LncRNA profile study reveals a seven-lncRNA signature predicts the prognosis of patients with colorectal cancer

An innovative pyroptosis-related long-noncoding-RNA signature predicts the prognosis of gastric cancer via affecting immune cell infiltration landscape

Background: Gastric cancer (GC) is a worldwide popular malignant tumor. However, the survival rate of advanced GC remains low. Pyroptosis and long non-coding RNAs (lncRNAs) are important in cancer progression. Thus, we aimed to find out a pyroptosis-related lncRNAs (PRLs) signature and use it to build a practical risk model with the purpose to predict the prognosis of patients with GC. Methods: Univariate Cox regression analysis was used to identify PRLs linked to GC patient's prognosis. Subsequently, to construct a PRLs signature, the least absolute shrinkage and selection operator regression, and multivariate Cox regression analysis were used. Kaplan-Meier analysis, principal component analysis, and receiver operating characteristic curve analysis were performed to assess our novel lncRNA signature. The correlation between risk signature and clinicopathological features was also examined. Finally, the relationship of pyroptosis and immune cells were evaluated through the CIBERSORT tool and single-sample lncRNA set enrichment analysis (ssGSEA). Results: A PRLs signature comprising eight lncRNAs was discerned as a self-determining predictor of prognosis. GC patients were sub-divided into high-risk and low-risk groups via this risk-model. Stratified analysis of different clinical factors also displayed that the PRLs signature was a good prognosis factor. According to the risk score and clinical characteristics, a nomogram was established. Moreover, the difference between the groups is significance in immune cells and immune pathways. Conclusion: This study established an effective prognostic signature consist of eight PRLs in GC, and constructed an efficient nomogram model. Further, the PRLs correlated with immune cells and immune pathways.

Identification and validation of an inflammation-related lncRNAs signature for improving outcomes of patients in colorectal cancer

Background: Colorectal cancer is the fourth most deadly cancer worldwide. Although current treatment regimens have prolonged the survival of patients, the prognosis is still unsatisfactory. Inflammation and lncRNAs are closely related to tumor occurrence and development in CRC. Therefore, it is necessary to establish a new prognostic signature based on inflammation-related lncRNAs to improve the prognosis of patients with CRC.

Methods: LASSO-penalized Cox analysis was performed to construct a prognostic signature. Kaplan-Meier curves were used for survival analysis and ROC curves were used to measure the performance of the signature. Functional enrichment analysis was conducted to reveal the biological significance of the signature. The R package “maftool” and GISTIC2.0 algorithm were performed for analysis and visualization of genomic variations. The R package “pRRophetic”, CMap analysis and submap analysis were performed to predict response to chemotherapy and immunotherapy.

Results: An effective and independent prognostic signature, IRLncSig, was constructed based on sixteen inflammation-related lncRNAs. The IRLncSig was proved to be an independent prognostic indicator in CRC and was superior to clinical variables and the other four published signatures. The nomograms were constructed based on inflammation-related lncRNAs and detected by calibration curves. All samples were classified into two groups according to the median value, and we found frequent mutations of the TP53 gene in the high-risk group. We also found some significantly amplificated regions in the high-risk group, 8q24.3, 20q12, 8q22.3, and 20q13.2, which may regulate the inflammatory activity of cancer cells in CRC. Finally, we identified chemotherapeutic agents for high-risk patients and found that these patients were more likely to respond to immunotherapy, especially anti-CTLA4 therapy.

Conclusion: In short, we constructed a new signature based on sixteen inflammation-related lncRNAs to improve the outcomes of patients in CRC. Our findings have proved that the IRLncSig can be used as an effective and independent marker for predicting the survival of patients with CRC.

Identification and Validation of an m6A-Related LncRNA Signature to Predict Progression-Free Survival in Colorectal Cancer

The RNA methylation of N6 adenosine (m6A) plays a crucial role in various biological processes. Strong evidence reveals that the dysregulation of long non-coding RNAs (lncRNA) brings about the abnormality of downstream signaling in multiple ways, thus influencing tumor initiation and progression. Currently, it is essential to discover effective and succinct molecular biomarkers for predicting colorectal cancer (CRC) prognosis. However, the prognostic value of m6A-related lncRNAs for CRC remains unclear, especially for progression-free survival (PFS). Here, we screened 24 m6A-related lncRNAs in 622 CRC patients and identified five lncRNAs (SLCO4A1-AS1, MELTF-AS1, SH3PXD2A-AS1, H19 and PCAT6) associated with patient PFS. Compared to normal samples, their expression was up-regulated in CRC tumors from TCGA dataset, which was validated in 55 CRC patients from our in-house cohort. We established an m6A-Lnc signature for predicting patient PFS, which was an independent prognostic factor by classification analysis of clinicopathologic features. Moreover, the signature was validated in 1,077 patients from six independent datasets (GSE17538, GSE39582, GSE33113, GSE31595, GSE29621, and GSE17536), and it showed better performance than three known lncRNA signatures for predicting PFS. In summary, our study demonstrates that the m6A-Lnc signature is a promising biomarker for forecasting patient PFS in CRC.

