Recently, the long noncoding RNA (lncRNA) plasmacytoma variant translocation 1 (PVT1) was reported to be involved in the pathogenesis of several cancers, including human colorectal cancer (CRC). However, the molecular basis for cancer initiation, development, and progression remains unclear. In this study, we observe that upregulated PVT1 is associated with poor prognosis and bad clinicopathological features of CRC patients. In vitro means of PVT1 loss in a CRC cell line inhibit cell proliferation, migration, and invasion. Furthermore, dual-luciferase reporter and RNA pull-down assays indicated that PVT1 binds to miR-16-5p, which has been shown to play strong tumor suppressive roles in CRC. Targeted loss of miR-16-5p partially rescues the suppressive effect induced by PVT1 knockdown. Vascular endothelial growth factor A (VEGFA), a direct downstream target of miR-16-5p, was suppressed by PVT1 knockdown in CRC cells. Overexpression of VEGFA is known to modulate the AKT signaling cascade by activating vascular endothelial growth factor receptor 1 (VEGFR1). We, therefore, show that PVT1 loss combined with miR-16-5p overexpression reduces tumor volume maximally when propagated within a mouse xenograft model. We conclude that the PVT1-miR-16-5p/VEGFA/VEGFR1/AKT axis directly coordinates the response in CRC pathogenesis and suggest PVT1 as a novel target for potential CRC therapy.

Interstitial fibrosis, tubular atrophy, and inflammation are major contributors to kidney allograft failure. Here we sought an objective, quantitative pathological assessment of these lesions to improve predictive utility and constructed a deep-learning–based pipeline recognizing normal vs. abnormal kidney tissue compartments and mononuclear leukocyte infiltrates. Periodic acid– Schiff stained slides of transplant biopsies (60 training and 33 testing) were used to quantify pathological lesions specific for interstitium, tubules and mononuclear leukocyte infiltration. The pipeline was applied to the whole slide images from 789 transplant biopsies (478 baseline [preimplantation] and 311 post-transplant 12-month protocol biopsies) in two independent cohorts (GoCAR: 404 patients, AUSCAD: 212 patients) of transplant recipients to correlate composite lesion features with graft loss. Our model accurately recognized kidney tissue compartments and mononuclear leukocytes. The digital features significantly correlated with revised Banff 2007 scores but were more sensitive to subtle pathological changes below the thresholds in the Banff scores. The Interstitial and Tubular Abnormality Score (ITAS) in baseline samples was highly predictive of one-year graft loss, while a Composite Damage Score in 12-month post-transplant protocol biopsies predicted later graft loss. ITASs and Composite Damage Scores outperformed Banff scores or clinical predictors with superior graft loss prediction accuracy. High/intermediate risk groups stratified by ITASs or Composite Damage Scores also demonstrated significantly higher incidence of estimated glomerular filtration rate decline and subsequent graft damage. Thus, our deep-learning approach accurately detected and quantified pathological lesions from baseline or post-transplant biopsies and demonstrated superior ability for prediction of post-transplant graft loss with potential application as a prevention, risk stratification or monitoring tool.

Background: Long Interspersed Nuclear Element-1 (LINE-1, L1) is increasingly regarded as a genetic risk for lung cancer. Transcriptionally active LINE-1 forms a L1-gene chimeric transcript (LCTs), through somatic L1 retrotransposition (LRT) or L1 antisense promoter (L1-ASP) activation, to play an oncogenic role in cancer progression. Methods: Here, we developed Retrotransposon-gene fusion estimation program (ReFuse), to identify and quantify LCTs in RNA sequencing data from TCGA lung cancer cohort (n = 1146) and a single cell RNA sequencing dataset then further validated those LCTs in an independent cohort (n = 134). We next examined the functional roles of a cancer specific LCT (L1-FGGY) in cell proliferation and tumor progression in LUSC cell lines and mice. Results: The LCT events correspond with specific metabolic processes and mitochondrial functions and was associated with genomic instability, hypomethylation, tumor stage and tumor immune microenvironment (TIME). Functional analysis of a tumor specific and frequent LCT involving FGGY (L1-FGGY) reveal that the arachidonic acid (AA) metabolic pathway was activated by the loss of FGGY through the L1-FGGY chimeric transcript to promote tumor growth, which was effectively targeted by a combined use of an anti-HIV drug (NVR) and a metabolic inhibitor (ML355). Lastly, we identified a set of transcriptomic signatures to stratify the LUSC patients with a higher risk for poor outcomes who may benefit from treatments using NVR alone or combined with an anti-metabolism drug. Conclusions: This study is the first to characterize the role of L1 in metabolic reprogramming of lung cancer and provide rationale for L1-specifc prognosis and potential for a therapeutic strategy for treating lung cancer. Trial registration: Study on the mechanisms of the mobile element L1-FGGY promoting the proliferation, invasion and immune escape of lung squamous cell carcinoma through the 12-LOX/Wnt pathway, Ek2020111. Registered 27 March 2020 ‐ Retrospectively registered.

