An interpretable single-cell RNA sequencing data clustering method based on latent Dirichlet allocation.

Single-cell RNA sequencing (scRNA-seq) detects whole transcriptome signals for large amounts of individual cells and is powerful for determining cell-to-cell differences and investigating the functional characteristics of various cell types. scRNA-seq datasets are usually sparse and highly noisy. Many steps in the scRNA-seq analysis workflow, including reasonable gene selection, cell clustering and annotation, as well as discovering the underlying biological mechanisms from such datasets, are difficult. In this study, we proposed an scRNA-seq analysis method based on the latent Dirichlet allocation (LDA) model. The LDA model estimates a series of latent variables, i.e. putative functions (PFs), from the input raw cell-gene data. Thus, we incorporated the 'cell-function-gene' three-layer framework into scRNA-seq analysis, as this framework is capable of discovering latent and complex gene expression patterns via a built-in model approach and obtaining biologically meaningful results through a data-driven functional interpretation process. We compared our method with four classic methods on seven benchmark scRNA-seq datasets. The LDA-based method performed best in the cell clustering test in terms of both accuracy and purity. By analysing three complex public datasets, we demonstrated that our method could distinguish cell types with multiple levels of functional specialization, and precisely reconstruct cell development trajectories. Moreover, the LDA-based method accurately identified the representative PFs and the representative genes for the cell types/cell stages, enabling data-driven cell cluster annotation and functional interpretation. According to the literature, most of the previously reported marker/functionally relevant genes were recognized.

Impact of mutations in SARS-COV-2 spike on viral infectivity and antigenicity.

Since the outbreak of SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, the viral genome has acquired numerous mutations with the potential to alter the viral infectivity and antigenicity. Part of mutations in SARS-CoV-2 spike protein has conferred virus the ability to spread more quickly and escape from the immune response caused by the monoclonal neutralizing antibody or vaccination. Herein, we summarize the spatiotemporal distribution of mutations in spike protein, and present recent efforts and progress in investigating the impacts of those mutations on viral infectivity and antigenicity. As mutations continue to emerge in SARS-CoV-2, we strive to provide systematic evaluation of mutations in spike protein, which is vitally important for the subsequent improvement of vaccine and therapeutic neutralizing antibody strategies.

Identification of alternative splicing-derived cancer neoantigens for mRNA vaccine development.

Messenger RNA (mRNA) vaccines have shown great potential for anti-tumor therapy due to the advantages in safety, efficacy and industrial production. However, it remains a challenge to identify suitable cancer neoantigens that can be targeted for mRNA vaccines. Abnormal alternative splicing occurs in a variety of tumors, which may result in the translation of abnormal transcripts into tumor-specific proteins. High-throughput technologies make it possible for systematic characterization of alternative splicing as a source of suitable target neoantigens for mRNA vaccine development. Here, we summarized difficulties and challenges for identifying alternative splicing-derived cancer neoantigens from RNA-seq data and proposed a conceptual framework for designing personalized mRNA vaccines based on alternative splicing-derived cancer neoantigens. In addition, several points were presented to spark further discussion toward improving the identification of alternative splicing-derived cancer neoantigens.

GID2 Interacts With CDKN3 and Regulates Pancreatic Cancer Growth and Apoptosis.

Dysregulation of deubiquitinase or ubiquitinase-mediated protein expression contributes to various diseases, including cancer. In the present study, we identified GID2, a subunit of the glucose-induced degradation-deficient (GID) complex that functions as an E3 ubiquitin ligase, as a potential key candidate gene in pancreatic cancer (PC) progression. The functional role and potential mechanism of GID2 in PC progression were investigated. Integrated bioinformatics analysis was performed to identify differentially expressed genes in PC based on the Gene Expression Profiling Interactive Analysis data sets. We found that GID2 was upregulated in PC tissues and that a high level of GID2 expression in clinical PC samples was positively associated with tumor stage and poor survival. Functional assays elucidated that GID2 expression promoted cell growth in vitro and accelerated tumor growth in vivo. GID2 knockdown effectively attenuated the malignant behaviors of PC cells and tumor formation. Furthermore, the protein network that interacted with the GID2 protein was constructed based on the GeneMANIA website. Cyclin-dependent kinase inhibitor 3 (CDKN3), a cell cycle regulator, was identified as a potential target of the GID2 protein. We revealed that GID2 positively regulated CDKN3 expression and inhibited CDKN3 ubiquitination. Furthermore, CDKN3 downregulation reversed the promoting effects of GID2 on PC progression. Therefore, the present study demonstrated that GID2 might regulate PC progression by maintaining the stability of the CDKN3 protein. These findings highlight the potential roles of the GID2/CDKN3 axis as a potential therapeutic target in PC.