Expression of POT1-AS1 in GC Tissue, Its Effect on Biological Behavior of Gastric Cancer, and Its Significance on Prognosis of Gastric Cancer

Objective

To study the correlation between gold in GC and biological indicators of gastric cancer (GC) and its effect on prognosis and correlation of POT1-AS1 with GC cellular growth, and to explore its impact in the processes of GC, to supply histological basis for medical treatment of GC.

Methods

From September 2019 to December 2021, 80 pairs of GAC specimens and healthy para-carcinoma tissue were immediately stored in paraformaldehyde solution. POT1-AS1 levels in 77 postoperative patients with GC were detected by immunohistochemical method. The correlation of the above indexes and the relationship between the above indexes and the biological behavior and prognosis of GC were analyzed.

Results

POT1-AS1 was strongly displayed in GAC specimens, and the difference between groups was statistically significant (P < 0.05). After sh-POT1-AS1 plasmid transfection, the relative expression of POT1-AS1 mRNA in SGC-7901 cells was remarkably lower compared to nontransfection group, and the difference between groups was statistically significant (P < 0.05). After POT1-AS1 knockdown, the SGC-7901 proliferation ability and the number of clones of SGC-7901 decreased remarkably. The relative level of cyclin D1 and cyclin-dependent kinase 4 (CDK4) in SGC-7901 reduced remarkably, while relative expression of cyclin-dependent kinase inhibitor 1A (CDKI1A) increased remarkably, and the difference between groups was statistically significant (P < 0.05). The positive expression of POT1-AS1 was found in GC and stromal cells. TIMP-1 in tumor stromal cells was related to the maximum diameter of tumor (P = 0.027), invasion depth (P = 0.001), lymph node metastasis (P = 0.006), and clinical stages (P = 0.006). TIMP-1 had an effect on the prognosis, while the strong positive group had a poor prognosis. The expression of TIMP-1 in GC cells was not related to clinical biological behavior and prognosis of GC. The VEGF level in GC was correlated to tumor maximum diameter (P < 0.05), invasive depth (P < 0.05), and lymph node metastasis (P < 0.05) that was linked to clinical phases, and the difference between groups was statistically significant (P < 0.05), which was positively correlated with Ki67-LI; the correlation coefficient was 0.254 and P = 0.026, which was not related to the positive expression of TIMP-1 in GC cells and stromal cells. VECF has an effect on the prognosis, and the outcomes of the positive group are worse.

Conclusion

The correlation between TIMP-1 of GASTRIC cancer mesenchymal cells of POT1-AS1 and VEGF and Ki-67-Li suggests that TIMP-1 produced by mesenchymal cells can facilitate tumor progression and lead to poor prognosis by promoting tumor cell proliferation. VEGF can strengthen tumor angiogenesis and then promote tumor cell proliferation, which has an adverse effect on the prognosis. Ki-67-LI is correlated to the medical biological behavior and prognosis of the tumor, reflecting the malignant process of the tumor.

Identification and Validation of a Novel Prognostic Gene Model for Colorectal Cancer

Aims

Colon cancer (CRC), with high morbidity and mortality, is a common and highly malignant cancer, which always has a bad prognosis. So it is urgent to employ a reasonable manner to assess the prognosis of patients. We developed and validated a gene model for predicting CRC risk.

Methods

The Gene Expression Omnibus (GEO) database was used to extract the gene expression profiles of CRC patients (N = 181) from GEO to identify genes that were differentially expressed between CRC patients and controls and then stable signature genes by firstly using both robust likelihood-based modeling with 1000 iterations and random survival forest variable hunting algorithms. Cluster analysis using the longest distance method was drawn out, and Kaplan–Meier (KM) survival analysis was used to compare the clusters. Meanwhile, the risk score was evaluated in three independent datasets including the GEO and Illumina HiSeq sequencing platforms. The corresponding risk index was calculated, and samples were clustered into high- and low-risk groups according to the median. And survival ROC analysis was used to evaluate the prognostic model. Finally, the Gene Set Enrichment Analysis (GSEA) was performed for further functional enrichment analyses.

