immune microenvironment (TiME).Herein we describe the utility of our new flexible mIF assays (FlexVUE™ panels) to provide the necessary relevant distribution of infiltrating immune cells in tumors coupled to the use of our new UltiStacker™ software. This hardware and software package allows a detailed spatial characterization of specific cell phenotypes defined by co- or lack of expression that may help in predicting clinical responses and mechanisms of resistance to therapy. The FlexVUE™ mIF assay was used to conduct 8-plex immunophenotyping of FFPE tumor sections. Slides were stained with a cocktail of eight primary antibodies using Leica Biosystems BOND RX autostainer and imaged on the Zeiss Axio Scan.Z1. Following the first round of imaging, the slides were decoverslipped and the signal was removed. New targets were probed on the same tissue section in a reagent incubation step on the BOND RX, termed ‘exchange.’ Slides were re-imaged on the Zeiss Axio Scan Z1 using the same dye channels. The image pairs were automatically aligned using UltiStacker™ software and exported for downstream analysis with Indica Labs HALOv3.1 software. Our approach demonstrates a streamlined off the shelf workflow requiring no assay development time that supports whole slide imaging of a flexible high plex panel. In addition, our assays enable traditional same slide H&amp;E fusion on a single tissue slide for comprehensive tissue immunophenotyping analysis of proliferating cells, tumor cells, tumor-infiltrating lymphocytes, macrophages or tertiary lymphoid structures. Citation Format: Yvette Cajigas, Peter Hamer, Alina Ainbinder, Angela Vasaturo, Douglas Wood, Kirsteen H. Maclean, Gourab Chatterjee. High throughput tissue phenotyping and imaging of the tumor immune microenvironment using novel FlexVUE™ multiplexed immunofluorescence assays [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3868.

Abstract 3863: Development of specific multiplexed immunofluorescence immune assays to study mouse models of tumorigenesis

Abstract Immunotherapy has transformed the treatment of metastatic and recurrent solid tumors. Advances in technology in the past few years have created unprecedented opportunities to identify biomarkers of disease processes, especially by using multi-omics technologies and datasets to derive valid and useful signatures of disease. Mouse tumor models are widely used tools to demonstrate activity of novel immunotherapies. Despite their widespread use, a comprehensive view of their tumor-immune compositions and their relevance to human tumors has only begun to emerge. The use of specific mouse tissue phenotyping and multiplex immunofluorescence (mIF) assays offer the unique advantage of preserving the architectural features of the tumor and revealing the spatial relationships between tumor cells and immune cells. The urgency to discover and implement new biomarkers lays bare the need to integrate a variety of advanced pre-clinical tools to probe the dynamic nature of events happening in the tumor immune microenvironment (TiME). Here, we present an established mIF assay, InSituPlex® technology, now enabled for rapid staining and visualization of dedicated 4-plex murine panels for deep, spatial phenotyping in the tumor microenvironment. Murine specific InSituPlex technology was used to perform singleplex and multiplex immune profiling on mouse FFPE tumor serial sections. Alternating serial sections were stained with a cocktail of four primary antibodies or a single primary antibody, in parallel, using Leica Biosystems BOND RX autostainer. Slides were imaged on the Zeiss Axio Scan Z1 providing high-quality images of the four targets (CD3, CD4, CD8, and FOXP3) in a single workday. The images were exported for downstream analysis with Indica Labs HALOv3.1 software. Concordance of the singleplex to the 4-plex mouse specific InSituPlex assay was assessed by quantifying the percent difference in cellular density of immune cell subtypes from 1-plex to 4-plex. Qualitative assessment and quantitative analysis revealed a high level of reproducibility of the assay, with each single markers’ coefficients of variation falling within an acceptable range. Each single marker tested showed an expected level of expression and expression pattern. The multiplex assay was highly concordant to the singleplex assay. Unique phenotypes from whole slide analysis followed the expected pattern, as indicated by the tumor type. Our protocol of mIF staining on mouse tissue provides an improved workflow to investigate the immune system, including the analysis of the tumor immune microenvironment and mechanisms of action of immune-related drugs in preclinical models. Citation Format: Yvette Cajigas, Peter Hamer, Alina Ainbinder, Gourab Chatterjee, Angela Vasaturo, Mael Manesse, Kirsteen Maclean. Development of specific multiplexed immunofluorescence immune assays to study mouse models of tumorigenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3863.

Signature Verification Using a Convolutional Neural Network

Many studies have been conducted on Handwritten Signature Verification. Researchers have taken many different approaches to accurately identify valid signatures from skilled forgeries which resemble the correct signature. Unique characteristics from signatures can be accurately and rapidly analyzed with multiple layers of receptive fields and hidden layers. The main contribution of this method is to find and decrease fraud committed, especially in the banking industry.

Toward Data Authenticity and Integrity for Blockchain-Based Mobile Edge Computing

Blockchain-based mobile edge computing (BMEC) solves the problem of limited computing resources of devices in the Internet of Things (IoT). However, there are some security threats in the blockchain environment, among which the attacks against consensus algorithms are particularly serious, such as double-spending attacks and long-range attacks. These attacks target the block proposal or validation process, which compromise the integrity of the BMEC by overwriting the correct block record with the wrong block record. In this article,we propose a scheme to solve the above problem by using the cooperative threshold signature and group management scheme to protect the block validity. The blockchain process of a single node is controlled in a group cooperative way, and each new block is regarded as a valid block only if it has a valid threshold signature for the group to which it belongs. Concretely, the system is initialized by a cooperative distributed key generation protocol, the device management protocol uses a resident node to ensure the security and efficiency of device join and leave, and the signature issuing protocol can tolerate a certain degree of exceptions of nodes within the group. We analyze the resistance ability of the scheme against consensus attacks, prove the security of the scheme, and analyze the computing and communication efficiency. Experimental evaluations on the protocols of the proposed scheme have been conducted on cloud virtual machines and embedded boards, including Raspberry Pi and UP2 Grove, which indicated that the scheme is efficient in practical scenarios.