Regional transcriptional vulnerability to basal forebrain functional dysconnectivity in mild cognitive impairment patients.

Nucleus basalis of Meynert (NbM), one of the earliest targets of Alzheimer's disease (AD), may act as a seed for pathological spreading to its connected regions. However, the underlying basis of regional vulnerability to NbM dysconnectivity remains unclear. NbM functional dysconnectivity was assessed using resting-state fMRI data of health controls and mild cognitive impairment (MCI) patients from the Alzheimer's disease Neuroimaging Initiative (ADNI2/GO phase). Transcriptional correlates of NbM dysconnectivity was explored by leveraging public intrinsic and differential post-mortem brain-wide gene expression datasets from Allen Human Brain Atlas (AHBA) and Mount Sinai Brain Bank (MSBB). By constructing an individual-level tissue-specific gene set risk score (TGRS), we evaluated the contribution of NbM dysconnectivity-correlated gene sets to change rate of cerebral spinal fluid (CSF) biomarkers during preclinical stage of AD, as well as to MCI onset age. An independent cohort of health controls and MCI patients from ADNI3 was used to validate our main findings. Between-group comparison revealed significant connectivity reduction between the right NbM and right middle temporal gyrus in MCI. This regional vulnerability to NbM dysconnectivity correlated with intrinsic expression of genes enriched in protein and immune functions, as well as with differential expression of genes enriched in cholinergic receptors, immune, vascular and energy metabolism functions. TGRS of these NbM dysconnectivity-correlated gene sets are associated with longitudinal amyloid-beta change at preclinical stages of AD, and contributed to MCI onset age independent of traditional AD risks. Our findings revealed the transcriptional vulnerability to NbM dysconnectivity and their crucial role in explaining preclinical amyloid-beta change and MCI onset age, which offer new insights into the early AD pathology and encourage more investigation and clinical trials targeting NbM.

Local white matter abnormalities in Parkinson's disease with mild cognitive impairment: Assessed with neurite orientation dispersion and density imaging.

Mild cognitive impairment is a nonmotor complication in Parkinson's disease (PD) that have a high risk of developing dementia. White matter is associated with cognitive function in PD and the alterations may occur before the symptoms of the disease. Previous diffusion tensor imaging (DTI) studies lacked specificity to characterize the concrete contributions of distinct white matter tissue properties. This may lead to inconsistent conclusions about the alteration of white matter microstructure. Here, we used neurite orientation dispersion and density imaging (NODDI) and white matter fiber clustering method to uncover local white matter microstructures in PD with mild cognitive impairment (PD-MCI). This study included 23 PD-MCI and 20 PD with normal cognition (PD-NC) and 21 healthy controls (HC). To probe specific and fine-grained differences, metrics of NODDI and DTI in white matter fiber clusters were evaluated using along-tract analysis. Our results showed that PD-MCI patients had significantly lower neurite density index (NDI) and orientation dispersion index (ODI) in white matter fiber clusters in the prefrontal region. Correlation analysis and receiver operating characteristic (ROC) analysis revealed that the diagnostic performance of NODDI-derived metrics in cingulum bundle (2 clusters) and thalamo-frontal (2 clusters) were superior to DTI metrics. Our study provides a more specific insight to uncover local white matter abnormalities in PD-MCI, which benefit understanding the underlying mechanism of cognitive decline in PD and predicting the disease in advance.

Comprehensive analysis of partial methylation domains in colorectal cancer based on single-cell methylation profiles.