Results

A 10-gene model was obtained, including 7 negative impact factors (SLC39A14, AACS, ERP29, LAMP3, TMEM106C, TMED2, and SLC25A3) and 3 positive ones (CNPY2, GRB10, and PBK), which related with several important oncogenic pathways (KRAS signaling, TNF-α signaling pathway, and WNT signaling pathway) and several cancer-related cellular processes (epithelial mesenchymal transition and cellular apoptosis). By using colon cancer datasets from The Cancer Genome Atlas (TCGA), the model was validated in KM survival analysis (P ≤ 0.001) and significant analysis with recurrence time (P = 0.0018).

Conclusions

This study firstly developed a stable and effective 10-gene model by using novel combined methods, and CRC patients might be able to use it as a prognostic marker for predicting their survival and monitoring their long-term treatment.

LncRNA profiles from Notch signaling: Implications for clinical management and tumor microenvironment of colorectal cancer

The interplay between long non-coding RNAs (lncRNAs) and the Notch pathway involves a variety of malignancies. However, Notch-derived lncRNAs and their latent clinical significance remain elusive in colorectal cancer (CRC). In this study, we introduced a framework that could screen Notch-derived lncRNAs (named “NLncer”) and ultimately identified 24 NLncers. To further explore the clinical significance of these NLncers, we performed LASSO and Cox regression in TCGA-CRC cohort (n = 584) and then retained six lncRNAs tightly associated with prognosis. The final model (termed “NLncS”) was subsequently tested in GSE38832 (n = 122), GSE39582 (n = 573), and an in-house clinical cohort (n = 115). Ultimately, our NLncS model could serve as an independent risk factor and afford a robust performance for assessing the prognosis of CRC patients. Additionally, patients with high NLncS risk scores were characterized by upregulation of immune pathways, strong immunogenicity, abundant CD8 + T-cell infiltration, and potentially higher response rates to CTLA4 blockers, which turned out to be suitable for immunotherapy. Aiming at globally observing the characteristics of high-risk patients, somatic mutation and methylation modification analysis provide us with evidence at the genomic and transcriptomic levels. To facilitate the clinical transformability, we mined deeply into the sensitive compounds targeting high-risk individuals and identified dasatinib as a candidate agent for patients with a high Notch risk score. In conclusion, our NLncS model is a promising biomarker for optimizing the clinical management of CRC patients.

Potentials of long non-coding RNAs as biomarkers of colorectal cancer

Colorectal cancer (CRC) is the third most common malignant tumor worldwide and the fourth major cause of cancer-related death, with high morbidity and increased mortality year by year. Although significant progress has been made in the therapy strategies for CRC, the great difficulty in early diagnosis, feeble susceptibility to radiotherapy and chemotherapy, and high recurrence rates have reduced therapeutic efficacy resulting in poor prognosis. Therefore, it is urgent to understand the pathogenesis of CRC and unravel novel biomarkers to improve the early diagnosis, treatment and prediction of CRC recurrence. Long non-coding RNAs (lncRNAs) are non-coding RNAs with a length of more than 200 nucleotides, which are abnormally expressed in tumor tissues and cell lines, activating or inhibiting specific genes through multiple mechanisms including transcription and translation. A growing number of studies have shown that lncRNAs are important regulators of microRNAs (miRNAs, miRs) expression in CRC and may be promising biomarkers and potential therapeutic targets in the research field of CRC. This review mainly summarizes the potential application value of lncRNAs as novel biomarkers in CRC diagnosis, radiotherapy, chemotherapy and prognosis. Additionally, the significance of lncRNA SNHGs family and lncRNA-miRNA networks in regulating the occurrence and development of CRC is mentioned, aiming to provide some insights for understanding the pathogenesis of CRC and developing new diagnostic and therapeutic strategies.

KRAS-related long noncoding RNAs in human cancers

KRAS is one of the most widely prevalent proto-oncogenes in human cancers. The constitutively active KRAS oncoprotein contributes to both tumor onset and cancer development by promoting cell proliferation and anchorage-independent growth in a MAPK pathway-dependent manner. The expression of microRNAs (miRNAs) and the KRAS oncogene are known to be dysregulated in various cancers, while long noncoding RNAs (lncRNAs) can act as regulators of the miRNAs targeting KRAS oncogene in different cancers and have gradually become a focus of research in recent years. In this review article, we summarize recent advances in the research on lncRNAs that have sponging effects on KRAS-targeting miRNAs as crucial mediators of KRAS expression in different cell types and organs. A deeper understanding of lncRNA function in KRAS-driven cancers is of major fundamental importance and will provide a valuable clinical tool for the diagnosis, prognosis, and eventual treatment of cancers.