Apolipoprotein L1 (APOL1) risk alleles in donor kidneys associate with graft loss, but whether recipient risk allele expression affects transplant outcomes is unclear. To test whether recipient APOL1 risk alleles independently correlate with transplant outcomes, we analyzed genome-wide SNP genotyping data on donors and recipients from 2 kidney transplant cohorts: Genomics of Chronic Allograft Rejection (GOCAR) and Clinical Trials in Organ Transplantation 01/17 (CTOT-01/17). We estimated genetic ancestry (quantified as the proportion of African ancestry, or pAFR) by ADMIXTURE and correlated APOL1 genotypes and pAFR with outcomes. In the GOCAR discovery set, we noted that the number of recipient APOL1 G1/G2 alleles (R-nAPOL1) associated with an increased risk of death-censored allograft loss (DCAL), independent of ancestry (HR = 2.14; P = 0.006), as well as within the subgroup of African American and Hispanic (AA/H) recipients (HR = 2.36; P = 0.003). R-nAPOL1 also associated with an increased risk of any T cell–mediated rejection (TCMR) event. These associations were validated in CTOT-01/17. Ex vivo studies of PMBCs revealed, unexpectedly, high expression levels of APOL1 in activated CD4⁺/CD8⁺ T cells and NK cells. We detected enriched immune response gene pathways in risk allele carriers compared with noncarriers on the kidney transplant waitlist and among healthy controls. Our findings demonstrate an immunomodulatory role for recipient APOL1 risk alleles associated with TCMR and DCAL. We believe this finding has broader implications for immune-mediated injury to native kidneys.

Cancer is a multifaceted disease that results from dysregulated normal cellular signaling networks caused by genetic, genomic and epigenetic alterations at cell or tissue levels. Uncovering the underlying protein signaling network changes, including cell cycle gene networks in cancer, aids in understanding the molecular mechanism of carcinogenesis and identifies the characteristic signaling network signatures unique for different cancers and specific cancer subtypes. The identified signatures can be used for cancer diagnosis, prognosis, and personalized treatment. During the past several decades, the available technology to study signaling networks has significantly evolved to include such platforms as genomic microarray (expression array, SNP array, CGH array, etc.) and proteomic analysis, which globally assesses genetic, epigenetic, and proteomic alterations in cancer. In this review, we compared Pathway Array analysis with other proteomic approaches in analyzing protein network involved in cancer and its utility serving as cancer biomarkers in diagnosis, prognosis and therapeutic target identification. With the advent of bioinformatics, constructing high complexity signaling networks is possible. As the use of signaling network-based cancer diagnosis, prognosis and treatment is anticipated in the near future, medical and scientific communities should be prepared to apply these techniques to further enhance personalized medicine.

Somatic long interspersed element-1 (LINE-1) retrotransposition is a genomic process that relates to gene disruption and tumor occurrence. However, the expression and function of LINE-1 retrotransposition in lung squamous cell carcinoma (LUSC) remain unclear. We analyzed the transcriptomes of LUSC samples in The Cancer Genome Atlas and observed LINE-1 retrotransposition in 90% of tumor samples. Thirteen LINE-1 retrotranspositions of high occurrence were identified and further validated from an independent Chinese LUSC cohort. Among them, LINE-1-FGGY (L1-FGGY) was identified as the most frequent LINE-1 retrotransposition in the Chinese cohort and significantly correlated with poor clinical outcome. L1-FGGY occurred with smoke-induced hypomethylation of the LINE-1 promoter and contributed to the development of local immune evasion and dysfunctional metabolism. Overexpression of L1-FGGY or knockdown of FGGY promoted cell proliferation and invasion in vitro, facilitated tumorigenesis in vivo, and dysregulated cell energy metabolism and cytokine/chemotaxin transcription. Importantly, specific reverse transcription inhibitors, nevirapine and efavirenz, dramatically countered L1-FGGY abundance, inhibited tumor growth, recovered metabolism dysfunction, and improved the local immune evasion. In conclusion, hypomethylation-induced L1-FGGY expression is a frequent genomic event that promotes the development and progression of LUSC and represents a promising predictive biomarker and therapeutic target in LUSC. Significance: LINE-1-FGGY is a prognosis predictive biomarker and potential therapeutic target to overcome local immune evasion in lung squamous cell carcinoma.