Epigenetic aberrations have played a significant role in affecting the pathophysiological state of colorectal cancer, and global DNA hypomethylation mainly occurs in partial methylation domains (PMDs). However, the distribution of PMDs in individual cells and the heterogeneity between cells are still unclear. In this study, the DNA methylation profiles of colorectal cancer detected by WGBS and scBS-seq were used to depict PMDs in individual cells for the first time. We found that more than half of the entire genome is covered by PMDs. Three subclasses of PMDS have distinct characteristics, and Gain-PMDs cover a higher proportion of protein coding genes. Gain-PMDs have extensive epigenetic heterogeneity between different cells of the same tumor, and the DNA methylation in cells is affected by the tumor microenvironment. In addition, abnormally elevated promoter methylation in Gain-PMDs may further promote the growth, proliferation and metastasis of tumor cells through silent transcription. The PMDs detected in this study have the potential as epigenetic biomarkers and provide a new insight for colorectal cancer research based on single-cell methylation data.

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Global characterization of B cell receptor repertoire in COVID-19 patients by single-cell V(D)J sequencing.

The world is facing a pandemic of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Adaptive immune responses are essential for SARS-CoV-2 virus clearance. Although a large body of studies have been conducted to investigate the immune mechanism in COVID-19 patients, we still lack a comprehensive understanding of the BCR repertoire in patients. In this study, we used the single-cell V(D)J sequencing to characterize the BCR repertoire across convalescent COVID-19 patients. We observed that the BCR diversity was significantly reduced in disease compared with healthy controls. And BCRs tend to skew toward different V gene segments in COVID-19 and healthy controls. The CDR3 sequences of heavy chain in clonal BCRs in patients were more convergent than that in healthy controls. In addition, we discovered increased IgG and IgA isotypes in the disease, including IgG1, IgG3 and IgA1. In all clonal BCRs, IgG isotypes had the most frequent class switch recombination events and the highest somatic hypermutation rate, especially IgG3. Moreover, we found that an IgG3 cluster from different clonal groups had the same IGHV, IGHJ and CDR3 sequences (IGHV4-4-CARLANTNQFYDSSSYLNAMDVW-IGHJ6). Overall, our study provides a comprehensive characterization of the BCR repertoire in COVID-19 patients, which contributes to the understanding of the mechanism for the immune response to SARS-CoV-2 infection.

Comprehensive analysis of TCR repertoire in COVID-19 using single cell sequencing.

T-cell receptor (TCR) is crucial in T cell-mediated virus clearance. To date, TCR bias has been observed in various diseases. However, studies on the TCR repertoire of COVID-19 patients are lacking. Here, we used single-cell V(D)J sequencing to conduct comparative analyses of TCR repertoire between 12 COVID-19 patients and 6 healthy controls, as well as other virus-infected samples. We observed distinct T cell clonal expansion in COVID-19. Further analysis of VJ gene combination revealed 6 VJ pairs significantly increased, while 139 pairs significantly decreased in COVID-19 patients. When considering the VJ combination of α and ß chains at the same time, the combination with the highest frequency on COVID-19 was TRAV12-2-J27-TRBV7-9-J2-3. Besides, preferential usage of V and J gene segments was also observed in samples infected by different viruses. Our study provides novel insights on TCR in COVID-19, which contribute to our understanding of the immune response induced by SARS-CoV-2.

sefOri: selecting the best-engineered sequence features to predict DNA replication origins.

MOTIVATION: Cell divisions start from replicating the double-stranded DNA, and the DNA replication process needs to be precisely regulated both spatially and temporally. The DNA is replicated starting from the DNA replication origins. A few successful prediction models were generated based on the assumption that the DNA replication origin regions have sequence level features like physicochemical properties significantly different from the other DNA regions. RESULTS: This study proposed a feature selection procedure to further refine the classification model of the DNA replication origins. The experimental data demonstrated that as large as 26% improvement in the prediction accuracy may be achieved on the yeast Saccharomyces cerevisiae. Moreover, the prediction accuracies of the DNA replication origins were improved for all the four yeast genomes investigated in this study. AVAILABILITY AND IMPLEMENTATION: The software sefOri version 1.0 was available at http://www.healthinformaticslab.org/supp/resources.php. An online server was also provided for the convenience of the users, and its web link may be found in the above-mentioned web page. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Alternative splicing associated with cancer stemness in kidney renal clear cell carcinoma.