Lower BCL11B expression is associated with adverse clinical outcome for patients with myelodysplastic syndrome

Myelodysplastic syndrome (MDS) is an aggressive and genetically heterogeneous disease with poor prognosis. Cellular immune disorder is a common characteristic of this disease and is thought to be related to clinical outcome. Alterations in T cell clonal expansion and T cell dysfunction has been detected in MDS patients. Little is known about whether there are immune biomarkers to evaluate the T cell alterations with clinical outcome. Previous studies have demonstrated that B-cell leukemia/lymphoma 11B (BCL11B) plays an important role in regulating T cell development and proliferation. In this study, the prognostic value of BCL11B for MDS patients was explored by analyzing RNA-seq data from 270 patients in two datasets in the Gene Expression Omnibus (GEO) database and real-time quantitative PCR data (qRT-PCR) of 31 bone marrow (BM) samples of MDS and 6 BM samples of patients with MDS progress to secondary acute myeloid leukemia (sAML) from our clinical center. The results demonstrated that BCL11B is significantly down-regulated in MDS patients as compared with healthy individuals (HIs). Importantly, lower BCL11B expression was found in MDS patients who were of high/very high risk, older than 60 y, or male and patients with sAML. Furthermore, low BCL11B expression appeared to be associated with poor overall survival (OS) for MDS patients, though the data were not yet significant enough at this point. In addition, BCL11B low-expressing MDS patients had shorter restricted mean survival time (RMST) than those with high BCL11B expression. Interestingly, BCL11B positively correlated with naive and activated memory CD4 + T cells, CD8 + T cells, and the T cell receptor complex genes CD3E and CD3G, but it negatively correlated with regulatory T cells (Treg). Additionally, co-occurrence of low BCL11B expression and CD3E and CD3G was associated with poor OS and shorter RMST. In conclusion, lower BCL11B expression in BM samples of MDS patients was associated with adverse clinical outcome.

LncRNAs serve as novel biomarkers for diagnosis and prognosis of childhood ALL

Background

Although some studies have demonstrated that lncRNAs are dysregulated in hematopoietic malignancies and may regulate the progression of leukemia, the detailed mechanism underlying tumorigenesis is still unclear. This study aimed to investigate lncRNAs that are differentially expressed in childhood B-cell acute lymphoblastic leukemia (B-ALL) and T-cell acute lymphoblastic leukemia (T-ALL) and their potential roles in the progression of childhood ALL.

Methods

Microarrays were used to detect differentially expressed lncRNAs and mRNAs. Several aberrantly expressed lncRNAs were validated by qRT-PCR. Leukemia-free survival was analyzed using the Kaplan–Meier method with a log-rank test. The co-expression correlations of lncRNAs and mRNAs were determined by Spearman’s correlation coefficient. CCK-8 assays and flow cytometry were performed to measure cell proliferation and apoptosis.

Results

We revealed that many lncRNAs were abnormally expressed in B-ALL and T-ALL. LncRNA/mRNA co-expression and the gene locus network showed that dysregulated lncRNAs are involved in diverse cellular processes. We also assessed the diagnostic value of the differentially expressed lncRNAs and confirmed the optimal combination of TCONS_00026679, uc002ubt.1, ENST00000411904, and ENST00000547644 with an area under the curve of 0.9686 [95 % CI: 0.9369–1.000, P < 0.001], with 90.7 % sensitivity and 92.19 % specificity, at a cut-off point of -0.5700 to distinguish childhood B-ALL patients from T-ALL patients, implying that these specific lncRNAs may have potential to detect subsets of childhood ALL. Notably, we found that the 8-year leukemia-free survival of patients with high TCONS_00026679 (p = 0.0081), ENST00000522339 (p = 0.0484), ENST00000499583 (p = 0.0381), ENST00000457217 (p = 0.0464), and ENST00000451368 (p = 0.0298) expression levels was significantly higher than that of patients with low expression levels of these lncRNAs, while patients with high uc002ubt.1 (p = 0.0499) and ENST00000547644 (p = 0.0451) expression levels exhibited markedly shorter 8-year leukemia-free survival. In addition, some lncRNAs were found to play different roles in cell proliferation and apoptosis in T-ALL and B-ALL.