Prognostic Stratification of Diffuse Large B-cell Lymphoma Using Clinico-genomic Models: Validation and Improvement of the LymForest-25 Model.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma. Despite notable therapeutic advances in the last decades, 30%–40% of affected patients develop relapsed or refractory disease that frequently precludes an infamous outcome. With the advent of new therapeutic options, it becomes necessary to predict responses to the standard treatment based on rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). In a recent communication, we presented a new machine learning model (LymForest-25) that was based on 25 clinical, biochemical, and gene expression variables. LymForest-25 achieved high survival prediction accuracy in patients with DLBCL treated with upfront immunochemotherapy. In this study, we aimed to evaluate the performance of the different features that compose LymForest-25 in a new UK-based cohort, which contained 481 patients treated with upfront R-CHOP for whom clinical, biochemical and gene expression information for 17 out of 19 transcripts were available. Additionally, we explored potential improvements based on the integration of other gene expression signatures and mutational clusters. The validity of the LymForest-25 gene expression signature was confirmed, and indeed it achieved a substantially greater precision in the estimation of mortality at 6 months and 1, 2, and 5 years compared with the cell-of-origin (COO) plus molecular high-grade (MHG) classification. Indeed, this signature was predictive of survival within the MHG and all COO subgroups, with a particularly high accuracy in the “unclassified” group. Integration of this signature with the International Prognostic Index (IPI) score provided the best survival predictions. However, the increased performance of molecular models with the IPI score was almost exclusively restricted to younger patients (<70 y). Finally, we observed a tendency towards an improved performance by combining LymForest-25 with the LymphGen mutation-based classification. In summary, we have validated the predictive capacity of LymForest-25 and expanded the potential for improvement with mutation-based prognostic classifications.

Comprehensive Analysis of TRP Channel-Related Genes for Estimating the Immune Microenvironment, Prognosis, and Therapeutic Effect in Patients With Esophageal Squamous Cell Carcinoma.

The Nobel Prize in Physiology or Medicine for the year 2021 was awarded to Ardem Patapoutian and David Julius for their discoveries of temperature-sensitive receptors (TRP channels) and tactile receptors (Piezo channels), both of which were previously unknown. TRP channels are at the heart of the human ability to detect temperature, and they also play crucial regulatory functions in the occurrence and progression of cancer. Despite this, there have been no research conducted on the prognostic significance of TRP channels in individuals with esophageal squamous cell carcinoma (ESCC). In GEO and TCGA cohorts, unsupervised clustering was first conducted based on 18 TRP channel-associated differentially expressed genes (DEGs) extracted from MSigDB database and KEGG database. Two TRP subtypes were identified and patients in subtype B had the best prognosis among the two subtypes. Significant differences in staging and grading existed among the different subtypes. In GEO cohort, univariate Cox analysis were performed to screen prognosis related genes. A TRP channel-related prognostic signature, which included 7 signature-related genes, was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression. Patients were divided into a high-risk group and low-risk group by the median risk score. In GEO and TCGA cohorts, Receiver operating characteristic (ROC) curves, principal component analysis (PCA), and univariate and multivariate Cox regression were performed to confirm the validity of signature. Following a more in-depth study of the TME based on the risk signature, it was discovered that the high-risk group had higher immune cell infiltration and lower tumor purity, indicating a bad prognosis. Patients with high risk scores also had increased immune checkpoint expression, indicating that these patients may be more likely to benefit from immunotherapy than other patients. We also found that paclitaxel, cisplatin, and 5-fluorouracil displayed a better response in treating the low-risk score ESCC patients. This study also adopted GTEx and qRT-PCR to perform experimental verification processes. In summary, we identified a TRP channel-associated prognostic signature. This signature can predict prognosis and immune microenvironment in ESCC.

Deep Learning-Based Pan-Cancer Classification Model Reveals Tissue-of-Origin Specific Gene Expression Signatures.

Simple SummaryGene expression data from different cancer types offer the opportunity to identify cancer tissue-of-origin specific biomarkers and targets. In this study, we used pan-cancer gene expression data to train a deep learning neural network model to identify cancer tissue-of-origin specific gene expression signatures. We identified 976 genes that can reliably classify different cancer types with >97% accuracy.Cancer tissue-of-origin specific biomarkers are needed for effective diagnosis, monitoring, and treatment of cancers. In this study, we analyzed transcriptomics data from 37 cancer types provided by The Cancer Genome Atlas (TCGA) to identify cancer tissue-of-origin specific gene expression signatures. We developed a deep neural network model to classify cancers based on gene expression data. The model achieved a predictive accuracy of >97% across cancer types indicating the presence of distinct cancer tissue-of-origin specific gene expression signatures. We interpreted the model using Shapley additive explanations to identify specific gene signatures that significantly contributed to cancer-type classification. We evaluated the model and the validity of gene signatures using an independent test data set from the International Cancer Genome Consortium. In conclusion, we present a robust neural network model for accurate classification of cancers based on gene expression data and also provide a list of gene signatures that are valuable for developing biomarker panels for determining cancer tissue-of-origin. These gene signatures serve as valuable biomarkers for determining tissue-of-origin for cancers of unknown primary.