BACKGROUD: Cancer stemness is associated with metastases in kidney renal clear cell carcinoma (KIRC) and negatively correlates with immune infiltrates. Recent stemness evaluation methods based on the absolute expression have been proposed to reveal the relationship between stemness and cancer. However, we found that existing methods do not perform well in assessing the stemness of KIRC patients, and they overlooked the impact of alternative splicing. Alternative splicing not only progresses during the differentiation of stem cells, but also changes during the acquisition of the stemness features of cancer stem cells. There is an urgent need for a new method to predict KIRC-specific stemness more accurately, so as to provide help in selecting treatment options. METHODS: The corresponding RNA-Seq data were obtained from the The Cancer Genome Atlas (TCGA) data portal. We also downloaded stem cell RNA sequence data from the Progenitor Cell Biology Consortium (PCBC) Synapse Portal. Independent validation sets with large sample size and common clinic pathological characteristics were obtained from the Gene Expression Omnibus (GEO) database. we constructed a KIRC-specific stemness prediction model using an algorithm called one-class logistic regression based on the expression and alternative splicing data to predict stemness indices of KIRC patients, and the model was externally validated. We identify stemness-associated alternative splicing events (SASEs) by analyzing different alternative splicing event between high- and low- stemness groups. Univariate Cox and multivariable logistic regression analysisw as carried out to detect the prognosis-related SASEs respectively. The area under curve (AUC) of receiver operating characteristic (ROC) was performed to evaluate the predictive values of our model. RESULTS: Here, we constructed a KIRC-specific stemness prediction model with an AUC of 0.968,and to provide a user-friendly interface of our model for KIRC stemness analysis, we have developed KIRC Stemness Calculator and Visualization (KSCV), hosted on the Shiny server, can most easily be accessed via web browser and the url https://jiang-lab.shinyapps.io/kscv/ . When applied to 605 KIRC patients, our stemness indices had a higher correlation with the gender, smoking history and metastasis of the patients than the previous stemness indices, and revealed intratumor heterogeneity at the stemness level. We identified 77 novel SASEs by dividing patients into high- and low- stemness groups with significantly different outcome and they had significant correlations with expression of 17 experimentally validated splicing factors. Both univariate and multivariate survival analysis demonstrated that SASEs closely correlated with the overall survival of patients. CONCLUSIONS: Basing on the stemness indices, we found that not only immune infiltration but also alternative splicing events showed significant different at the stemness level. More importantly, we highlight the critical role of these differential alternative splicing events in poor prognosis, and we believe in the potential for their further translation into targets for immunotherapy.

Accurate identification of RNA D modification using multiple features.

As one of the common post-transcriptional modifications in tRNAs, dihydrouridine (D) has prominent effects on regulating the flexibility of tRNA as well as cancerous diseases. Facing with the expensive and time-consuming sequencing techniques to detect D modification, precise computational tools can largely promote the progress of molecular mechanisms and medical developments. We proposed a novel predictor, called iRNAD_XGBoost, to identify potential D sites using multiple RNA sequence representations. In this method, by considering the imbalance problem using hybrid sampling method SMOTEEEN, the XGBoost-selected top 30 features are applied to construct model. The optimized model showed high Sn and Sp values of 97.13% and 97.38% over jackknife test, respectively. For the independent experiment, these two metrics separately achieved 91.67% and 94.74%. Compared with iRNAD method, this model illustrated high generalizability and consistent prediction efficiencies for positive and negative samples, which yielded satisfactory MCC scores of 0.94 and 0.86, respectively. It is inferred that the chemical property and nucleotide density features (CPND), electron-ion interaction pseudopotential (EIIP and PseEIIP) as well as dinucleotide composition (DNC) are crucial to the recognition of D modification. The proposed predictor is a promising tool to help experimental biologists investigate molecular functions.