Conclusions

Dysregulated lncRNAs involved in different regulatory mechanisms underlying the progression of childhood T-ALL and B-ALL might serve as novel biomarkers to distinguish ALL subsets and indicate poor outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40364-021-00303-x.

Identification of a Genome Instability-Associated LncRNA Signature for Prognosis Prediction in Colon Cancer

Long non-coding RNAs (lncRNAs) were reported to have the potential in maintaining genome instability, but the identification of lncRNAs related to genome instability and their prognostic value have not been largely explored in colon cancer. In this study, we obtained 155 genome instability-associated lncRNAs based on somatic mutation profiles in colon cancer from The Cancer Genome Atlas (TCGA) database. Functional enrichment analysis revealed the possible roles of genes co-expressed with those lncRNAs involved in some cancer, genome instability and immune related biological processes. Combined with overall survival data, a seven-lncRNA signature was established for prognosis prediction. According to the risk score calculated by this signature, high-risk patients characterized by high somatic mutation count, high microsatellite instability, significantly poorer clinical outcomes and specific tumor immune infiltration status compared with low-risk patients. The lncRNA signature was validated to be an independent prognostic indicator with good predictive performance in TCGA cohort. Furthermore, the prognostic value of the ZNF503-AS1 in lncRNA signature was confirmed in another independent dataset from Gene Expression Omnibus database. In summary, the genome instability-associated lncRNA signature in this study could be a promising tool for effectively predicting survival outcomes in colon cancer.

Antibody-drug conjugates for the treatment of lymphoma: clinical advances and latest progress

Antibody-drug conjugates (ADCs) are a promising class of immunotherapies with the potential to specifically target tumor cells and ameliorate the therapeutic index of cytotoxic drugs. ADCs comprise monoclonal antibodies, cytotoxic payloads with inherent antitumor activity, and specialized linkers connecting the two. In recent years, three ADCs, brentuximab vedotin, polatuzumab vedotin, and loncastuximab tesirine, have been approved and are already establishing their place in lymphoma treatment. As the efficacy and safety of ADCs have moved in synchrony with advances in their design, a plethora of novel ADCs have garnered growing interest as treatments. In this review, we provide an overview of the essential elements of ADC strategies in lymphoma and elucidate the up-to-date progress, current challenges, and novel targets of ADCs in this rapidly evolving field.

Elevated LOXL2 expression by LINC01347/miR-328-5p axis contributes to 5-FU chemotherapy resistance of colorectal cancer

Chemotherapy resistance after curative surgery is a major contributor to the mortality of colorectal cancer (CRC). Detailed mechanism studies of specific molecular alterations are critical to improving the available therapies for long-term disease administration. We explored the functional role of LINC01347 in chemotherapy resistance of CRC. Elevated LINC01347 expression was correlated with CRC disease progression during chemotherapy treatment. However, the functional role of LINC01347 and mechanism remained undefined. In this study, we demonstrated that elevated LINC01347 expression was correlated with late clinical stage and poor prognosis in CRC tumor tissues with TCGA data. Exogenous LINC01347 expression promoted cell proliferation and 5-FU resistance of CRC cells, while LINC01347 knockdown attenuated cell growth and 5-FU resistance in vitro and in vivo. Molecular analysis indicated that LINC01347 participated in the transcriptional regulation of LOXL2 by sponging miR-328-5p. LOXL2 knockdown impaired the LINC01347 overexpression induced 5-FU resistance in CRC cells. The clinical analysis supported miR-328-5p/LOXL2 as a candidate biomarker for chemotherapy resistance of CRC patients. Our study provided a molecular basis for the development of 5-FU based chemotherapy resistance in CRC by LINC01347/miR-328/LOXL2 axis. We identified LINC01347 as a prognostic biomarker and potential therapeutic target against 5-FU based chemotherapy resistance of CRC.

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possible regulatory contexts in which these molecules are involved. Herein, we propose the use of the pipeline deposited in the RTN package to build lncRNAs co-expression networks using TCGA breast cancer (BC) cohort data. Worldwide, BC is the most common cancer in women and has great molecular heterogeneity. We identified an enriched co-expression network for the validation of relevant cell processes in the context of BC, including LINC00504. This lncRNA has increased expression in luminal subtype A samples, and is associated with prognosis in basal-like subtype. Silencing this lncRNA in luminal A cell lines resulted in decreased cell viability and colony formation. These results highlight the relevance of the proposed method for the identification of lncRNAs in specific biological contexts.

Long Non-coding RNAs in Cancer: Implications for Diagnosis, Prognosis, and Therapy

Long non-coding RNAs (lncRNAs) are major components of cellular transcripts that are arising as important players in various biological pathways. They have received extensive attention in recent years, regarded to be involved in both developmental processes and various diseases. Due to their specific expression and functional diversity in a variety of cancers, lncRNAs have promising applications in cancer diagnosis, prognosis and therapy. Studies have shown that lncRNAs with high specificity and accuracy have the potential to become biomarkers in cancers. LncRNAs can be noninvasively extracted from body fluids, tissues and cells, and can be used as independent or auxiliary biomarkers to improve the accuracy of diagnosis or prognosis. Currently, the most well-recognized lncRNA is PCA3, which has been approved for use in the diagnosis of prostate cancer. Moreover, the underlying mechanisms of lncRNAs were explored as therapeutic targets, which have been investigated in clinical trials of several cancers. In this review, we presented a compilation of recent publications, clinical trials and patents, addressing the potential of lncRNAs that could be considered as biomarkers or therapeutic targets, with the hopes of providing promised implications for future cancer therapy.

A seven immune-related lncRNA signature predicts the survival of patients with colon adenocarcinoma

This study aimed to explore immune-related lncRNAs for predicting the overall survival of patients with colon adenocarcinoma. RNA-sequencing data were downloaded from the TCGA data portal. The immune-related lncRNAs with differential expression were identified with Cox and LASSO regression analysis. With the stepwise regression analysis, a seven lncRNA signature was established for calculating the Risk Score with following formula: Risk Score = [Expression level of AC027307.2 * (0.156)] + [Expression level of AC074117.1 * (0.294)] + [Expression level of AC103702.2 * (-0.025)] + [Expression level of CYTOR * (0.205)] + [Expression level of LINC02381 * (0.251)] + [Expression level of MIR200CHG * (0.052)] + [Expression level of SNHG16 * (-0.101)]. The Risk Score was validated with survival analysis, achieving moderate area under the curve (AUC) of receiver operating characteristic (ROC) curve over 0.7. GSEA and immune-cell abundance analysis further supported the involved lncRNAs were immune-relevant. Finally, the prognosis prediction efficacy was verified with clinical samples with an AUC of 0.674 in ROC curve. Both the Risk Score and involved immune-related lncRNAs presented promising clinical significance.

Artificial Intelligence Applied to in vitro Gene Expression Testing (IVIGET) to Predict Trivalent Inactivated Influenza Vaccine Immunogenicity in HIV Infected Children

The number of patients affected by chronic diseases with special vaccination needs is burgeoning. In this scenario, predictive markers of immunogenicity, as well as signatures of immune responses are typically missing even though it would especially improve the identification of personalized immunization practices in these populations. We aimed to develop a predictive score of immunogenicity to Influenza Trivalent Inactivated Vaccination (TIV) by applying deep machine learning algorithms using transcriptional data from sort-purified lymphocyte subsets after in vitro stimulation. Peripheral blood mononuclear cells (PBMCs) collected before TIV from 23 vertically HIV infected children under ART and virally controlled were stimulated in vitro with p09/H1N1 peptides (stim) or left unstimulated (med). A multiplexed-qPCR for 96 genes was made on fixed numbers of 3 B cell subsets, 3 T cell subsets and total PBMCs. The ability to respond to TIV was assessed through hemagglutination Inhibition Assay (HIV) and ELIspot and patients were classified as Responders (R) and Non Responders (NR). A predictive modeling framework was applied to the data set in order to define genes and conditions with the higher predicted probability able to inform the final score. Twelve NR and 11 R were analyzed for gene expression differences in all subsets and 3 conditions [med, stim or Δ (stim-med)]. Differentially expressed genes between R and NR were selected and tested with the Adaptive Boosting Model to build a prediction score. The score obtained from subsets revealed the best prediction score from 46 genes from 5 different subsets and conditions. Calculating a combined score based on these 5 categories, we achieved a model accuracy of 95.6% and only one misclassified patient. These data show how a predictive bioinformatic model applied to transcriptional analysis deriving from in-vitro stimulated lymphocytes subsets may predict poor or protective vaccination immune response in vulnerable populations, such as HIV-infected individuals. Future studies on larger cohorts are needed to validate such strategy in the context of vaccination trials.