Imidazole-graphyne: a new 2D carbon nitride with a direct bandgap and strong IR refraction.

Six-membered rings are common building blocks of many carbon structures. Recent studies have shown that penta-graphene composed of five-membered carbon rings have properties very different from that of graphene. This has motivated the search for new carbon structures. Among this is cp-graphyne, composed of carbon pentagons and bridged by acetylenic linkers. However, the bandgap of cp-graphyne, like that of graphene, is zero, making it unsuitable for applications in electronics. Herein, we show that a new two-dimensional (2D) carbon nitride structure formed by assembling the five-membered imidazole molecules with acetylenic linker can overcome this limitation. Named ID-GY, this new material not only has a direct band gap of 1.10 eV, but it is dynamically and mechanically stable and can withstand temperatures up to 1200 K. In addition, due to its porous and anisotropic geometry, the Young's modulus of ID-GY along the diagonal direction is lower than that of most 2D materials reported previously. Equally important, ID-GY exhibits strong refraction near infrared (IR) and has potential for applications in nanoelectronics and optical devices. These results, based on density functional theory, can stimulate experimental studies.

LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse.

Long non-coding RNAs (lncRNAs) play crucial roles in regulating gene expression, and a growing number of researchers have focused on the identification of target genes of lncRNAs. However, no online repository is available to collect the information on target genes regulated by lncRNAs. To make it convenient for researchers to know what genes are regulated by a lncRNA of interest, we developed a database named lncRNA2Target to provide a comprehensive resource of lncRNA target genes in 2015. To update the database this year, we retrieved all new lncRNA-target relationships from papers published from 1 August 2014 to 30 April 2018 and RNA-seq datasets before and after knockdown or overexpression of a specific lncRNA. LncRNA2Target database v2.0 provides a web interface through which its users can search for the targets of a particular lncRNA or for the lncRNAs that target a particular gene, and is freely accessible at http://123.59.132.21/lncrna2target.

Prediction of antioxidant proteins using hybrid feature representation method and random forest.

Natural antioxidant proteins are mainly found in plants and animals, which interact to eliminate excessive free radicals and protect cells and DNA from damage, prevent and treat some diseases. Therefore, accurate identification of antioxidant proteins is important for the development of new drugs and research of related diseases. This article proposes novel method based on the combination of random forest and hybrid features that can accurately predict antioxidant proteins. Four single feature extraction methods (188D, profile-based Auto-cross covariance (ACC-PSSM), N-gram, and g-gap) and hybrid feature representation methods were used to feature extraction. Three feature selection methods (MRMD, t-SNE, and the optimal feature set selection) were adopted to determine the optimal features. The new hybrid feature vectors derived by combining 188D with the other three features all have indicators ranging from 0.9550 to 0.9990. The novel method showed better performance compared with the other methods.

Fabrication of Hollow CoP/TiOx Heterostructures for Enhanced Oxygen Evolution Reaction.

Transition-metal phosphides have flourished as promising candidates for oxygen evolution reaction (OER) electrocatalysts. Herein, it is demonstrated that the electrocatalytic OER performance of CoP can be greatly improved by constructing a hybrid CoP/TiOx heterostructure. The CoP/TiOx heterostructure is fabricated using metal-organic framework nanocrystals as templates, which leads to unique hollow structures and uniformly distributed CoP nanoparticles on TiOx . The strong interactions between CoP and TiOx in the CoP/TiOx heterostructure and the conductive nature of TiOx with Ti3+ sites endow the CoP-TiOx hybrid material with high OER activity comparable to the state-of-the-art IrO2 or RuO2 OER electrocatalysts. In combination with theoretical calculations, this work reveals that the formation of CoP/TiOx heterostructure can generate a pathway for facile electron transport and optimize the water adsorption energy, thus promoting the OER electrocatalysis.

Low thermal conductivity of peanut-shaped carbon nanotube and its insensitive response to uniaxial strain.

Motivated by the experimental synthesis of peanut-shaped carbon nanotubes (PSNTs) that combine the novel features of fullerene and carbon nanotubes (CNTs), we study the thermal conductivity of a PSNT (1dp08) and its response to different strains by using non-equilibrium molecular dynamics simulations and lattice dynamics together with density functional theory. We find that the thermal conductivity of the PSNT is reduced by more than 90% as compared to that of CNTs, and remains almost the same when different strains applied, exhibiting very different behaviors from that of CNTs, where the thermal conductivity decreases monotonically with the increase of strain. Through phonon mode calculations, we show that the reduced phonon group velocity, phonon lifetime and the vibrational mismatch are responsible for the low thermal conductivity of the PSNT, and the insensitive response of thermal conductivity to strain is due to the insensitivity of its phonon density of states and group velocity to strain. These features endow the PSNT with the potential applications in thermal devices, and add new features to one-dimensional carbon nanomaterials going beyond conventional CNTs.

BIN1 rs744373 variant shows different association with Alzheimer's disease in Caucasian and Asian populations.

BACKGROUND: The association between BIN1 rs744373 variant and Alzheimer's disease (AD) had been identified by genome-wide association studies (GWASs) as well as candidate gene studies in Caucasian populations. But in East Asian populations, both positive and negative results had been identified by association studies. Considering the smaller sample sizes of the studies in East Asian, we believe that the results did not have enough statistical power. RESULTS: We conducted a meta-analysis with 71,168 samples (22,395 AD cases and 48,773 controls, from 37 studies of 19 articles). Based on the additive model, we observed significant genetic heterogeneities in pooled populations as well as Caucasians and East Asians. We identified a significant association between rs744373 polymorphism with AD in pooled populations (P = 5 × 10- 07, odds ratio (OR) = 1.12, and 95% confidence interval (CI) 1.07-1.17) and in Caucasian populations (P = 3.38 × 10- 08, OR = 1.16, 95% CI 1.10-1.22). But in the East Asian populations, the association was not identified (P = 0.393, OR = 1.057, and 95% CI 0.95-1.15). Besides, the regression analysis suggested no significant publication bias. The results for sensitivity analysis as well as meta-analysis under the dominant model and recessive model remained consistent, which demonstrated the reliability of our finding. CONCLUSIONS: The large-scale meta-analysis highlighted the significant association between rs744373 polymorphism and AD risk in Caucasian populations but not in the East Asian populations.

Long noncoding RNA LINC00473 drives the progression of pancreatic cancer via upregulating programmed death-ligand 1 by sponging microRNA-195-5p.

Pancreatic cancer (PC) is a great health burden to patients owing to its poor overall survival rate. Long noncoding RNAs (lncRNAs) interact with microRNAs (miRs) to participate in tumorigenesis. Therefore, we aim to uncover the role and related mechanism of LINC00473 in PC through the modulation of miR-195-5p and programmed death-ligand 1 (PD-L1). Increased LINC00473 and PD-L1 but declined miR-195-5p were determined in PC tissues and cell lines, and it was found that LINC00473 mainly situated in the cytoplasm. Also, miR-195-5p was verified to bind with both LINC00473 and PD-L1. Next, with the aim to examine the ability of LINC00473, miR-195-5p, and PD-L1 on the PC progression, the expression of LINC00473, miR-195-5p and PD-L1 were altered with mimics, inhibitors, overexpression vectors or siRNAs in PC cells and cocultured CD8+ T cells. It was demonstrated that LINC00473 sponged miR-195-5p to upregulate PD-L1 expression. More important, the obtained results revealed that LINC00473 silencing or miR-195-5p upregulation elevated the expression of Bcl-2 associated X protein (Bax), interferon (IFN)-γ, and interleukin (IL)-4 but reduced the expression of B-cell lymphoma-2 (Bcl-2), matrix metalloproteinase (MMP)-2, MMP-9, and IL-10, thus inducing the enhancement of the apoptosis as along with the inhibition of proliferation, invasion, and migration of the PC cells. LINC00473 silencing or miR-195-5p elevation activated the CD8+ T cells. Taken together, LINC00473 silencing blocked the PC progression through enhancing miR-195-5p-targeted downregulation of PD-L1. This finding offers new therapeutic options for treating this devastating disease.